Molecular markers have drastically changed and improved our understanding of biological processes. In combination with PCR, markers revolutionized the study of all organisms, even tiny insects, and eukaryotic pathogens amongst others. Microsatellite markers were the most prominent and successful ones. Their success started in the early 1990s. They were used for population genetic studies, mapping of genes and genomes, and paternity testing and inference of relatedness. Their popularity is based on some of their characteristics as codominance, the high polymorphism information content, and their ease of isolation (Schlötterer 2004). Still, microsatellites are the marker of choice for a range of non-model organisms as next-generation sequencing technologies produce a huge amount of single nucleotide polymorphisms (SNPs), but often at expense of sample size and higher costs.
 
The high level of polymorphism of microsatellite markers, which consist of one to six base-pair nucleotide motifs replicated up to 10 or 20 times, results from slippage events during DNA replication. Short hairpin loops might shorten the template strand or extend the new strand. However, such slippage events might occur during PCR amplification resulting in additional bands or peaks. Such stutter alleles often appear to differ by one repeat unit and might be hard to interpret but definitively reduce automated scoring of microsatellite results.
 
A standalone software package available to handle stuttering is Microchecker (van Oosterhout et al., 2004, which nowadays faces incompatibilities with updated versions of different operating systems. Thus, de Meeûs and Noûs (2022), in their manuscript, tackled the stuttering issue by developing an OS-independent analysis pipeline based on standard spreadsheet software such as Microsoft Office (Excel) or Apache Open Office (Calc). The authors use simulated populations differing in the mating system (pangamic, selfing (30%), clonal) and a different number of subpopulations and individuals per subpopulation to test for differences among the null model (no stuttering), a test population with 2 out of 20 loci (10%) with stuttering, and the latter with stuttering cured. Further to this, the authors also re-analyse data from previous studies utilising organisms differing in the mating system to understand whether control of stuttering changes major parameter estimates and conclusions of those studies.
 
Stuttering of microsatellite loci might result in increased heterozygote deficits. The authors utilise the FIS (inbreeding coefficient) as a tool to compare the different treatments of the simulated populations. Their method detected stuttering in pangamic and selfing populations, while the detection of stuttering in clonal organisms is more difficult. The cure for stuttering resulted in FIS values similar to those populations lacking stuttering. The re-analysis of four previously published studies indicated that the new method presented here is more accurate than Microchecker (van Oosterhout et al., 2004) in a direct comparison. For the Lyme disease-transmitting tick Ixodes scapularis (De Meeûs et al., 2021), three loci showed stuttering and curing these resulted in data that are in good agreement with pangamic reproduction. In the tsetse fly Glossina palpalis palpalis (Berté et al., 2019), two out of seven loci were detected as stuttering. Curing them resulted in decreased FIS for one locus, while the other showed an increased FIS, an indication of other problems such as the occurrence of null alleles. Overall, in dioecious pangamic populations, the method works well, and the cure of stuttering improves population genetic parameter estimates, although FST and FIS might be slightly overestimated. In monoecious selfers, the detection and cure work well, if other factors such as null alleles do not interfere. In clonal organisms, only loci with extremely high FIS might need a cure to improve parameter estimates.
 
This spreadsheet-based method helps to automate microsatellite analysis at very low costs and thus improves the accuracy of parameter estimates. This might certainly be very useful for a range of non-model organisms, parasites, and their vectors, for which microsatellites are still the marker of choice. 
 
References

Berté D, De Meeus T, Kaba D, Séré M, Djohan V, Courtin F, N'Djetchi KM, Koffi M, Jamonneau V, Ta BTD, Solano P, N’Goran EK, Ravel S (2019) Population genetics of Glossina palpalis palpalis in sleeping sickness foci of Côte d'Ivoire before and after vector control. Infection Genetics and Evolution 75, 103963. https://doi.org/0.1016/j.meegid.2019.103963

de Meeûs T, Chan CT, Ludwig JM, Tsao JI, Patel J, Bhagatwala J, Beati L (2021) Deceptive combined effects of short allele dominance and stuttering: an example with Ixodes scapularis, the main vector of Lyme disease in the U.S.A. Peer Community Journal 1, e40. https://doi.org/10.24072/pcjournal.34

de Meeûs T, Noûs C (2022) A simple procedure to detect, test for the presence of stuttering, and cure stuttered data with spreadsheet programs. Zenodo, v5, peer-reviewed and recommended by PCI Zoology. https://doi.org/10.5281/zenodo.7029324

Schlötterer C (2004) The evolution of molecular markers - just a matter of fashion? Nature Reviews Genetics 5, 63-69. https://doi.org/10.1038/nrg1249

van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes 4, 535-538. https://doi.org/10.1111/j.1471-8286.2004.00684.x

The Western honeybee, Apis mellifera L., is one of the best-studied social insects. It shows a reproductive division of labour, cooperative brood care, and age-related polyethism. Furthermore, honeybees regulate the temperature in the hive. Although bees are invertebrates that are usually ectothermic, this is still true for individual worker bees, but the colony maintains a very narrow range of temperature, especially within the brood nest. This is quite important as the development of individuals is dependent on ambient temperature, with higher temperatures resulting in accelerated development and vice versa. In honeybees, a feedback mechanism couples developmental temperature and the foraging behaviour of the colony and the future population development (Tautz et al., 2003). Bees raised under lower temperatures are more likely to perform in-hive tasks, while bees raised under higher temperatures are better foragers. To maintain optimal levels of worker population growth and foraging rates, it is adaptive to regulate temperature to ensure optimal levels of developing brood. Moreover, this allows honeybees to decouple the internal developmental processes from ambient temperatures enhancing the ecological success of the species. 

In every system of thermoregulation, whether it is endothermic under the utilization of energetic resources as in mammals or the honeybee or ectothermic as in lower vertebrates and invertebrates through differential exposure to varying environmental temperature gradients, there is a need for precision (low variability) and accuracy (hitting the target temperature). However, in honeybees, the temperature is regulated by workers through muscle contraction and fanning of the wings and thus, a higher number of workers could be better at achieving precise and accurate temperature within the brood nest. Alternatively, the amount of brood could trigger responses with more brood available, a need for more precise and accurate temperature control. The authors aimed at testing these two important factors on the precision and accuracy of within-colony temperature regulation by monitoring 28 colonies equipped with temperature sensors for two years (Godeau et al., 2023).

They found that the number of brood cells predicted the mean temperature (accuracy of thermoregulation). Other environmental factors had a small effect. However, the model incorporating these factors was weak in predicting the temperature as it overestimated temperatures in lower ranges and underestimated temperatures in higher ranges. In contrast, the variability of the target temperature (precision of thermoregulation) was positively affected by the external temperature, while all other factors did not show a significant effect. Again, the model was weak in predicting the data. Overall colony size measured in categories of the number of workers and the number of brood cells did not show major differences in variability of the mean temperature, but a slight positive effect for the number of bees on the mean temperature. 

Unfortunately, the temperature was a poor predictor of colony size. The latter is important as the remote control of beehives using Internet of Things (IoT) technologies get more and more incorporated into beekeeping management. These IoT technologies and their success are dependent on good proxies for the control of the status of the colony. Amongst the factors to monitor, the colony size (number of bees and/or amount of brood) is extremely important, but temperature measurements alone will not allow us to predict colony sizes. Nevertheless, this study showed clearly that the number of brood cells is a crucial factor for the accuracy of thermoregulation in the beehive, while ambient temperature affects the precision of thermoregulation. In the view of climate change, the latter factor seems to be important, as more extreme environmental conditions in the future call for measures of mitigation to ensure the proper functioning of the bee colony, including the maintenance of homeostatic conditions inside of the nest to ensure the delivery of the ecosystem service of pollination.

REFERENCES

Godeau U, Pioz M, Martin O, Rüger C, Crauser D, Le Conte Y, Henry M, Alaux C (2023) Brood thermoregulation effectiveness is positively linked to the amount of brood but not to the number of bees in honeybee colonies. EcoEvoRxiv, ver. 5 peer-reviewed and recommended by Peer Community in Zoology. https://doi.org/10.32942/osf.io/9mwye 

Tautz J, Maier S, Claudia Groh C, Wolfgang Rössler W, Brockmann A (2003) Behavioral performance in adult honey bees is influenced by the temperature experienced during their pupal development. PNAS 100: 7343–7347. https://doi.org/10.1073/pnas.1232346100