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 intricate relationships between parasites and their hosts often involve a choreography of behavioral changes, with parasites manipulating their hosts in a way that enhances - or seemingly enhances – their transmission (Hughes et al., 2012; Moore, 2002; Poulin, 2010). Host manipulation is increasingly acknowledged as a pervasive adaptive transmission strategy employed by parasites, and as such is one of the most remarkable manifestations of the extended phenotype (Dawkins, 1982).

In this laboratory study, Rigaud et al. (2023) delved into the time course of antipredator behavioral modifications induced by the acanthocephalan Pomphorhynchus laevis in its amphipod intermediate host Gammarus pulex. This system has a good foundation of prior knowledge (Bakker et al., 2017; Fayard et al., 2020; Perrot-Minnot et al., 2023), nicely drawn upon for the present work. This parasite orchestrates a switch from predation suppression, during the noninfective phase, to predation enhancement upon maturation. Specifically, G. pulex infected with the non-infective acanthella stage of the parasite can exhibit increased refuge use and reduced activity compared to uninfected individuals (Dianne et al., 2011, 2014), leading to decreased predation by trout (Dianne et al., 2011). In contrast, upon reaching the infective cystacanth stage, the parasite can enhance the susceptibility of its host to trout predation (Dianne et al., 2011).

The present work aimed to understand the temporal sequence of these behavioral changes across the entire ontogeny of the parasite. The results confirmed the protective role of P. laevis during the acanthella stage, wherein infected amphipods exhibited heightened refuge use. This protective manipulation, however, became significant only later in the parasite's ontogeny, suggesting a delayed investment strategy, possibly influenced by the extended developmental time of P. laevis. The protective component wanes upon reaching the cystacanth stage, transitioning into an exposure strategy, aligning with theoretical predictions and previous empirical work (Dianne et al., 2011; Parker et al., 2009). The switch was behavior-specific. Unlike the protective behavior, a decline in the amphipod activity rate manifested early in the acanthella stage and persisted throughout development, suggesting potential benefits of reduced activity for the parasite across multiple stages. Furthermore, the findings challenge previous assumptions regarding the condition-dependency of manipulation, revealing that the parasite-induced behavioral changes predominantly occurred in the presence of cues signaling potential predators. Finally, while amphipods infected with acanthella stages displayed survival rates comparable to their uninfected counterparts, increased mortality was observed in those infected with cystacanth stages.

Understanding the temporal sequence of host behavioral changes is crucial for deciphering whether it is adaptive to the parasite or not. This study stands out for its meticulous examination of multiple behaviors over the entire ontogeny of the parasite highlighting the complexity and condition-dependent nature of manipulation. The protective-then-expose strategy emerges as a dynamic process, finely tuned to the developmental stages of the parasite and the ecological challenges faced by the host. The delayed emergence of protective behaviors suggests a strategic investment by the parasite, with implications for the host's survival and the parasite's transmission success. The differential impact of infection on refuge use and activity rate further emphasizes the need for a multidimensional approach in studying parasitic manipulation (Fayard et al., 2020). This complexity demands further exploration, particularly in deciphering how trophically-transmitted parasites shape the behavioral landscape of their intermediate hosts and its temporal dynamic (Herbison, 2017; Perrot-Minnot & Cézilly, 2013).  As we discover the many subtleties of these parasitic manipulations, new avenues of research are unfolding, promising a deeper understanding of the ecology and evolution of host-parasite interactions.

References

Bakker, T. C. M., Frommen, J. G., & Thünken, T. (2017). Adaptive parasitic manipulation as exemplified by acanthocephalans. Ethology, 123(11), 779–784. https://doi.org/10.1111/eth.12660

Dawkins, R. (1982). The extended phenotype: The long reach of the gene (Reprinted). Oxford University Press.

Dianne, L., Perrot-Minnot, M.-J., Bauer, A., Gaillard, M., Léger, E., & Rigaud, T. (2011). Protection first then facilitation: A manipulative parasite modulates the vulnerability to predation of its intermediate host according to its own developmental stage. Evolution, 65(9), 2692–2698. https://doi.org/10.1111/j.1558-5646.2011.01330.x

Dianne, L., Perrot-Minnot, M.-J., Bauer, A., Guvenatam, A., & Rigaud, T. (2014). Parasite-induced alteration of plastic response to predation threat: Increased refuge use but lower food intake in Gammarus pulex infected with the acanothocephalan Pomphorhynchus laevis. International Journal for Parasitology, 44(3–4), 211–216. https://doi.org/10.1016/j.ijpara.2013.11.001

Fayard, M., Dechaume‐Moncharmont, F., Wattier, R., & Perrot‐Minnot, M. (2020). Magnitude and direction of parasite‐induced phenotypic alterations: A meta‐analysis in acanthocephalans. Biological Reviews, 95(5), 1233–1251. https://doi.org/10.1111/brv.12606

Herbison, R. E. H. (2017). Lessons in Mind Control: Trends in Research on the Molecular Mechanisms behind Parasite-Host Behavioral Manipulation. Frontiers in Ecology and Evolution, 5, 102. https://doi.org/10.3389/fevo.2017.00102

Hughes, D. P., Brodeur, J., & Thomas, F. (2012). Host manipulation by parasites. Oxford university press.

Moore, J. (2002). Parasites and the behavior of animals. Oxford University Press.

Parker, G. A., Ball, M. A., Chubb, J. C., Hammerschmidt, K., & Milinski, M. (2009). When should a trophically transmitted parasite manipulate its host? Evolution, 63(2), 448–458. https://doi.org/10.1111/j.1558-5646.2008.00565.x

Perrot-Minnot, M.-J., & Cézilly, F. (2013). Investigating candidate neuromodulatory systems underlying parasitic manipulation: Concepts, limitations and prospects. Journal of Experimental Biology, 216(1), 134–141. https://doi.org/10.1242/jeb.074146

Perrot-Minnot, M.-J., Cozzarolo, C.-S., Amin, O., Barčák, D., Bauer, A., Filipović Marijić, V., García-Varela, M., Servando Hernández-Orts, J., Yen Le, T. T., Nachev, M., Orosová, M., Rigaud, T., Šariri, S., Wattier, R., Reyda, F., & Sures, B. (2023). Hooking the scientific community on thorny-headed worms: Interesting and exciting facts, knowledge gaps and perspectives for research directions on Acanthocephala. Parasite, 30, 23. https://doi.org/10.1051/parasite/2023026

Poulin, R. (2010). Parasite Manipulation of Host Behavior. In Advances in the Study of Behavior (Vol. 41, pp. 151–186). Elsevier. https://doi.org/10.1016/S0065-3454(10)41005-0

Rigaud, T., Balourdet, A., & Bauer, A. (2023). Time-course of antipredator behavioral changes induced by the helminth Pomphorhynchus laevis in its intermediate host Gammarus pulex: The switch in manipulation according to parasite developmental stage differs between behaviors. bioRxiv, ver. 6 peer-reviewed and recommended by Peer Community in Zoology. https://doi.org/10.1101/2023.04.25.538244

The critical role that gut microbiota play in many aspects of an animal’s life, including pathogen resistance, detoxification, digestion, and nutritional physiology, is becoming more and more apparent (Engel and Moran 2013; Lindsay et al., 2020). Gut microbiota recruitment and maintenance can be largely affected by the surrounding environment (Chandler et al., 2011; Callens et al., 2020). The environment may thus dictate gut microbiota composition and diversity, which in turn can affect organismal responses to stress. Only few studies have, however, taken the gut microbiota into account to estimate life histories in response to multiple stressors in aquatic systems (Macke et al., 2016). 

Houwenhuyse et al., investigate how the microbiome affects life histories in response to ecologically relevant single and multiple biotic stressors (an oomycete-like parasite, and a toxic cyanobacterium) in Daphnia magna (Houwenhuyse et al., 2023). Daphnia is an excellent model, because this aquatic system lends itself extremely well for gut microbiota transplantation and manipulation. This is due to the possibility to sterilize eggs (making them free of bacteria), horizontal transmission of bacteria from the environment, and the relative ease of culturing genetically similar Daphnia clones in large numbers. 

The authors use an elegant experimental design to show that the Daphnia gut microbial community differs when derived from a laboratory versus natural inoculum, the latter being more diverse. The authors subsequently show that key life history traits (survival, fecundity, and body size) depend on the stressors (and combination thereof), the microbiota (structure and diversity), and Daphnia genotype. A key finding is that Daphnia exposed to both biotic stressors show an antagonistic interaction effect on survival (being higher), but only in individuals containing laboratory gut microbiota. The exact mechanism remains to be determined, but the authors propose several interesting hypotheses as to why Daphnia with more diverse gut microbiota do less well. This could be due, for example, to increased inter-microbe competition or an increased chance of contracting opportunistic, parasitic bacteria. For Daphnia with less diverse laboratory gut microbiota, a monopolizing species may be particularly beneficial for stress tolerance. Alongside these interesting findings, the paper also provides extensive information about the gut microbiota composition (available in the supplementary files), which is a very useful resource for other researchers. 

Overall, this study reveals that multiple, interacting factors affect the performance of Daphnia under stressful conditions. Of importance is that laboratory studies may be based on simpler microbiota systems, meaning that stress responses measured in the laboratory may not accurately reflect what is happening in nature. 

REFERENCES

Callens M, De Meester L, Muylaert K, Mukherjee S, Decaestecker E. The bacterioplankton community composition and a host genotype dependent occurrence of taxa shape the Daphnia magna gut bacterial community. FEMS Microbiology Ecology. 2020;96(8):fiaa128. https://doi.org/10.1093/femsec/fiaa128

Chandler JA, Lang JM, Bhatnagar S, Eisen JA, Kopp A. Bacterial communities of diverse Drosophila species: ecological context of a host-microbe model system. PLOS Genetics. 2011;7(9):e1002272. https://doi.org/10.1371/journal.pgen.1002272

Engel P, Moran NA. The gut microbiota of insects - diversity in structure and function. FEMS Microbiology Reviews. 2013;37(5):699-735. https://doi.org/10.1111/1574-6976.12025

Houwenhuyse S, Bulteel L, Vanoverberghe I, Krzynowek A, Goel N et al. Microbiome mediated tolerance to biotic stressors: a case study of the interaction between a toxic cyanobacterium and an oomycete-like infection in Daphnia magna. 2023. OSF, ver. 2 peer-reviewed and recommended by Peer Community in Zoology. https://doi.org/10.31219/osf.io/9n4mg

Lindsay EC, Metcalfe NB, Llewellyn MS. The potential role of the gut microbiota in shaping host energetics and metabolic rate. Journal of Animal Ecology. 2020;89(11):2415-2426. https://doi.org/10.1111/1365-2656.13327

Macke E, Tasiemski A, Massol F, Callens M, Decaestecker E. Life history and eco-evolutionary dynamics in light of the gut microbiota. Oikos. 2017;126(4):508-531. https://doi.org/10.1111/oik.03900

The popularity of iNaturalist and other online biodiversity databases to which the general public and specialists alike contribute observations has skyrocketed in recent years (Dance 2022). The AI-based algorithms (computer vision) which provide the first identification of a given organism on an uploaded photograph have become very sophisticated, suggesting initial identifications often down to species level with a surprisingly high degree of accuracy. The initial identifications are then confirmed or improved by feedback from the community, which works particularly well for organismal groups to which many active community members contribute, such as the birds. Hence, providing initial observations and identifying observations of others, as well as browsing the recorded biodiversity for given locales or the range of occurrences of individual taxa has become a meaningful and satisfying experience for the interested naturalist. Furthermore, several research studies have now been published relying on observations uploaded to iNaturalist (Szentivanyi and Vincze 2022). However, using the enormous amount of natural history data available on iNaturalist in a systematic way has remained challenging, since this requires not only retrieving numerous observations from the database (in the hundreds or even thousands), but also some level of transparent quality control.

Billotte (2022) provides a protocol and R scripts for the quality assessment of downloaded observations from iNaturalist, allowing an efficient and reproducible stepwise approach to prepare a high-quality data set for further analysis. First, observations with their associated metadata are downloaded from iNaturalist, along with the corresponding entries from the Global Biodiversity Information Facility (GBIF). In addition, a taxonomic reference list is obtained (these are available online for many taxa), which is used to assess the taxonomic consistency in the dataset. Second, the geo-tagging is assessed by comparing the iNaturalist and GBIF metadata. Lastly, the image quality is assessed using pyBRISQUE. The approach is illustrated using spiders (Araneae) as an example. Spiders are a very diverse taxon and an excellent taxonomic reference list is available (World Spider Catalogue 2022). However, spiders are not well known to most non-specialists, and it is not easy to take good pictures of spiders without using professional equipment. Therefore, the ability of iNaturalist’s computer vision to provide identifications is limited to this date and the community of specialists active on iNaturalist is comparatively small. Hence, spiders are a good taxon to demonstrate how the pipeline results in a quality-controlled dataset based on crowed-sourced data. Importantly, the software employed is free to use, although inevitably, the initial learning curve to use R scripts can be steep, depending on prior expertise with R/RStudio. Furthermore, the approach is employable with databases other than iNaturalist.

In summary, Billotte's (2022) pipeline allows researchers to use the wealth of observations on iNaturalist and other databases to produce large metadata and image datasets of high-quality in a reproducible way. This should pave the way for more studies, which could include, for example, the assessment of range expansions of invasive species or the evaluation of the presence of endangered species, potentially supporting conservation efforts.

References

Billotte J (2022) A pipeline for assessing the quality of images and metadata from crowd-sourced databases. BiorXiv, 2022.04.29.490112, ver 5 peer reviewed and recommended by Peer Community In Zoology. https://doi.org/10.1101/2022.04.29.490112

Dance A (2022) Community science draws on the power of the crowd. Nature, 609, 641–643. https://doi.org/10.1038/d41586-022-02921-3

Szentivanyi T, Vincze O (2022) Tracking wildlife diseases using community science: an example through toad myiasis. European Journal of Wildlife Research, 68, 74. https://doi.org/10.1007/s10344-022-01623-5

World Spider Catalog (2022). World Spider Catalog. Version 23.5. Natural History Museum Bern, online at http://wsc.nmbe.ch. https://doi.org/10.24436/2

Insects exhibit a great diversity of life-history traits that often vary not only between species but also between populations of the same species (Flatt and Heyland, 2011). A better understanding of the variation in these traits can be of paramount importance when it comes to species of economic and agricultural interest (Wilby and Thomas, 2002). In particular, the control of the development and expansion of agricultural pests generally requires a good understanding of the parameters that favour the reproduction of these pests and/or the reproduction of the species used to control them (Bianchi et al., 2013; Gäde and Goldsworthy, 2003).

Parasitoid wasps of the genus Trichogramma are a classic example of insects involved in pest control (Smith, 1996). This genus comprises over 200 species worldwide, which have been used to control populations of a wide range of lepidopteran pests since the 1900s (Flanders, 1930; Hassan, 1993). Despite its common use, the egg-laying preference of this genus is only partially known. For example, all Trichogramma species are often thought to have positive phototaxis (or negative geotaxis) (e.g. Brower & Cline, 1984; van Atta et al., 2015), but comprehensive studies simultaneously testing this (or other) parameter among Trichogramma species and populations remain rare.

This is exactly the aim of the present study (Burte et al., 2021). Using a new experimental approach based on automatic image analysis, the authors compared the photo- and geo-tactic oviposition preference among 5 Trichogramma species from 25 populations. Their results first confirm that most Trichogramma species and populations prefer light to shade, and higher to lower positions for oviposition. Interestingly, they also reveal that the levels of preference for light and gravity show inter- and intraspecific variation (probably due to local adaptation to different strata) and that both preferences tend to relax over time.

Overall, this study provides important information for improving the use of Trichogramma species as biological agents. For example, it may help to establish breeding lines adapted to the microhabitat and/or growing parts of plants on which agricultural pests lay eggs most. Similarly, it suggests that the use of multiple strains with different microhabitat selection preferences could lead to better coverage of host plants, as well as a reduction in intraspecific competition in the preferred parts. Finally, this study provides a new methodology to efficiently and automatically study oviposition preferences in Trichogramma, which could be used in other insects with a particularly small size.

References

Bianchi, F. J. J. A., Schellhorn, N. A. and Cunningham, S. A. (2013). Habitat functionality for the ecosystem service of pest control: reproduction and feeding sites of pests and natural enemies. Agricultural and Forest Entomology, 15, 12–23. https://doi.org/10.1111/j.1461-9563.2012.00586.x

Burte V., Perez G., Ayed F., Groussier G., Mailleret L, van Oudenhove L. and Calcagno V. (2021). Up and to the light: intra- and interspecific variability of photo-and geo-tactic oviposition preferences in genus Trichogramma. bioRxiv, 2021.03.30.437671, ver. 4 peer-reviewed and recommended by PCI Zoology. https://doi.org/10.1101/2021.03.30.437671

Brower, J. H. and Cline, L. D. (1984). Response of Trichogramma pretiosum and T. evanescens to Whitelight, Blacklight or NoLight Suction Traps. The Florida Entomologist, 67, 262–268. https://doi.org/10.2307/3493947

Flanders, S. E. (1930). Mass production of egg parasites of the genus Trichogramma. Hilgardia, 4, 465–501. https://doi.org/10.3733/hilg.v04n16p465

Flatt, T. and Heyland, A. (2011). Mechanisms of life history evolution: the genetics and physiology of life history traits and trade-offs. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199568765.001.0001

Gäde, G. and Goldsworthy, G. J. (2003). Insect peptide hormones: a selective review of their physiology and potential application for pest control. Pest Management Science, 59, 1063–1075. https://doi.org/10.1002/ps.755

Hassan, S. A. (1993). The mass rearing and utilization of Trichogramma to control lepidopterous pests: Achievements and outlook. Pesticide Science, 37, 387–391. https://doi.org/10.1002/ps.2780370412

Smith, S. M. (1996). Biological Control with Trichogramma : Advances, Successes, and Potential of Their Use. Annual Review of Entomology, 41, 375–406. https://doi.org/10.1146/annurev.en.41.010196.002111

van Atta, K. J., Potter, K. A. and Woods, H. A. (2015). Effects of UV-B on Environmental Preference and Egg Parasitization by Trichogramma Wasps (Hymenoptera: Trichogrammatidae). Journal of Entomological Science, 50, 318–325. https://doi.org/10.18474/JES15-09.1

Wilby, A. and Thomas, M. B. (2002). Natural enemy diversity and pest control: patterns of pest emergence with agricultural intensification. Ecology Letters, 5, 353–360. https://doi.org/10.1046/j.1461-0248.2002.00331.x