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

Host-parasitoid interactions have been the focus of extensive ecological research for decades. One the of the major reasons is the importance host-parasitoid interactions play for the biological control of crop pests. Parasitoids are the main natural regulators for a large number of economically important pest insects, and in many cases they could be the only viable crop protection strategy. Parasitoids are also integral part of complex food webs whose structure and diversity display large spatio-temporal variations [1-3]. With the increasing globalization of human activities, the generalized spread and establishment of invasive species is a major cause of disruption in local community and food web spatio-temporal dynamics. In particular, the deliberate introduction of non-native parasitoids as part of biological control programs, aiming the suppression of established, and also highly invasive crop pests, is a common practice with potentially significant, yet poorly understood effects on local food web dynamics (e.g. [4]).
In their study, Muru et al. [5] took advantage of an existing biological control program focusing on the Asian chestnut gall wasp Dryocosmus kuriphilus, an invasive (and highly damaging) pest of chestnut trees. The species is currently a successful invader in many geographic regions, including southern France, where local parasitoid communities failed to provide an adequate control since its widespread establishment in 2010 [6]. In response, the non-native parasitoid species Torymus sinensis, which is highly-specific to the Asian chestnut gall wasp, was massively released in commercial chestnut orchards across several regions in France and the island of Corsica. The pest population outbreak was successfully contained, and thanks to the vast amount of host-parasitoid interaction data collected as part of the program, the authors were able to explore the effects of the large fluctuations in Asian chestnut gall wasp natural abundances on native parasitoid communities, immediately before, and up to five years following the introduction of its natural enemy T. sinensis.
Using co-occurrence and clustering analyses, Muru et al. [5] demonstrate that the invasion and the consecutive (efficient) control of the Asian chestnut gall wasp by the parasitoid T. sinensis have a significant impact on the structure of local parasitoid food webs. In particular, following decline in the Asian chestnut gall wasp’s populations, native parasitoids markedly switched to alternative hosts, most likely due to their respectively higher relative abundances. This pattern seemed to be driven by the degree of generalism in native parasitoid species. Indeed, when its abundances were still relatively high, the Asian chestnut gall wasp was primarily attacked by species capable of exploiting a broad range of hosts, while at low population densities only specialist parasitoids such as Mesolobus sericeus were able to persist and compete with the non-native T. sinensis.
The current study is important for two major reasons. First, it underscores the value of long-term species interaction data in order to understand the dynamic nature of food webs, namely their structural flexibility in response to changes in the environment or, as in this case, large fluctuation in abundances of a major pest species. In this context, biological control programs could be a great source of data for exploring long-term, large-scale dynamics of species interactions, and their use in ecological studies deserves to be further emphasized. Second, the study adds to the increasing empirical evidence that mobile generalist foragers can display adaptive, frequency-dependent switching behaviour ([1], [7]), which has been suggested to act as a key stabilizing mechanism in food webs by buffering fluctuating population dynamics at larger spatial scales ([8- 10]).
However, the timing of such buffering seems important, especially in systems such as commercial chestnut orchards. Despite their capacity to adaptively switch their foraging behaviour, the response of the native parasitoid communities to the new, unfamiliar resource was not fast enough in order to contain the primary outbreak under an appropriate damage threshold, thus requiring the introduction of the more specialized parasitoid T. sinensis. Nevertheless, based on current ecological theory, results presented by Muru et al. [5] suggest that the response of native parasitoid community to fluctuating host dynamics – i.e. shifts in parasitoid foraging behaviour based on their traits – could be predictable. This is encouraging considering the growing impact of biological invasions and insect pest outbreaks, but also the need to implement efficient, yet sustainable strategies for crop protection. Future studies would show at what extent observations by Muru et al. [5] are generalizable over longer time periods or other model systems. Noticeably, better understanding about population dynamics and interactions with the broader community of hosts available across habitats should allow to fine-tune predictions about parasitoids’ response to fluctuating resources.

References

[1] Eveleigh ES, McCann KS, McCarthy PC, Pollock SJ, Lucarotti CJ, Morin B, McDougall GA, Strongman DB, Huber JT, Umbanhowar J, Faria LDB (2007). Fluctuations in density of an outbreak species drive diversity cascades in food webs. Proc. Natl. Acad. Sci. USA 104, 16976-16981. doi: 10.1073/pnas.0704301104
[2] Tylianakis JM, Tscharntke T, Lewis OT (2007). Habitat modification alters the structure of tropical host–parasitoid food webs. Nature 445, 202-205. doi: 10.1038/nature05429
[3] Murakami M, Hirao T, Kasei A (2008). Effects of habitat configuration on host–parasitoid food web structure. Ecol. Res. 23, 1039-1049. doi: 10.1007/s11284-008-0478-0
[4] Geslin B, Gauzens B, Baude M, Dajoz I, Fontaine C, Henry M, Ropars L, Rollin O, Thébault E, Vereecken NJ (2016). Massively introduced managed species and their consequences for plant–pollinator interactions. Adv. Ecol. Res. 57, 147-199. doi: 10.1016/bs.aecr.2016.10.007
[5] Muru D, Borowiec N, Thaon M, Ris N, Viciriuc M I, Warot S, Vercken E (2020) The open bar is closed: restructuration of a native parasitoid community following successful control of an invasive pest. bioRxiv, 2019.12.20.884908, ver. 6 peer-reviewed and recommended by PCI Zoology. doi: 10.1101/2019.12.20.884908
[6] Borowiec N, Thaon M, Brancaccio L, Warot S, Vercken E, Fauvergue X, Ris N, Malausa J-C (2014). Classical biological control against the chestnut gall wasp 'Dryocosmus kuriphilus' (Hymenoptera, Cynipidae) in France. Plant Prot. Q. 29, 7-10.
[7] Bartley TJ, McCann KS, Bieg C, Cazelles K, Granados M, Guzzo MM, MacDougall AS, Tunney TD, McMeans BC (2019). Food web rewiring in a changing world. Nat. Ecol. Evol. 3, 345–354. doi: 10.1038/s41559-018-0772-3
[8] Kondoh M (2003). Foraging adaptation and the relationship between food-web complexity and stability. Science. 299, 1388-1391. doi: 10.1126/science.1079154
[9] McCann KS, Rooney N (2009). The more food webs change, the more they stay the same. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1789-801. doi: 10.1098/rstb.2008.0273
[10] Valdovinos FS, Ramos-Jiliberto R, garay-Narváez L, Urbani P, Dunne JA (2010). Consequences of adaptive behaviour for the structure and dynamics of food webs. Ecol. Lett. 13, 1546-1559. doi: 10.1111/j.1461-0248.2010.01535.x

Genetic (bio)diversity is one of three recognised levels of biodiversity, besides species and ecosystem diversity. Its importance for species survival and adaptation is increasingly highlighted and its monitoring recommended (e.g. O’Brien et al 2022). Especially the management of ex-situ populations has a long history of taking into account genetic aspects (through pedigree analysis but increasingly also by applying molecular tools). As in-situ and ex-situ efforts are nowadays often aligned (in a One-Plan-Approach), genetic management is becoming more the standard (supported by quickly developing genomic techniques). However, rarely quantitative genetic aspects are raised in this issue, while its relevance cannot be underestimated. Hence, the current manuscript by Sauve et al (2022) is a welcome contribution, in order to improve conservation efforts. The authors give a clear overview on how quantitative genetic analysis can aid the measurement, monitoring, prediction and management of adaptive genetic variation. The main tools are pedigrees (mainly of ex-situ populations) and the Animal Model. The main goal is to prevent adaption to captivity and altered genetics in general (in reintroduction projects). The confounding factors to take into account (like inbreeding, population structure, differences between facilities, sample size and parental/social effects) are well described by the authors. As such, I fully recommend this manuscript for publication, hoping increased interest in quantitative analysis will benefit the quality of species conservation management.

References

O'Brien D, Laikre L, Hoban S, Bruford MW et al. (2022) Bringing together approaches to reporting on within species genetic diversity. Journal of Applied Ecology, 00, 1–7. https://doi/10.1111/1365-2664.14225

Sauve D., Spero J., Steiner J., Wheeler H., Lynch C., Chabot A.A. (2022) Improving species conservation plans under IUCN’s One Plan Approach using quantitative genetic methods. EcoEvoRxiv, ver. 9 peer-reviewed and recommended by Peer Community in Zoology. https://doi.org/10.32942/osf.io/n3zxp

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