Rodents are well known as one of the main animal groups responsible for human-transmitted pathogens. As such, it seems logical to try and survey what kinds of pathogenic microbes might be harboured by wild rodents, in order to establish some baseline surveillance and prevent future zoonotic outbreaks (Bernstein et al., 2022). This is exactly what Abbate et al. (2023) endeavoured and their findings are intimidating. Based on quite a large sampling effort, they collected more than 700 rodents of seven species around two villages in northeastern France. They looked for molecular markers indicative of viral and bacterial infections and proceeded to analyze their pathogen communities using multivariate techniques.

Variation in the prevalence of the different pathogens was found among host species, with e.g. signs of CPXV more prevalent in Cricetidae while some Mycoplasma strains were more prevalent in Muridae. Co-circulation of pathogens was found in all species, with some evidencing signs of up to 12 different pathogen taxa. The diversity of co-circulating pathogens was markedly different between host species and higher in adult hosts, but not affected by sex. The dataset also evinced some slight differences between habitats, with meadows harbouring a little more diversity of rodent pathogens than forests. Less intuitively, some pathogen associations seemed quite repeatable, such as the positive association of Bartonella spp. with CPXV in the montane water vole. The study allowed the authors to test several associations already described in the literature, including associations between different hemotropic Mycoplasma species.

I strongly invite colleagues interested in zoonoses, emerging pandemics and more generally One Health to read the paper of Abbate et al. (2023) and try to replicate them across the world. To prevent the next sanitary crises, monitoring rodents, and more generally vertebrates, population demographics is a necessary and enlightening step (Johnson et al., 2020), but insufficient. Following the lead of colleagues working on rodent ectoparasites (Krasnov et al., 2014), we need more surveys like the one described by Abbate et al. (2023) to understand the importance of the dilution effect in the prevalence and transmission of microbial pathogens (Andreazzi et al., 2023) and the formation of epidemics. We also need other similar studies to assess the potential of different rodent species to carry pathogens more or less capable of infecting other mammalian species (Morand et al., 2015), in other places in the world.

References

Abbate, J. L., Galan, M., Razzauti, M., Sironen, T., Voutilainen, L., Henttonen, H., Gasqui, P., Cosson, J.-F. & Charbonnel, N. (2023) Pathogen community composition and co-infection patterns in a wild community of rodents. BioRxiv, ver.4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.02.09.940494 

Andreazzi, C. S., Martinez-Vaquero, L. A., Winck, G. R., Cardoso, T. S., Teixeira, B. R., Xavier, S. C. C., Gentile, R., Jansen, A. M. & D'Andrea, P. S. (2023) Vegetation cover and biodiversity reduce parasite infection in wild hosts across ecological levels and scales. Ecography, 2023, e06579.
https://doi.org/10.1111/ecog.06579
 
Bernstein, A. S., Ando, A. W., Loch-Temzelides, T., Vale, M. M., Li, B. V., Li, H., Busch, J., Chapman, C. A., Kinnaird, M., Nowak, K., Castro, M. C., Zambrana-Torrelio, C., Ahumada, J. A., Xiao, L., Roehrdanz, P., Kaufman, L., Hannah, L., Daszak, P., Pimm, S. L. & Dobson, A. P. (2022) The costs and benefits of primary prevention of zoonotic pandemics. Science Advances, 8, eabl4183.
https://doi.org/10.1126/sciadv.abl4183
 
Johnson, C. K., Hitchens, P. L., Pandit, P. S., Rushmore, J., Evans, T. S., Young, C. C. W. & Doyle, M. M. (2020) Global shifts in mammalian population trends reveal key predictors of virus spillover risk. Proceedings of the Royal Society B: Biological Sciences, 287, 20192736.
https://doi.org/10.1098/rspb.2019.2736
 
Krasnov, B. R., Pilosof, S., Stanko, M., Morand, S., Korallo-Vinarskaya, N. P., Vinarski, M. V. & Poulin, R. (2014) Co-occurrence and phylogenetic distance in communities of mammalian ectoparasites: limiting similarity versus environmental filtering. Oikos, 123, 63-70.
https://doi.org/10.1111/j.1600-0706.2013.00646.x
 
Morand, S., Bordes, F., Chen, H.-W., Claude, J., Cosson, J.-F., Galan, M., Czirjak, G. Á., Greenwood, A. D., Latinne, A., Michaux, J. & Ribas, A. (2015) Global parasite and Rattus rodent invasions: The consequences for rodent-borne diseases. Integrative Zoology, 10, 409-423.
https://doi.org/10.1111/1749-4877.12143

"Good and evil are so close as to be chained together in the soul"
Robert Louis Stevenson, Dr. Jekyll and Mr. Hyde

Maternally inherited symbionts—microorganisms that pass from a female host to her progeny—have two main ways of increasing their own fitness. First, they can increase the fecundity or viability of infected females. This “positive fitness effects” strategy is the one commonly used by mutualistic symbionts, such as Buchnera aphidicola—the bacterial endosymbiont of the pea aphid, Acyrthosiphon pisum [4]. Second, maternally inherited symbionts can manipulate the reproduction of infected females in a way that enhances symbiont transmission at the expense of host fitness. A famous example of this “reproductive parasitism” strategy is the cytoplasmic incompatibility (CI) [3] induced by bacteria of the genus Wolbachia in their arthropod and nematode hosts. CI works as a toxin-antidote system, whereby the sperm of infected males is modified in a lethal way (toxin) that can only be reverted if the egg is also infected (antidote) [1]. As a result, CI imposes a kind of conditional sterility on their hosts: while infected females are compatible with both infected and uninfected males, uninfected females experience high offspring mortality if (and only if) they mate with infected males [7].

These two symbiont strategies (positive fitness effects versus reproductive parasitism) have been traditionally studied separately, both empirically and theoretically. However, it has become clear that the two strategies are not mutually exclusive, and that a reproductive parasite can simultaneously act as a mutualist—an infection type that has been dubbed “Jekyll and Hyde” [6], after the famous novella by Robert Louis Stevenson about kind scientist Dr. Jekyll and his evil alter ego, Mr. Hyde. In important previous work, Zug and Hammerstein [7] analyzed the consequences of positive fitness effects on the dynamics of different kind of infections, including “Jekyll and Hyde” infections characterized by CI and other reproductive parasitism strategies. Building on this and related modeling framework, Karisto et al. [2] re-investigate and expand on the interplay between positive fitness effects and reproductive parasitism in Wolbachia infections by focusing on CI in both diplodiploid and haplodiploid populations, and by paying particular attention to the mathematical assumption structure underlying their results.

Karisto et al. begin by reviewing classic models of Wolbachia infections in diplodiploid populations that assume a “negative fitness effect” (modeled as a fertility penalty on infected females), characteristic of a pure strategy of reproductive parasitism. Together with the positive frequency-dependent effects due to CI (whereby the fitness benefits to symbionts infecting females increase with the proportion of infected males in the population) this results in population dynamics characterized by two stable equilibria (the Wolbachia-free state and an interior equilibrium with a high frequency of Wolbachia-carrying hosts) separated by an unstable interior equilibrium. Wolbachia can then spread once the initial frequency is above a threshold or an invasion barrier, but is prevented from fixing by a proportion of infections failing to be passed on to offspring. Karisto et al. show that, given the assumption of negative fitness effects, the stable interior equilibrium can never feature a Wolbachia prevalence below one-half. Moreover, they convincingly argue that a prevalence greater than but close to one-half is difficult to maintain in the presence of stochastic fluctuations, as in these cases the high-prevalence stable equilibrium would be too close to the unstable equilibrium signposting the invasion barrier.

Karisto et al. then relax the assumption of negative fitness effects and allow for positive fitness effects (modeled as a fertility premium on infected females) in a diplodiploid population. They show that positive fitness effects may result in situations where the original invasion threshold is now absent, the bistable coexistence dynamics are transformed into purely co-existence dynamics, and Wolbachia symbionts can now invade when rare. Karisto et al. conclude that positive fitness effects provide a plausible and potentially testable explanation for the low frequencies of symbiont-carrying hosts that are sometimes observed in nature, which are difficult to reconcile with the assumption of negative fitness effects. 

Finally, Karisto et al. extend their analysis to haplodiploid host populations (where all fertilized eggs develop as females). Here, they investigate two types of cytoplasmic incompatibility: a female-killing effect, similar to the CI effect studied in diplodiploid populations (the “Leptopilina type” of Vavre et al. [5]) and a masculinization effect, where CI leads to the loss of paternal chromosomes and to the development of the offspring as a male (the “Nasonia type” of Vavre et al. [5]). The models are now two-sex, which precludes a complete analytical treatment, in particular regarding the stability of fixed points. Karisto et al. compensate by conducting large numerical analyses that support their claims. Importantly, all main conclusions regarding the interplay between positive fitness effects and reproductive parasitism continue to hold under haplodiploidy. 

All in all, the analysis and results by Karisto et al. suggest that it is not necessary to resort to classical (but depending on the situation, unlikely) mechanisms, such as ongoing invasion or source-sink dynamics, to explain arthropod populations featuring low-prevalent Wolbachia infections. Instead, low-frequency equilibria might be simply due to reproductive parasites conferring beneficial fitness effects, or Wolbachia symbionts playing Dr. Jekyll (positive fitness effects) and Mr. Hyde (cytoplasmatic incompatibility). 

References

[1] Beckmann JF, Bonneau M, Chen H, Hochstrasser M, Poinsot D, Merçot H, Weill M, Sicard M, Charlat S (2019) The Toxin–Antidote Model of Cytoplasmic Incompatibility: Genetics and Evolutionary Implications. Trends in Genetics, 35, 175–185. https://doi.org/10.1016/j.tig.2018.12.004

[2] Karisto P, Duplouy A, Vries C de, Kokko H (2022) Positive fitness effects help explain the broad range of Wolbachia prevalences in natural populations. bioRxiv, 2022.04.11.487824, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.04.11.487824

[3] Laven H (1956) Cytoplasmic Inheritance in Culex. Nature, 177, 141–142. https://doi.org/10.1038/177141a0

[4] Perreau J, Zhang B, Maeda GP, Kirkpatrick M, Moran NA (2021) Strong within-host selection in a maternally inherited obligate symbiont: Buchnera and aphids. Proceedings of the National Academy of Sciences, 118, e2102467118. https://doi.org/10.1073/pnas.2102467118

[5] Vavre F, Fleury F, Varaldi J, Fouillet P, Bouletreau M (2000) Evidence for Female Mortality in Wolbachia-Mediated Cytoplasmic Incompatibility in Haplodiploid Insects: Epidemiologic and Evolutionary Consequences. Evolution, 54, 191–200. https://doi.org/10.1111/j.0014-3820.2000.tb00019.x

[6] Zug R, Hammerstein P (2015) Bad guys turned nice? A critical assessment of Wolbachia mutualisms in arthropod hosts. Biological Reviews, 90, 89–111. https://doi.org/10.1111/brv.12098

[7] Zug R, Hammerstein P (2018) Evolution of reproductive parasites with direct fitness benefits. Heredity, 120, 266–281. https://doi.org/10.1038/s41437-017-0022-5

In social species conflicts among group members typically lead to the formation of dominance hierarchies with dominant individuals outcompeting other groups members and, in some extreme cases, suppressing reproduction of subordinates. It has therefore been typically assumed that dominant individuals have a higher breeding success than subordinates. However, previous work on mammals (mostly primates) revealed high variation, with some populations showing no evidence for a link between female dominance reproductive success, and a meta-analysis on primates suggests that the strength of this relationship is stronger for species with a longer lifespan [1]. Therefore, there is now a need to understand 1) whether dominance and reproductive success are generally associated across social mammals (and beyond) and 2) which factors explains the variation in the strength (and possibly direction) of this relationship.
In their preregistration, Shivani et al. [2] plan to perform a meta-analysis on 86 social mammal species to address these two points. More specifically, they will investigate whether the relationship between female dominance and reproductive success vary according to life history traits (e.g. stronger for species with large litter size), ecological conditions (e.g. stronger when resources are limited) and the social environment (e.g. stronger for cooperative breeders than for plural breeders).
The two reviewers and I were particularly positive and enthusiastic about this preregistration and only had minor comments that were nicely addressed by the authors. We found the background well-grounded in the existing literature and that the predictions were therefore clear and well-motivated. The methods were particularly transparent with a nicely annotated R script and the authors even simulated a dataset with the same structure as the actual data in order to make sure that the coding of the data handling and statistical analyses were appropriate (without being tempted to look at model outputs from the true dataset).
Perhaps one limitation to keep in mind once we will have the chance to look at the outcome of this study if that the dataset may not be fully representative of social species with dominance hierarchies. For example, the current dataset contains only one aquatic mammal (Mirounga angustirostris) as far as I can see, which is likely due to a lack of knowledge on such systems. Furthermore, not only mammals exhibit dominance hierarchies and it will be interesting to see if the results of the proposed study hold for other social taxa (and if not, what may explain their differences).
That being said, the proposed study will already offer a much broader overview of the relationship between dominance and reproductive success in animal societies and a better understanding for its variation. The reviewers and I believe it will make an important contribution to the fields of socio-ecology and evolutionary ecology. I therefore strongly recommend this preregistration and we are particularly looking forward to seeing the outcome of this exciting study.

References

[1] Majolo, B., Lehmann, J., de Bortoli Vizioli, A., & Schino, G. (2012). Fitness‐related benefits of dominance in primates. American journal of physical anthropology, 147(4), 652-660. doi: 10.1002/ajpa.22031
[2] Shivani, Huchard, E., Lukas, D. (2020). Preregistration - The effect of dominance rank on female reproductive success in social mammals In principle acceptance by PCI Ecology of the version 1.2 on 07 July 2020. https://github.com/dieterlukas/FemaleDominanceReproductionMetaAnalysis/blob/trunk/PreregistrationMetaAnalysis_RankSuccess.Rmd

In the field of ecology, there is a growing interest in machine (including deep) learning for processing and automatizing repetitive analyses on large amounts of images collected from camera traps, drones and smartphones, among others. These analyses include species or individual recognition and classification, counting or tracking individuals, detecting and classifying behavior. By saving countless times of manual work and tapping into massive amounts of data that keep accumulating with technological advances, machine learning is becoming an essential tool for ecologists. We refer to recent papers for more details on machine learning for ecology and evolution (Besson et al. 2022, Borowiec et al. 2022, Christin et al. 2019, Goodwin et al. 2022, Lamba et al. 2019, Nazir & Kaleem 2021, Perry et al. 2022, Picher & Hartig 2023, Tuia et al. 2022, Wäldchen & Mäder 2018).

In their paper, Nakagawa et al. (2023) conducted a systematic review of the literature on machine learning for wildlife imagery. Interestingly, the authors used a method unfamiliar to ecologists but well-established in medicine called rapid review, which has the advantage of being quickly completed compared to a fully comprehensive systematic review while being representative (Lagisz et al., 2022). Through a rigorous examination of more than 200 articles, the authors identified trends and gaps, and provided suggestions for future work. Listing all their findings would be counterproductive (you’d better read the paper), and I will focus on a few results that I have found striking, fully assuming a biased reading of the paper. First, Nakagawa et al. (2023) found that most articles used neural networks to analyze images, in general through collaboration with computer scientists. A challenge here is probably to think of teaching computer vision to the generations of ecologists to come (Cole et al. 2023). Second, the images were dominantly collected from camera traps, with an increase in the use of aerial images from drones/aircrafts that raise specific challenges. Third, the species concerned were mostly mammals and birds, suggesting that future applications should aim to mitigate this taxonomic bias, by including, e.g., invertebrate species. Fourth, most papers were written by authors affiliated with three countries (Australia, China, and the USA) while India and African countries provided lots of images, likely an example of scientific colonialism which should be tackled by e.g., capacity building and the involvement of local collaborators. Last, few studies shared their code and data, which obviously impedes reproducibility. Hopefully, with the journals’ policy of mandatory sharing of codes and data, this trend will be reversed. 

REFERENCES

Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF (2022) Towards the fully automated monitoring of ecological communities. Ecology Letters, 25, 2753–2775. https://doi.org/10.1111/ele.14123

Borowiec ML, Dikow RB, Frandsen PB, McKeeken A, Valentini G, White AE (2022) Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13, 1640–1660. https://doi.org/10.1111/2041-210X.13901

Christin S, Hervet É, Lecomte N (2019) Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632–1644. https://doi.org/10.1111/2041-210X.13256

Cole E, Stathatos S, Lütjens B, Sharma T, Kay J, Parham J, Kellenberger B, Beery S (2023) Teaching Computer Vision for Ecology. https://doi.org/10.48550/arXiv.2301.02211

Goodwin M, Halvorsen KT, Jiao L, Knausgård KM, Martin AH, Moyano M, Oomen RA, Rasmussen JH, Sørdalen TK, Thorbjørnsen SH (2022) Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook†. ICES Journal of Marine Science, 79, 319–336. https://doi.org/10.1093/icesjms/fsab255

Lagisz M, Vasilakopoulou K, Bridge C, Santamouris M, Nakagawa S (2022) Rapid systematic reviews for synthesizing research on built environment. Environmental Development, 43, 100730. https://doi.org/10.1016/j.envdev.2022.100730

Lamba A, Cassey P, Segaran RR, Koh LP (2019) Deep learning for environmental conservation. Current Biology, 29, R977–R982. https://doi.org/10.1016/j.cub.2019.08.016

Nakagawa S, Lagisz M, Francis R, Tam J, Li X, Elphinstone A, Jordan N, O’Brien J, Pitcher B, Sluys MV, Sowmya A, Kingsford R (2023) Rapid literature mapping on the recent use of machine learning for wildlife imagery. EcoEvoRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.32942/X2H59D

Nazir S, Kaleem M (2021) Advances in image acquisition and processing technologies transforming animal ecological studies. Ecological Informatics, 61, 101212. https://doi.org/10.1016/j.ecoinf.2021.101212

Perry GLW, Seidl R, Bellvé AM, Rammer W (2022) An Outlook for Deep Learning in Ecosystem Science. Ecosystems, 25, 1700–1718. https://doi.org/10.1007/s10021-022-00789-y

Pichler M, Hartig F Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, n/a. https://doi.org/10.1111/2041-210X.14061

Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T (2022) Perspectives in machine learning for wildlife conservation. Nature Communications, 13, 792. https://doi.org/10.1038/s41467-022-27980-y

Wäldchen J, Mäder P (2018) Machine learning for image-based species identification. Methods in Ecology and Evolution, 9, 2216–2225. https://doi.org/10.1111/2041-210X.13075

Environmental Risk Assessment (ERA) is a strategic conceptual framework to characterize the nature and magnitude of risks, to humans and biodiversity, of the release of chemical contaminants in the environment. Several measures have been suggested to enhance the science and application of ERA, including the identification and acknowledgment of uncertainties that potentially influence the outcome of risk assessments, and the appropriate consideration of temporal scale and its linkage to assessment endpoints [1].
Baudrot & Charles [2] proposed to approach these questions by coupling toxicokinetics-toxicodynamics models, which describe the time-course of processes leading to the adverse effects of a toxicant, with Bayesian inference. TKTD models separate processes influencing an organismal internal exposure (´toxicokinetics´, i.e., the uptake, bioaccumulation, distribution, biotransformation and elimination of a toxicant) from processes leading to adverse effects and ultimately its death (´toxicodynamics´) [3]. Although species and substance specific, the mechanistic nature of TKTD models facilitates the comparison of different toxicants, species, life stages, environmental conditions and endpoints [4].
Baudrot & Charles [2] investigated the use of a Bayesian framework to assess the uncertainties surrounding the calibration of General Unified Threshold Models of Survival (a category of TKTD) with data from standard toxicity tests, and their propagation to predictions of regulatory toxicity endpoints such as LC(x,t) [the lethal concentration affecting any x% of the population at any given exposure duration of time t] and MF(x,t) [an exposure multiplication factor leading to any x% effect reduction due to the contaminant at any time t].
Once calibrated with empirical data, GUTS models were used to explore individual survival over time, and under untested exposure conditions. Lethal concentrations displayed a strong curvilinear decline with time of exposure. For a given total amount of contaminant, pulses separated by short time intervals yielded higher mortality than pulses separated by long time intervals, as did few pulses of high amplitude when compared to multiple pulses of low amplitude. The response to a pulsed contaminant exposure was strongly influenced by contaminant depuration times. These findings highlight one important contribution of TKTD modelling in ecotoxicology: they represent just a few of the hundreds of exposure scenarios that could be mathematically explored, and that would be unfeasible or even unethical to conduct experimentally.
GUTS models were also used for interpolations or extrapolations of assessment endpoints, and their marginal distributions. A case in point is the incipient lethal concentration. The responses of model organisms to contaminants in standard toxicity tests are typically assessed at fixed times of exposure (e.g. 24h or 48h in the Daphnia magna acute toxicity test). However, because lethal concentrations are strongly time-dependent, it has been suggested that a more meaningful endpoint would be the incipient (i.e. asymptotic) lethal concentration when time of exposure increases to infinity. The authors present a mathematical solution for calculating the marginal distribution of such incipient lethal concentration, thereby providing both more relevant information and a way of comparing experiments, compounds or species tested for different periods of time.
Uncertainties were found to change drastically with time of exposure, being maximal at extreme values of x for both LC(x,t) and MF(x,t). In practice this means that assessment endpoints estimated when the effects of the contaminant are weak (such as LC10, the contaminant concentration resulting in the mortality of 10% of the experimental population), a commonly used assessment value in ERA, are prone to be highly variable.
The authors end with recommendations for improved experimental design, including (i) using assessment endpoints at intermediate values of x (e.g., LC50 instead of LC10) (ii) prolonging exposure and recording mortality over the course of the experiment (iii) experimenting one or few peaks of high amplitude close to each other when assessing pulsed exposure. Whereas these recommendations are not that different from current practices, they are based on a more coherent mechanistic grounding.
Overall, this and other contributions from Charles, Baudrot and their research group contribute to turn TKTD models into a real tool for Environmental Risk Assessment. Further enhancement of ERA´s science and application could be achieved by extending the use of TKTD models to sublethal rather than lethal effects, and to chronic rather than acute exposure, as these are more controversial issues in decision-making regarding contaminated sites.

References

[1] Dale, V. H., Biddinger, G. R., Newman, M. C., Oris, J. T., Suter, G. W., Thompson, T., ... & Chapman, P. M. (2008). Enhancing the ecological risk assessment process. Integrated environmental assessment and management, 4(3), 306-313. doi: 10.1897/IEAM_2007-066.1
[2] Baudrot, V., & Charles, S. (2018). Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics models. bioRxiv, 356469, ver. 3 peer-reviewed and recommended by PCI Ecol. doi: 10.1101/356469
[3] EFSA Panel on Plant Protection Products and their Residues (PPR), Ockleford, C., Adriaanse, P., Berny, P., Brock, T., Duquesne, S., ... & Kuhl, T. (2018). Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms. EFSA Journal, 16(8), e05377. doi: 10.2903/j.efsa.2018.5377
[4] Jager, T., Albert, C., Preuss, T. G., & Ashauer, R. (2011). General unified threshold model of survival-a toxicokinetic-toxicodynamic framework for ecotoxicology. Environmental science & technology, 45(7), 2529-2540. doi: 10.1021/es103092a