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

Although monitoring the prevalence of pathogens in wildlife is crucial, there are logistical constraints that make this difficult, costly, and unpractical. This problem is often compounded when attempting to measure the temporal dynamics of prevalence. To improve the detection rate, a commonly used technique is pooling samples, where multiple individuals are analyzed at once. Yet, this introduces further potential biases: low-prevalence samples are effectively diluted through pooling, creating a false negative risk; negative samples are masked by the inclusion of positive samples, possibly artificially inflating the estimate of prevalence (and masking the inter-sample variability).

In their contribution, Borremans et al. (2024) come up with a modelling technique to provide accurate predictions of prevalence dynamics using a mix of pooled and individual samples. Because this model represents the pooling of individual samples as a complete mixing process, it can accurately estimate the prevalence dynamics from pooled samples only.

It is particularly noteworthy that the model provides an estimation of the false negative rate of the test. When there are false negatives (or more accurately, when the true rate at which false negatives happens), the value of the effect coefficients for individual-level covariates are likely to be off, potentially by a substantial amount. But besides more accurate coefficient estimation, the actual false negative rate is important information about the overall performance of the infection test.

The model described in this article also allows for a numerical calculation of the probability density function of infection. It is worth spending some time on how this is achieved, as I found the approach relying on combinatorics to be particularly interesting. When pooling, both the number of individuals that are mixed is known, and so is the measurement made on the pooled samples. The question is to figure out the number of individuals that because they are infectious, contribute to this score. The approach used by the authors is to draw (with replacement) possible positive and negative test outcomes assuming a number of positive individuals, and from this to estimate a pathogen concentration in the positive samples. This pathogen concentration can be transformed into its test outcome, and this value taken over all possible combinations is a conditional estimate of the test outcome, knowing the number of pooled individuals, and estimating the number of positive ones.

This approach is where the use of individual samples informs the model: by providing additional corrections for the relative volume of sample each individual provides, and by informing the transformation of test values into virus concentrations.

The authors make a strong case that their model can provide robust estimates of prevalence even in the presence of common field epidemiology pitfalls, and notably incomplete individual-level information. More importantly, because the model can work from pooled samples only, it gives additional value to samples that would otherwise have been discarded because they did not allow for prevalence estimates.

References

Benny Borremans, Caylee A. Falvo, Daniel E. Crowley, Andrew Hoegh, James O. Lloyd-Smith, Alison J. Peel, Olivier Restif, Manuel Ruiz-Aravena, Raina K. Plowright (2024) Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples. bioRxiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2023.11.02.565200

Which biological traits modulate species distribution has historically been and still is one of the core questions of the macroecology and biogeography agenda [1, 2]. As most of the Earth surface has been modified by human activities [3] understanding the strategies that allow species to inhabit human-dominated landscapes will be key to explain species geographic distribution in the Anthropocene. In this vein, Logan et al. [4] are working on a long-term and integrative project aimed to investigate how great-tailed grackles rapidly expanded their geographic range into North America [4]. Particularly, they want to determine which is the role of behavioral flexibility, i.e. an individual’s ability to modify its behavior when circumstances change based on learning from previous experience [5], in rapid geographic range expansions. The authors are already working in a set of complementary questions described in pre-registrations that have already been recommended at PCI Ecology: (1) Do individuals with greater behavioral flexibility rely more on causal cognition [6]? (2) Which are the mechanisms that lead to behavioral flexibility [7]? (3) Does the manipulation of behavioral flexibility affect exploration, but not boldness, persistence, or motor diversity [8]? (4) Can context changes improve behavioral flexibility [9]?
In this new pre-registration, they aim to determine whether the more behaviorally flexible individuals have more flexible foraging behaviors (i.e. use a wider variety of foraging techniques in the wild and eat a larger number of different foods), habitat use (i.e. higher microhabitat richness) and social relationships (i.e., are more likely to have a greater number of bonds or stronger bonds with other individuals; [4]). The project is ambitious, combining both the experimental characterization of individuals’ behavioral flexibility and the field characterization of the foraging and social behavior of those individuals and of wild ones.
The current great-tailed grackles project will be highly relevant to understand rapid geographic range expansions in a changing world. In this vein, this pre-registration will particularly help to go one step further in our understanding of behavioral flexibility as a determinant of species geographic distribution. Logan et al. [4] pre-registration is very well designed, main and alternative hypotheses have been thought and written and methods are presented in a very detailed way, which includes the R codes that authors will use in their analyses. Authors have answered in a very detailed way each comment that reviewers have pointed out and modified the pre-registration accordingly, which we consider highly improved the quality of this work. That is why we strongly recommend this pre-registration and look forward to see the results.

References

[1] Gaston K. J. (2003) The structure and dynamics of geographic ranges. Oxford series in Ecology and Evolution. Oxford University Press, New York.
[2] Castro-Insua, A., Gómez‐Rodríguez, C., Svenning, J.C., and Baselga, A. (2018) A new macroecological pattern: The latitudinal gradient in species range shape. Global ecology and biogeography, 27(3), 357-367. doi: 10.1111/geb.12702
[3] Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., et al. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520(7545), 45–50. doi: 10.1038/nature14324
[4] Logan CJ, McCune K, Bergeron L, Folsom M, Lukas D. (2019). Is behavioral flexibility related to foraging and social behavior in a rapidly expanding species? In principle recommendation by Peer Community In Ecology. http://corinalogan.com/Preregistrations/g_flexforaging.html
[5] Mikhalevich, I., Powell, R., and Logan, C. (2017). Is Behavioural Flexibility Evidence of Cognitive Complexity? How Evolution Can Inform Comparative Cognition. Interface Focus 7: 20160121. doi: 10.1098/rsfs.2016.0121.
[6] Fronhofer, E. (2019) From cognition to range dynamics: advancing our understanding of macroecological patterns. Peer Community in Ecology, 100014. doi: 10.24072/pci.ecology.100014
[7] Vogel, E. (2019) Adapting to a changing environment: advancing our understanding of the mechanisms that lead to behavioral flexibility. Peer Community in Ecology, 100016. doi: 10.24072/pci.ecology.100016
[8] Van Cleve, J. (2019) Probing behaviors correlated with behavioral flexibility. Peer Community in Ecology, 100020. doi: 10.24072/pci.ecology.100020
[9] Coulon, A. (2019) Can context changes improve behavioral flexibility? Towards a better understanding of species adaptability to environmental changes. Peer Community in Ecology, 100019. doi: 10.24072/pci.ecology.100019

Alarm calls are an important acoustic signal that can decide the life or death of an individual. Many birds are able to vary their alarm calls to provide more accurate information on e.g. urgency or even the type of a threatening predator. According to the acoustic adaptation hypothesis, the habitat plays an important role too in how acoustic patterns get transmitted. This is of particular interest for range-expanding species that will face new environmental conditions along the leading edge. One could hypothesize that the alarm call repertoire of a species could increase in newly founded ranges to incorporate new habitats and threats individuals might face. Hence selection for a larger active vocabulary might be beneficial for new colonizers. Using the Great-Tailed Grackle (Quiscalus mexicanus) as a model species, Samantha Bowser from Arizona State University and Maggie MacPherson from Louisiana State University want to find out exactly that. 

The Great-Tailed Grackle is an appropriate species given its high vocal diversity. Also, the species consists of different subspecies that show range expansions along the northern range edge yet to a varying degree. Using vocal experiments and field recordings the researchers have a high potential to understand more about the acoustic adaptation hypothesis within a range dynamic process. 

Over the course of this assessment, the authors incorporated the comments made by two reviewers into a strong revision of their research plans. With that being said, the few additional comments made by one of the initial reviewers round up the current stage this interesting research project is in. 

To this end, I can only fully recommend the revised research plan and am much looking forward to the outcomes from the author’s experiments, modeling, and field data. With the suggestions being made at such an early stage I firmly believe that the final outcome will be highly interesting not only to an ornithological readership but to every ecologist and biogeographer interested in drivers of range dynamic processes.

References

Bowser, S., MacPherson, M. (2021). A study on the role of social information sharing leading to range expansion in songbirds with large vocal repertoires: Enhancing our understanding of the Great-Tailed Grackle (Quiscalus mexicanus) alarm call. In principle recommendation by PCI Ecology. https://doi.org/10.17605/OSF.IO/2UFJ5. Version 3

Along with the generalization of open-access practices, large, heterogeneous datasets are becoming increasingly available to ecologists (Farley et al. 2018). While such data offer exciting opportunities for unveiling original patterns and trends, they also raise new challenges regarding how to extract relevant information and actually improve our knowledge of complex ecological systems, beyond purely descriptive correlations (Dietze 2017, Farley et al. 2018).

In this work, Caumette et al. (2024) develop an original ecoinformatics approach to relate multi-scale environmental factors to the temporal dynamics of a major pest in mango orchards. Their method relies on the recent tree-boosting method GPBoost (Sigrist 2022) to hierarchize the influence of environmental factors of heterogeneous nature (e.g., orchard composition and management; landscape structure; climate) on the emergence date of the oriental fruit fly, Bactrocera dorsalis. As boosting methods allows the analysis of high-dimensional data, they are particularly adapted to the exploration of such datasets, to uncover unexpected, potentially complex dependencies between ecological dynamics and multiple environmental factors (Farley et al. 2018). In this article, Caumette et al. (2024) make a special effort to guide the reader step by step through their complex analysis pipeline to make it broadly understandable to the average ecologist, which is no small feat. I particularly welcome this commitment, as making new, cutting-edge analytical methods accessible to a large community of science practitioners with varying degrees of statistical or programming expertise is a major challenge for the future of quantitative ecology. 

The main result of Caumette et al. (2024) is that temperature and humidity conditions both at the local and regional scales are the main predictors of B. dorsalis emergence date, while orchard management practices seem to have relatively little influence. This suggests that favourable climatic conditions may allow the persistence of small populations of B. dorsalis over the dry season, which may then act as a propagule source for early re-infestations. However, as the authors explain, the resulting regression model is not designed for predictive purposes and should not at this stage be used for decision-making in pest management. Its main interest rather resides in identifying potential key factors favoring early infestations of B. dorsalis, and help focusing future experimental field studies on the most relevant levers for integrated pest management in mango orchards.

In a wider perspective, this work also provides a convincing proof-of-concept for the use of boosting methods to identify the most influential factors in large, multivariate datasets in a variety of ecological systems. It is also crucial to keep in mind that the current exponential growth in high-throughput environmental data (Lucivero 2020) could quickly come into conflict with the need to reduce the environmental footprint of research (Mariette et al. 2022). In this context, robust and accessible methods for extracting and exploiting all the information available in already existing datasets might prove essential to a sustainable pursuit of science.

References
 
Caumette C, Diatta P, Piry S, Chapuis M-P, Faye E, Sigrist F, Martin O, Papaïx J, Brévault T, Berthier K. 2024. Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese orchards. bioRxiv 2023.11.10.566583, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.1101/2023.11.10.566583

Dietze MC. 2017. Ecological Forecasting. Princeton University Press
 
Farley SS, Dawson A, Goring SJ, Williams JW. 2018. Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions. BioScience, 68, 563–576, https://doi.org/10.1093/biosci/biy068
 
Lucivero F. 2020. Big Data, Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives. Science and Engineering Ethics 26, 1009–1030. https://doi.org/10.1007/s11948-019-00171-7

Mariette J, Blanchard O, Berné O, Aumont O, Carrey J, Ligozat A-L, Lellouch E, Roche P-E, Guennebaud G, Thanwerdas J, Bardou P, Salin G, Maigne E, Servan S, Ben-Ari T 2022. An open-source tool to assess the carbon footprint of research. Environmental Research: Infrastructure and Sustainability, 2022. https://dx.doi.org/10.1088/2634-4505/ac84a4
 
Sigrist F. 2022. Gaussian process boosting. The Journal of Machine Learning Research, 23, 10565-10610. https://jmlr.org/papers/v23/20-322.html