Population dynamics and stock assessment models are only as good as the data used to parameterise them. For Atlantic salmon (Salmo salar) populations, a critical parameter may be frequency of precocious maturation. Indeed, the young males (parrs) that mature early, before leaving the river to reach the ocean, can contribute to reproduction but have much lower survival rates afterwards. The authors cite evidence of the potentially major consequences of this alternate reproductive strategy. So, to be parameterised correctly, it needs to be assessed correctly. Cue the ultrasound machine.

Through a thorough analysis of data collected on 850 individuals [1], over three years, the authors clearly show that the non-invasive examination of the internal cavity of young fishes to look for gonads, using a portable ultrasound machine, provides reliable and replicable evidence of precocious maturation. They turned into OB-GYN for salmons (albeit for male salmons!) and it worked. While using ultrasounds to detect fish gonads is not a new idea (early attempts for salmonids date back to the 80s [2]), the value here is in the comparison with the classic visual inspection technique (which turns out to be less reliable) and the fact that ultrasounds can now easily be carried out in the field.

Beyond the potentially important consequences of this new technique for the correct assessment of salmon population dynamics, the authors also make the case for the acquisition of more reliable individual-level data in ecological studies, which I applaud.

References.

[1] Nevoux M, Marchand F, Forget G, Huteau D, Tremblay J, and Destouches J-P. (2019). Field assessment of precocious maturation in salmon parr using ultrasound imaging. bioRxiv 425561, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/425561
[2] Reimers E, Landmark P, Sorsdal T, Bohmer E, Solum T. (1987). Determination of salmonids’ sex, maturation and size: an ultrasound and photocell approach. Aquaculture Magazine.13:41-44.

In his 1972 paper “Will a Large Complex System Be Stable?” [1], May challenges the idea that large communities are more stable than small ones. This was the beginning of a fundamental debate that still structures an entire research area in ecology: the diversity-stability debate [2]. The most salient strength of May’s work was to use a mathematical argument to refute an idea based on the observations that simple communities are less stable than large ones. Using the formalism of dynamical systems and a major results on the distribution of the eigen values for random matrices, May demonstrated that the addition of random interactions destabilizes ecological communities and thus, rich communities with a higher number of interactions should be less stable. But May also noted that his mathematical argument holds true only if ecological interactions are randomly distributed and thus concluded that this must not be true! This is how the contradiction between mathematics and empirical observations led to new developments in the study of ecological networks.
Since 1972, the theoretical corpus of ecology has advanced, building on the formalism of dynamical systems, ecologists have revealed that ecological interactions are indeed not randomly distributed [3,4], but general rules are still missing and we are far from understanding what determine the exact network topology of a given community. One promising avenue is to understand the relationship between different facets of the concept of stability [5,6]. Indeed, the classical approach to determine whether a system is stable is qualitative: if a system returns to its equilibrium when it is slightly moved away from it, then the system is considered stable. But there are several other aspects that are worth scrutinizing. For instance, when a system returns to its equilibrium, one can characterize the corresponding transient dynamics [7,8], that is asking fundamental questions such as: what is the trajectory of return? How long does it take to return to the equilibrium? Another fundamental question is whether the system remains qualitatively stable when the distributions of interactions strengths change? From a biological standpoint, all of these questions matter as all these aspects of stability may partially explain the actual structure of ecological networks, and hence, frameworks that integrate several facets of stability are much needed.
The study by Arnoldi et al. [9] is a significant step towards such a framework. The strength of their formalism is threefold. First, instead of considering separately the system and its perturbations, they considering the fluctuations of a perturbed ecological systems and thus, perturbations are parts of the ecological system. Second, they use of a broad definition of perturbation that encompasses the types of perturbations (whether the individual respond synchronously or not), their intensity and their direction (how the perturbations are correlated across species). Third, they quantify the instability of the system using variability which integrates the consequences of perturbations over the whole set of species of a community: such a measure is comparable across communities and accounts for the trivial effect of the perturbations on the system dynamics.
Using this framework, the authors show that interactions within a stable community leads to a general relationship between variability and the abundance of individually perturbed species: if individuals of species respond in synchrony to a perturbation, then the more abundant the species perturbed the higher the variability of the system, but the relationship is reverse when individual respond asynchronously. A direct implications of these results for the classical debate is that the diversity-stability relationship is negative for the former type of perturbations (as in May’s seminal paper) but positive for the latter type. Hence, the rigorous work of Arnoldi and colleagues sheds a new light upon the classical debate: the nature of the perturbation regime prevailing within a community affects the slope of the diversity-stability relationships and given the vast diversity of ecological communities, this may very well be one of the reasons why the debate still endures.
From a historical perspective, it is interesting that ecologists have gone from looking at random webs to structured webs and now, in a sense, Arnoldi et al. are unpacking the role of differentially structured perturbations. The work they achieved will doubtlessly be followed by further theoretical investigations. One natural research avenue is to revisit the role of the topology of ecological networks with this framework: how the distribution of interactions and their strength affect the general relationship they unravel? Finally, this study demonstrate that the impact of the abundance of a species on the variability of the system depends on the nature of the perturbation regime and so the distribution of species abundances within a community should be determined by the prevailing perturbation regime which is a prediction that remains to be tested.

References

[1] May, Robert M (1972). Will a Large Complex System Be Stable? Nature 238, 413–414. doi: 10.1038/238413a0
[2] McCann, Kevin Shear (2000). The Diversity–Stability Debate. Nature 405, 228–233. doi: 10.1038/35012234
[3] Rooney, Neil, Kevin McCann, Gabriel Gellner, and John C. Moore (2006). Structural Asymmetry and the Stability of Diverse Food Webs. Nature 442, 265–269. doi: 10.1038/nature04887
[4] Jacquet, Claire, Charlotte Moritz, Lyne Morissette, Pierre Legagneux, François Massol, Philippe Archambault, and Dominique Gravel (2016). No Complexity–Stability Relationship in Empirical Ecosystems. Nature Communications 7, 12573. doi: 10.1038/ncomms12573
[5] Donohue, Ian, Helmut Hillebrand, José M. Montoya, Owen L. Petchey, Stuart L. Pimm, Mike S. Fowler, Kevin Healy, et al. (2016). Navigating the Complexity of Ecological Stability. Ecology Letters 19, 1172–1185. doi: 10.1111/ele.12648
[6] Arnoldi, Jean-François, and Bart Haegeman (2016). Unifying Dynamical and Structural Stability of Equilibria. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 472, 20150874. doi: 10.1098/rspa.2015.0874
[7] Caswell, Hal, and Michael G. Neubert (2005). Reactivity and Transient Dynamics of Discrete-Time Ecological Systems. Journal of Difference Equations and Applications 11, 295–310. doi: 10.1080/10236190412331335382
[8] Arnoldi, J-F., M. Loreau, and B. Haegeman (2016). Resilience, Reactivity and Variability: A Mathematical Comparison of Ecological Stability Measures. Journal of Theoretical Biology 389, 47–59. doi: 10.1016/j.jtbi.2015.10.012
[9] Arnoldi, Jean-Francois, Michel Loreau, and Bart Haegeman. (2019). The Inherent Multidimensionality of Temporal Variability: How Common and Rare Species Shape Stability Patterns.” BioRxiv, 431296, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/431296

Behavioral plasticity, which is a subset of phenotypic plasticity, is an important component of foraging, defense against predators, mating, and many other behaviors. More specifically, behavioral flexibility, in this study, captures how quickly individuals adapt to new circumstances. In cases where individuals disperse to new environments, which often occurs in range expansions, behavioral flexibility is likely crucial to the chance that individuals can establish in these environments. Thus, it is important to understand how best to measure behavioral flexibility and how measures of such flexibility might vary across individuals and behavioral contexts and with other measures of learning and problem solving.
In this preregistration, Logan and colleagues propose to use a long-term study of the great-tailed grackle to measure how much they can manipulate behavioral flexibility in a reversal learning task, how much behavioral flexibility in one task predicts flexibility in another task and in problem solving a new task, and how robust these patterns are within individuals and across tasks. Logan and colleagues lay out their hypotheses and predictions for each experiment in a clear and concise manner. They also are very clear about the details of their study system, such as how they determined the number of trials they use in their learning reversal experiments, and how those details have influenced their experimental design. Further, given that the preregistration uses RMarkdown and is stored on GitHub (as are other studies in the larger project), their statistical code and its history of modification are easily available. This is a crucial component of making research more reproducible, which is a recent emphasis in behavioral sciences more broadly.
Reviewers of this preregistration found the study of substantial merit. The authors have responded to the reviewers' comments and their revisions have made the preregistration much clearer and cogent. I am happy to recommend this preregistration.

Parasitism requires parasites and hosts to meet and is therefore conditioned by their respective dispersal abilities. While dispersal has been studied in a number of wild vertebrates (including in relation to infection risk), we still have poor knowledge of the movements of their parasites. Yet we know that many parasites, and in particular vectors transmitting pathogens from host to host, possess the ability to move actively during at least part of their lives.
So... how far does a vector go – and is this reflected in the population structure of the pathogens they transmit? This is the question addressed by Rataud et al. [1], who provide the first attempt at using capture-mark-recapture to estimate not only functional dispersal, but also detection probability and survival in a wild parasite that is also a vector for other pathogens.
The authors find that (i) functional dispersal of soft ticks within a gull colony is very limited. Moreover, they observe unexpected patterns: (ii) experimental displacement of ticks does not induce homing behaviour, and (iii) despite lower survival, tick dispersal was lower in nests not containing hosts than in successful nests.
These results contrast with expectations based on the distribution of infectious agents. Low tick dispersal within the colony, combined with host territoriality during breeding and high site fidelity between years should result in a spatially structured distribution of infectious agents carried by ticks. This is not the case here. One possible explanation could be that soft ticks live for much longer than a breeding season, and that they disperse at other times of year to a larger extent than usually assumed.
This study represents one chapter of a story that will likely keep unfolding. It raises fascinating questions, and illustrates the importance of basic knowledge of parasite ecology and behaviour to better understand pathogen dynamics in the wild.

References
[1] Rataud A., Dupraz M., Toty C., Blanchon T., Vittecoq M., Choquet R. & McCoy K.D. (2019). Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite. Zenodo, 2592114. Ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.5281/zenodo.2592114

What kinds of studies are most needed to understand the effects of global change on nature? Two deficiencies stand out: lack of long-term studies [1] and lack of data on species interactions [2]. The paper by Mennerat and colleagues [3] is particularly valuable because it addresses both of these shortcomings. The first one is obvious. Our understanding of the impact of climate on biota improves with longer times series of observations. Mennerat et al. [3] analysed an impressive 18-year series from multiple sites to search for trends in parasitism rates across a range of temperatures. The second deficiency (lack of species interaction data) is perhaps not yet fully appreciated, despite studies pointing this out ten years ago [2,4]. The focus is often on species range limits and how taking species interactions into account changes species range predictions based on climate alone (climate envelope models; [5]). But range limits are not everything, as the function of a species (or community, network, etc.) ultimately depends on the strengths of species interactions and not only on the presence or absence of a given species [2,4]. Mennerat et al. [3] show that in the case of birds and their nest parasites, it is the strength of the interaction that has changed, while the species involved stayed the same. Mennerat et al. [3] found nest parasitism to increase with temperature at the nestling stage. They have also searched for trends of parasitism dynamics dependence on the host, but did not find any, probably because the nest parasites are generalists and attack other bird species within the study sites. This study thus draws attention to wider networks of interacting species, and we urgently need more data to predict how interaction networks will rewire with progressing environmental change [6,7].

References

[1] Lindenmayer, D.B., Likens, G.E., Andersen, A., Bowman, D., Bull, C.M., Burns, E., et al. (2012). Value of long-term ecological studies. Austral Ecology, 37(7), 745–57. doi: 10.1111/j.1442-9993.2011.02351.x
[2] Tylianakis, J.M., Didham, R.K., Bascompte, J. & Wardle, D.A. (2008). Global change and species interactions in terrestrial ecosystems. Ecology Letters, 11(12), 1351–63. doi: 10.1111/j.1461-0248.2008.01250.x
[3] Mennerat, A., Charmantier, A., Hurtrez-Bousses, S., Perret, P. & Lambrechts, M.M. (2019). Parasite intensity is driven by temperature in a wild bird. bioRxiv, 323311. Ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/323311
[4] Gilman, S.E., Urban, M.C., Tewksbury, J., Gilchrist, G.W. & Holt, R.D. (2010). A framework for community interactions under climate change. Trends in Ecology & Evolution, 25(6), 325–31. doi: 10.1016/j.tree.2010.03.002
[5] Louthan, A.M., Doak, D.F. & Angert, A.L. (2015). Where and when do species interactions set range limits? Trends in Ecology & Evolution, 30(12), 780–92. doi: 10.1016/j.tree.2015.09.011
[6] Bartley, T.J., McCann, K.S., Bieg, C., Cazelles, K., Granados, M., Guzzo, M.M., et al. (2019). Food web rewiring in a changing world. Nature Ecology & Evolution, 3(3), 345–54. doi: 10.1038/s41559-018-0772-3
[7] Staniczenko, P.P.A., Lewis, O.T., Jones, N.S. & Reed-Tsochas, F. (2010). Structural dynamics and robustness of food webs. Ecology Letters, 13(7), 891–9. doi: 10.1111/j.1461-0248.2010.01485.x