Submit a preprint

Latest recommendationsrsstwitter

IdTitleAuthorsAbstractPictureThematic fieldsRecommenderReviewers▲Submission date
24 May 2023
article picture

Evolutionary determinants of reproductive seasonality: a theoretical approach

When does seasonal reproduction evolve?

Recommended by ORCID_LOGO based on reviews by Francois-Xavier Dechaume-Moncharmont, Nigel Yoccoz and 1 anonymous reviewer

Have you ever wondered why some species breed seasonally while others do not? You might think it is all down to lattitude and the harshness of winters but it turns out it is quite a bit more complicated than that. A consequence of this is that climate change may result in the evolution of the degree of seasonal reproduction, with some species perhaps becoming less seasonal and others more so even in the same habitat. 

Burtschell et al. (2023) investigated how various factors influence seasonal breeding by building an individual-based model of a baboon population from which they calculated the degree of seasonality for the fittest reproductive strategy. They then altered key aspects of their model to examine how these changes impacted the degree of seasonality in the reproductive strategy. What they found is fascinating. 

The degree of seasonality in reproductive strategy is expected to increase with increased seasonality in the environment, decreased food availability, increased energy expenditure, and how predictable resource availability is. Interestingly, neither female cycle length nor extrinsic infant mortality influenced the degree of seasonality in reproduction.

What this means in reality for seasonal species is more challenging to understand. Some environments appear to be becoming more seasonal yet less predictable, and some species appear to be altering their daily energy budgets in response to changing climate in quite complex ways. As with pretty much everything in biology, Burtschell et al.'s work reveals much nuance and complexity, and that predicting how species might alter their reproductive timing is fraught with challenges.

The paper is very well written. With a simpler model it may have proven possible to achieve analytical solutions, but this is a very minor gripe. The reviewers were positive about the paper, and I have little doubt it will be well-cited. 

REFERENCES

Burtschell L, Dezeure J, Huchard E, Godelle B (2023) Evolutionary determinants of reproductive seasonality: a theoretical approach. bioRxiv, 2022.08.22.504761, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.08.22.504761

Evolutionary determinants of reproductive seasonality: a theoretical approachLugdiwine Burtschell, Jules Dezeure, Elise Huchard, Bernard Godelle<p style="text-align: justify;">Reproductive seasonality is a major adaptation to seasonal cycles and varies substantially among organisms. This variation, which was long thought to reflect a simple latitudinal gradient, remains poorly understood ...Evolutionary ecology, Life history, Theoretical ecologyTim Coulson Nigel Yoccoz2022-08-23 21:37:28 View
10 Oct 2018
article picture

Detecting within-host interactions using genotype combination prevalence data

Combining epidemiological models with statistical inference can detect parasite interactions

Recommended by based on reviews by Samuel Díaz Muñoz, Erick Gagne and 1 anonymous reviewer

There are several important topics in the study of infectious diseases that have not been well explored due to technical difficulties. One such topic is pursued by Alizon et al. in “Modelling coinfections to detect within-host interactions from genotype combination prevalences” [1]. Both theory and several important examples have demonstrated that interactions among co-infecting strains can have outsized impacts on disease outcomes, transmission dynamics, and epidemiology. Unfortunately, empirical data on pathogen interactions and their outcomes is often correlational making results difficult to decipher.
The analytical framework developed by Alizon et al. [1] infers the presence and strength of pathogen interactions through their impact on transmission dynamics using a novel application of Approximate Bayesian Computation (ABC)-regression to epidemiological data. Traditional analytic approaches identify pathogen interactions when the observed distribution of pathogens among hosts differ from ‘neutral’ expectations. However, deviations from this expectation are not only a result of inter-strain interactions but can be caused by many ecological interactions, such as heterogeneity in host contact networks. To overcome this difficulty, Alizon et al [1] develop an analytical framework that incorporates explicit epidemiological models to allow inference of interactions among strains of Human Papillomaviruses (HPV) even with other ecological interactions that impact the distribution of strains among hosts. Alizon et al also demonstrate that using more of the available data, including the specific combination of strains present in hosts and knowledge of the connectivity of the hosts (i.e., super-spreaders), leads to more accurate inferences of the strength and direction of within-host interactions among coinfecting strains. This method successfully identified data generated from models with high and moderate inter-strain interaction intensity when the host population was homogeneous and was only slightly less successful when the host population was heterogeneous (super-spreaders present). By comparison, some previously published analytical methods could identify only some inter-strain interactions in datasets generated from models with homogeneous host populations, but host heterogeneity obscured these interactions.
This manuscript makes seamless connections between basic viral biology and its epidemiological consequences by tying them together with realistic models, illustrating the fundamental utility of biological modeling. This analytical framework provides crucial tools for experimentalists, facilitating collaborations with theoreticians to better understand the epidemiological consequences of co-infections. In addition, the method is simple enough to be applied by a broad base of experimentalists to the many pathogens where co-infections are common. Thus, this paper has the potential to impact several research fields and public health practice. Those attempting to apply this method should note the potential limitations noted by the authors. For example, it is not designed to detect the mechanisms of inter-strain interactions (there is no within host component of the models) but to identify the existence of interactions through patterns indicative of these interactions while ruling out other sources that could cause the pattern. This approach is likely to be most accurate when strain identification within hosts is precise and unbiased - which is unlikely in many systems where samples are taken only from symptomatic cases and strain detection is not sufficiently sensitive – and when host contact networks can be reasonably estimated. Importantly, a priori knowledge of the set of possible epidemiological models is needed for accurate parameter estimates, which may be true for several prominent pathogens, but not be so for many other pathogens and symbionts. We look forward to future extensions of this framework where this restriction is relaxed. Alizon et al. [1] have provided a framework that will facilitate theoretical and empirical work on the impact of coinfections on infectious disease and should shape future public health data collection standards.

References

[1] Alizon, S., Murall, C.L., Saulnier, E., & Sofonea, M.T. (2018). Detecting within-host interactions using genotype combination prevalence data. bioRxiv, 256586, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/256586

Detecting within-host interactions using genotype combination prevalence dataSamuel Alizon, Carmen Lía Murall, Emma Saulnier, Mircea T Sofonea<p>Parasite genetic diversity can provide information on disease transmission dynamics but most methods ignore the exact combinations of genotypes in infections. We introduce and validate a new method that combines explicit epidemiological modelli...Eco-immunology & Immunity, Epidemiology, Host-parasite interactions, Statistical ecologyDustin Brisson Samuel Díaz Muñoz, Erick Gagne2018-02-01 09:23:26 View
24 May 2024
article picture

Effects of water nutrient concentrations on stream macroinvertebrate community stoichiometry: a large-scale study

The influence of water phosphorus and nitrogen loads on stream macroinvertebrate community stoichiometry

Recommended by ORCID_LOGO based on reviews by Thomas Guillemaud, Jun Zuo and 1 anonymous reviewer

The manuscript by Beck et al. (2024) investigates the effects of water phosphorus and nitrogen loads on stream macroinvertebrate community stoichiometry across France. Utilizing data from over 1300 standardized sampling events, this research finds that community stoichiometry is significantly influenced by water phosphorus concentration, with the strongest effects at low nitrogen levels.

The results demonstrate that the assumptions of Ecological Stoichiometry Theory apply at the community level for at least two dominant taxa and across a broad spatial scale, with probable implications for nutrient cycling and ecosystem functionality.

This manuscript contributes to ecological theory, particularly by extending Ecological Stoichiometry Theory to include community-level interactions, clarifying the impact of nutrient concentrations on community structure and function, and informing nutrient management and conservation strategies.

In summary, this study not only addresses a gap in community-level stoichiometric research but also delivers crucial empirical support for advancing ecological science and promoting environmental stewardship.

References

Beck M, Billoir E, Usseglio-Polatera P, Meyer A, Gautreau E and Danger M (2024) Effects of water nutrient concentrations on stream macroinvertebrate community stoichiometry: a large-scale study. bioRxiv, 2024.02.01.574823, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2024.02.01.574823

Effects of water nutrient concentrations on stream macroinvertebrate community stoichiometry: a large-scale studyMiriam Beck, Elise Billoir, Philippe Usseglio-Polatera, Albin Meyer, Edwige Gautreau, Michael Danger<p>Basal resources generally mirror environmental nutrient concentrations in the elemental composition of their tissue, meaning that nutrient alterations can directly reach consumer level. An increased nutrient content (e.g. phosphorus) in primary...Community ecology, Ecological stoichiometryHuihuang Chen Thomas Guillemaud, Jun Zuo, Anonymous2024-02-02 10:14:01 View
03 Jan 2024
article picture

Diagnosis of planktonic trophic network dynamics with sharp qualitative changes

A new approach to describe qualitative changes of complex trophic networks

Recommended by based on reviews by Tim Coulson and 1 anonymous reviewer

Modelling the temporal dynamics of trophic networks has been a key challenge for community ecologists for decades, especially when anthropogenic and natural forces drive changes in species composition, abundance, and interactions over time. So far, most modelling methods fail to incorporate the inherent complexity of such systems, and its variability, to adequately describe and predict temporal changes in the topology of trophic networks. 

Taking benefit from theoretical computer science advances, Gaucherel and colleagues (2024) propose a new methodological framework to tackle this challenge based on discrete-event Petri net methodology. To introduce the concept to naïve readers the authors provide a useful example using a simplistic predator-prey model.

The core biological system of the article is a freshwater trophic network of western France in the Charente-Maritime marshes of the French Atlantic coast. A directed graph describing this system was constructed to incorporate different functional groups (phytoplankton, zooplankton, resources, microbes, and abiotic components of the environment) and their interactions. Rules and constraints were then defined to, respectively, represent physiochemical, biological, or ecological processes linking network components, and prevent the model from simulating unrealistic trajectories. Then the full range of possible trajectories of this mechanistic and qualitative model was computed.

The model performed well enough to successfully predict a theoretical trajectory plus two trajectories of the trophic network observed in the field at two different stations, therefore validating the new methodology introduced here. The authors conclude their paper by presenting the power and drawbacks of such a new approach to qualitatively model trophic networks dynamics.

Reference

Cedric Gaucherel, Stolian Fayolle, Raphael Savelli, Olivier Philippine, Franck Pommereau, Christine Dupuy (2024) Diagnosis of planktonic trophic network dynamics with sharp qualitative changes. bioRxiv 2023.06.29.547055, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.06.29.547055

Diagnosis of planktonic trophic network dynamics with sharp qualitative changesCedric Gaucherel, Stolian Fayolle, Raphael Savelli, Olivier Philippine, Franck Pommereau, Christine Dupuy<p>Trophic interaction networks are notoriously difficult to understand and to diagnose (i.e., to identify contrasted network functioning regimes). Such ecological networks have many direct and indirect connections between species, and these conne...Community ecology, Ecosystem functioning, Food webs, Freshwater ecology, Interaction networks, Microbial ecology & microbiologyFrancis Raoul Tim Coulson2023-07-03 10:42:34 View
23 Oct 2023
article picture

The Moa the Merrier: Resolving When the Dinornithiformes Went Extinct

Are Moas ancient Lazarus species?

Recommended by ORCID_LOGO based on reviews by Tim Coulson and Richard Holdaway

Ancient human colonisation often had catastrophic consequences for native fauna. The North American Megafauna went extinct shortly after humans entered the scene and Madagascar suffered twice, before 1500 CE and around 1700 CE after the Malayan and European colonisation. Maoris colonised New Zealand by about 1300 and a century later the giant Moa birds (Dinornithiformes) sharply declined. But did they went extinct or are they an ancient example of Lazarus species, species thought to be extinct but still alive? Scattered anecdotes of late sightings of living Moas even up to the 20th century seem to suggest the latter. The quest for later survival has also a criminal aspect. Who did it, the Maoris or the white colonisers in the late 18th century?

The present work by Floe Foxon (2023) tries to settle this question. It uses a survival modelling approach and an assessment of the reliability of nearly 100 alleged sightings. The model favours the so-called overkill hypothesis, that Moas probably went extinct in the 15th century shortly after Maori colonisation. A small but still remarkable probability remained for survival up to 1770. Later sightings turned out to be highly unreliable.

The paper is important as it does not rely on subjective discussions of late sightings but on a probabilistic modelling approach with sensitivity testing prior applied to marsupials. As common in probabilistic approaches, the study does not finally settle the case. A probability of as much as 20% remained for late survival after 1450 CE. This is not improbable as New Zealand was sufficiently unexplored in those days to harbour a few refuges for late survivors. However, in this respect, it is a bit unfortunate that at the end of the discussion, the paper cites Heuvelmans, the founder of cryptozoology, and it mentions the ivory-billed woodpecker, which has recently been redetected. No Moa remains were found after 1450.

References

Foxon F (2023) The Moa the Merrier: Resolving When the Dinornithiformes Went Extinct. bioRxiv, 2023.08.07.552261, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.08.07.552261

The Moa the Merrier: Resolving When the Dinornithiformes Went ExtinctFloe Foxon<p style="text-align: justify;">The Moa (Aves: Dinornithiformes) are an extinct group of the ratite clade from New Zealand. The overkill hypothesis asserts that the first New Zealand settlers hunted the Moa to extinction by 1450 CE, whereas the st...Conservation biology, Human impact, Statistical ecology, ZoologyWerner Ulrich Tim Coulson, Richard Holdaway2023-08-08 17:14:30 View
20 Sep 2018
article picture

When higher carrying capacities lead to faster propagation

When the dispersal of the many outruns the dispersal of the few

Recommended by ORCID_LOGO based on reviews by Yuval Zelnik and 1 anonymous reviewer

Are biological invasions driven by a few pioneers, running ahead of their conspecifics? Or are these pioneers constantly being caught up by, and folded into, the larger flux of propagules from the established populations behind them?
In ecology and beyond, these two scenarios are known as "pulled" and "pushed" fronts, and they come with different expectations. In a pushed front, invasion speed is not just a matter of how good individuals are at dispersing and settling new locations. It becomes a collective, density-dependent property of population fluxes. And in particular, it can depend on the equilibrium abundance of the established populations inside the range, i.e. the species’ carrying capacity K, factoring in its abiotic environment and biotic interactions.
This realization is especially important because it can flip around our expectations about which species expand fast, and how to manage them. We tend to think of initial colonization and long-term abundance as two independent axes of variation among species or indeed as two ends of a spectrum, in the classic competition-colonization tradeoff [1]. When both play into invasion speed, good dispersers might not outrun good competitors. This is useful knowledge, whether we want to contain an invasion or secure a reintroduction.
In their study "When higher carrying capacities lead to faster propagation", Haond et al [2] combine mathematical analysis, Individual-Based simulations and experiments to show that various mechanisms can cause pushed fronts, whose speed increases with the carrying capacity K of the species. Rather than focus on one particular angle, the authors endeavor to demonstrate that this qualitative effect appears again and again in a variety of settings.
It is perhaps surprising that this notable and general connection between K and invasion speed has managed to garner so little fame in ecology. A large fraction of the literature employs the venerable Fisher-KPP reaction-diffusion model, which combines local logistic growth with linear diffusion in space. This model has prompted both considerable mathematical developments [3] and many applications to modelling real invasions [4]. But it only allows pulled fronts, driven by the small populations at the edge of a species range, with a speed that depends only on their initial growth rate r.
This classic setup is, however, singular in many ways. Haond et al [2] use it as a null model, and introduce three mechanisms or factors that each ensure a role of K in invasion speed, while giving less importance to the pioneers at the border.
Two factors, the Allee effect and demographic stochasticity, make small edge populations slower to grow or less likely to survive. These two factors are studied theoretically, and to make their claims stronger, the authors stack the deck against K. When generalizing equations or simulations beyond the null case, it is easy to obtain functional forms where the parameter K does not only play the role of equilibrium carrying capacity, but also affects dynamical properties such as the maximum or mean growth rate. In that case, it can trivially change the propagation speed, without it meaning anything about the role of established populations behind the front. Haond et al [2] avoid this pitfall by disentangling these effects, at the cost of slightly more peculiar expressions, and show that varying essentially nothing but the carrying capacity can still impact the speed of the invasion front.
The third factor, density-dependent dispersal, makes small populations less prone to disperse. It is well established empirically and theoretically that various biological mechanisms, from collective organization to behavioral switches, can prompt organisms in denser populations to disperse more, e.g. in such a way as to escape competition [5]. The authors demonstrate how this effect induces a link between carrying capacity and invasion speed, both theoretically and in a dispersal experiment on the parasitoid wasp, Trichogramma chilonis.
Overall, this study carries a simple and clear message, supported by valuable contributions from different angles. Although some sections are clearly written for the theoretical ecology crowd, this article has something for everyone, from the stray physicist to the open-minded manager. The collaboration between theoreticians and experimentalists, while not central, is worthy of note. Because the narrative of this study is the variety of mechanisms that can lead to the same qualitative effect, the inclusion of various approaches is not a gimmick, but helps drive home its main message. The work is fairly self-contained, although one could always wish for further developments, especially in the direction of more quantitative testing of these mechanisms.
In conclusion, Haond et al [2] effectively convey the widely relevant message that, for some species, invading is not just about the destination, it is about the many offspring one makes along the way.

References

[1] Levins, R., & Culver, D. (1971). Regional Coexistence of Species and Competition between Rare Species. Proceedings of the National Academy of Sciences, 68(6), 1246–1248. doi: 10.1073/pnas.68.6.1246
[2] Haond, M., Morel-Journel, T., Lombaert, E., Vercken, E., Mailleret, L., & Roques, L. (2018). When higher carrying capacities lead to faster propagation. BioRxiv, 307322. doi: 10.1101/307322
[3] Crooks, E. C. M., Dancer, E. N., Hilhorst, D., Mimura, M., & Ninomiya, H. (2004). Spatial segregation limit of a competition-diffusion system with Dirichlet boundary conditions. Nonlinear Analysis: Real World Applications, 5(4), 645–665. doi: 10.1016/j.nonrwa.2004.01.004
[4] Shigesada, N., & Kawasaki, K. (1997). Biological Invasions: Theory and Practice. Oxford University Press, UK.
[5] Matthysen, E. (2005). Density-dependent dispersal in birds and mammals. Ecography, 28(3), 403–416. doi: 10.1111/j.0906-7590.2005.04073.x

When higher carrying capacities lead to faster propagationMarjorie Haond, Thibaut Morel-Journel, Eric Lombaert, Elodie Vercken, Ludovic Mailleret & Lionel Roques<p>This preprint has been reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100004). Finding general patterns in the expansion of natural populations is a major challenge in ecology and invasion biology...Biological invasions, Colonization, Dispersal & Migration, Experimental ecology, Population ecology, Spatial ecology, Metacommunities & Metapopulations, Theoretical ecologyMatthieu Barbier Yuval Zelnik2018-04-25 10:18:48 View