Submit a preprint

Latest recommendationsrsstwitter

IdTitleAuthorsAbstractPictureThematic fieldsRecommender▲ReviewersSubmission date
20 Jun 2019
article picture

Sexual segregation in a highly pagophilic and sexually dimorphic marine predator

Sexual segregation in a sexually dimorphic seabird: a matter of spatial scale

Recommended by based on reviews by Dries Bonte and 1 anonymous reviewer

Sexual segregation appears in many taxa and can have important ecological, evolutionary and conservation implications. Sexual segregation can take two forms: either the two sexes specialise in different habitats but share the same area (habitat segregation), or they occupy the same habitat but form separate, unisex groups (social segregation) [1,2]. Segregation would have evolved as a way to avoid, or at least, reduce intersexual competition.
Testing whether social or habitat segregation is at play necessitates the use of combined approaches to determine the spatial scale at which segregation occurs. This enterprise is even more challenging when studying marine species, which travel over long distances to reach their foraging areas. This is what Barbraud et al. [3] have endeavoured on the snow petrel (Pagodroma nivea), a sexually dimorphic, polar seabird. Studying sexual segregation at sea requires tools for indirect measures of habitat use and foraging tactics. During the incubation period, in a colony based at Pointe Geologie, Adelie land, East Antarctica, the team has equipped birds with GPS loggers to analyse habitat use and foraging behaviour. It has also compared short-, mid-, and long-term stable isotopic profiles, from plasma, blood cells, and feather samples, respectively.
Barbraud et al. [3] could not detect any evidence for sexual segregation in space use. Furthermore, the two sexes showed similar δ13C profiles, illustrating similar foraging latitudes, and indicating no sexual segregation at large spatial scales. Snow petrels all forage exclusively in the sea ice environment formed over the deep Antarctic continental shelf. The authors, however, found other forms of segregation: males consistently foraged at higher sea ice concentrations than females. Males also fed on higher trophic levels than females. Therefore, male and female snow petrels segregate at a smaller spatial scale, and use different foraging tactics and diet specialisations. Females also took shorter foraging trips than males, with higher mass gain that strongly benefit from higher sea ice concentration. Mass gain in males increased with the length of their foraging trip at sea ice areas.
The authors conclude that high sea ice concentration offers the most favourable foraging habitat for snow petrels, and thus that intersexual competition may drive females away from high sea ice areas. This study shows that combining information from different tools provides an elegant way of isolating the potential factors driving sexual segregation and the spatial scales at which it occurs.


[1] Conradt, L. (2005). Definitions, hypotheses, models and measures in the study of animal segregation. In Sexual segregation in vertebrates: ecology of the two sexes (Ruckstuhl K.E. and Neuhaus, P. eds). Cambridge University Press, Cambridge, United Kingdom. Pp:11–34.
[2] Ruckstuhl, K. E. (2007). Sexual segregation in vertebrates: proximate and ultimate causes. Integrative and Comparative Biology, 47(2), 245-257. doi: 10.1093/icb/icm030
[3] Barbraud, C., Delord, K., Kato, A., Bustamante, P., & Cherel, Y. (2018). Sexual segregation in a highly pagophilic and sexually dimorphic marine predator. bioRxiv, 472431, ver. 3 peer-reviewed and recommended bt PCI Ecology. doi: 10.1101/472431

Sexual segregation in a highly pagophilic and sexually dimorphic marine predatorChristophe Barbraud, Karine Delord, Akiko Kato, Paco Bustamante, Yves Cherel<p>Sexual segregation is common in many species and has been attributed to intra-specific competition, sex-specific differences in foraging efficiency or in activity budgets and habitat choice. However, very few studies have simultaneously quantif...Foraging, Marine ecologyDenis Réale Dries Bonte, Anonymous2018-11-19 13:40:59 View
08 Aug 2020
article picture

Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator

While the quoll’s away, the mice will play… and the seeds will pay

Recommended by based on reviews by 2 anonymous reviewers

A predator can strongly influence the demography of its prey, which can have profound carryover effects on the trophic network; so-called density-mediated indirect interactions (DMII; Werner and Peacor 2003; Schmitz et al. 2004; Trussell et al. 2006). Furthermore, a novel predator can alter the phenotypes of its prey for traits that will change prey foraging efficiency. These trait-mediated indirect interactions may in turn have cascading effects on the demography and features of the basal resources consumed by the intermediate consumer (TMIII; Werner and Peacor 2003; Schmitz et al. 2004; Trussell et al. 2006), but very few studies have looked for these effects (Trusell et al. 2006). The study “Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator”, by Jolly et al. (2020) is therefore a much-needed addition to knowledge in this field. The authors have profited from a rare introduction of Northern quolls (Dasyurus hallucatus) on an Australian island, to examine both the density-mediated and trait-mediated indirect interactions with grassland melomys (Melomys burtoni) and the vegetation of their woodland habitat.
Jolly et al. (2020) compared melomys populations in four quoll-invaded and three quoll-free sites on the same island. Using capture-mark-recapture methods, they found a lower survival and decreased population size in quoll-invaded sites compared to quoll-free sites. Although they acknowledge that this decline could be attributable to either the direct effects of the predator or to a wildfire that occurred early in the experiment in the quoll-invaded sites, the authors argue that the wildfire alone cannot explain all of their results.
Beyond demographic effects, Jolly et al. (2020) also examined risk taking, foraging behaviour, and predator avoidance in melomys. Quoll presence was first associated with a strong decrease in risk taking in melomys, but the difference disappeared over the three years of study, indicating a possible adjustment by the prey. In quoll-invaded sites, though, melomys continued to be more neophobic than in the quoll-free sites throughout the study. Furthermore, in a seed (i.e. wheat) removal experiment, Jolly et al. (2020) measured how melomys harvested seeds in the presence or absence of predator scents. In both quoll-invaded and quoll-free sites, melomys density increased seed harvest efficiency. Melomys also removed less seeds in quoll-invaded sites than in quoll-free sites, supporting both the DMII and TMII hypotheses. However, in the quoll-invaded sites only, melomys foraged less on predator-scented seed patches than on unscented ones, trading foraging efficiency for an increased safety against predators, and this effect increased across the years. This last result indicates that predators can indirectly influence seed consumption through the trade-off between foraging and predator avoidance, strongly supporting the TMII hypothesis.
Ideally, the authors would have run a nice before-after, impact-control design, but nature does not always allow for ideal experimental designs. Regardless, the results of such an “experiment in the wild” predation study are still valuable, as they are very rare (Trussell et al. 2006), and they provide crucial information on the direct and indirect interactions along a trophic cascade. Furthermore, the authors have effectively addressed any concerns about potential confounding factors, and thus have a convincing argument that their results represent predator-driven demographic and behavioural changes.
One important question remains from an evolutionary ecology standpoint: do the responses of melomys to the presence of quolls represent phenotypically plastic changes or rapid evolutionary changes caused by novel selection pressures? Classically, TMII are assumed to be mostly caused by phenotypic plasticity (Werner and Peacor 2003), and this might be the case when the presence of the predator is historical. Phenotypic plasticity allows quick and reversible adjustments of the prey population to changes in the predator density. When the predator population declines, such rapid phenotypic changes can be reversed, reducing the cost associated with anti-predator behaviour (e.g., lower foraging efficiency) in the absence of predators. In the case of a novel predator, however, short-term evolutionary responses by the prey may play role in the TMII, as they would allow a phenotypic shift in prey’s traits along the trade-off between foraging efficiency and anti-predator response that will probably more advantageous over the longer term, if the predator does not disappear. The authors state that they could not rule out one or the other of these hypotheses. However, future work estimating the relative importance of phenotypic plasticity and evolutionary changes in the quoll-melomys system would be valuable. Phenotypic selection analysis, for example, by estimating the link between survival and the traits measured, might help test for a fitness advantage to altered behaviour in the presence of a predator. Common garden experiments, comparing the quoll-invaded and the quoll-free melomys populations, might also provide information on any potential evolutionary changes caused by predation. More work could also analyse the potential effects on the seed populations. Not only might the reduction in seed predation have consequences on the landscape in the future, as the authors mention, but it may also mean that the seeds themselves could be subject to novel selection pressures, which may affect their phenology, physiology or life history. Off course, the authors will have to switch from wheat to a more natural situation, and evaluate the effects of changes in the melomys population on the feature of the local vegetation and the ecosystem.
Finally, the authors have not yet found that the observed changes in the traits have translated into a demographic rebound for melomys. Here again, I can see an interesting potential for further studies. Should we really expect an evolutionary rescue (Bell and Gonzalez 2009) in this system? Alternatively, should the changes in behaviour be accompanied by permanent changes in life history, such as a slower pace-of-life (Réale et al. 2010) that could possibly lead to lower melomys density?
This paper provides nice in natura evidence for density- and trait-mediated indirect interactions hypotheses. I hope it will be the first of a long series of work on this interesting quoll-melomys system, and that the authors will be able to provide more information on the eco-evolutionary consequences of a novel predator on a trophic network.


-Bell G, Gonzalez A (2009) Evolutionary rescue can prevent extinction following environmental change. Ecology letters, 12(9), 942-948.
-Jolly CJ, Smart AS, Moreen J, Webb JK, Gillespie GR, Phillips BL (2020) Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator. bioRxiv, 856997, ver. 6 peer-reviewed and recommended by PCI Ecology. 10.1101/856997
-Matassa C, Ewanchuk P, Trussell G (2018) Cascading effects of a top predator on intraspecific competition at intermediate and basal trophic levels. Functional Ecology, 32(9), 2241-2252.
-Réale D, Garant D, Humphries MM, Bergeron P, Careau V, Montiglio PO (2010) Personality and the emergence of the pace-of-life syndrome concept at the population level. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1560), 4051-4063.
-Schmitz O, Krivan V, Ovadia O (2004) Trophic cascades: the primacy of trait‐mediated indirect interactions. Ecology Letters 7(2), 153-163.
-Trussell G, Ewanchuk P, Matassa C (2006). Habitat effects on the relative importance of trait‐ and density‐mediated indirect interactions. Ecology Letters, 9(11), 1245-1252.
-Werner EE, Peacor SD (2003) A review of trait‐mediated indirect interactions in ecological communities. Ecology, 84(5), 1083-1100.[1083:AROTII]2.0.CO;2

Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predatorChris J Jolly, Adam S Smart, John Moreen, Jonathan K Webb, Graeme R Gillespie and Ben L Phillips<p>The arrival of novel predators can trigger trophic cascades driven by shifts in prey numbers. Predators also elicit behavioural change in prey populations, via phenotypic plasticity and/or rapid evolution, and such changes may also contribute t...Behaviour & Ethology, Biological invasions, Evolutionary ecology, Experimental ecology, Foraging, Herbivory, Population ecology, Terrestrial ecology, Tropical ecologyDenis Réale2019-11-27 21:39:44 View
02 Jun 2021
article picture

Identifying drivers of spatio-temporal variation in survival in four blue tit populations

Blue tits surviving in an ever-changing world

Recommended by based on reviews by Ana Sanz-Aguilar and Vicente García-Navas

How long individuals live has a large influence on a number of biological processes, both for the individuals themselves as well as for the populations they live in. For a given species, survival is often summarized in curves showing the probability to survive from one age to the next. However, these curves often hide a large amount of variation in survival. Variation can occur from chance, or if individuals have different genotypes or phenotypes that can influence how long they might live, or if environmental conditions are not the same across time or space. Such spatiotemporal variations in the conditions that individuals experience can lead to complex patterns of evolution (Kokko et al. 2017) but because of the difficulties to obtain the relevant data they have not been studied much in natural populations.
In this manuscript, Bastianelli and colleagues (2021) identify which environmental and population conditions are associated with variation in annual survival of blue tits. The analyses are based on an impressive dataset, tracking a total of almost 5500 adults in four populations studied for at least 19 years. The authors describe two core results. First, average annual survival is lower in deciduous forests compared to evergreen forests. The differences in average annual survival between the forest types link with previously described differences, with individuals having larger clutches (Charmantier et al. 2016) and higher aggression (Dubuc-Messier et al. 2017) in the populations where adult survival is lower. Second, there are huge fluctuations from one year to the next in the percentage of individuals surviving which occur similarly in all populations. Even though survival covaried across the four populations, this variation was not associated with any of the local or global climate indices the authors investigated.
Studies like these are fundamental to our understanding of population change. They are important from an applied side as they can reveal the sustainability of populations and inform potential management options. On a basic research side, they reveal how evolution operates in populations. Theoretical studies predict that individuals are often not adapted to average conditions they experience, but either selected to balance the extremes they encounter  or to make the best during harsh conditions when it really matters (Lewontin & Cohen 1969).
This study also opens the door to new research, highlighting that demographic studies should pay attention to variation in survival and other life history traits. For blue tits specifically, the study shows that in order to understand the demography of populations we need a better mechanistic understanding of the environmental and physiological pressures influencing whether individuals die or not to make predictions whether and how climate or other ecological effects shape variation in survival.
Bastianelli O, Robert A, Doutrelant C, Franceschi C de, Giovannini P, Charmantier A (2021) Identifying drivers of spatio-temporal variation in survival in four blue tit populations. bioRxiv, 2021.01.28.428563, ver. 4 peer-reviewed and recommended by Peer community in Ecology.

Charmantier A, Doutrelant C, Dubuc-Messier G, Fargevieille A, Szulkin M (2016) Mediterranean blue tits as a case study of local adaptation. Evolutionary Applications, 9, 135–152.

Dubuc-Messier G, Réale D, Perret P, Charmantier A (2017) Environmental heterogeneity and population differences in blue tits personality traits. Behavioral Ecology, 28, 448–459.

Kokko H, Chaturvedi A, Croll D, Fischer MC, Guillaume F, Karrenberg S, Kerr B, Rolshausen G, Stapley J (2017) Can Evolution Supply What Ecology Demands? Trends in Ecology & Evolution, 32, 187–197.

Lewontin RC, Cohen D (1969) On Population Growth in a Randomly Varying Environment. Proceedings of the National Academy of Sciences, 62, 1056–1060.

Identifying drivers of spatio-temporal variation in survival in four blue tit populationsOlivier Bastianelli, Alexandre Robert, Claire Doutrelant, Christophe de Franceschi, Pablo Giovannini, Anne Charmantier<p style="text-align: justify;">In a context of rapid climate change, the influence of large-scale and local climate on population demography is increasingly scrutinized, yet studies are usually focused on one population. Demographic parameters, i...Climate change, Demography, Evolutionary ecology, Life history, Population ecologyDieter Lukas2021-01-29 15:24:23 View
25 Oct 2021
article picture

The taxonomic and functional biogeographies of phytoplankton and zooplankton communities across boreal lakes

The difficult interpretation of species co-distribution

Recommended by based on reviews by Anthony Maire and Emilie Macke

Ecology is the study of the distribution of organisms in space and time and their interactions. As such, there is a tradition of studies relating abiotic environmental conditions to species distribution, while another one is concerned by the effects of consumers on the abundance of their resources.  Interestingly, joining the dots appears more difficult than it would suggest: eluding the effect of species interactions on distribution remains one of the greatest challenges to elucidate nowadays (Kissling et al. 2012). Theory suggests that yes, species interactions such as predation and competition should influence range limits (Godsoe et al. 2017), but the common intuition among many biogeographers remains that over large areas such as regions and continents, environmental drivers like temperature and precipitation overwhelm their local effects. Answering this question is of primary importance in the context where species are moving around with climate warming.  Inconsistencies in food web structure may arise with asynchronized movements of consumers and their resources, leading to a major disruption in regulation and potentially ecosystem functioning. Solving this problem, however, remains very challenging because we have to rely on observational data since experiments are hard to perform at the biogeographical scale. 

The study of St-Gelais is an interesting step forward to solve this problem. Their main objective was to assess the strength of the association between phytoplankton and zooplankton communities at a large spatial scale, looking at the spatial covariation of both taxonomic and functional composition. To do so, they undertook a massive survey of more than 100 lakes across three regions of the boreal region of Québec. Species and functional composition were recorded, along with a set of abiotic variables. Classic community ecology at this point. The difficulty they faced was to disentangle the multiple causal relationships involved in the distribution of both trophic levels. Teasing apart bottom-up and top-down forces driving the assembly of plankton communities using observational data is not an easy task. On the one hand, both trophic levels could respond to variations in temperature, nutrient availability and dissolved organic carbon. The interpretation is fairly straightforward if the two levels respond to different factors, but the situation is much more complicated when they do respond similarly. There are potentially three possible underlying scenarios. First, the phyto and zooplankton communities may share the same environmental requirements, thereby generating a joint distribution over gradients such as temperature and nutrient availability. Second, the abiotic environment could drive the distribution of the phytoplankton community, which would then propagate up and influence the distribution of the zooplankton community. Alternatively, the abiotic environment could constrain the distribution of the zooplankton, which could then affect the one of phytoplankton. In addition to all of these factors, St-Gelais et al also consider that dispersal may limit the distribution, well aware of previous studies documenting stronger dispersal limitations for zooplankton communities. 

Unfortunately, there is not a single statistical approach that could be taken from the shelf and used to elucidate drivers of co-distribution. Joint species distribution was once envisioned as a major step forward in this direction (Warton et al. 2015), but there are several limits preventing the direct interpretation that co-occurrence is linked to interactions (Blanchet et al. 2020). Rather, St-Gelais used a variety of multivariate statistics to reveal the structure in their observational data. First, using a Procrustes analysis (a method testing if the spatial variation of one community is correlated to the structure of another community), they found a significant correlation between phytoplankton and zooplankton communities, indicating a taxonomic coupling between the groups. Interestingly, this observation was maintained for functional composition only when interaction-related traits were considered. At this point, these results strongly suggest that interactions are involved in the correlation, but it's hard to decipher between bottom-up and top-down perspectives. A complementary analysis performed with a constrained ordination, per trophic level, provided complementary pieces of information. First observation was that only functional variation was found to be related to the different environmental variables, not taxonomic variation. Despite that trophic levels responded to water quality variables, spatial autocorrelation was more important for zooplankton communities and the two layers appear to respond to different variables. 

It is impossible with those results to formulate a strong conclusion about whether grazing influence the co-distribution of phytoplankton and zooplankton communities. That's the mere nature of observational data. While there is a strong spatial association between them, there are also diverging responses to the different environmental variables considered. But the contrast between taxonomic and functional composition is nonetheless informative and it seems that beyond the idiosyncrasies of species composition, trait distribution may be more informative and general. Perhaps the most original contribution of this study is the hierarchical approach to analyze the data, combined with the simultaneous analysis of taxonomic and functional distributions. Having access to a vast catalog of multivariate statistical techniques, a careful selection of analyses helps revealing key features in the data, rejecting some hypotheses and accepting others. Hopefully, we will see more and more of such multi-trophic approaches to distribution because it is now clear that the factors driving distribution are much more complicated than anticipated in more traditional analyses of community data. Biodiversity is more than a species list, it is also all of the interactions between them, influencing their distribution and abundance (Jordano 2016).


Blanchet FG, Cazelles K, Gravel D (2020) Co-occurrence is not evidence of ecological interactions. Ecology Letters, 23, 1050–1063.

Godsoe W, Jankowski J, Holt RD, Gravel D (2017) Integrating Biogeography with Contemporary Niche Theory. Trends in Ecology & Evolution, 32, 488–499.

Jordano P (2016) Chasing Ecological Interactions. PLOS Biology, 14, e1002559.

Kissling WD, Dormann CF, Groeneveld J, Hickler T, Kühn I, McInerny GJ, Montoya JM, Römermann C, Schiffers K, Schurr FM, Singer A, Svenning J-C, Zimmermann NE, O’Hara RB (2012) Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. Journal of Biogeography, 39, 2163–2178.

St-Gelais NF, Vogt RJ, Giorgio PA del, Beisner BE (2021) The taxonomic and functional biogeographies of phytoplankton and zooplankton communities across boreal lakes. bioRxiv, 373332, ver. 4 peer-reviewed and recommended by Peer community in Ecology.

Warton DI, Blanchet FG, O’Hara RB, Ovaskainen O, Taskinen S, Walker SC, Hui FKC (2015) So Many Variables: Joint Modeling in Community Ecology. Trends in Ecology & Evolution, 30, 766–779.

Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, Dormann CF, Forchhammer MC, Grytnes J-A, Guisan A, Heikkinen RK, Høye TT, Kühn I, Luoto M, Maiorano L, Nilsson M-C, Normand S, Öckinger E, Schmidt NM, Termansen M, Timmermann A, Wardle DA, Aastrup P, Svenning J-C (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88, 15–30.

The taxonomic and functional biogeographies of phytoplankton and zooplankton communities across boreal lakesNicolas F St-Gelais, Richard J Vogt, Paul A del Giorgio, Beatrix E Beisner<p>Strong trophic interactions link primary producers (phytoplankton) and consumers (zooplankton) in lakes. However, the influence of such interactions on the biogeographical distribution of the &nbsp;taxa and functional traits of planktonic organ...Biogeography, Community ecology, Species distributionsDominique Gravel2018-07-24 15:01:51 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.


[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
29 Jan 2020
article picture

Stoichiometric constraints modulate the effects of temperature and nutrients on biomass distribution and community stability

On the importance of stoichiometric constraints for understanding global change effects on food web dynamics

Recommended by based on reviews by 2 anonymous reviewers

The constraints associated with the mass balance of chemical elements (i.e. stoichiometric constraints) are critical to our understanding of ecological interactions, as outlined by the ecological stoichiometry theory [1]. Species in ecosystems differ in their elemental composition as well as in their level of elemental homeostasis [2], which can determine the outcome of interactions such as herbivory or decomposition on species coexistence and ecosystem functioning [3, 4].
Despite their importance, stoichiometric constraints are still often ignored in theoretical studies exploring the consequences of environmental perturbations on food web stability. Meanwhile, drivers of global change strongly alter biochemical cycles and the balance of chemical elements in ecosystems [5]. An important challenge is thus to understand how stoichiometric constraints affect food web responses to global changes.
The study of Sentis et al. [6] makes a step in that direction. This article investigates how stoichiometric constraints affect the response of consumer-resource dynamics to increasing temperature and nutrient inputs. It shows that the stoichiometric flexibility of the resource, coupled with lower consumer assimilation efficiency when stoichiometric unbalance between the resource and the consumer is higher, dampens the destabilizing effects of nutrient enrichment on species dynamics but reduces consumer persistence at extreme temperatures. Interestingly, these effects of stoichiometric constraints arise not only from changes in species assimilation efficiencies and carrying capacities but also from stoichiometric negative feedback loops on resource and consumer populations.
The results of this study are a call to further include stoichiometric constraints into food web models to better understand and predict the consequences of global changes on ecological communities. Many perspectives exist on that issue. For instance, it would be interesting to assess the effects of other stoichiometric mechanisms (e.g. changes in the element limiting growth [3]) on food web stability and its response to nutrient enrichment, as well as the effects of other global change drivers associated with altered biochemical cycles (e.g. carbon dioxide increase).


[1] Sterner, R. W. and Elser, J. J. (2017). Ecological Stoichiometry, The Biology of Elements from Molecules to the Biosphere. doi: 10.1515/9781400885695
[2] Elser, J. J., Sterner, R. W., Gorokhova, E., Fagan, W. F., Markow, T. A., Cotner, J. B., Harrison, J.F., Hobbie, S.E., Odell, G.M., Weider, L. W. (2000). Biological stoichiometry from genes to ecosystems. Ecology Letters, 3(6), 540–550. doi: 10.1111/j.1461-0248.2000.00185.x
[3] Daufresne, T., and Loreau, M. (2001). Plant–herbivore interactions and ecological stoichiometry: when do herbivores determine plant nutrient limitation? Ecology Letters, 4(3), 196–206. doi: 10.1046/j.1461-0248.2001.00210.x
[4] Zou, K., Thébault, E., Lacroix, G., and Barot, S. (2016). Interactions between the green and brown food web determine ecosystem functioning. Functional Ecology, 30(8), 1454–1465. doi: 10.1111/1365-2435.12626
[5] Peñuelas, J., Poulter, B., Sardans, J., Ciais, P., van der Velde, M., Bopp, L., Boucher, O., Godderis, Y., Hinsinger, P., Llusia, J., Nardin, E., Vicca, S., Obersteiner, M., Janssens, I. A. (2013). Human-induced nitrogen–phosphorus imbalances alter natural and managed ecosystems across the globe. Nature Communications, 4(1), 1–10. doi: 10.1038/ncomms3934
[6] Sentis, A., Haegeman, B. & Montoya, J.M. (2020). Stoichiometric constraints modulate the effects of temperature and nutrients on biomass distribution and community stability. bioRxiv, 589895, ver. 7 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/589895

Stoichiometric constraints modulate the effects of temperature and nutrients on biomass distribution and community stability Arnaud Sentis, Bart Haegeman, and José M. Montoya<p>Temperature and nutrients are two of the most important drivers of global change. Both can modify the elemental composition (i.e. stoichiometry) of primary producers and consumers. Yet their combined effect on the stoichiometry, dynamics, and s...Climate change, Community ecology, Food webs, Theoretical ecology, Thermal ecologyElisa Thebault2019-08-08 12:20:08 View
21 Oct 2020
article picture

Why scaling up uncertain predictions to higher levels of organisation will underestimate change

Uncertain predictions of species responses to perturbations lead to underestimate changes at ecosystem level in diverse systems

Recommended by based on reviews by Carlos Melian and 1 anonymous reviewer

Different sources of uncertainty are known to affect our ability to predict ecological dynamics (Petchey et al. 2015). However, the consequences of uncertainty on prediction biases have been less investigated, especially when predictions are scaled up to higher levels of organisation as is commonly done in ecology for instance. The study of Orr et al. (2020) addresses this issue. It shows that, in complex systems, the uncertainty of unbiased predictions at a lower level of organisation (e.g. species level) leads to a bias towards underestimation of change at higher level of organisation (e.g. ecosystem level). This bias is strengthened by larger uncertainty and by higher dimensionality of the system.
This general result has large implications for many fields of science, from economics to energy supply or demography. In ecology, as discussed in this study, these results imply that the uncertainty of predictions of species’ change increases the probability of underestimation of changes of diversity and stability at community and ecosystem levels, especially when species richness is high. The uncertainty of predictions of species’ change also increases the probability of underestimation of change when multiple ecosystem functions are considered at once, or when the combined effect of multiple stressors is considered.
The consequences of species diversity on ecosystem functions and stability have received considerable attention during the last decades (e.g. Cardinale et al. 2012, Kéfi et al. 2019). However, since the bias towards underestimation of change increases with species diversity, future studies will need to investigate how the general statistical effect outlined by Orr et al. might affect our understanding of the well-known relationships between species diversity and ecosystem functioning and stability in response to perturbations.


Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Larigauderie A, Srivastava DS, Naeem S (2012) Biodiversity loss and its impact on humanity. Nature, 486, 59–67.
Kéfi S, Domínguez‐García V, Donohue I, Fontaine C, Thébault E, Dakos V (2019) Advancing our understanding of ecological stability. Ecology Letters, 22, 1349–1356.
Orr JA, Piggott JJ, Jackson A, Arnoldi J-F (2020) Why scaling up uncertain predictions to higher levels of organisation will underestimate change. bioRxiv, 2020.05.26.117200.
Petchey OL, Pontarp M, Massie TM, Kéfi S, Ozgul A, Weilenmann M, Palamara GM, Altermatt F, Matthews B, Levine JM, Childs DZ, McGill BJ, Schaepman ME, Schmid B, Spaak P, Beckerman AP, Pennekamp F, Pearse IS (2015) The ecological forecast horizon, and examples of its uses and determinants. Ecology Letters, 18, 597–611.

Why scaling up uncertain predictions to higher levels of organisation will underestimate changeJames Orr, Jeremy Piggott, Andrew Jackson, Jean-François Arnoldi<p>Uncertainty is an irreducible part of predictive science, causing us to over- or underestimate the magnitude of change that a system of interest will face. In a reductionist approach, we may use predictions at the level of individual system com...Community ecology, Ecosystem functioning, Theoretical ecologyElisa ThebaultAnonymous2020-06-02 15:41:12 View
26 May 2021
article picture

Spatial distribution of local patch extinctions drives recovery dynamics in metacommunities

Unity makes strength: clustered extinctions have stronger, longer-lasting effects on metacommunities dynamics

Recommended by based on reviews by David Murray-Stoker and Frederik De Laender

In this article, Saade et al. (2021) investigate how the rate of local extinctions and their spatial distribution affect recolonization dynamics in metacommunities. They use an elegant combination of microcosm experiments with metacommunities of freshwater ciliates and mathematical modelling mirroring their experimental system. Their main findings are (i) that local patch extinctions increase both local (α-) and inter-patch (β-) diversity in a transient way during the recolonization process, (ii) that these effects depend more on the spatial distribution of extinctions (dispersed or clustered) than on their amount, and (iii) that they may spread regionally.
Microcosm experiments are already quite cool just by themselves and have contributed largely to conceptual advances in community ecology (see Fraser and Keddy 1997, or Jessup et al. 2004 for reviews on this topic), but they are here exploited to a whole further level by the fitting of a metapopulation dynamics model. The model allows both to identify the underlying mechanisms most likely to generate the patterns observed (here, competitive interactions) and to assess the robustness of these patterns when considering larger spatial or temporal scales. This release of experimental limitations allows here for the analysis of quantitative metrics of spatial structure, like the distance to the closest patch, which gives an interesting insight into the functional basis of the effect of the spatial distribution of extinctions.

A major strength of this study is that it highlights the importance of considering the spatial structure explicitly. Recent work on ecological networks has shown repeatedly that network structure affects the propagation of pathogens (Badham and Stocker 2010), invaders (Morel-Journel et al. 2019), or perturbation events (Gilarranz et al. 2017). Here, the spatial structure of the metacommunity is a regular grid of patches, but the distribution of extinction events may be either regularly dispersed (i.e., extinct patches are distributed evenly over the grid and are all surrounded by non-extinct patches only) or clustered (all extinct patches are neighbours). This has a direct effect on the neighbourhood of perturbed patches, and because perturbations have mostly local effects, their recovery dynamics are dominated by the composition of this immediate neighbourhood. In landscapes with dispersed extinctions, the neighbourhood of a perturbed patch is not affected by the amount of extinctions, and neither is its recovery time. In contrast, in landscapes with clustered extinctions, the amount of extinctions affects the depth of the perturbed area, which takes longer to recover when it is larger. Interestingly, the spatial distribution of extinctions here is functionally equivalent to differences in connectivity between perturbed and unperturbed patches, which results in contrasted “rescue recovery” and “mixing recovery” regimes as described by Zelnick et al. (2019).
Furthermore, this study focuses on local dynamics of competition and short-term, transient patterns that may have been overlooked by more classical, equilibrium-based approaches of dynamical systems of metacommunities. Indeed, in a metacommunity composed of several competitors, early theoretical work demonstrated that species coexistence is possible at the regional scale only, provided that spatial heterogeneity creates spatial variance in fitness or precludes the superior competitor from accessing certain habitat patches (Skellam 1951, Levins 1969). In the spatially homogeneous experimental system of Saade et al., one of the three ciliate species ends up dominating the community at equilibrium. However, following local, one-time extinction events, the community endures a recolonization process in which differences in dispersal may provide temporary spatial niches for inferior competitors. These transient patterns might prove essential to understand and anticipate the resilience of natural systems that are under increasing pressure, and enduring ever more frequent and intense perturbations (IPBES 2019). Spatial autocorrelation in extinction events was previously identified as a risk for stability and persistence of metacommunities (Ruokolainen 2013, Kahilainen et al. 2018). These new results show that autocorrelated perturbations also have longer-lasting effects, which is likely to increase their overall impact on metacommunity dynamics. As spatial and temporal autocorrelation of temperature and extreme climatic events are expected to increase (Di Cecco and Gouthier 2018), studies that investigate how metacommunities respond to the structure of the distribution of perturbations are more necessary than ever.

Badham J, Stocker R (2010) The impact of network clustering and assortativity on epidemic behaviour. Theoretical Population Biology, 77, 71–75.
Di Cecco GJ, Gouhier TC (2018) Increased spatial and temporal autocorrelation of temperature under climate change. Scientific Reports, 8, 14850.
Fraser LH, Keddy P (1997) The role of experimental microcosms in ecological research. Trends in Ecology & Evolution, 12, 478–481.
Gilarranz LJ, Rayfield B, Liñán-Cembrano G, Bascompte J, Gonzalez A (2017) Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science, 357, 199–201.
IPBES (2019) Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. 
Jessup CM, Kassen R, Forde SE, Kerr B, Buckling A, Rainey PB, Bohannan BJM (2004) Big questions, small worlds: microbial model systems in ecology. Trends in Ecology & Evolution, 19, 189–197.
Kahilainen A, van Nouhuys S, Schulz T, Saastamoinen M (2018) Metapopulation dynamics in a changing climate: Increasing spatial synchrony in weather conditions drives metapopulation synchrony of a butterfly inhabiting a fragmented landscape. Global Change Biology, 24, 4316–4329.

Levins R (1969) Some Demographic and Genetic Consequences of Environmental Heterogeneity for Biological Control1. Bulletin of the Entomological Society of America, 15, 237–240.
Morel-Journel T, Assa CR, Mailleret L, Vercken E (2019) Its all about connections: hubs and invasion in habitat networks. Ecology Letters, 22, 313–321.

Ruokolainen L (2013) Spatio-Temporal Environmental Correlation and Population Variability in Simple Metacommunities. PLOS ONE, 8, e72325.

Saade C, Kefi S, Gougat-Barbera C, Rosenbaum B, Fronhofer EA (2021) Spatial distribution of local patch extinctions drives recovery dynamics in metacommunities. bioRxiv, 2020.12.03.409524, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.
Skellam JG (1951) Random Dispersal in Theoretical Populations. Biometrika, 38, 196–218.
Zelnik YR, Arnoldi J-F, Loreau M (2019) The three regimes of spatial recovery. Ecology, 100, e02586.

Spatial distribution of local patch extinctions drives recovery dynamics in metacommunitiesCamille Saade, Sonia Kéfi, Claire Gougat-Barbera, Benjamin Rosenbaum, and Emanuel A. Fronhofer<p style="text-align: justify;">Human activities lead more and more to the disturbance of plant and animal communities with local extinctions as a consequence. While these negative effects are clearly visible at a local scale, it is less clear how...Biodiversity, Coexistence, Colonization, Community ecology, Competition, Dispersal & Migration, Experimental ecology, Landscape ecology, Spatial ecology, Metacommunities & MetapopulationsElodie Vercken2020-12-08 15:55:20 View
06 May 2022
article picture

Effects of climate warming on the pine processionary moth at the southern edge of its range: a retrospective analysis on egg survival in Tunisia

Even the current climate change winners could end up being losers

Recommended by based on reviews by Matt Hill, Philippe Louapre, José Hodar and Corentin Iltis

Climate change is accelerating (IPCC 2022), and so applies ever stronger selective pressures on biodiversity (Segan et al. 2016). Possible responses include range shifts or adaptations to new climatic conditions (Bellard et al. 2012), but there is still much uncertainty about the extent of most species' adaptive capacities and the impact of extreme climatic events.
The pine processionary is a major pest of pine trees in the Mediterranean area. It is notably one of the few species for which a clear link between recent climate change and its northward expansion has been established (Battisti et al. 2005), and as such is often considered as globally benefitting from climate change. However, recent results show a retraction of its range at the southern limit (Bourougaaoui et al. 2021), exposed to high warming (+1.4°C in Tunisia since 1901 as opposed to +1.12°C on average in the Northern hemisphere) and extreme summer temperature events (Verner et al. 2013). Thus, it is possible that the species' adaptive abilities are being challenged at the southern limit of its native range by the magnitude of observed climate change.
In this work, Bourougaaoui et al. (2022) investigate how climate change over the last 30 years has impacted the reproductive success of the pine processionary moth in Tunisia. A major methodological interest of this study is that they used data both from historical collections and from recent samplings, which raised a challenge for running a longitudinal analysis as sampling locations differed between the two periods. By applying a grouping method to local climatic data, the authors were able to define several large climatic clusters within the country, and analyze long-term data from different sites within the same clusters. They find that both fecundity and hatching rate decreased over the period, while at the same time both the average temperature increased and climate variability increased. One of the main conclusions is that recurrent episodes of extreme heat during summer might have a larger impact than the long-term increase of average temperature, which strongly echoes how the intensification of weather extremes is currently proving one of the most important dimensions of climate change.
However, a most interesting hypothesis also arises from the analysis of the differences between climatic clusters: preexisting adaptations to heat, for instance, phenological shifts that allow the most sensitive stages to develop earlier in the season before the extreme heat events are most likely to occur, might actually reduce impacts in the historically warmest areas. Thus the greatest climate vulnerability might not always stand where one expects it.

Battisti A, Stastny M, Netherer S, Robinet C, Schopf A, Roques A, Larsson S (2005) Expansion of Geographic Range in the Pine Processionary Moth Caused by Increased Winter Temperatures. Ecological Applications, 15, 2084–2096.

Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecology Letters, 15, 365–377.

Bourougaaoui A, Ben Jamâa ML, Robinet C (2021) Has North Africa turned too warm for a Mediterranean forest pest because of climate change? Climatic Change, 165, 46.

Bourougaaoui A, Robinet C, Jamaa MLB, Laparie M (2022) Effects of climate warming on the pine processionary moth at the southern edge of its range: a retrospective analysis on egg survival in Tunisia. bioRxiv, 2021.08.17.456665, ver. 5 peer-reviewed and recommended by Peer Community in Ecology.

IPCC. 2022. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. In Press.

Segan DB, Murray KA, Watson JEM (2016) A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Global Ecology and Conservation, 5, 12–21.

Verner D (2013) Tunisia in a Changing Climate : Assessment and Actions for Increased Resilience and Development. World Bank, Washington, DC.  

Effects of climate warming on the pine processionary moth at the southern edge of its range: a retrospective analysis on egg survival in TunisiaAsma Bourougaaoui, Christelle Robinet, Mohamed Lahbib Ben Jamâa, Mathieu Laparie<p style="text-align: justify;">In recent years, ectotherm species have largely been impacted by extreme climate events, essentially heatwaves. In Tunisia, the pine processionary moth (PPM), <em>Thaumetopoea pityocampa</em>, is a highly damaging p...Climate change, Dispersal & Migration, Life history, Phenotypic plasticity, Species distributions, Terrestrial ecology, Thermal ecology, ZoologyElodie Vercken2021-08-19 11:03:13 View
31 Jan 2019
article picture

Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed grackles

From cognition to range dynamics: advancing our understanding of macroecological patterns

Recommended by based on reviews by 2 anonymous reviewers

Understanding the distribution of species on earth is one of the fundamental challenges in ecology and evolution. For a long time, this challenge has mainly been addressed from a correlative point of view with a focus on abiotic factors determining a species abiotic niche (classical bioenvelope models; [1]). It is only recently that researchers have realized that behaviour and especially plasticity in behaviour may play a central role in determining species ranges and their dynamics [e.g., 2-5]. Blaisdell et al. propose to take this even one step further and to analyse how behavioural flexibility and possibly associated causal cognition impacts range dynamics.
The current preregistration is integrated in an ambitious long-term research plan that aims at addressing the above outlined question and focuses specifically on investigating whether more behaviourally flexible individuals are better at deriving causal inferences. The model system the authors plan on using are Great-tailed Grackles which have expanded their range into North America during the last century. The preregistration by Blaisdell et al. is a great example of the future of scientific research: it includes conceptual models, alternative hypotheses and testable predictions along with a sound sampling and analysis plan and embraces the principles of Open Science. Overall, the research the authors propose is fascinating and of highest relevance, as it aims at bridging scales from the microscopic mechanisms that underlie animal behaviour to macroscopic, macroecological consequences (see also [3]). I am very much looking forward to the results the authors will report.

[1] Elith, J. & Leathwick, J. R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40: 677-697. doi: 10.1146/annurev.ecolsys.110308.120159
[2] Kubisch, A.; Degen, T.; Hovestadt, T. & Poethke, H. J. (2013) Predicting range shifts under global change: the balance between local adaptation and dispersal. Ecography 36: 873-882. doi: 10.1111/j.1600-0587.2012.00062.x
[3] Keith, S. A. & Bull, J. W. (2017) Animal culture impacts species' capacity to realise climate-driven range shifts. Ecography, 40: 296-304. doi: 10.1111/ecog.02481
[4] Sullivan, L. L.; Li, B.; Miller, T. E.; Neubert, M. G. & Shaw, A. K. (2017) Density dependence in demography and dispersal generates fluctuating invasion speeds. Proc. Natl. Acad. Sci. USA, 114: 5053-5058. doi: 10.1073/pnas.1618744114
[5] Fronhofer, E. A.; Nitsche, N. & Altermatt, F. (2017) Information use shapes the dynamics of range expansions into environmental gradients. Glob. Ecol. Biogeogr. 26: 400-411. doi: 10.1111/geb.12547

Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed gracklesAaron Blaisdell, Zoe Johnson-Ulrich, Luisa Bergeron, Carolyn Rowney, Benjamin Seitz, Kelsey McCune, Corina LoganThis PREREGISTRATION has undergone one round of peer reviews. We have now revised the preregistration and addressed reviewer comments. The DOI was issued by OSF and refers to the whole GitHub repository, which contains multiple files. The specific...Behaviour & Ethology, Preregistrations, ZoologyEmanuel A. Fronhofer2018-08-20 11:09:48 View