Dispersal is a key element of the natural dynamics of meta-communities, and plays a central role in the success of populations colonizing new landscapes. Understanding how demographic processes may affect the speed at which alien species spread through environmentally-heterogeneous habitat fragments is therefore of key importance to manage biological invasions. This requires studying together the complex interplay of dispersal and population processes, two inextricably related phenomena that can produce many possible outcomes. Stochastic models offer an opportunity to describe this kind of process in a meaningful way, but to ensure that they are realistic (sensu Levins 1966) it is also necessary to combine model simulations with empirical data (Snäll et al. 2007).

Morel-Journel et al. (2023) put together stochastic models and experimental data to study how population density may affect the speed at which alien species spread through a heterogeneous landscape. They do it by focusing on what they call ‘colonisation debt’, which is merely the impact that population density at the invasion front may have on the speed at which the species colonizes patches of different carrying capacities. They investigate this issue through two largely independent approaches. First, a stochastic model of dispersal throughout the patches of a linear, 1-dimensional landscape, which accounts for different degrees of density-dependent growth. And second, a microcosm experiment of a parasitoid wasp colonizing patches with different numbers of host eggs. In both cases, they compare the velocity of colonization of patches with lower or higher carrying capacity than the previous one (i.e. what they call upward or downward gradients).

Their results show that density-dependent processes influence the speed at which new fragments are colonized is significantly reduced by positive density dependence. When either population growth or dispersal rate depend on density, colonisation debt limits the speed of invasion, which turns out to be dependent on the strength and direction of the gradient between the conditions of the invasion front, and the newly colonized patches. Although this result may be quite important to understand the meta-population dynamics of dispersing species, it is important to note that in their study the environmental differences between patches do not take into account eventual shifts in the scenopoetic conditions (i.e. the values of the environmental parameters to which species niches’ respond to; Hutchinson 1978, see also Soberón 2007). Rather, differences arise from variations in the carrying capacity of the patches that are consecutively invaded, both in the in silico and microcosm experiments. That is, they account for potential differences in the size or quality of the invaded fragments, but not on the costs of colonizing fragments with different environmental conditions, which may also determine invasion speed through niche-driven processes. This aspect can be of particular importance in biological invasions or under climate change-driven range shifts, when adaptation to new environments is often required (Sakai et al. 2001; Whitney & Gabler 2008; Hill et al. 2011).

The expansion of geographical distribution ranges is the result of complex eco-evolutionary processes where meta-community dynamics and niche shifts interact in a novel physical space and/or environment (see, e.g., Mestre et al. 2020). Here, the invasibility of native communities is determined by niche variations and how similar are the traits of alien and native species (Hui et al. 2023). Within this context, density-dependent processes will build upon and heterogeneous matrix of native communities and environments (Tischendorf et al. 2005), to eventually determine invasion success. What the results of Morel-Journel et al. (2023) show is that, when the invader shows density dependence, the invasion process can be slowed down by variations in the carrying capacity of patches along the dispersal front. This can be particularly useful to manage biological invasions; ongoing invasions can be at least partially controlled by manipulating the size or quality of the patches that are most adequate to the invader, controlling host populations to reduce carrying capacity. But further, landscape manipulation of such kind could be used in a preventive way, to account in advance for the effects of the introduction of alien species for agricultural exploitation or biological control, thereby providing an additional safeguard to practices such as the introduction of parasitoids to control plagues. These practical aspects are certainly worth exploring further, together with a more explicit account of the influence of the abiotic conditions and the characteristics of the invaded communities on the success and speed of biological invasions.

REFERENCES

Hill, J.K., Griffiths, H.M. & Thomas, C.D. (2011) Climate change and evolutionary adaptations at species' range margins. Annual Review of Entomology, 56, 143-159. https://doi.org/10.1146/annurev-ento-120709-144746

Hui, C., Pyšek, P. & Richardson, D.M. (2023) Disentangling the relationships among abundance, invasiveness and invasibility in trait space. npj Biodiversity, 2, 13. https://doi.org/10.1038/s44185-023-00019-1

Hutchinson, G.E. (1978) An introduction to population biology. Yale University Press, New Haven, CT.

Levins, R. (1966) The strategy of model building in population biology. American Scientist, 54, 421-431. 

Mestre, A., Poulin, R. & Hortal, J. (2020) A niche perspective on the range expansion of symbionts. Biological Reviews, 95, 491-516. https://doi.org/10.1111/brv.12574

Morel-Journel, T., Haond, M., Duan, L., Mailleret, L. & Vercken, E. (2023) Colonisation debt: when invasion history impacts current range expansion. bioRxiv, 2022.11.13.516255, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.11.13.516255

Snäll, T., B. O'Hara, R. & Arjas, E. (2007) A mathematical and statistical framework for modelling dispersal. Oikos, 116, 1037-1050. https://doi.org/10.1111/j.0030-1299.2007.15604.x

Sakai, A.K., Allendorf, F.W., Holt, J.S., Lodge, D.M., Molofsky, J., With, K.A., Baughman, S., Cabin, R.J., Cohen, J.E., Ellstrand, N.C., McCauley, D.E., O'Neil, P., Parker, I.M., Thompson, J.N. & Weller, S.G. (2001) The population biology of invasive species. Annual Review of Ecology and Systematics, 32, 305-332. https://doi.org/10.1146/annurev.ecolsys.32.081501.114037

Soberón, J. (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10, 1115-1123. https://doi.org/10.1111/j.1461-0248.2007.01107.x

Tischendorf, L., Grez, A., Zaviezo, T. & Fahrig, L. (2005) Mechanisms affecting population density in fragmented habitat. Ecology and Society, 10, 7. https://doi.org/10.5751/ES-01265-100107

Whitney, K.D. & Gabler, C.A. (2008) Rapid evolution in introduced species, 'invasive traits' and recipient communities: challenges for predicting invasive potential. Diversity and Distributions, 14, 569-580. https://doi.org/10.1111/j.1472-4642.2008.00473.x

Reduce the impact the intensification of human activities has on the environmental is the challenge the humanity faces today, a major challenge that could be compared to climbing Everest without an oxygen supply. Indeed, over-population, pollution, burning fossil fuels, and deforestation are all evils which have had hugely detrimental effects on the environment such as climate change, soil erosion, poor air quality, and scarcity of drinking water to name but a few. In response to the ever-growing consumer demand, agriculture has intensified massively along with a drastic increase in the use of chemicals to ensure an adequate food supply while controlling crop pests. In this context, to address the disastrous effects of the intensive usage of pesticides on both human health and biodiversity, organic farming (OF) revealed as a miracle remedy with multiple benefits. Delattre et al. (2023) present a powerful modelling approach to decipher the crossed effects of the landscape structure and the OF expansion scenario on the pest abundance, both in organic and conventional (CF) crop fields. To this end, the authors ingeniously combined a grid-based landscape model with a spatially explicit predator-pest model. Based on an extensive in silico simulation process, they explore a diversity of landscape structures differing in their amount of semi-natural habitats (SHN) and in their fragmentation, to finally propose a ranking of various expansion scenarios according to the pest control methods in organic farming as well as to the pest and predators’ dissemination capacities. In total, 9 landscape structures (3 proportions of SHN x 3 fragmentation levels) were crossed with 3 expansion scenarios (RD = a random distribution of OF and CF in the grid; IP = isolated CF are converted; GP = CF within aggregates are converted), 4 pest management practices, 3 initial densities and 36 biological parameter combinations driving the predator’ and pest’s population dynamics. This exhaustive exploration of possible combinations of landscape and farming practices highlighted the main drivers of the various OF expansion scenarios, such as increased spillover of predators in isolated OF/CF fields, increased pest management efficiency in large patches of CF and the importance of the distance between OF and CF. In the end, this study brings to light the crucial role that landscape planning plays when OF practices have limited efficiency on pests. It also provides convincing arguments to the fact that converting to organic isolated CF as a priority seems to be the most promising scenario to limit pest densities in CF crops while improving predator to pest ratios (considered as a proxy of conservation biological control) in OF ones without increasing pest densities. Once further completed with model calibration validation based on observed life history traits data for both predators and pests, this work should be very helpful in sustaining policy makers to convince farmers of engaging in organic farming.

REFERENCES

Delattre T, Memah M-M, Franck P, Valsesia P, Lavigne C (2023) Best organic farming deployment scenarios for pest control: a modeling approach. bioRxiv, 2022.05.31.494006, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.05.31.494006

A network includes a set of vertices or nodes (e.g., species in an interaction network), and a set of edges or links (e.g., interactions between species). Whether and how networks vary in space and/or time are questions often addressed in ecological research. 

Two ecological networks can differ in several extents: in that species are different in the two networks and establish new interactions (species turnover), or in that species that are present in both networks establish different interactions in the two networks (rewiring). The ecological meaning of changes in network structure is quite different according to whether species turnover or interaction rewiring plays a greater role. Therefore, much attention has been devoted in recent years on quantifying and interpreting the relative changes in network structure due to species turnover and/or rewiring.

Poisot et al. (2012) proposed to partition the global variation in structure between networks, \( \beta_{WN} \) (WN = Whole Network) into two terms: \( \beta_{OS} \) (OS = Only Shared species) and \( \beta_{ST} \) (ST = Species Turnover), such as \( \beta_{WN} = \beta_{OS} + \beta_{ST} \).

The calculation lays on enumerating the interactions between species that are common or not to two networks, as illustrated on Figure 1 for a simple case. Specifically, Poisot et al. (2012) proposed to use a Sorensen type measure of network dissimilarity, i.e., \( \beta_{WN} = \frac{a+b+c}{(2a+b+c)/2} -1=\frac{b+c}{2a+b+c} \) , where \( a \) is the number of interactions shared between the networks, while \( b \) and \( c \) are interaction numbers unique to one and the other network, respectively. \( \beta_{OS} \) is calculated based on the same formula, but only for the subnetworks including the species common to the two networks, in the form \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a_{OS}+b_{OS}+c_{OS}} \) (e.g., Fig. 1). \( \beta_{ST} \) is deduced by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and represents in essence a "dissimilarity in interaction structure introduced by dissimilarity in species composition" (Poisot et al. 2012).

Figure 1. Ecological networks exemplified in Fründ (2021) and discussed in Poisot (2022). a is the number of shared links (continuous lines in right figures), while b+c is the number of edges unique to one or the other network (dashed lines in right figures).

Alternatively, Fründ (2021) proposed to define \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a+b+c} \) and \( \beta_{ST} = \frac{b_{ST}+c_{ST}}{2a+b+c} \), where \( b_{ST}=b-b_{OS} \)  and \( c_{ST}=c-c_{OS} \) , so that the components \( \beta_{OS} \) and \( \beta_{ST} \) have the same denominator. In this way, Fründ (2021) partitioned the count of unique \( b+c=b_{OS}+b_{ST}+c_{ST} \) interactions, so that \( \beta_{OS} \) and \( \beta_{ST} \) sums to \( \frac{b_{OS}+c_{OS}+b_{ST}+c_{ST}}{2a+b+c} = \frac{b+c}{2a+b+c} = \beta_{WN} \). Fründ (2021) advocated that this partition allows a more sensible comparison of \( \beta_{OS} \) and \( \beta_{ST} \), in terms of the number of links that contribute to each component.

For instance, let us consider the networks 1 and 2 in Figure 1 (left panel) such as \( a_{OS}=2 \) (continuous lines in right panel), \( b_{ST} + c_{ST} = 1 \) and \( b_{OS} + c_{OS} = 1 \) (dashed lines in right panel), and thereby \( a = 2 \), \( b+c=2 \), \( \beta_{WN} = 1/3 \). Fründ (2021) measured \( \beta_{OS}=\beta_{ST}=1/6 \) and argued that it is appropriate insofar as it reflects that the number of unique links in the OS and ST components contributing to network dissimilarity (dashed lines) are actually equal. Conversely, the formula of Poisot et al. (2012) yields \( \beta_{OS}=1/5 \), hence \( \beta_{ST} = \frac{1}{3}-\frac{1}{5}=\frac{2}{15}<\beta_{OS} \). Fründ (2021) thus argued that the method of Poisot tends to underestimate the contribution of species turnover.

To clarify and avoid misinterpretation of the calculation of \( \beta_{OS} \) and \( \beta_{ST} \) in Poisot et al. (2012), Poisot (2022) provides a new, in-depth mathematical analysis of the decomposition of \( \beta_{WN} \). Poisot et al. (2012) quantify in \( \beta_{OS} \) the actual contribution of rewiring in network structure for the subweb of common species. Poisot (2022) thus argues that \( \beta_{OS} \) relates only to the probability of rewiring in the subweb, while the definition of \( \beta_{OS} \) by Fründ (2021) is relative to the count of interactions in the global network (considered in denominator), and is thereby dependent on both rewiring probability and species turnover. Poisot (2022) further clarifies the interpretation of \( \beta_{ST} \). \( \beta_{ST} \) is obtained by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and thus represents the influence of species turnover in terms of the relative architectures of the global networks and of the subwebs of shared species. Coming back to the example of Fig.1., the Poisot et al. (2012) formula posits that \( \frac{\beta_{ST}}{\beta_{WN}}=\frac{2/15}{1/3}=2/5 \), meaning that species turnover contributes two-fifths of change in network structure, while rewiring in the subweb of common species contributed three fifths.  Conversely, the approach of Fründ (2021) does not compare the architectures of global networks and of the subwebs of shared species, but considers the relative contribution of unique links to network dissimilarity in terms of species turnover and rewiring. 

Poisot (2022) concludes that the partition proposed in Fründ (2021) does not allow unambiguous ecological interpretation of rewiring. He provides guidelines for proper interpretation of the decomposition proposed in Poisot et al. (2012).

References

Fründ J (2021) Dissimilarity of species interaction networks: how to partition rewiring and species turnover components. Ecosphere, 12, e03653. https://doi.org/10.1002/ecs2.3653

Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361. https://doi.org/10.1111/ele.12002

Poisot T (2022) Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. EcoEvoRxiv Preprints, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/gxhu2

In temperate regions, food abundance and quality vary greatly throughout the year, and the ability of organisms to synchronise their phenology to these changes is a key determinant of their reproductive success. Successful synchronisation requires that cues are perceived prior to change, leaving time for physiological adjustments.
But what are the cues used to anticipate seasonal changes? Abiotic factors like temperature and photoperiod are known for their driving role in the phenology of a wide range of plant an animal species [1,2] . Arguably though, biotic cues directly linked to upcoming changes in food abundance could be as important as abiotic factors, but the response of organisms to these cues remains relatively unexplored.
Biotic cues may be particularly important for higher trophic levels because of their tight interaction with the hosts or preys they depend on. In this study Tougeron and colleagues [3] address this topic using interacting insects, namely herbivorous aphids and the parasitic wasps (or parasitoids) that feed on them. The key finding of the study by Tougeron et al. [3] is that the host morph in which parasitic wasp larvae develop is a major driver of diapause induction. More importantly, the aphid morph that triggers diapause in the wasp is the one that will lay overwintering eggs in autumn at the onset of harsh winter conditions. Its neatly designed experimental setup also provides evidence that this response may vary across populations as host-dependent diapause induction was only observed in a wasp population that originated from a cold area. As the authors suggests, this may be caused by local adaptation to environmental conditions because, relative to warmer regions, missing the time window to enter diapause in colder regions may have more dramatic consequences. The study also shows that different aphid morphs differ greatly in their chemical composition, and points to particular types of metabolites like sugars and polyols as specific cues for diapause induction.
This study provides a nice example of the complexity of biological interactions, and of the importance of phenological synchrony between parasites and their hosts. The authors provide evidence that phenological synchrony is likely to be achieved via chemical cues derived from the host. A similar approach was used to demonstrate that the herbivorous beetle Leptinotarsa decemlineata uses plant chemical cues to enter diapause [4]. Beetles fed on plants exposed to pre-wintering conditions entered diapause in higher proportions than those fed on control plants grown at normal conditions. As done by Tougeron et al. [3], in [4] the authors associated diapause induction to changes in the composition of metabolites in the plant. In both studies, however, the missing piece is to unveil the particular chemical involved, an answer that may be provided by future experiments.
Latitudinal clines in diapause induction have been described in a number of insect species [5]. Correlative studies, in which the phenology of different trophic levels has been monitored, suggest that these clines may in part be governed by lower trophic levels. For example, Phillimore et al. [6] explored the relative contribution of temperature and of host plant phenology on adult flight periods of the butterfly Anthocharis cardamines. Tougeron et al. [3], by using aphids and their associated parasitoids, take the field further by moving from observational studies to experiments. Besides, aphids are not only a tractable host-parasite system in the laboratory, they are important agricultural pests. Improving our basic knowledge of their ecological interactions may ultimately contribute to improving pest control techniques. The study by Tougeron et al. [3] exemplifies the multiple benefits that can be gained from addressing fundamental questions in species that are also directly relevant to society.

References

[1] Tauber, M. J., Tauber, C. A., and Masaki, S. (1986). Seasonal Adaptations of Insects. Oxford, New York: Oxford University Press.
[2] Bradshaw, W. E., and Holzapfel, C. M. (2007). Evolution of Animal Photoperiodism. Annual Review of Ecology, Evolution, and Systematics, 38(1), 1–25. doi: 10.1146/annurev.ecolsys.37.091305.110115
[3] Tougeron, K., Brodeur, J., Baaren, J. van, Renault, D., and Lann, C. L. (2019b). Sex makes them sleepy: host reproductive status induces diapause in a parasitoid population experiencing harsh winters. bioRxiv, 371385, ver. 6 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/371385
[4] Izzo, V. M., Armstrong, J., Hawthorne, D., and Chen, Y. (2014). Time of the season: the effect of host photoperiodism on diapause induction in an insect herbivore, Leptinotarsa decemlineata. Ecological Entomology, 39(1), 75–82. doi: 10.1111/een.12066
[5] Hut Roelof A., Paolucci Silvia, Dor Roi, Kyriacou Charalambos P., and Daan Serge. (2013). Latitudinal clines: an evolutionary view on biological rhythms. Proceedings of the Royal Society B: Biological Sciences, 280(1765), 20130433. doi: 10.1098/rspb.2013.0433
[6] Phillimore, A. B., Stålhandske, S., Smithers, R. J., and Bernard, R. (2012). Dissecting the Contributions of Plasticity and Local Adaptation to the Phenology of a Butterfly and Its Host Plants. The American Naturalist, 180(5), 655–670. doi: 10.1086/667893

For decades, the effect of population density on individual performance has been studied by ecologists using both theoretical, observational, and experimental approaches. The generally accepted definition of the Allee effect is a positive correlation between population density and average individual fitness that occurs at low population densities, while individual fitness is typically decreased through intraspecific competition for resources at high population densities.  Allee effects are very relevant in conservation biology because species at low population densities would then be subjected to much higher extinction risks. 

However, due to all kinds of stochasticity, low population numbers are always more vulnerable to extinction than larger population sizes. This effect by itself cannot be necessarily ascribed to lower individual performance at low densities, i.e, Allee effects. Vercken and colleagues (2021) address this challenging question and measure the extent to which average individual fitness is affected by population density analyzing 30 experimental populations. As a model system, they use populations of parasitoid wasps of the genus Trichogramma. They report Allee effect in 8 out 30 experimental populations. Vercken and colleagues's work has several strengths. 

First of all, it is nice to see that they put theory at work. This is a very productive way of using theory in ecology. As a starting point, they look at what simple theoretical population models say about Allee effects (Lewis and Kareiva 1993; Amarasekare 1998; Boukal and Berec 2002). These models invariably predict a one-humped relation between population-density and per-capita growth rate. It is important to remark that pure logistic growth, the paradigm of density-dependence, would never predict such qualitative behavior. It is only when there is a depression of per-capita growth rates at low densities that true Allee effects arise. Second, these authors manage to not only experimentally test this main prediction but also report additional demographic traits that are consistently affected by population density. 

In these wasps, individual performance can be measured in terms of the average number of individuals every adult is able to put into the next generation ---the lambda parameter in their analysis. The first panel in figure 3 shows that the per-capita growth rates are lower in populations presenting Allee effects, the ones showing a one-humped behavior in the relation between per-capita growth rates and population densities (see figure 2). Also other population traits, such maximum population size and exitinction probability, change in a correlated and consistent manner. 

In sum, Vercken and colleagues's results are experimentally solid and based on theory expectations. However, they are very intriguing. They find the signature of Allee effects in only 8 out 30 populations, all from the same genus Trichogramma, and some populations belonging to the same species (from different sampling sites) do not show consistently Allee effects. Where does this population variability comes from? What are the reasons underlying this within- and between-species variability? What are the individual mechanisms driving Allee effects in these populations? Good enough, this piece of work generates more intriguing questions than the question is able to clearly answer. Science is not a collection of final answers but instead good questions are the ones that make science progress. 

References

Amarasekare P (1998) Allee Effects in Metapopulation Dynamics. The American Naturalist, 152, 298–302. https://doi.org/10.1086/286169

Boukal DS, Berec L (2002) Single-species Models of the Allee Effect: Extinction Boundaries, Sex Ratios and Mate Encounters. Journal of Theoretical Biology, 218, 375–394. https://doi.org/10.1006/jtbi.2002.3084

Lewis MA, Kareiva P (1993) Allee Dynamics and the Spread of Invading Organisms. Theoretical Population Biology, 43, 141–158. https://doi.org/10.1006/tpbi.1993.1007

Vercken E, Groussier G, Lamy L, Mailleret L (2021) The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populations. HAL, hal-02570868, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://hal.archives-ouvertes.fr/hal-02570868