​​In this article, the authors discuss the results of their study investigating the effects of heat stress and moisture stress on a terrestrial isopod Armadilldium vulgare, the common woodlouse [1]. Specifically, the authors have assessed how increased temperature or decreased moisture affects life history traits (such as growth, survival, and reproduction) as well as physiological traits (immune cell parameters and \( beta \)-galactosidase activity). This article quantitatively evaluates the effects of the two stressors on woodlouse. Terrestrial isopods like woodlouse are sensitive to thermal and moisture stress [2; 3] and are therefore good models to test hypotheses in global change biology and for monitoring ecosystem health.

​An important feature of this study is the combination of experimental, laboratory, and analytical techniques. Experiments were conducted under controlled conditions in the laboratory by modulating temperature and moisture, life history and physiological traits were measured/analyzed and then tested using models. Both stressors had negative impacts on survival and reproduction of woodlouse, and result in premature ageing. Although thermal stress did not affect survival, it slowed woodlouse growth. Moisture stress did not have a detectable effect on woodlouse growth but decreased survival and reproductive success. An important insight from this study is that effects of heat and moisture stressors on woodlouse are not necessarily linear, and experimental approaches can be used to better elucidate the mechanisms and understand how these organisms respond to environmental stress.

​This article is timely given the increasing attention on biological monitoring and ecosystem health.​

References:

[1] Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. bioRxiv, 2022.09.26.509512., ver. 3 peer-reviewd and recommended by PCI Ecology. https://doi.org/10.1101/2022.09.26.509512

[2] ​Warburg MR, Linsenmair KE, Bercovitz K (1984) The effect of climate on the distribution and abundance of isopods. In: Sutton SL, Holdich DM, editors. The Biology of Terrestrial Isopods. Oxford: Clarendon Press. pp. 339–367.​

[3] Hassall M, Helden A, Goldson A, Grant A (2005) Ecotypic differentiation and phenotypic plasticity in reproductive traits of Armadillidium vulgare (Isopoda: Oniscidea). Oecologia 143: 51–60.​ https://doi.org/10.1007/s00442-004-1772-3

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.

References

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. https://doi.org/10.1038/nature11148
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. https://doi.org/10.1111/ele.13340
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. https://doi.org/10.1101/2020.05.26.117200
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. https://doi.org/10.1111/ele.12443

Tritrophic interactions have a central role in ecological theory and applications [1-3]. Particularly, systems comprised of plants, herbivores and predators have historically received wide attention given their ubiquity and economic importance [4]. Although ecologists have long aimed to understand the forces that govern alternating ecological effects at successive trophic levels [5], several key open questions remain (at least partially) unanswered [6]. In particular, the analysis of complex food webs has questioned whether ecosystems can be viewed as a series of trophic chains [7,8]. Moreover, whether systems are mostly controlled by top-down (trophic cascades) or bottom-up processes remains an open question [6].
Traditionally, studies have addressed how species diversity at different food chain compartments affect the strength and direction of trophic cascades [9]. For example, many studies have tested whether biological control was more efficient with more than one species of natural enemies [10-12]. Much less attention has been given to the role of within-species variation in shaping trophic cascades [13]. In particular, whereas the impact of trait variation within species of plants or predators on successive trophic levels has been recently addressed [14,15], the impact of intraspecific herbivore variation is in its infancy (but see [16]). This is at odds with the resurgent acknowledgment of the importance of individual variation for several ecological processes operating at higher levels of biological organization [17].
Sources of variation within species can come in many flavours. In herbivores, striking ecological variation can be found among populations occurring on different host plants, which become genetically differentiated, thus forming host races [18,19]. Curiously, the impact of variation across host races on the strength of trophic cascades has, to date, not been explored. This is the gap that the manuscript by Sentis and colleagues [20] fills. They experimentally studied a curious tri-trophic system where the primary consumer, pea aphids, specializes in different plant hosts, creating intraspecific variation across biotypes. Interestingly, there is also ecological variation across lineages from the same biotype. The authors set up experimental food chains, where pea aphids from different lineages and biotypes were placed in their universal legume host (broad bean plants) and then exposed to a voracious but charming predator, ladybugs. The full factorial design of this experiment allowed the authors to measure vertical effects of intraspecific variation in herbivores on both plant productivity (top-down) and predator individual growth (bottom-up).
The results nicely uncover the mechanisms by which intraspecific differences in herbivores precipitates vertical modulation in food chains. Herbivore lineage and host-plant specialization shaped the strength of trophic cascades, but curiously these effects were not modulated by density-dependence. Further, ladybugs consuming pea aphids from different lineages and biotypes grew at distinct rates, revealing bottom-up effects of intraspecific variation in herbivores.
These findings are novel and exciting for several reasons. First, they show how intraspecific variation in intermediate food chain compartments can simultaneously reverberate both top-down and bottom-up effects. Second, they bring an evolutionary facet to the understanding of trophic cascades, providing valuable insights on how genetically differentiated populations play particular ecological roles in food webs. Finally, Sentis and colleagues’ findings [20] have critical implications well beyond their study systems. From an applied perspective, they provide an evident instance on how consumers’ evolutionary specialization matters for their role in ecosystems processes (e.g. plant biomass production, predator conversion rate), which has key consequences for biological control initiatives and invasive species management. From a conceptual standpoint, their results ignite the still neglected value of intraspecific variation (driven by evolution) in modulating the functioning of food webs, which is a promising avenue for future theoretical and empirical studies.

References

[1] Price, P. W., Bouton, C. E., Gross, P., McPheron, B. A., Thompson, J. N., & Weis, A. E. (1980). Interactions among three trophic levels: influence of plants on interactions between insect herbivores and natural enemies. Annual review of Ecology and Systematics, 11(1), 41-65. doi: 10.1146/annurev.es.11.110180.000353
[2] Olff, H., Brown, V.K. & Drent, R.H. (1999). Herbivores: between plants and predators. Blackwell Science, Oxford.
[3] Tscharntke, T. & Hawkins, B.A. (2002). Multitrophic level interactions. Cambridge University Press. doi: 10.1017/CBO9780511542190
[4] Agrawal, A. A. (2000). Mechanisms, ecological consequences and agricultural implications of tri-trophic interactions. Current opinion in plant biology, 3(4), 329-335. doi: 10.1016/S1369-5266(00)00089-3
[5] Pace, M. L., Cole, J. J., Carpenter, S. R., & Kitchell, J. F. (1999). Trophic cascades revealed in diverse ecosystems. Trends in ecology & evolution, 14(12), 483-488. doi: 10.1016/S0169-5347(99)01723-1
[6] Abdala‐Roberts, L., Puentes, A., Finke, D. L., Marquis, R. J., Montserrat, M., Poelman, E. H., ... & Mooney, K. (2019). Tri‐trophic interactions: bridging species, communities and ecosystems. Ecology letters, 22(12), 2151-2167. doi: 10.1111/ele.13392
[7] Polis, G.A. & Winemiller, K.O. (1996). Food webs. Integration of patterns and dynamics. Chapmann & Hall, New York. doi: 10.1007/978-1-4615-7007-3
[8] Torres‐Campos, I., Magalhães, S., Moya‐Laraño, J., & Montserrat, M. (2020). The return of the trophic chain: Fundamental vs. realized interactions in a simple arthropod food web. Functional Ecology, 34(2), 521-533. doi: 10.1111/1365-2435.13470
[9] Polis, G. A., Sears, A. L., Huxel, G. R., Strong, D. R., & Maron, J. (2000). When is a trophic cascade a trophic cascade?. Trends in Ecology & Evolution, 15(11), 473-475. doi: 10.1016/S0169-5347(00)01971-6
[10] Sih, A., Englund, G., & Wooster, D. (1998). Emergent impacts of multiple predators on prey. Trends in ecology & evolution, 13(9), 350-355. doi: 10.1016/S0169-5347(98)01437-2
[11] Diehl, E., Sereda, E., Wolters, V., & Birkhofer, K. (2013). Effects of predator specialization, host plant and climate on biological control of aphids by natural enemies: a meta‐analysis. Journal of Applied Ecology, 50(1), 262-270. doi: 10.1111/1365-2664.12032
[12] Snyder, W. E. (2019). Give predators a complement: conserving natural enemy biodiversity to improve biocontrol. Biological control, 135, 73-82. doi: 10.1016/j.biocontrol.2019.04.017
[13] Des Roches, S., Post, D. M., Turley, N. E., Bailey, J. K., Hendry, A. P., Kinnison, M. T., ... & Palkovacs, E. P. (2018). The ecological importance of intraspecific variation. Nature Ecology & Evolution, 2(1), 57-64. doi: 10.1038/s41559-017-0402-5
[14] Bustos‐Segura, C., Poelman, E. H., Reichelt, M., Gershenzon, J., & Gols, R. (2017). Intraspecific chemical diversity among neighbouring plants correlates positively with plant size and herbivore load but negatively with herbivore damage. Ecology Letters, 20(1), 87-97. doi: 10.1111/ele.12713
[15] Start, D., & Gilbert, B. (2017). Predator personality structures prey communities and trophic cascades. Ecology letters, 20(3), 366-374. doi: 10.1111/ele.12735
[16] Turcotte, M. M., Reznick, D. N., & Daniel Hare, J. (2013). Experimental test of an eco-evolutionary dynamic feedback loop between evolution and population density in the green peach aphid. The American Naturalist, 181(S1), S46-S57. doi: 10.1086/668078
[17] Bolnick, D. I., Amarasekare, P., Araújo, M. S., Bürger, R., Levine, J. M., Novak, M., ... & Vasseur, D. A. (2011). Why intraspecific trait variation matters in community ecology. Trends in ecology & evolution, 26(4), 183-192. doi: 10.1016/j.tree.2011.01.009
[18] Drès, M., & Mallet, J. (2002). Host races in plant–feeding insects and their importance in sympatric speciation. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 357(1420), 471-492. doi: 10.1098/rstb.2002.1059
[19] Magalhães, S., Forbes, M. R., Skoracka, A., Osakabe, M., Chevillon, C., & McCoy, K. D. (2007). Host race formation in the Acari. Experimental and Applied Acarology, 42(4), 225-238. doi: 10.1007/s10493-007-9091-0
[20] Sentis, A., Bertram, R., Dardenne, N., Simon, J.-C., Magro, A., Pujol, B., Danchin, E. and J.-L. Hemptinne (2020) Intraspecific difference among herbivore lineages and their host-plant specialization drive the strength of trophic cascades. bioRxiv, 722140, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/722140

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

The spatial distribution of a species is driven by both biotic and abiotic factors that may change over time (Soberón & Nakamura, 2009; Paquette & Hargreaves, 2021).  Therefore, species ranges are dynamic, especially in humanized landscapes where changes occur at high speeds (Sirén & Morelli, 2020). The distribution of many species is being reduced because of human impacts; however, some species are expanding their distributions, even over their niche (Lustenhouwer & Parker, 2022). One of the factors that may lead to a geographic niche expansion is behavioral flexibility (Mikhalevich et al., 2017), but the mechanisms determining range expansion through behavioral changes are not fully understood. 

The PCI Ecology study by Summers et al. (2023) uses a very large database on the current and historic distribution of two species of grackles that have shown different trends in their distribution. The great-tailed grackle has largely expanded its range over the 20th century, while the range of the boat-tailed grackle has remained very similar. They take advantage of this differential response in the distribution of the two species and run several analyses to test whether it was a change in habitat availability, in the realized niche, in habitat connectivity or in in the other traits or conditions that previously limited the species range, what is driving the observed distribution of the species. The study finds a change in the niche of great-tailed grackle, consistent with the high behavioral flexibility of the species.

The two reviewers and I have seen a lot of value in this study because 1) it addresses a very timely question, especially in the current changing world; 2) it is a first step to better understanding if behavioral attributes may affect species’ ability to change their niche; 3) it contrasts the results using several complementary statistical analyses, reinforcing their conclusions; 4) it is based on the preregistration Logan et al (2021), and any deviations from it are carefully explained and justified in the text and 5) the limitations of the study have been carefully discussed. It remains to know if the boat-tailed grackle has more limited behavioral flexibility than the great-tailed grackle, further confirming the results of this study.
 
References

Logan CJ, McCune KB, Chen N, Lukas D (2021) Implementing a rapid geographic range expansion - the role of behavior and habitat changes. http://corinalogan.com/Preregistrations/gxpopbehaviorhabitat.html

Lustenhouwer N, Parker IM (2022) Beyond tracking climate: Niche shifts during native range expansion and their implications for novel invasions. Journal of Biogeography, 49, 1481–1493. https://doi.org/10.1111/jbi.14395

Mikhalevich I, Powell R, Logan C (2017) Is behavioural flexibility evidence of cognitive complexity? How evolution can inform comparative cognition. Interface Focus, 7, 20160121. https://doi.org/10.1098/rsfs.2016.0121

Paquette A, Hargreaves AL (2021) Biotic interactions are more often important at species’ warm versus cool range edges. Ecology Letters, 24, 2427–2438. https://doi.org/10.1111/ele.13864

Sirén APK, Morelli TL (2020) Interactive range-limit theory (iRLT): An extension for predicting range shifts. Journal of Animal Ecology, 89, 940–954. https://doi.org/10.1111/1365-2656.13150

Soberón J, Nakamura M (2009) Niches and distributional areas: Concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106, 19644–19650. https://doi.org/10.1073/pnas.0901637106

Summers JT, Lukas D, Logan CJ, Chen N (2022) The role of climate change and niche shifts in divergent range dynamics of a sister-species pair. EcoEvoRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/879pe