Mutualistic interactions are the weird uncles of population and community ecology. They are everywhere, from the microbes aiding digestion in animals’ guts to animal-pollination services in ecosystems; They increase productivity through facilitation; They fascinate us when small birds pick the teeth of a big-mouthed crocodile. Yet, mutualistic interactions are far less studied and understood than competition or predation. Possibly because we are naively convinced that there is no mystery here: isn’t it obvious that mutualistic interactions necessarily facilitate species coexistence? Since mutualistic species benefit from one another, if one species evolves, the other should just follow, isn’t that so?

It is not as simple as that, for several reasons. First, because simple mutualistic Lotka-Volterra models showed that most of the time mutualistic systems should drift to infinity and be unstable (e.g. Goh 1979). This is not what happens in natural populations, so something is missing in simple models. At a larger scale, that of communities, this is even worse, since we are still far from understanding the link between the topology of mutualistic networks and the stability of a community. Second, interactions are context-dependent: mutualistic species exchange resources, and thus from the point of view of one species the interaction is either beneficial or not, depending on the net gain of energy (e.g. Holland and DeAngelis 2010). In other words, considering interactions as mutualistic per se is too caricatural. Third, since evolution is blind, the evolutionary response of a species to an environmental change can have any effect on its mutualistic partner, and not necessarily a neutral or positive effect. This latter reason is particularly highlighted by the paper by A. Weinbach et al. (2021).

Weinbach et al. considered a simple two-species mutualistic Lotka-Volterra model and analyzed the evolutionary dynamics of a trait controlling for the rate of interaction between the two species by using the classical Adaptive Dynamics framework. They showed that, depending on the form of the trade-off between this interaction trait and its effect on the intrinsic growth rate, several situations can occur at evolutionary equilibrium: species can stably coexist and maintain their interaction, or the interaction traits can evolve to zero where species can coexist without any interactions.

Weinbach et al. then investigated the fate of the two-species system if a partner species is strongly affected by environmental change, for instance, a large decrease of its growth rate. Because of the supposed trade-off between the interaction trait and the growth rate, the interaction trait in the focal species tends to decrease as an evolutionary response to the decline of the partner species. If environmental change is too large, the interaction trait can evolve to zero and can lead the partner species to extinction. An “evolutionary murder”.

Even though Weinbach et al. interpreted the results of their model through the lens of plant-pollinators systems, their model is not specific to this case. On the contrary, it is very general, which has advantages and caveats. By its generality, the model is informative because it is a proof of concept that the evolution of mutualistic interactions can have unexpected effects on any category of mutualistic systems. Yet, since the model lacks many specificities of plant-pollinator interactions, it is hard to evaluate how their result would apply to plant-pollinators communities.

I wanted to recommend this paper as a reminder that it is certainly worth studying the evolution of mutualistic interactions, because i) some unexpected phenomenons can occur, ii) we are certainly too naive about the evolution and ecology of mutualistic interactions, and iii) one can wonder to what extent we will be able to explain the stability of mutualistic communities without accounting for the co-evolutionary dynamics of mutualistic species.

References

Goh BS (1979) Stability in Models of Mutualism. The American Naturalist, 113, 261–275. http://www.jstor.org/stable/2460204.

Holland JN, DeAngelis DL (2010) A consumer–resource approach to the density-dependent population dynamics of mutualism. Ecology, 91, 1286–1295. https://doi.org/10.1890/09-1163.1

Weinbach A, Loeuille N, Rohr RP (2021) Eco-evolutionary dynamics further weakens mutualistic interaction and coexistence under population decline. bioRxiv, 570580, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/570580

Across the temporal expanse of history, the impact of human activities on global landscapes has manifested as a complex interplay of ecological alterations. From the advent of early agricultural practices to the successive waves of industrialization characterizing the 18th and 19th centuries, anthropogenic forces have exerted profound and enduring transformations upon Earth's ecosystems. Indeed, by 2017, more than 80% of the terrestrial biosphere was transformed by human populations and land use, and just 19% remains as wildlands (Ellis et al. 2021).
 
Urbanization engenders profound alterations in environmental conditions, exerting substantial impacts on biological communities. The expansion of built infrastructure, modification of land use patterns, and the introduction of impervious surfaces and habitat fragmentation are key facets of urbanization (Faeth et al. 2011). These alterations generate biodiversity loss, changes in the composition of biological communities, disruptions in access and availability of food and nutrients, and a loss of efficiency in the immune system's control of infections, etc. (Reyes et al. 2013).
 
In this study, Caizergues et al. (2023) investigated the prevalence and diversity of avian malaria parasites (Plasmodium/Haemoproteus sp. and Leucocytozoon sp.) in great tits (Parus major) living across an urbanization gradient. The study reveals nuanced patterns of avian malaria prevalence and lineage diversity in great tits across urban and non-urban environments. While overall parasite diversity remains consistent, there are marked differences in prevalence between life stages and habitats. They observed a high prevalence in adult birds (from 95% to 100%), yet lower prevalence in fledglings (from 0% to 38%). Notably, urban nestlings exhibit higher parasite prevalence than their non-urban counterparts, suggesting a potential link between early malaria infection and the urban heat island effect. This finding underscores the importance of considering both spatial and temporal aspects of urbanization in understanding disease dynamics. Parasite lineages were not habitat-specific. The results suggest a potential parasitic burden in more urbanized areas, with a marginal but notable effect of nest-level urbanization on Plasmodium prevalence. This challenges the common perception of lower parasitic prevalence in urban environments and highlights the need for further investigation into the factors influencing parasite prevalence at finer spatial scales.
 
The discussion emphasizes the significance of examining vector distributions, abundance, and diversity in urban areas, which may be influenced by ecological niches and the presence of suitable habitats such as marshes. The identification of habitat-specific Haemosporidian lineages, particularly those occurring more frequently in urban areas, raises intriguing questions about the factors influencing parasite diversity. The presence of rare lineages in urban environments, such as AFR065, DELURB4, and YWT4, suggests a potential connection between urban bird communities and specific parasite strains.
 
Future research should empirically demonstrate these relationships to enhance our understanding of urban parasitology. This finding has broader implications for wildlife epidemiology, especially when introducing or keeping exotic wildlife in contact with native species. The study highlights the importance of considering not only the prevalence but also the specific lineages of parasites in understanding the dynamics of avian malaria in urban and non-urban habitats. This preprint contributes valuable insights to the ongoing discourse on the intricate interplay between ecological repercussions of human-induced changes (urbanization), biological communities, and the prevalence of vector-borne diseases.
 
References

Caizergues AE, Robira B, Perrier C, Jeanneau M, Berthomieu A, Perret S, Gandon S, Charmantier A (2023) Cities as parasitic amplifiers? Malaria prevalence and diversity in great tits along an urbanization gradient. bioRxiv, 2023.05.03.539263, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.05.03.539263

Ellis EC, Gauthier N, Klein Goldewijk K, Bliege Bird R, Boivin N, Díaz S, Fuller DQ, Gill JL, Kaplan JO, Kingston N, Locke H, McMichael CNH, Ranco D, Rick TC, Shaw MR, Stephens L, Svenning JC, Watson JEM. People have shaped most of terrestrial nature for at least 12,000 years. Proc Natl Acad Sci U S A. 2021 Apr 27;118(17):e2023483118. https://doi.org/10.1073/pnas.2023483118. 

Faeth  SH, Bang  C, Saari  S (2011) Urban biodiversity: Patterns and mechanisms. Ann N Y Acad Sci 1223:69–81. https://doi.org/10.1111/j.1749-6632.2010.05925.x

Faeth  SH, Bang  C, Saari  S (2011) Urban biodiversity: Patterns and mechanisms. Ann N Y Acad Sci 1223:69–81. https://doi.org/10.1111/j.1749-6632.2010.05925.x

Reyes  R, Ahn  R, Thurber  K, Burke  TF (2013) Urbanization and Infectious Diseases: General Principles, Historical Perspectives, and Contemporary Challenges. Challenges Infect Dis 123. https://doi.org/10.1007/978-1-4614-4496-1_4

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

Despite the increasing refinement of statistical methods, a robust experimental design is still one of the most important cornerstones to answer ecological and evolutionary questions. However, there is a strong trade-off between a perfect design and its feasibility. A common mantra is that more data is always better, but how much is enough is complex to answer, specially when we want to capture the spatial and temporal variability of a given process. Gardarin and Valantin-Morison [1] make an effort to answer these questions for a practical case: How many pitfalls traps, of which type, and over which extent, do we need to detect shifts in arthropod community composition in agricultural landscapes. There is extense literature on how to approach these challenges using preliminary data in combination with simulation methods [e.g. 2], but practical cases are always welcomed to illustrate the complexity of the decisions to be made. A key challenge in this situation is the nature of simplified and patchy agricultural arthropod communities. In this context, small effect sizes are expected, but those small effects are relevant from an ecological point of view because small increases at low biodiversity may produce large gains in ecosystem functioning [3].
The paper shows that some variables are not important, such as the type of fluid used to fill the pitfall traps. This is good news for potential comparisons among studies using slightly different protocols. However, the bad news are that the sampling effort needed for detecting community changes is larger than the average effort currently implemented. A potential solution is to focus on Community Weighed Mean metrics (CWM; i.e. a functional descriptor of the community body size distribution) rather than on classic metrics such as species richness, as detecting changes on CWM requires a lower sampling effort and it has a clear ecological interpretation linked to ecosystem functioning.
Beyond the scope of the data presented, which is limited to a single region over two years, and hence it is hard to extrapolate to other regions and years, the big message of the paper is the need to incorporate statistical power simulations as a central piece of the ecologist's toolbox. This is challenging, especially when you face questions such as: Should I replicate over space, or over time? The recommended paper is accompanied by the statistical code used, which should facilitate this task to other researchers. Furthermore, we should be aware that some important questions in ecology are highly variable in space and time, and hence, larger sampling effort across space and time is needed to detect patterns. Larger and longer monitoring schemes require a large effort (and funding), but if we want to make relevant ecology, nobody said it would be easy.

References

[1] Gardarin, A. and Valantin-Morison, M. (2019). Which pitfall traps and sampling efforts should be used to evaluate the effects of cropping systems on the taxonomic and functional composition of arthropod communities? Zenodo, 3468920, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.5281/zenodo.3468920
[2] Johnson, P. C., Barry, S. J., Ferguson, H. M., and Müller, P. (2015). Power analysis for generalized linear mixed models in ecology and evolution. Methods in ecology and evolution, 6(2), 133-142. doi: 10.1111/2041-210X.12306
[3] Cardinale, B. J. et al. (2012). Biodiversity loss and its impact on humanity. Nature, 486(7401), 59-67. doi: 10.1038/nature11148

Individuals can respond to environmental changes that they undergo directly (within-generation plasticity) but also through transgenerational plasticity, providing lasting effects that are transmitted to the next generations (Donelson et al. 2012; Munday et al. 2013; Kuijper & Hoyle 2015; Auge et al. 2017, Tariel et al. 2020). These parental effects can affect offspring via various mechanisms, notably via maternal transmission of hormones to the eggs or growing embryos (Mousseau & Fox 1998). While the effects of environmental quality may simply carry-over to the next generation (e.g., females in stressful environments give birth to offspring in poorer condition), parental effects may also be a mechanism that adjusts offspring phenotype in response to environmental variation and predictability, and thereby match offspring's phenotype to future environmental conditions (Gluckman et al. 2005; Marshall & Uller 2007; Dey et al. 2016; Yin et al. 2019), for example by preparing their offspring to an expected stressful environment.

When females experience stress during gestation or egg formation, elevations in glucocorticoids (GC) are expected to affect offspring phenotype in many ways, from the offspring's own GC levels, to their growth and survival (Sheriff et al. 2017). This is a well established idea, but how strong is the evidence for this? A meta-analysis on birds found no clear effect of corticosterone manipulation on offspring traits (38 studies on 9 bird species for corticosterone manipulation; Podmokła et al. 2018). Another meta-analysis including 14 vertebrate species found no clear effect of prenatal stress on offspring GC (Thayer et al. 2018). Finally, a meta-analysis on wild vertebrates (23 species) found no clear effect of GC-mediated maternal effects on offspring traits (MacLeod et al. 2021). As often when facing such inconclusive results, context dependence has been suggested as one potential reason for such inconsistencies, for exemple sex specific effects (Groothuis et al. 2019, 2020). However, sex specific measures on offspring are scarce (Podmokła et al. 2018). Moreover, the literature available is still limited to a few, mostly “model” species.

With their study, Amin et al. (2024) show the way to improve our understanding on GC transmission from mother to offspring and its effects in several aspects. First they used innovative non-invasive methods (which could broaden the range of species available to study) by quantifying cortisol metabolites from faecal samples collected from pregnant females, as proxy for maternal GC level, and relating it to GC levels from hairs of their neonate offspring. Second they used a free ranging large mammal (taxa from which literature is missing): the fallow deer (Dama dama). Third, they provide sex specific measures of GC levels. And finally but importantly, they are exemplary in their transparency regarding 1) the exploratory nature of their study, 2) their statistical thinking and procedure, and 3) the study limitations (e.g., low sample size and high within individual variation of measurements). I hope this study will motivate more research (on the fallow deer, and on other species) to broaden and strengthen our understanding of sex specific effects of maternal stress and CG levels on offspring phenotype and fitness.

References

Amin, B., Fishman, R., Quinn, M., Matas, D., Palme, R., Koren, L., & Ciuti, S. (2024). Sex differences in the relationship between maternal and foetal glucocorticoids in a free-ranging large mammal. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.05.04.538920 

Auge, G.A., Leverett, L.D., Edwards, B.R. & Donohue, K. (2017). Adjusting phenotypes via within-and across-generational plasticity. New Phytologist, 216, 343–349. https://doi.org/10.1111/nph.14495

Dey, S., Proulx, S.R. & Teotonio, H. (2016). Adaptation to temporally fluctuating environments by the evolution of maternal effects. PLoS biology, 14, e1002388. https://doi.org/10.1371/journal.pbio.1002388

Donelson, J.M., Munday, P.L., McCormick, M.I. & Pitcher, C.R. (2012). Rapid transgenerational acclimation of a tropical reef fish to climate change. Nature Climate Change, 2, 30. https://doi.org/10.1038/nclimate1323

Gluckman, P.D., Hanson, M.A. & Spencer, H.G. (2005). Predictive adaptive responses and human evolution. Trends in ecology & evolution, 20, 527–533. https://doi.org/10.1016/j.tree.2005.08.001

Groothuis, Ton GG, Bin-Yan Hsu, Neeraj Kumar, and Barbara Tschirren. "Revisiting mechanisms and functions of prenatal hormone-mediated maternal effects using avian species as a model." Philosophical Transactions of the Royal Society B 374, no. 1770 (2019): 20180115. https://doi.org/10.1098/rstb.2018.0115

Groothuis, Ton GG, Neeraj Kumar, and Bin-Yan Hsu. "Explaining discrepancies in the study of maternal effects: the role of context and embryo." Current Opinion in Behavioral Sciences 36 (2020): 185-192. https://doi.org/10.1016/j.cobeha.2020.10.006 

Kuijper, B. & Hoyle, R.B. (2015). When to rely on maternal effects and when on phenotypic plasticity? Evolution, 69, 950–968. https://doi.org/10.1111/evo.12635   

MacLeod, Kirsty J., Geoffrey M. While, and Tobias Uller. "Viviparous mothers impose stronger glucocorticoid‐mediated maternal stress effects on their offspring than oviparous mothers." Ecology and Evolution 11, no. 23 (2021): 17238-17259.

Marshall, D.J. & Uller, T. (2007). When is a maternal effect adaptive? Oikos, 116, 1957–1963. https://doi.org/10.1111/j.2007.0030-1299.16203.x       

Mousseau, T.A. & Fox, C.W. (1998). Maternal effects as adaptations. Oxford University Press.

Munday, P.L., Warner, R.R., Monro, K., Pandolfi, J.M. & Marshall, D.J. (2013). Predicting evolutionary responses to climate change in the sea. Ecology Letters, 16, 1488–1500. https://doi.org/10.1111/ele.12185

Podmokła, Edyta, Szymon M. Drobniak, and Joanna Rutkowska. "Chicken or egg? Outcomes of experimental manipulations of maternally transmitted hormones depend on administration method–a meta‐analysis." Biological Reviews 93, no. 3 (2018): 1499-1517. https://doi.org/10.1111/brv.12406 

Sheriff, M. J., Bell, A., Boonstra, R., Dantzer, B., Lavergne, S. G., McGhee, K. E., MacLeod, K. J., Winandy, L., Zimmer, C., & Love, O. P. (2017). Integrating ecological and evolutionary context in the study of maternal stress. Integrative and Comparative Biology, 57(3), 437–449. https://doi.org/10.1093/icb/icx105

Tariel, Juliette, Sandrine Plénet, and Émilien Luquet. "Transgenerational plasticity in the context of predator-prey interactions." Frontiers in Ecology and Evolution 8 (2020): 548660. https://doi.org/10.3389/fevo.2020.548660 

Thayer, Zaneta M., Meredith A. Wilson, Andrew W. Kim, and Adrian V. Jaeggi. "Impact of prenatal stress on offspring glucocorticoid levels: A phylogenetic meta-analysis across 14 vertebrate species." Scientific Reports 8, no. 1 (2018): 4942. https://doi.org/10.1038/s41598-018-23169-w 

Yin, J., Zhou, M., Lin, Z., Li, Q.Q. & Zhang, Y.-Y. (2019). Transgenerational effects benefit offspring across diverse environments: a meta-analysis in plants and animals. Ecology letters, 22, 1976–1986. https://doi.org/10.1111/ele.13373