Aboveground – belowground interactions is a fascinating field that has developed in ecology since about 20 years [1]. This field has been very fruitful as measured by the numerous articles published but also by the particular role it has played in the development of soil ecology. While soil ecology has for a long time developed partially independently from “general ecology” [2], the field of aboveground – belowground interactions has shown that all ecological interactions occurring within the soil are likely to impact plant growth and plant physiology because they have their roots within the soil. In turns, this should impact the aerial system of plants (higher or lower biomasses, changes in leaf quality…), which should cascade on the aboveground food web. Conversely, all ecological interactions occurring aboveground likely impact plant growth, which should cascade to their root systems, and thus to the soil functioning and the soil food web (through changes in the emission of exudates or inputs of dead roots…). Basically, plants are linking the belowground and aboveground worlds because, as terrestrial primary producers, they need to have (1) leaves to capture CO2 and exploit light and (2) roots to absorb water and mineral nutrients. The article I presently recommend [3] tackles this general issue through the prism of the impact of large herbivores on the decomposition of leaf litter.
This issue is a relatively old one [4, 5] but still deserves efforts because there have been relatively few studies on the subject and because the issue is relatively complex due to the diversity of mechanisms involved and the difficulty to disentangle them. I recommend this article because the authors have cleverly taken advantage of a ‘‘natural’’ long-term experiment, i.e. three islands with contrasted deer densities, to test whether these large mammals are able to impact leaf litter decomposition and whether they are able to do so through changes in litter quality (because they browse the vegetation) or through changes in soil characteristics (either physical or chemical characteristics or the composition of the decomposer community). They have found that deer decrease litter decomposition, mainly through a decrease in litter quality (increase in its C:N ratio). I particularly appreciate the combination of statistics achieved to test the different hypotheses and the fair and in-depth discussion of the results.
I have to confess that I have two small regrets with this work. First, all replications are implemented within the same three islands, so that it cannot be fully excluded that measured effects should not be attributed to any other possible difference between the three islands. I am fairly sure this is not the case (at least because the three islands have the same environments) but I hope that future studies or meta-analyses will be able analyse independent deer density treatments. Second, as a soil ecologist, I am eager to see results on the decomposer communities, both microorganisms and macrofauna, of the three islands.

References

[1] Hooper, D. U., Bignell, D. E., Brown, V. K., Brussard, L., Dangerfield, J. M., Wall, D. H. and Wolters, V. (2000). Interactions between Aboveground and Belowground Biodiversity in Terrestrial Ecosystems: Patterns, Mechanisms, and Feedbacks. BioScience, 50(12), 1049-1061. doi: 10.1641/0006-3568(2000)050[1049:ibaabb]2.0.co;2
[2] Barot, S., Blouin, M., Fontaine, S., Jouquet, P., Lata, J.-C., and Mathieu, J. (2007). A Tale of Four Stories: Soil Ecology, Theory, Evolution and the Publication System. PLOS ONE, 2(11), e1248. doi: 10.1371/journal.pone.0001248
[3] Chollet S., Maillard M., Schörghuber J., Grayston S. and Martin J.-L. (2019). Deer slow down litter decomposition by reducing litter quality in a temperate forest. bioRxiv, 690032, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/690032
[4] Wardle, D. A., Barker, G. M., Yeates, G. W., Bonner, K. I., and Ghani, A. (2001). Introduced browsing mammals in New Zealand natural forests: aboveground and belowground consequences. Ecological Monographs, 71(4), 587-614. doi: 10.1890/0012-9615(2001)071[0587:ibminz]2.0.co;2
[5] Bardgett, R. D., and Wardle, D. A. (2003). Herbivore-mediated linkages between aboveground and belowground communities. Ecology, 84(9), 2258-2268. doi: 10.1890/02-0274

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

References

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

Coastal lagoons are among the most productive natural ecosystems on Earth. These relatively closed basins are important habitats and nursery for endemic and endangered species and are extremely vulnerable to nutrient input from the surrounding catchment; therefore, they are highly susceptible to anthropogenic influence, pollution and invasion (Pérez-Ruzafa et al., 2019). In general, coastal lagoons exhibit great spatial and temporal variability in their physicochemical water characteristics due to the sporadic mixing of freshwater with marine influx. One of the alternatives for monitoring communities or target species in aquatic ecosystems is the environmental DNA (eDNA), since overcomes some limitations from traditional methods and enables the investigation of multiple species from a single sample (Thomsen and Willerslev, 2015). In coastal lagoons, where the water turbidity is highly variable, there is a major challenge for monitoring the eDNA because filtering turbid water to obtain the eDNA is problematic (filters get rapidly clogged, there is organic and inorganic matter accumulation, etc.). 

The study by Turba et al. (2023) analyzes different ways of dealing with eDNA sampling and processing in turbid waters and sediments of coastal lagoons, and offers guidelines to obtain unbiased results from the subsequent sequencing using 12S (fish) and 16S (Bacteria and Archaea) universal primers. They analyzed the effect on taxa detection of: i) freezing or not prior to filtering; ii) freezing prior to centrifugation to obtain a sample pellet; and iii) using frozen sediment samples as a proxy of what happens in the water. The authors propose these different alternatives (freeze, do not freeze, sediment sampling) because they consider that they are the easiest to carry out. They found that freezing before filtering using a 3 µm pore size filter had no effects on community composition for either primer, and therefore it is a worthwhile approach for comparison of fish, bacteria and archaea in this kind of system. However, significantly different bacterial community composition was found for sediment compared to water samples. Also, in sediment samples the replicates showed to be more heterogeneous, so the authors suggest increasing the number of replicates when using sediment samples. Something that could be a concern with the study is that the rarefaction curves based on sequencing effort for each protocol did not saturate in any case, this being especially evident in sediment samples. The authors were aware of this, used the slopes obtained from each curve as a measure of comparison between samples and considering that the sequencing depth did not meet their expectations, they managed to achieve their goal and to determine which protocol is the most promising for eDNA monitoring in coastal lagoons. Although there are details that could be adjusted in relation to this item, I consider that the approach is promising for this type of turbid system.

References

Pérez-Ruzafa A, Campillo S, Fernández-Palacios JM, García-Lacunza A, García-Oliva M, Ibañez H, Navarro-Martínez PC, Pérez-Marcos M, Pérez-Ruzafa IM, Quispe-Becerra JI, Sala-Mirete A, Sánchez O, Marcos C (2019) Long-Term Dynamic in Nutrients, Chlorophyll a, and Water Quality Parameters in a Coastal Lagoon During a Process of Eutrophication for Decades, a Sudden Break and a Relatively Rapid Recovery. Frontiers in Marine Science, 6. https://doi.org/10.3389/fmars.2019.00026

Thomsen PF, Willerslev E (2015) Environmental DNA – An emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation, 183, 4–18. https://doi.org/10.1016/j.biocon.2014.11.019

Turba R, Thai GH, Jacobs DK (2023) Different approaches to processing environmental DNA samples in turbid waters have distinct effects for fish, bacterial and archaea communities. bioRxiv, 2022.06.17.495388, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.06.17.495388

Amongst the many challenges and forms of environmental change that organisms face in our era of global change, climate change is perhaps one of the most straightforward and amenable to investigation. First, measurements of day-to-day temperatures are relatively feasible and accessible, and predictions regarding the expected trends in Earth surface temperature are probably some of the most reliable we have. It appears quite clear, in particular, that beyond the overall increase in average temperature, the heat waves locally experienced by organisms in their natural habitats are bound to become more frequent, more intense, and more long-lasting [1]. Second, it is well appreciated that temperature is a major environmental factor with strong impacts on different facets of organismal development and life-history [2-4]. These impacts have reasonably clear mechanistic underpinnings, with definite connections to biochemistry, physiology, and considerations on energetics. Third, since variation in temperature is a challenge already experienced by natural populations across their current and historical ranges, it is not a completely alien form of environmental change. Therefore, we already learnt quite a lot about it in several species, and so did the species, as they may be expected to have evolved dedicated adaptive mechanisms to respond to elevated temperatures. Last, but not least, temperature is quite amenable to being manipulated as an experimental factor.
For all these reasons, experimental studies of the consequences of increased temperature hit some of a sweetspot and are a source of very nice research, in many different organisms. The work by Leicht and Seppala [5] complements a sequence of earlier studies by this group, using the freshwater snail Lymnaea stagnalis as their model system [6-7].
In the present study, the authors investigate how a heat wave (a period of abnormally elevated temperature, here 25°C versus a normal 15°C) may have indirect effects on the next generation, through maternal effects. They question whether such indirect effects exist, and if they exist, how they compare, in terms of effect size, with the (more straightforward) direct effects observed in individuals that directly experience a heat wave. Transgenerational effects are well-known to occur following periods of physiological stress, and might thus have non negligible contributions to the overall effect of warming.
In this freshwater snail, heat has very strong direct effects: mortality increases at high temperature, but survivors grow much bigger, with a greater propensity to lay eggs and a (spectacular) three-fold increase in the number of eggs laid [6]. Considering that, it is easy to consider that transgenerational effects should be small game. And indeed, the present study also observes the big and obvious direct effects of elevated temperature: higher mortality, but greater propensity to oviposit. However, it was also found that the eggs were smaller if from mothers exposed to high temperature, with a correspondingly smaller size of hatchlings. This suggests that a heat wave causes the snails to lay more eggs, but smaller ones, reminiscent of a size-number trade-off. Unfortunately, clutch size could not be measured in this experiment, so this cannot be investigated any further. For this trait, the indirect effect may indeed be regarded as small game : eggs and hatchlings were about 15 % smaller, an effect size pretty small compared to the mammoth direct positive effect of temperature on shell length (see Figure 4 ; and also [6]). The same is true for developmental time (Figure 3).
However, for some traits the story was different. In particular, it was found that the (smaller) eggs produced from heated mothers were more likely to hatch by almost 10% (Figure 2). Here the indirect effect not only goes against the direct effect (hatching rate is lower at high temperature), but it also has similar effect size. As a consequence, taking into account both the indirect and direct effects, hatching success is essentially the same at 15°C and 25°C (Figure 2). Survival also had comparable effect sizes for direct and indirect effects. Indeed, survival was reduced by about 20% regardless of whom endured the heat stress (the focal individual or her mother; Figure 4). Interestingly, the direct and indirect effects were not quite cumulative: if a mother experienced a heat wave, heating up the offspring did not do much more damage, as though the offspring were ‘adapted’ to the warmer conditions (but keep in mind that, surprisingly, the authors’ stats did not find a significant interaction; Table 2).
At the end of the day, even though at first heat seems a relatively simple and understandable component of environmental change, this study shows how varied its effects can be effects on different components of individual fitness. The overall impact most likely is a mix of direct and indirect effects, of shifts along allocation trade-offs, and of maladaptive and adaptive responses, whose overall ecological significance is not so easy to grasp. That said, this study shows that direct and indirect (maternal) effects can sometimes go against one another and have similar intensities. Indirect effects should therefore not be overlooked in this kind of studies. It also gives a hint of what an interesting challenge it is to understand the adaptive or maladaptive nature of organism responses to elevated temperatures, and to evaluate their ultimate fitness consequences.

References

[1] Meehl, G. A., & Tebaldi, C. (2004). More intense, more frequent, and longer lasting heat waves in the 21st century. Science (New York, N.Y.), 305(5686), 994–997. doi: 10.1126/science.1098704
[2] Adamo, S. A., & Lovett, M. M. E. (2011). Some like it hot: the effects of climate change on reproduction, immune function and disease resistance in the cricket Gryllus texensis. The Journal of Experimental Biology, 214(Pt 12), 1997–2004. doi: 10.1242/jeb.056531
[3] Deutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B., & Naylor, R. L. (2018). Increase in crop losses to insect pests in a warming climate. Science (New York, N.Y.), 361(6405), 916–919. doi: 10.1126/science.aat3466
[4] Sentis, A., Hemptinne, J.-L., & Brodeur, J. (2013). Effects of simulated heat waves on an experimental plant–herbivore–predator food chain. Global Change Biology, 19(3), 833–842. doi: 10.1111/gcb.12094
[5] Leicht, K., & Seppälä, O. (2019). Direct and transgenerational effects of an experimental heat wave on early life stages in a freshwater snail. BioRxiv, 449777, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/449777
[6] Leicht, K., Seppälä, K., & Seppälä, O. (2017). Potential for adaptation to climate change: family-level variation in fitness-related traits and their responses to heat waves in a snail population. BMC Evolutionary Biology, 17(1), 140. doi: 10.1186/s12862-017-0988-x
[7] Leicht, K., Jokela, J., & Seppälä, O. (2013). An experimental heat wave changes immune defense and life history traits in a freshwater snail. Ecology and Evolution, 3(15), 4861–4871. doi: 10.1002/ece3.874

Temperature-size rule (TSR) is a phenomenon of plastic changes in body size in response to temperature, originally observed in more than 80% of ectothermic organisms representing various groups (Atkinson 1994). In particular, ectotherms were observed to grow faster and reach smaller size at higher temperature and grow slower and achieve larger size at lower temperature. This response has fired the imagination of researchers since its invention, due to its counterintuitive pattern from an evolutionary perspective (Berrigan and Charnov 1994). The main question to be resolved is: why do organisms grow fast and achieve smaller sizes under more favourable conditions (= relatively higher temperature), while they grow longer and achieve larger sizes under less favourable conditions (relatively lower temperature), if larger size means higher fitness, while longer development may be risky? 

This evolutionary conundrum still awaits an ultimate explanation (Angilletta Jr et al. 2004; Angilletta and Dunham 2003; Verberk et al. 2021). Although theoretical modelling has shown that such a growth pattern can be achieved as a response to temperature alone, with a specific combination of energetic parameters and external mortality (Kozłowski et al. 2004), it has been suggested that other temperature-dependent environmental variables may be the actual drivers of this pattern. One of the most frequently invoked variable is the relative oxygen availability in the environment (e.g., Atkinson et al. 2006; Audzijonyte et al. 2019; Verberk et al. 2021; Woods 1999), which decreases with temperature increase. Importantly, this effect is more pronounced in aquatic systems (Forster et al. 2012). However, other temperature-dependent parameters are also being examined in the context of their possible effect on TSR induction and strength.

Food availability is among the interfering factors in this regard. In aquatic systems, nutritional conditions are generally better at higher temperature, while a range of relatively mild thermal conditions is considered. However, there are no conclusive results so far on how nutritional conditions affect the plastic body size response to acute temperature changes. A study by Bazin et al. (2023) examined this particular issue, the effects of food and temperature on TSR, in medaka fish. An important value of the study was to relate the patterns found to fitness. This is a rare and highly desirable approach since evolutionary significance of any results cannot be reliably interpreted unless shown as expressed in light of fitness. 

The authors compared the body size of fish kept at 20°C and 30°C under conditions of food abundance or food restriction. The results showed that the TSR (smaller body size at 30°C compared to 20°C) was observed in both food treatments, but the effect was delayed during fish development under food restriction. Regarding the relevance to fitness, increased temperature resulted in more eggs laid but higher mortality, while food restriction increased survival but decreased the number of eggs laid in both thermal treatments. Overall, food restriction seemed to have a more severe effect on development at 20°C than at 30°C, contrary to the authors’ expectations. 

I found this result particularly interesting. One possible interpretation, also suggested by the authors, is that the relative oxygen availability, which was not controlled for in this study, could have affected this pattern. According to theoretical predictions confirmed in quite many empirical studies so far, oxygen restriction is more severe at higher temperatures. Perhaps for these particular two thermal treatments and in the case of the particular species studied, this restriction was more severe for organismal performance than the food restriction. This result is an example that all three variables, temperature, food and oxygen, should be taken into account in future studies if the interrelationship between them is to be understood in the context of TSR. It also shows that the reasons for growing smaller in warm may be different from those for growing larger in cold, as suggested, directly or indirectly, in some previous studies (Hessen et al. 2010; Leiva et al. 2019). 

Since medaka fish represent predatory vertebrates, the results of the study contribute to the issue of global warming effect on food webs, as the authors rightly point out. This is an important issue because the general decrease in the size or organisms in the aquatic environment with global warming is a fact (e.g., Daufresne et al. 2009), while the question of how this might affect entire communities is not trivial to resolve (Ohlberger 2013). 

REFERENCES

Angilletta Jr, M. J., T. D. Steury & M. W. Sears, 2004. Temperature, growth rate, and body size in ectotherms: fitting pieces of a life–history puzzle. Integrative and Comparative Biology 44:498-509. https://doi.org/10.1093/icb/44.6.498

Angilletta, M. J. & A. E. Dunham, 2003. The temperature-size rule in ectotherms: Simple evolutionary explanations may not be general. American Naturalist 162(3):332-342. https://doi.org/10.1086/377187

Atkinson, D., 1994. Temperature and organism size – a biological law for ectotherms. Advances in Ecological Research 25:1-58. https://doi.org/10.1016/S0065-2504(08)60212-3

Atkinson, D., S. A. Morley & R. N. Hughes, 2006. From cells to colonies: at what levels of body organization does the 'temperature-size rule' apply? Evolution & Development 8(2):202-214 https://doi.org/10.1111/j.1525-142X.2006.00090.x

Audzijonyte, A., D. R. Barneche, A. R. Baudron, J. Belmaker, T. D. Clark, C. T. Marshall, J. R. Morrongiello & I. van Rijn, 2019. Is oxygen limitation in warming waters a valid mechanism to explain decreased body sizes in aquatic ectotherms? Global Ecology and Biogeography 28(2):64-77 https://doi.org/10.1111/geb.12847

Bazin, S., Hemmer-Brepson, C., Logez, M., Sentis, A. & Daufresne, M. 2023. Distinct impacts of food restriction and warming on life history traits affect population fitness in vertebrate ectotherms. HAL, ver.3  peer-reviewed and recommended by PCI Ecology. https://hal.inrae.fr/hal-03738584v3

Berrigan, D. & E. L. Charnov, 1994. Reaction norms for age and size at maturity in response to temperature – a puzzle for life historians. Oikos 70:474-478. https://doi.org/10.2307/3545787

Daufresne, M., K. Lengfellner & U. Sommer, 2009. Global warming benefits the small in aquatic ecosystems. Proceedings of the National Academy of Sciences USA 106(31):12788-93 https://doi.org/10.1073/pnas.0902080106

Forster, J., A. G. Hirst & D. Atkinson, 2012. Warming-induced reductions in body size are greater in aquatic than terrestrial species. Proceedings of the National Academy of Sciences of the United States of America 109(47):19310-19314. https://doi.org/10.1073/pnas.1210460109

Hessen, D. O., P. D. Jeyasingh, M. Neiman & L. J. Weider, 2010. Genome streamlining and the elemental costs of growth. Trends in Ecology & Evolution 25(2):75-80. https://doi.org/10.1016/j.tree.2009.08.004

Kozłowski, J., M. Czarnoleski & M. Dańko, 2004. Can optimal resource allocation models explain why ectotherms grow larger in cold? Integrative and Comparative Biology 44(6):480-493. https://doi.org/10.1093/icb/44.6.480

Leiva, F. P., P. Calosi & W. C. E. P. Verberk, 2019. Scaling of thermal tolerance with body mass and genome size in ectotherms: a comparison between water- and air-breathers. Philosophical Transactions of the Royal Society B 374:20190035. https://doi.org/10.1098/rstb.2019.0035

Ohlberger, J., 2013. Climate warming and ectotherm body szie - from individual physiology to community ecology. Functional Ecology 27:991-1001. https://doi.org/10.1111/1365-2435.12098

Verberk, W. C. E. P., D. Atkinson, K. N. Hoefnagel, A. G. Hirst, C. R. Horne & H. Siepel, 2021. Shrinking body sizes in response to warming: explanations for the temperature-size rule with special emphasis on the role of oxygen. Biological Reviews 96:247-268. https://doi.org/10.1111/brv.12653

Woods, H. A., 1999. Egg-mass size and cell size: effects of temperature on oxygen distribution. American Zoologist 39:244-252. https://doi.org/10.1093/icb/39.2.244