Scale is a big topic in ecology [1]. Environmental variation happens at particular scales. The typical scale at which organisms disperse is species-specific, but, as a first approximation, an ensemble of similar species, for instance, trees, could be considered to share a typical dispersal scale. Finally, characteristic spatial scales of species interactions are, in general, different from the typical scales of dispersal and environmental variation. Therefore, conceptually, we can distinguish these three characteristic spatial scales associated with three different processes: species selection for a given environment (E), dispersal (D), and species interactions (I), respectively.  

From the famous species-area relation to the spatial distribution of biomass and species richness, the different macro-ecological patterns we usually study emerge from an interplay between dispersal and local interactions in a physical environment that constrains species establishment and persistence in every location. To make things even more complicated, local environments are often modified by the species that thrive in them, which establishes feedback loops.  It is usually assumed that local interactions are short-range in comparison with species dispersal, and dispersal scales are typically smaller than the scales at which the environment varies (I < D < E, see [2]), but this should not always be the case. 

The authors of this paper [2] relax this typical assumption and develop a theoretical framework to study how diversity and ecosystem functioning are affected by different relations between the typical scales governing interactions, dispersal, and environmental variation. This is a huge challenge. First, diversity and ecosystem functioning across space and time have been empirically characterized through a wide variety of macro-ecological patterns. Second, accommodating local interactions, dispersal and environmental variation and species environmental preferences to model spatiotemporal dynamics of full ecological communities can be done also in a lot of different ways. One can ask if the particular approach suggested by the authors is the best choice in the sense of producing robust results, this is, results that would be predicted by alternative modeling approaches and mathematical analyses [3]. The recommendation here is to read through and judge by yourself.  

The main unusual assumption underlying the model suggested by the authors is non-local species interactions. They introduce interaction kernels to weigh the strength of the ecological interaction with distance, which gives rise to a system of coupled integro-differential equations. This kernel is the key component that allows for control and varies the scale of ecological interactions. Although this is not new in ecology [4], and certainly has a long tradition in physics ---think about the electric or the gravity field, this approach has been widely overlooked in the development of the set of theoretical frameworks we have been using over and over again in community ecology, such as the Lotka-Volterra equations or, more recently, the metacommunity concept [5].

In Physics, classic fields have been revised to account for the fact that information cannot travel faster than light. In an analogous way, a focal individual cannot feel the presence of distant neighbors instantaneously. Therefore, non-local interactions do not exist in ecological communities. As the authors of this paper point out, they emerge in an effective way as a result of non-random movements, for instance, when individuals go regularly back and forth between environments (see [6], for an application to infectious diseases), or even migrate between regions. And, on top of this type of movement, species also tend to disperse and colonize close (or far) environments. Individual mobility and dispersal are then two types of movements, characterized by different spatial-temporal scales in general. Species dispersal, on the one hand, and individual directed movements underlying species interactions, on the other, are themselves diverse across species, but it is clear that they exist and belong to two distinct categories. 

In spite of the long and rich exchange between the authors' team and the reviewers, it was not finally clear (at least, to me and to one of the reviewers) whether the model for the spatio-temporal dynamics of the ecological community (see Eq (1) in [2]) is only presented as a coupled system of integro-differential equations on a continuous landscape for pedagogical reasons, but then modeled on a discrete regular grid for computational convenience. In the latter case, the system represents a regular network of local communities,  becomes a system of coupled ODEs, and can be numerically integrated through the use of standard algorithms. By contrast,  in the former case, the system is meant to truly represent a community that develops on continuous time and space, as in reaction-diffusion systems. In that case, one should keep in mind that numerical instabilities can arise as an artifact when integrating both local and non-local spatio-temporal systems. Spatial patterns could be then transient or simply result from these instabilities. Therefore, when analyzing spatiotemporal integro-differential equations, special attention should be paid to the use of the right numerical algorithms. The authors share all their code at https://zenodo.org/record/5543191, and all this can be checked out. In any case, the whole discussion between the authors and the reviewers has inherent value in itself, because it touches on several limitations and/or strengths of the author's approach,  and I highly recommend checking it out and reading it through.

Beyond these methodological issues, extensive model explorations for the different parameter combinations are presented. Several results are reported, but, in practice, what is then the main conclusion we could highlight here among all of them?  The authors suggest that "it will be difficult to manage landscapes to preserve biodiversity and ecosystem functioning simultaneously, despite their causative relationship", because, first, "increasing dispersal and interaction scales had opposing
effects" on these two patterns, and, second, unexpectedly, "ecosystems attained the highest biomass in scenarios which also led to the lowest levels of biodiversity". If these results come to be fully robust, this is, they pass all checks by other research teams trying to reproduce them using alternative approaches, we will have to accept that we should preserve biodiversity on its own rights and not because it enhances ecosystem functioning or provides particular beneficial services to humans. 

References

[1] Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73:1943–1967. https://doi.org/10.2307/1941447

[2] Yuval R. Zelnik, Matthieu Barbier, David W. Shanafelt, Michel Loreau, Rachel M. Germain. 2023. Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.1101/2021.10.11.463913

[3] Baron, J. W. and Galla, T. 2020. Dispersal-induced instability in complex ecosystems. Nature Communications  11, 6032. https://doi.org/10.1038/s41467-020-19824-4

[4] Cushing, J. M. 1977. Integrodifferential equations and delay models in population dynamics 
 Springer-Verlag, Berlin. https://doi.org/10.1007/978-3-642-93073-7

[5] M. A. Leibold, M. Holyoak, N. Mouquet, P. Amarasekare, J. M. Chase, M. F. Hoopes, R. D. Holt, J. B. Shurin, R. Law, D. Tilman, M. Loreau, A. Gonzalez. 2004. The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters, 7(7): 601-613. https://doi.org/10.1111/j.1461-0248.2004.00608.x

[6] M. Pardo-Araujo, D. García-García, D. Alonso, and F. Bartumeus. 2023. Epidemic thresholds and human mobility. Scientific reports 13 (1), 11409. https://doi.org/10.1038/s41598-023-38395-0

The preregistration "Investigating sex differences in genetic relatedness in great-tailed grackles in Tempe, Arizona to infer potential sex biases in dispersal" [1] presents the analysis plan that will be used to genetically and spatially investigate sex-biased dispersal in great-tailed grackles (Quiscalus mexicanus).
Several hypotheses implying mating systems, intrasexual competition or sex-related handicaps have been proposed to explain the diversity of dispersal patterns between or within species according to their ecological requirements, environmental factors such as seasonality [2], or individual characteristics such as age [3] or sex [4].
In birds, females are classically the dispersing sex, while males remain close to the place they were hatched [5], with potential benefits that males derive from knowing the local environment to establish territories [6].
In great-tailed grackles the males hold territories and the females choose which territory to place their nest in [7]. In this context, the main hypothesis is that females are the dispersing sex in this species. The authors of this preregistration plan to investigate this hypothesis and its 3 alternatives ((i) the males are the dispersing sex, (ii) both sexes disperse or (iii) neither of the two sexes disperse), investigating the spatial distribution of genetic relatives.
The authors plan to measure the genetic relatedness (using SNP markers) and geographic distances among all female dyads and among all male dyads in the fine geographic scale (Tempe campus, Arizona). If females disperse away from relatives, the females will be less likely to be found geographically close to genetic relatives.
This pre-registration shows that the authors are well aware of the possible limitations of their study, particularly in relation to their population of 57 individuals, on a small scale. But they will use methods that should be able to detect a signal. They were very good at incorporating the reviewers' comments and suggestions, which enabled them to produce a satisfactory and interesting version of the manuscript presenting their hypotheses, limitations and the methods they plan to use. Another point I would like to stress is that this pre-registration practice is a very good one that makes it possible to anticipate the challenges and the type of analyses to be carried out, in particular by setting out the working hypotheses and confronting them (as well as the methods envisaged) with peers from this stage. I therefore recommend this manuscript and thank all the contributors (authors and reviewers) for their work. I look forward to seeing the outcomes of this study.

References

[1] Sevchik A., Logan C. J., Folsom M., Bergeron L., Blackwell A., Rowney C., and Lukas D. (2019). Investigating sex differences in genetic relatedness in great-tailed grackles in Tempe, Arizona to infer potential sex biases in dispersal. In principle recommendation by Peer Community In Ecology. corinalogan.com/Preregistrations/gdispersal.html
[2] Fies, M. L., Puckett, K. M., and Larson-Brogdon, B. (2002). Breeding season movements and dispersal of Northern Bobwhites in fragmented habitats of Virginia. Vol. 5 , Article 35. Available at: trace.tennessee.edu/nqsp/vol5/iss1/35
[3] Marvá, M., and San Segundo, F. (2018). Age-structure density-dependent fertility and individuals dispersal in a population model. Mathematical biosciences, 300, 157-167. doi: 10.1016/j.mbs.2018.03.029
[4] Trochet, A., Courtois, E. A., Stevens, V. M., Baguette, M., Chaine, A., Schmeller, D. S., Clobert, J., and Wiens, J. J. (2016). Evolution of sex-biased dispersal. The Quarterly Review of Biology, 91(3), 297-320. doi: 10.1086/688097
[5] Greenwood, P. J., and Harvey, P. H. (1982). The natal and breeding dispersal of birds. Annual review of ecology and systematics, 13(1), 1-21. doi: 10.1146/annurev.es.13.110182.000245
[6] Greenwood, P. J. (1980). Mating systems, philopatry and dispersal in birds and mammals. Animal behaviour, 28(4), 1140-1162. doi: 10.1016/S0003-3472(80)80103-5
[7] Johnson, K., DuVal, E., Kielt, M., and Hughes, C. (2000). Male mating strategies and the mating system of great-tailed grackles. Behavioral Ecology, 11(2), 132-141. doi: 10.1093/beheco/11.2.132

The symbiosis with autotrophic dinoflagellate algae has enabled heterotrophic Cnidaria to thrive in nutrient-poor tropical waters (Muscatine and Porter 1977; Stanley 2006). In particular, mixotrophy, i.e. the ability to acquire nutrients through both autotrophy and heterotrophy, confers a competitive edge in oligotrophic waters, allowing photosymbiotic Cnidaria to outcompete benthic organisms limited to a single diet (e.g., McCook 2001). However, the relative importance of autotrophy vs heterotrophy in sustaining symbiotic cnidarian’s nutrition is still the subject of intense research. In fact, figuring out the cellular mechanisms by which symbiotic Cnidaria acquire a balanced diet for their metabolism and growth is relevant to our understanding of their physiology under varying environmental conditions and in response to anthropogenic perturbations.

In this study's long-term starvation experiment, Radecker & Meibom (2023) investigated the survival of the photosymbiotic sea anemone Aiptasia in the absence of heterotrophic feeding. After one year of heterotrophic starvation, Apitasia anemones remained fully viable but showed an 85 % reduction in biomass. Using 13C-bicarbonate and 15N-ammonium labeling, electron microscopy and NanoSIMS imaging, the authors could clearly show that the contribution of algal-derived nutrients to the host metabolism remained unaffected as a result of increased algal photosynthesis and more efficient carbon translocation. At the same time, the absence of heterotrophic feeding caused severe nitrogen limitation in the starved Apitasia anemones.

Overall, this study provides valuable insights into nutrient exchange within the symbiosis between Cnidaria and dinoflagellate algae at the cellular level and sheds new light on the importance of heterotrophic feeding as a nitrogen acquisition strategy for holobiont growth in oligotrophic waters.

REFERENCES

McCook L (2001) Competition between corals and algal turfs along a gradient of terrestrial influence in the nearshore central Great Barrier Reef. Coral Reefs 19:419–425. https://doi.org/10.1007/s003380000119

Muscatine L, Porter JW (1977) Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience 27:454–460. https://doi.org/10.2307/1297526

Radecker N, Meibom A (2023) Symbiotic nutrient cycling enables the long-term survival of Aiptasia in the absence of heterotrophic food sources. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.12.07.519152

Stanley GD Jr (2006) Photosymbiosis and the evolution of modern coral reefs. Science 312:857–858. https://doi.org/10.1126/science.1123701

One of the long-standing questions in evolutionary ecology is on the mechanisms involved in arms race coevolution. One way to address this question is to understand the conditions under which one species evolves traits in response to the presence of a second species and so on. However, specialized pairwise interactions are by far less common in nature than interactions involving a higher number of interacting species (Bascompte, Jordano 2013). While interactions between large sets of species are the norm rather than the exception in mutualistic (pollination, seed dispersal), and antagonist (herbivory, parasitism) relationships, few is known on the way species identify, process, and respond to information provided by other interacting species under field conditions (Schaefer, Ruxton 2011). 

Zu et al. (2020) addressed this general question by developing an interesting information theory-based approach that hypothesized conditional entropy in chemical communication plays a role as proxy of fitness in plant-herbivore communities. More specifically, plant fitness was assumed to be related to the efficiency to code signals by plant species, and herbivore fitness to the capacity to decode plant signals. In this way, from the plant perspective, the elaboration of plant signals that elude decoding by herbivores is expected to be favored, as herbivores are expected to attack plants with simple chemical signals. The empirical observation upon which the model was tested was the redundancy in volatile organic compounds (VOC) found across plant species in a plant-herbivore community. Interestingly, Zu et al.’s model predicted successfully that VOC redundancy in the plant community associates with increased conditional entropy, which conveys herbivore confusion and plant protection against herbivory. In this way, plant species that evolve VOCs already present in the community might be benefitted, ultimately leading to the patterns of VOC redundancy commonly observed in nature.

Bass & Kessler performed a series of interesting observations on Zu et al. (2020), that can be organized along three lines of reasoning. First, from an evolutionary perspective, Bass & Kessler note the important point that accepting that conditional information entropy, estimated from the contribution of every plant species to volatile redundancy implies that average plant fitness seems to depend on community-level properties (i.e., what the other species in the community are doing) rather than on population-level characteristics (I.e., what the individuals belonging a population are doing). While the level at which selection acts upon is a longstanding debate (e.g., Goodnight, 1990; Williams, 1992), the model seems to contradict one of the basic tenets of Darwinian evolution. The extent to which this important observation invalidates the contribution of Zu et al. (2020) is open to scrutiny. However, one can indulge the evolutionary criticism by arguing that every theoretical model performs a number of assumptions to preserve the simplicity of analyses. Furthermore, even accepting the criticism, the overall information-based framework is valuable as it provides a fresh perspective to the way coding and decoding chemical information in plant-herbivore interactions may result in arm race coevolution. The question to be assessed by members of the scientific community is how strong the evolutionary assumptions are to be acceptable. A second line of reasoning involves consideration of additional routes of chemical information transfer. If chemical volatiles are involved in another ecological function unrelated to arm race (as they are) such as toxicity, crypsis, aposematism, etc., the conditional information indices considered as proxy to plant and herbivore fitness may be only secondarily related to arms race. This is an interesting observation, which suggests that VOC production may have more than one ecological function, as it often happens in “pleiotropic” traits (Strauss, Irwin 2004). This is an exciting avenue for future research. Finally, a third category of comments involves the relationship between conditional information entropy and plant and herbivore fitness. Bass & Kessler developed a Bayesian treatment of the community-level information developed by Zu et al. (2020) that permitted to estimate fitness on a species rather than community level. Their results revealed that community conditional entropies fail to align with species-level indices, suggesting that conclusions of Strauss & Irwin (2004) are not commensurate with fitness at the species level, where the analysis seems to be pertinent. In general, I strongly recommend Bass & Kessler’s contribution as it provides a series of observations and new perspectives to Zu et al. (2020). Rather than restricting their manuscript to blind criticisms, Bass & Kessler provides new interesting perspectives, which is always welcome as it improves the value and scope of the original work.

References

Bascompte J, Jordano P (2013) Mutualistic Networks. Princeton University Press. https://doi.org/10.23943/princeton/9780691131269.001.0001

Bass E, Kessler A (2022) Comment on “Information arms race explains plant-herbivore chemical communication in ecological communities.” EcoEvoRxiv, ver. 8 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.32942/osf.io/xsbtm

Goodnight CJ (1990) Experimental Studies of Community Evolution I: The Response to Selection at the Community Level. Evolution, 44, 1614–1624. https://doi.org/10.1111/j.1558-5646.1990.tb03850.x

Schaefer HM, Ruxton GD (2011) Plant-Animal Communication. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:osobl/9780199563609.001.0001

Strauss SY, Irwin RE (2004) Ecological and Evolutionary Consequences of Multispecies Plant-Animal Interactions. Annual Review of Ecology, Evolution, and Systematics, 35, 435–466. https://doi.org/10.1146/annurev.ecolsys.35.112202.130215

Williams GC (1992) Natural Selection: Domains, Levels, and Challenges. Oxford University Press, Oxford, New York.

Zu P, Boege K, del-Val E, Schuman MC, Stevenson PC, Zaldivar-Riverón A, Saavedra S (2020) Information arms race explains plant-herbivore chemical communication in ecological communities. Science, 368, 1377–1381. https://doi.org/10.1126/science.aba2965

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