In this paper, Geoffroy et al. [1] deal with partner choice as a mechanism of maintaining cooperation, and argues that rather than being unequivocally a force towards improved payoffs to everyone through cooperation, partner choice can lead to “over-cooperation” where individuals can evolve to invest so much in cooperation that the costs of cooperating partially or fully negate the benefits from it. This happens when partner choice is consequential and effective, i.e., when interactions are long (so each decision to accept or reject a partner is a bigger stake) and when meeting new partners is frequent when unpaired (so that when one leaves an interaction one can find a new partner quickly). Geoffroy et al. [1] show that this tendency to select for overcooperation under such regimes can be counteracted if individuals base their acceptance-rejection of partners not just on the partner cooperativeness, but also on their own. By using tools from matching theory in economics, they show that plastic partner choice generates positive assortment between cooperativeness of the partners, and in the extreme case of perfectly assortative pairings, makes the pair the unit of selection, which selects for maximum total payoff.
This study is a nice contribution to the literature that illustrates potential complexities with partner choice as a mechanism for cooperation, including how the proximate mechanisms of partner choice can significantly alter the evolutionary trajectory of cooperation. Modeling choice as a reaction norm that depends on one’s own traits also adds a layer of realism to partner choice theory.
The authors are also to be commended for the revisions they made through the review process. Earlier versions of the model somewhat overstated the tendency for fixed partner choice strategies to lead to over cooperation, missing some of the important features in previous models, notably McNamara et al. [2] that can counter this tendency. In this version, the authors acknowledge these factors, mainly, mortality during partner choice (which increases the opportunity cost of forgoing a current partner) and also the fact that endogenous distribution of alternative partners (which will tend to be worse than the overall population distribution, because more cooperative types spend more time attached and less cooperative types more time unattached). These two factors can constrain cooperation from “running away” as the authors put it, but the main point of Geoffroy et al. [1] that plastic choice can create selection against inefficient cooperation stands.
I think the paper will be very stimulating to theoretical and empirical researchers working on partner choice and social behaviors, and happy to recommend it.

References

[1] Geoffroy, F., Baumard, N., & Andre, J.-B. (2019). Why cooperation is not running away. bioRxiv, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/316117
[2] McNamara, J. M., Barta, Z., Fromhage, L., & Houston, A. I. (2008). The coevolution of choosiness and cooperation. Nature, 451, 189–192. doi: 10.1038/nature06455

In their perspective article, F Pouyet and KJ Gilbert (2020), propose an interesting overview of all the processes that sculpt patterns of molecular evolution. This well documented article covers most (if not all) important facets of the recurrent debate that has marked the history of molecular evolution: the relative importance of natural selection and neutral processes (i.e. genetic drift). I particularly enjoyed reading this review, that instead of taking a clear position on the debate, catalogs patiently every pieces of information that can help understand how patterns we observed at the genome level, can be understood from a selectionnist point of view, from a neutralist one, and, to quote their title, from "everything in between". The review covers the classical objects of interest in population genetics (genetic drift, selection, demography and structure) but also describes several genomic processes (meiotic drive, linked selection, gene conversion and mutation processes) that obscure the interpretation of these population processes. The interplay between all these processes is very complex (to say the least) and have resulted in many cases in profound confusions while analyzing data. It is always very hard to fully acknowledge our ignorance and we have many times payed the price of model misspecifications. This review has the grand merit to improve our awareness in many directions. Being able to cover so many aspects of a wide topic, while expressing them simply and clearly, connecting concepts and observations from distant fields, is an amazing "tour de force". I believe this article constitutes an excellent up-to-date introduction to the questions and problems at stake in the field of molecular evolution and will certainly also help established researchers by providing them a stimulating overview supported with many relevant references.

References

[1] Pouyet F, Gilbert KJ (2020) Towards an improved understanding of molecular evolution: the relative roles of selection, drift, and everything in between. arXiv:1909.11490 [q-bio]. ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. url:https://arxiv.org/abs/1909.11490

Mathematical models play an essential role in current evolutionary biology, and evolutionary epidemiology is not an exception [1]. While the issues of virulence evolution and drug-resistance evolution resonate in the literature for quite some time [2, 3], the study by Alizon [4] is one of a few that consider co-evolution of both these traits [5]. The idea behind this study is the following: treating individuals with more severe symptoms at a higher rate (which appears to be quite natural) leads to an appearance of virulent drug-resistant strains, via treatment failure. The author then shows that virulence in drug-resistant strains may face different selective pressures than in drug-sensitive strains and hence proceed at different rates. Hence, treatment itself modulates evolution of virulence. As one of the reviewers emphasizes, the present manuscript offers a mathematical view on why the resistant and more virulent strains can be selected in epidemics. Also, we both find important that the author highlights that the topic and results of this study can be attributed to public health policies and development of optimal treatment protocols [6].
Mathematical models are simplified representations of reality, created with a particular purpose. It can be simple as well as complex, but even simple models can produce relatively complex and knitted results. The art of modelling thus lies not only in developing a model, but also in interpreting and unknitting the results. And this is what Alizon [4] indeed does carefully and exhaustively. Using two contrasting theoretical approaches to study co-evolution, the Price equation approach to study short-term evolution and the adaptive dynamics approach to study long-term evolution, Alizon [4] shows that a positive correlation between the rate of treatment and infection severity causes virulence in drug-sensitive strains to decrease. Clearly, no single model can describe and explain an examined system in its entirety, and even this aspect of the work is taken seriously. Many possible extensions of the study are laid out, providing a wide opportunity to pursue this topic even further. Personally, I have had an opportunity to read many Alizon’s papers and use, teach or discuss many of his models and results. All, including the current one, keep high standard and pursue the field of theoretical (evolutionary) epidemiology.

References

[1] Gandon S, Day T, Metcalf JE, Grenfell BT (2016) Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends Ecol Evol 31: 776-788. doi: https://doi.org/10.1016/j.tree.2016.07.010
[2] Berngruber TW, Froissart R, Choisy M, Gandon S (2013) Evolution of virulence in emerging epidemics. PLoS Pathog 9(3): e1003209. doi: https://doi.org/10.1371/journal.ppat.1003209
[3] Spicknall IH, Foxman B, Marrs CF, Eisenberg JNS (2013) A modeling framework for the evolution and spread of antibiotic resistance: literature review and model categorization. Am J Epidemiol 178: 508-520. doi: https://doi.org/10.1093/aje/kwt017
[4] Alizon S (2020) Treating symptomatic infections and the co-evolution of virulence and drug resistance. bioRxiv, 2020.02.29.970905, ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.02.29.970905
[5] Carval D, Ferriere R (2010) A unified model for the coevolution of resistance, tolerance, and virulence. Evolution 64: 2988–3009. doi: https://doi.org/10.1111/j.1558-5646.2010.01035.x
[6] Read AF, T Day, and S Huijben (2011). The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc Natl Acad Sci USA 108 Suppl 2, 10871–7. doi: https://doi.org/10.1073/pnas.1100299108

The genome of eukaryotic species is a complex structure that experience many different interactions within itself and with the surrounding environment. The genetic architecture of a phenotype (that is, the set of genetic elements affecting a trait of the organism) plays a fundamental role in understanding the adaptation process of a species to, for example, different climate environments, or to its interaction with other species. Thus, it is fundamental to study the different aspects of the genetic architecture of the species and its relationship with its surronding environment. Aspects such as modularity (the number of genetic units and the degree to which each unit is affecting a trait of the organism), pleiotropy (the number of different effects that a genetic unit can have on an organism) or linkage (the degree of association between the different genetic units) are essential to understand the genetic architecture and to interpret the effects of selection on the genome. Indeed, the knowledge of the different aspects of the genetic architecture could clarify whether genes are affected by multiple aspects of the environment or, on the contrary, are affected by only specific aspects [1,2].

The work performed by Lotterhos et al. [3] sought to understand the genetic architecture of the adaptation to different environments in lodgepole pine (Pinus contorta), considering as candidate SNPs those previously detected as a result of its extreme association patterns to different environmental variables or to extreme population differentiation. This consideration is very important because the study is only relevant if the studied markers are under the effect of selection. Otherwise, the genetic architecture of the adaptation to different environments would be masked by other (neutral) kind of associations that would be difficult to interpret [4,5]. In order to understand the relationship between genetic architecture and adaptation, it is relevant to detect the association networks of the candidate SNPs with climate variables (a way to measure modularity) and if these SNPs (and loci) are affected by single or multiple environments (a way to measure pleiotropy).

The authors used co-association networks, an innovative approach in this field, to analyse the interaction between the environmental information and the genetic polymorphism of each individual. This methodology is more appropriate than other multivariate methods - such as analysis based on principal components - because it is possible to cluster SNPs based on associations with similar environmental variables. In this sense, the co-association networks allowed to both study the genetic and physical linkage between different co-associations modules but also to compare two different models of evolution: a Modular environmental response architecture (specific genes are affected by specific aspects of the environment) or a Universal pleiotropic environmental response architecture (all genes are affected by all aspects of the environment). The representation of different correlations between allelic frequency and environmental factors (named galaxy biplots) are especially informative to understand the effect of the different clusters on specific aspects of the environment (for example, the co-association network ‘Aridity’ shows strong associations with hot/wet versus cold/dry environments).

The analysis performed by Lotterhos et al. [3], although it has some unavoidable limitations (e.g., only extreme candidate SNPs are selected, limiting the results to the stronger effects; the genetic and physical map is incomplete in this species), includes relevant results and also implements new methodologies in the field. To highlight some of them: the preponderance of a Modular environmental response architecture (evolution in separated modules), the detection of physical linkage among SNPs that are co-associated with different aspects of the environment (which was unexpected a priori), the implementation of co-association networks and galaxy biplots to see the effect of modularity and pleiotropy on different aspects of environment. Finally, this work contains remarkable introductory Figures and Tables explaining unambiguously the main concepts [6] included in this study. This work can be treated as a starting point for many other future studies in the field.

References

[1] Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, Sperone FG, Toomajian C, Roux F & Bergelson J. 2011. Adaptation to climate across the Arabidopsis thaliana genome. Science 334: 83–86. doi: 10.1126/science.1209244
[2] Wagner GP & Zhang J. The pleiotropic structure of the genotype­phenotype map: the evolvability of complex organisms. Nature Review Genetics 12: 204–213. doi: 10.1038/nrg2949
[3] Lotterhos KE, Yeaman S, Degner J, Aitken S, Hodgins K. 2018. Modularity of genes involved in local adaptation to climate despite physical linkage. bioRxiv 202481, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/202481
[4] Lotterhos KE & Whitlock MC. 2014. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Molecular Ecology 23: 2178–2192. doi: 10.1111/mec.12725
[5] Lotterhos KE & Whitlock MC. 2015. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Molecular Ecology 24: 1031–1046. doi: 10.1111/mec.13100
[6] Paaby AB & Rockman MV. 2013. The many faces of pleiotropy. Trends in Genetics 29: 66-73. doi: 10.1016/j.tig.2012.10.010

The Latitudinal Diversity Gradient (LDG) has fascinated natural historians, ecologists and evolutionary biologists ever since [1] described it about 200 years ago [2]. Despite such interest, agreement on the origin and nature of this gradient has been elusive. Several tens of hypotheses and models have been put forward as explanations for the LDG [2-3], that can be grouped in ecological, evolutionary and historical explanations [4] (see also [5]). These explanations can be reduced to no less than 26 hypotheses, which account for variations in ecological limits for the establishment of progressively larger assemblages, diversification rates, and time for species accumulation [5]. Besides that, although in general the tropics hold more species, different taxa show different shapes and rates of spatial variation [6], and a considerable number of groups show reverse patterns, with richer assemblages in cold temperate regions (see e.g. [7-9]).
Understanding such complexity needs integrating ecological and evolutionary research into the wide temporal and spatial perspectives provided by the burgeoning field of biogeography. This integrative discipline ¬–that traces back to Humboldt himself (e.g. [10])– seeks to put together historical and functional explanations to explain the complex dynamics of Earth’s biodiversity. Different to quantum physicists, biogeographers cannot pursue the ultimate principle behind the patterns we observe in nature due to the interplay of causes and effects, which in fact tell us that there is not such a single principle. Rather, they need to identify an array of basic principles coming from different perspectives, to then integrate them into models that provide realistic –but never simple– explanations to biodiversity gradients such as LDG (see, e.g., [5; 11]). That is, rather than searching for a sole explanation, research on the LDG must aim to identify as many signals hidden in the pattern as possible, and provide hypotheses or models that account for these signals. To later integrate them and, whenever possible, to validate them with empirical data on the organisms’ distribution, ecology and traits, phylogenies, fossils, etc.
Within this context, Meseguer & Condamine [12] provide a novel perspective to LDG research using phylogenetic and fossil evidence on the origin and extinction of taxa within the turtle, crocodile and lizard (i.e. squamate) lineages. By digging into deep time down to the Triassic (about 250 Myr ago) they are able to identify several episodes of flattening and steepening of the LDG for these three clades. Strikingly, their results show similar diversification rates in the northern hemisphere and in the equator during the over 100 Myr long global greenhouse period that extends from the late Jurassic to the Cretaceous and early Neogene. During this period, the LDG for these three groups would have appeared quite even across a mainly tropical Globe, although the equatorial regions were apparently much more evolutionarily dynamic. The equator shows much higher rates of origination and extinction of branches throughout the Cretaceous, but they counteract each other so net diversification is similar to that of the northern hemisphere in all three groups. The transition to a progressively colder Earth in the Paleogene (starting around 50 Myr ago) provokes a mass extinction in the three clades, which is compensated in the equator by the dispersal of many taxa from the areas that currently pertain to the Holarctic biogeographical realm. Finally, during the coldhouse Earth’s climatic conditions of the Neogene only squamates show significant positive diversification rates in extratropical areas, while the diversity of testudines remains, and crocodiles continue declining progressively towards oblivion in the whole world.
Meseguer & Condamine [12] attribute these temporal patterns to the so-called asymmetric gradient of extinction and dispersal (AGED) framework. Here, the dynamics of extinction-at and dispersal-from high latitudes during colder periods increase the steepness of the LDG. Whereas the gradient flattens when Earth warms up as a result of dispersal from the equator followed by increased diversification in extratropical regions. This idea in itself is not new, for the influence of climatic oscillations on diversification rates is well known, at least for the Pleistocene Ice Ages [13], as is the effect of niche conservatism on the LDG [14]. Nevertheless, Meseguer & Condamine’s AGED provides a synthetic verbal model that could allow integrating the three main types of processes behind the LDG into a single framework. To do this it would be necessary to combine AGED’s cycles of dispersal and diversification with realistic models of: (1) the ecological limits to host rich assemblages in the colder and less productive temperate climatic domains; (2) the variations in diversification rates with shifts in temperature and/or energy regimes; and (3) the geographical patterns of climatic oscillation through time that determine the time for species accumulation in each region.
Integrating these models may allow transposing Meseguer & Condamine’s [12] framework into the more mechanistic macroecological models advocated by Pontarp et al. [5]. This type of mechanistic models has been already used to understand the development of biodiversity gradients through the climatic oscillations of the Pleistocene and the Quaternary (e.g. [11]). So the challenge in this case would be to generate a realistic scenario of geographical dynamics that accounts for plate tectonics and long-term climatic oscillations. This is still a major gap and we would benefit from the integrated work by historical geologists and climatologists here. For instance, there is little doubt about the progressive cooling through the Cenozoic based in isotope recording in sea floor sediments [15]. Meseguer & Condamine [12] use this evidence for separating greenhouse, transition and coldhouse world scenarios, which should not be a problem for these rough classes. However, a detailed study of the evolutionary correlation of true climate variables across the tree of life is still pending, as temperature is inferred only for sea water in an ice-free ocean, say earlier half of the Cenozoic [15]. Precipitation regime is even less known. Such scenario would provide a scaffold upon which the temporal dynamics of several aspects of the generation and loss of biodiversity can be modelled. Additionally, one of the great advantages of selecting key clades to study the LDG would be to determine the functional basis of diversification. There are species traits that are well known to affect speciation and extinction probabilities, such as reproductive strategies or life histories (e.g. [16]). Whereas these traits might also be a somewhat redundant effect of climatic causes, they might foster (i.e. “extended reinforcement”, [17]) or slow diversification. Even so, it is unlikely that such a model would account for all the latitudinal variation in species richness. But it will at least provide a baseline for the main latitudinal variations in the diversity of the regional communities (sensu [18]) worldwide. Within this context the effects of recent ecological, evolutionary and historical processes, such as environmental heterogeneity, current diversification rates or glacial cycles, will only modify the general LDG pattern resulting from the main processes contained in Meseguer & Condamine’s AGED, thereby providing a more comprehensive understanding of the geographical gradients of diversity.

References
[1] Humboldt, A. v. (1808). Ansichten der Natur, mit wissenschaftlichen Erläuterungen. J. G. Cotta, Tübingen.
[2] Hawkins, B. A. (2001). Ecology's oldest pattern? Trends in Ecology & Evolution, 16, 470. doi: 10.1016/S0169-5347(01)02197-8
[3] Lomolino, M. V., Riddle, B. R. & Whittaker, R. J. (2017). Biogeography. Fifth Edition. Sinauer Associates, Inc., Sunderland, Massachussets.
[4] Mittelbach, G. G., Schemske, D. W., Cornell, H. V., Allen, A. P., Brown, J. M., Bush, M. B., Harrison, S. P., Hurlbert, A. H., Knowlton, N., Lessios, H. A., McCain, C. M., McCune, A. R., McDade, L. A., McPeek, M. A., Near, T. J., Price, T. D., Ricklefs, R. E., Roy, K., Sax, D. F., Schluter, D., Sobel, J. M. & Turelli, M. (2007). Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecology Letters, 10, 315-331. doi: 10.1111/j.1461-0248.2007.01020.x
[5] Pontarp, M., Bunnefeld L., Cabral, J. S., Etienne, R. S., Fritz, S. A., Gillespie, R. Graham, C. H., Hagen, O., Hartig, F., Huang, S., Jansson, R., Maliet, O., Münkemüller, T., Pellissier, L., Rangel, T. F., Storch, D., Wiegand, T. & Hurlbert, A. H. (2019). The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends in ecology & evolution, 34, 211-223. doi: 10.1016/j.tree.2018.11.009
[6] Hillebrand, H. (2004). On the generality of the latitudinal diversity gradient. The American Naturalist, 163, 192-211. doi: 10.1086/381004
[7] Santos, A. M. C. & Quicke, D. L. J. (2011). Large-scale diversity patterns of parasitoid insects. Entomological Science, 14, 371-382. doi: 10.1111/j.1479-8298.2011.00481.x
[8] Morinière, J., Van Dam, M. H., Hawlitschek, O., Bergsten, J., Michat, M. C., Hendrich, L., Ribera, I., Toussaint, E. F. A. & Balke, M. (2016). Phylogenetic niche conservatism explains an inverse latitudinal diversity gradient in freshwater arthropods. Scientific Reports, 6, 26340. doi: 10.1038/srep26340
[9] Weiser, M. D., Swenson, N. G., Enquist, B. J., Michaletz, S. T., Waide, R. B., Zhou, J. & Kaspari, M. (2018). Taxonomic decomposition of the latitudinal gradient in species diversity of North American floras. Journal of Biogeography, 45, 418-428. doi: 10.1111/jbi.13131
[10] Humboldt, A. v. (1805). Essai sur la geographie des plantes; accompagné d'un tableau physique des régions equinoxiales. Levrault, Paris.
[11] Rangel, T. F., Edwards, N. R., Holden, P. B., Diniz-Filho, J. A. F., Gosling, W. D., Coelho, M. T. P., Cassemiro, F. A. S., Rahbek, C. & Colwell, R. K. (2018). Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves. Science, 361, eaar5452. doi: 10.1126/science.aar5452
[12] Meseguer, A. S. & Condamine, F. L. (2019). Ancient tropical extinctions contributed to the latitudinal diversity gradient. bioRxiv, 236646, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/236646
[13] Jansson, R., & Dynesius, M. (2002). The fate of clades in a world of recurrent climatic change: Milankovitch oscillations and evolution. Annual review of ecology and systematics, 33(1), 741-777. doi: 10.1146/annurev.ecolsys.33.010802.150520
[14] Wiens, J. J., & Donoghue, M. J. (2004). Historical biogeography, ecology and species richness. Trends in ecology & evolution, 19, 639-644. doi: 10.1016/j.tree.2004.09.011
[15] Zachos, J. C., Dickens, G. R., & Zeebe, R. E. (2008). An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. Nature, 451, 279-283. doi: 10.1038/nature06588
[16] Zúñiga-Vega, J. J., Fuentes-G, J. A., Ossip-Drahos, A. G., & Martins, E. P. (2016). Repeated evolution of viviparity in phrynosomatid lizards constrained interspecific diversification in some life-history traits. Biology letters, 12, 20160653. doi: 10.1098/rsbl.2016.0653
[17] Butlin, R. K., & Smadja, C. M. (2018). Coupling, reinforcement, and speciation. The American Naturalist, 191, 155-172. doi: 10.1086/695136
[18] Ricklefs, R. E. (2015). Intrinsic dynamics of the regional community. Ecology letters, 18, 497-503. doi: 10.1111/ele.12431