Understanding why and how species coexist in local communities is one of the central questions in ecology. There is general agreement that species distribution and coexistence are determined by a number of key mechanisms, including the environmental requirements of species, dispersal, evolutionary constraints, resource availability and selection, metapopulation dynamics, and biotic interactions (e.g. Soberón & Nakamura 2009; Colwell & Rangel 2009; Ricklefs 2015). These factors are however intricately intertwined in a scale-structured fashion (Hortal et al. 2010; D’Amen et al. 2017), making it particularly difficult to tease apart the effects of each one of them. This could be addressed by the novel field of Joint Species Distribution Modelling (JSDM; Okasvainen & Abrego 2020), as it allows assessing the effects of several sets of factors and the co-occurrence and/or covariation in abundances of potentially interacting species at the same time (Pollock et al. 2014; Ovaskainen et al. 2016; Dormann et al. 2018). However, the development of JSDM has been hampered by the general lack of good-quality detailed data on species co-occurrences and abundances (see Hortal et al. 2015).

Facon et al. (2021) use a particularly large compilation of field surveys to study the abundance and co-occurrence of Tephritidae fruit flies in c. 400 orchards, gardens and natural areas throughout the island of Réunion. Further, they combine such information with lab data on their host-selection fundamental niche (i.e. in the absence of competitors), codifying traits of female choice and larval performances in 21 host species. They use Poisson Log-Normal models, a type of mixed model that allows one to jointly model the random effects associated with all species, and retrieve the covariations in abundance that are not explained by environmental conditions or differences in sampling effort. Then, they use a series of models to evaluate the effects on these matrices of ecological covariates (date, elevation, habitat, climate and host plant), species interactions (by comparing with a constrained residual variance-covariance matrix) and the species’ host-selection fundamental niches (through separate models for each fly species).

The eight Tephritidae species inhabiting Réunion include both generalists and specialists in Solanaceae and Cucurbitaceae with a known history of interspecific competition. Facon et al. (2021) use a comprehensive JSDM approach to assess the effects of different factors separately and altogether. This allows them to identify large effects of plant hosts and the fundamental host-selection niche on species co-occurrence, but also to show that ecological covariates and weak –though not negligible– species interactions are necessary to account for all residual variance in the matrix of joint species abundances per site. Further, they also find evidence that the fitness per host measured in the lab has a strong influence on the abundances in each host plant in the field for specialist species, but not for generalists. Indeed, the stronger effects of competitive exclusion were found in pairs of Cucurbitaceae specialist species. However, these analyses fail to provide solid grounds to assess why generalists are rarely found in Cucurbitaceae and Solanaceae. Although they argue that this may be due to Connell’s (1980) ghost of competition past (past competition that led to current niche differentiation), further data on the evolutionary history of these fruit flies is needed to assess this hypothesis.

Finding evidence for the effects of competitive interactions on species’ occurrences and spatial distributions is often difficult, perhaps because these effects occur over longer time scales than the ones usually studied by ecologists (Yackulic 2017). The work by Facon and colleagues shows that weak effects of competition can be detected also at the short ecological timescales that determine coexistence in local communities, under the virtuous combination of good-quality data and sound analytical designs that account for several aspects of species’ niches, their biotopes and their joint population responses. This adds a new dimension to the application of Hutchinson’s (1978) niche framework to understand the spatial dynamics of species and communities (see also Colwell & Rangel 2009), although further advances to incorporate dispersal-driven metacommunity dynamics (see, e.g., Ovaskainen et al. 2016; Leibold et al. 2017) are certainly needed. Nonetheless, this work shows the potential value of in-depth analyses of species coexistence based on combining good-quality field data with well-thought out JSDM applications. If many studies like this are conducted, it is likely that the uprising field of Joint Species Distribution Modelling will improve our understanding of the hierarchical relationships between the different factors affecting species coexistence in ecological communities in the near future.

References

Colwell RK, Rangel TF (2009) Hutchinson’s duality: The once and future niche. Proceedings of the National Academy of Sciences, 106, 19651–19658. https://doi.org/10.1073/pnas.0901650106

Connell JH (1980) Diversity and the Coevolution of Competitors, or the Ghost of Competition Past. Oikos, 35, 131–138. https://doi.org/10.2307/3544421

D’Amen M, Rahbek C, Zimmermann NE, Guisan A (2017) Spatial predictions at the community level: from current approaches to future frameworks. Biological Reviews, 92, 169–187. https://doi.org/10.1111/brv.12222

Dormann CF, Bobrowski M, Dehling DM, Harris DJ, Hartig F, Lischke H, Moretti MD, Pagel J, Pinkert S, Schleuning M, Schmidt SI, Sheppard CS, Steinbauer MJ, Zeuss D, Kraan C (2018) Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecology and Biogeography, 27, 1004–1016. https://doi.org/10.1111/geb.12759

Facon B, Hafsi A, Masselière MC de la, Robin S, Massol F, Dubart M, Chiquet J, Frago E, Chiroleu F, Duyck P-F, Ravigné V (2021) Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of community structure in fruit flies. bioRxiv, 2020.12.07.414326. ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.1101/2020.12.07.414326

Hortal J, de Bello F, Diniz-Filho JAF, Lewinsohn TM, Lobo JM, Ladle RJ (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46, 523–549. https://doi.org/10.1146/annurev-ecolsys-112414-054400

Hortal J, Roura‐Pascual N, Sanders NJ, Rahbek C (2010) Understanding (insect) species distributions across spatial scales. Ecography, 33, 51–53. https://doi.org/10.1111/j.1600-0587.2009.06428.x

Hutchinson, G.E. (1978) An introduction to population biology. Yale University Press, New Haven, CT.

Leibold MA, Chase JM, Ernest SKM (2017) Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology, 98, 909–919. https://doi.org/10.1002/ecy.1697

Ovaskainen O, Abrego N (2020) Joint Species Distribution Modelling: With Applications in R. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108591720

Ovaskainen O, Roy DB, Fox R, Anderson BJ (2016) Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution, 7, 428–436. https://doi.org/10.1111/2041-210X.12502

Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5, 397–406. https://doi.org/10.1111/2041-210X.12180

Ricklefs RE (2015) Intrinsic dynamics of the regional community. Ecology Letters, 18, 497–503. https://doi.org/10.1111/ele.12431

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

Yackulic CB (2017) Competitive exclusion over broad spatial extents is a slow process: evidence and implications for species distribution modeling. Ecography, 40, 305–313. https://doi.org/10.1111/ecog.02836

For decades, the effect of population density on individual performance has been studied by ecologists using both theoretical, observational, and experimental approaches. The generally accepted definition of the Allee effect is a positive correlation between population density and average individual fitness that occurs at low population densities, while individual fitness is typically decreased through intraspecific competition for resources at high population densities.  Allee effects are very relevant in conservation biology because species at low population densities would then be subjected to much higher extinction risks. 

However, due to all kinds of stochasticity, low population numbers are always more vulnerable to extinction than larger population sizes. This effect by itself cannot be necessarily ascribed to lower individual performance at low densities, i.e, Allee effects. Vercken and colleagues (2021) address this challenging question and measure the extent to which average individual fitness is affected by population density analyzing 30 experimental populations. As a model system, they use populations of parasitoid wasps of the genus Trichogramma. They report Allee effect in 8 out 30 experimental populations. Vercken and colleagues's work has several strengths. 

First of all, it is nice to see that they put theory at work. This is a very productive way of using theory in ecology. As a starting point, they look at what simple theoretical population models say about Allee effects (Lewis and Kareiva 1993; Amarasekare 1998; Boukal and Berec 2002). These models invariably predict a one-humped relation between population-density and per-capita growth rate. It is important to remark that pure logistic growth, the paradigm of density-dependence, would never predict such qualitative behavior. It is only when there is a depression of per-capita growth rates at low densities that true Allee effects arise. Second, these authors manage to not only experimentally test this main prediction but also report additional demographic traits that are consistently affected by population density. 

In these wasps, individual performance can be measured in terms of the average number of individuals every adult is able to put into the next generation ---the lambda parameter in their analysis. The first panel in figure 3 shows that the per-capita growth rates are lower in populations presenting Allee effects, the ones showing a one-humped behavior in the relation between per-capita growth rates and population densities (see figure 2). Also other population traits, such maximum population size and exitinction probability, change in a correlated and consistent manner. 

In sum, Vercken and colleagues's results are experimentally solid and based on theory expectations. However, they are very intriguing. They find the signature of Allee effects in only 8 out 30 populations, all from the same genus Trichogramma, and some populations belonging to the same species (from different sampling sites) do not show consistently Allee effects. Where does this population variability comes from? What are the reasons underlying this within- and between-species variability? What are the individual mechanisms driving Allee effects in these populations? Good enough, this piece of work generates more intriguing questions than the question is able to clearly answer. Science is not a collection of final answers but instead good questions are the ones that make science progress. 

References

Amarasekare P (1998) Allee Effects in Metapopulation Dynamics. The American Naturalist, 152, 298–302. https://doi.org/10.1086/286169

Boukal DS, Berec L (2002) Single-species Models of the Allee Effect: Extinction Boundaries, Sex Ratios and Mate Encounters. Journal of Theoretical Biology, 218, 375–394. https://doi.org/10.1006/jtbi.2002.3084

Lewis MA, Kareiva P (1993) Allee Dynamics and the Spread of Invading Organisms. Theoretical Population Biology, 43, 141–158. https://doi.org/10.1006/tpbi.1993.1007

Vercken E, Groussier G, Lamy L, Mailleret L (2021) The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populations. HAL, hal-02570868, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://hal.archives-ouvertes.fr/hal-02570868

Whether environmental conditions –in particular energy and water availability– are sufficient to account for species richness gradients (e.g. Currie 1991), or the effects of other biotic and historical or regional factors need to be considered as well (e.g. Ricklefs 1987), was the subject of debate during the 1990s and 2000s (e.g. Francis & Currie 2003; Hawkins et al. 2003, 2006; Currie et al. 2004; Ricklefs 2004). The metabolic theory of ecology (Brown et al. 2004) provided a solid and well-rooted theoretical support for the preponderance of energy as the main driver for richness variations. As any good piece of theory, it provided testable predictions about the sign and shape (i.e. slope) of the relationship between temperature –a key aspect of ambient energy– and species richness. However, these predictions were not supported by empirical evaluations (e.g. Kreft & Jetz 2007; Algar et al. 2007; Hawkins et al. 2007a), as the effects of a myriad of other environmental gradients, regional factors and evolutionary processes result in a wide variety of richness–temperature responses across different groups and regions (Hawkins et al. 2007b; Hortal et al. 2008). So, in a textbook example of how good theoretical work helps advancing science even if proves to be (partially) wrong, the evaluation of this aspect of the metabolic theory of ecology led to current understanding that, while species richness does respond to current climatic conditions, many other ecological, evolutionary and historical factors do modify such response across scales (see, e.g., Ricklefs 2008; Hawkins 2008; D’Amen et al. 2017). And the kinetic model linking mean annual temperature and species richness (Allen et al. 2002; Brown et al. 2004) was put aside as being, perhaps, another piece of the puzzle of the origin of current diversity gradients.

Segovia (2021) puts together an elegant way of reinvigorating this part of the metabolic theory of ecology. He uses quantile regressions to model just the upper parts of the relationship between species richness and mean annual temperature, rather than modelling its central tendency through the classical linear regression family of methods –as was done in the past. This assumes that the baseline effect of ambient energy does produce the negative linear relationship between richness and temperature predicted by the kinetic model (Allen et al. 2002), but also that this effect only poses an upper limit for species richness, and the effects of other factors may result in lower levels of species co-occurrence, thus producing a triangular rather than linear relationship. The results of Segovia’s simple and elegant analytical design show unequivocally that the predictions of the kinetic model become progressively more explanatory towards the upper quartiles of the relationship between species richness and temperature along over 10,000 tree local inventories throughout the Americas, reaching over 70% of explanatory power for the upper 5% of the relationship (i.e. the 95% quantile). This confirms to a large extent his reformulation of the predictions of the kinetic model. 

Further, the neat study from Segovia (2021) also provides evidence confirming that the well-known spatial non-stationarity in the richness–temperature relationship (see Cassemiro et al. 2007) also applies to its upper-bound segment. Both the explanatory power and the slope of the relationship in the 95% upper quantile vary widely between biomes, reaching values similar to the predictions of the kinetic model only in cold temperate environments ­–precisely where temperature becomes more important than water availability as a constrain to plant life (O’Brien 1998; Hawkins et al. 2003). Part of these variations are indeed related with changes in water deficit and number of frost days along the XXth Century, as shown by the residuals of this paper (Segovia 2021) and a more detailed separate study (Segovia et al. 2020). This pinpoints the importance of the relative balance between water and energy as two of the main climatic factors constraining species diversity gradients, confirming the value of hypotheses that date back to Humboldt’s work (see Hawkins 2001, 2008). There is however a significant amount of unexplained variation in Segovia’s analyses, in particular in the progressive departure of the predictions of the kinetic model as we move towards the tropics, or downwards along the lower quantiles of the richness–temperature relationship. This calls for a deeper exploration of the factors that modify the baseline relationship between richness and energy, opening a new avenue for the macroecological investigation of how different forces and processes shape up geographical diversity gradients beyond the mere energetic constrains imposed by the basal limitations of multicellular life on Earth.

References

Algar, A.C., Kerr, J.T. and Currie, D.J. (2007) A test of Metabolic Theory as the mechanism underlying broad-scale species-richness gradients. Global Ecology and Biogeography, 16, 170-178. doi: https://doi.org/10.1111/j.1466-8238.2006.00275.x

Allen, A.P., Brown, J.H. and Gillooly, J.F. (2002) Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science, 297, 1545-1548. doi: https://doi.org/10.1126/science.1072380

Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M. and West, G.B. (2004) Toward a metabolic theory of ecology. Ecology, 85, 1771-1789. doi: https://doi.org/10.1890/03-9000

Cassemiro, F.A.d.S., Barreto, B.d.S., Rangel, T.F.L.V.B. and Diniz-Filho, J.A.F. (2007) Non-stationarity, diversity gradients and the metabolic theory of ecology. Global Ecology and Biogeography, 16, 820-822. doi: https://doi.org/10.1111/j.1466-8238.2007.00332.x

Currie, D.J. (1991) Energy and large-scale patterns of animal- and plant-species richness. The American Naturalist, 137, 27-49. doi: https://doi.org/10.1086/285144

Currie, D.J., Mittelbach, G.G., Cornell, H.V., Field, R., Guegan, J.-F., Hawkins, B.A., Kaufman, D.M., Kerr, J.T., Oberdorff, T., O'Brien, E. and Turner, J.R.G. (2004) Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecology Letters, 7, 1121-1134. doi: https://doi.org/10.1111/j.1461-0248.2004.00671.x

D'Amen, M., Rahbek, C., Zimmermann, N.E. and Guisan, A. (2017) Spatial predictions at the community level: from current approaches to future frameworks. Biological Reviews, 92, 169-187. doi: https://doi.org/10.1111/brv.12222

Francis, A.P. and Currie, D.J. (2003) A globally consistent richness-climate relationship for Angiosperms. American Naturalist, 161, 523-536. doi: https://doi.org/10.1086/368223

Hawkins, B.A. (2001) Ecology's oldest pattern? Trends in Ecology & Evolution, 16, 470. doi: https://doi.org/10.1016/S0169-5347(01)02197-8 

Hawkins, B.A. (2008) Recent progress toward understanding the global diversity gradient. IBS Newsletter, 6.1, 5-8. https://escholarship.org/uc/item/8sr2k1dd

Hawkins, B.A., Field, R., Cornell, H.V., Currie, D.J., Guégan, J.-F., Kaufman, D.M., Kerr, J.T., Mittelbach, G.G., Oberdorff, T., O'Brien, E., Porter, E.E. and Turner, J.R.G. (2003) Energy, water, and broad-scale geographic patterns of species richness. Ecology, 84, 3105-3117. doi: https://doi.org/10.1890/03-8006

Hawkins, B.A., Diniz-Filho, J.A.F., Jaramillo, C.A. and Soeller, S.A. (2006) Post-Eocene climate change, niche conservatism, and the latitudinal diversity gradient of New World birds. Journal of Biogeography, 33, 770-780. doi: https://doi.org/10.1111/j.1365-2699.2006.01452.x

Hawkins, B.A., Albuquerque, F.S., Araújo, M.B., Beck, J., Bini, L.M., Cabrero-Sañudo, F.J., Castro Parga, I., Diniz-Filho, J.A.F., Ferrer-Castán, D., Field, R., Gómez, J.F., Hortal, J., Kerr, J.T., Kitching, I.J., León-Cortés, J.L., et al. (2007a) A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients. Ecology, 88, 1877-1888. doi:10.1890/06-1444.1. doi: https://doi.org/10.1890/06-1444.1

Hawkins, B.A., Diniz-Filho, J.A.F., Bini, L.M., Araújo, M.B., Field, R., Hortal, J., Kerr, J.T., Rahbek, C., Rodríguez, M.Á. and Sanders, N.J. (2007b) Metabolic theory and diversity gradients: Where do we go from here? Ecology, 88, 1898–1902. doi: https://doi.org/10.1890/06-2141.1

Hortal, J., Rodríguez, J., Nieto-Díaz, M. and Lobo, J.M. (2008) Regional and environmental effects on the species richness of mammal assemblages. Journal of Biogeography, 35, 1202–1214. doi: https://doi.org/10.1111/j.1365-2699.2007.01850.x

Kreft, H. and Jetz, W. (2007) Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences USA, 104, 5925-5930. doi: https://doi.org/10.1073/pnas.0608361104

O'Brien, E. (1998) Water-energy dynamics, climate, and prediction of woody plant species richness: an interim general model. Journal of Biogeography, 25, 379-398. doi: https://doi.org/10.1046/j.1365-2699.1998.252166.x

Ricklefs, R.E. (1987) Community diversity: Relative roles of local and regional processes. Science, 235, 167-171. doi: https://doi.org/10.1126/science.235.4785.167

Ricklefs, R.E. (2004) A comprehensive framework for global patterns in biodiversity. Ecology Letters, 7, 1-15. doi: https://doi.org/10.1046/j.1461-0248.2003.00554.x

Ricklefs, R.E. (2008) Disintegration of the ecological community. American Naturalist, 172, 741-750. doi: https://doi.org/10.1086/593002

Segovia, R.A. (2021) Temperature predicts the maximum tree-species richness and water and frost shape the residual variation. bioRxiv, 836338, ver. 4 peer-reviewed and recommended by Peer community in Ecology. doi: https://doi.org/10.1101/836338

Segovia, R.A., Pennington, R.T., Baker, T.R., Coelho de Souza, F., Neves, D.M., Davis, C.C., Armesto, J.J., Olivera-Filho, A.T. and Dexter, K.G. (2020) Freezing and water availability structure the evolutionary diversity of trees across the Americas. Science Advances, 6, eaaz5373. doi: https://doi.org/10.1126/sciadv.aaz5373

We know that herbivory can have profound influences on plant communities with respect to their distribution and productivity (recently reviewed by Jia et al. 2018). However, the degree to which these effects are realized belowground in the rhizosphere is far less understood. Indeed, many independent studies and synthesis find that the environmental context can be more important than the direct effects of herbivore activity and its removal of plant biomass (Andriuzzi and Wall 2017, Schrama et al. 2013). In spite of dedicated attention, generalizable conclusions remain a bit elusive (Sitters and Venterink 2015). Picon-Cochard and colleagues (2021) help address this research conundrum in an elegant analysis that demonstrates the interaction between long-term cattle grazing and climatic variability on primary production aboveground and belowground. 

Over the course of two years, Picon-Cochard et al. (2021) measured above and belowground net primary productivity in French grasslands that had been subject to ten years of managed cattle grazing. When they compared these data with climatic trends, they find an interesting interaction among grazing intensity and climatic factors influencing plant growth.  In short, and as expected, plants allocate more resources to root growth in dry years and more to above ground biomass in wet and cooler years. However, this study reveals the degree to which this is affected by cattle grazing. Grazed grasslands support warmer and dryer soils creating feedback that further and significantly promotes root growth over green biomass production.  

The implications of this work to understanding the capacity of grassland soils to store carbon is profound. This study addresses one brief moment in time of the long trajectory of this grazed ecosystem. The legacy of grazing does not appear to influence soil ecosystem functioning with respect to root growth except within the environmental context, in this case, climate. This supports the notion that long-term research in animal husbandry and grazing effects on landscapes is deeded. It is my hope that this study is one of many that can be used to synthesize many different data sets and build a deeper understanding of the long-term effects of grazing and herd management within the context of a changing climate.  Herbivory has a profound influence upon ecosystem health and the distribution of plant communities (Speed and Austrheim 2017), global carbon storage (Chen and Frank 2020) and nutrient cycling (Sitters et al. 2020). The analysis and results presented by Picon-Cochard (2021) help to resolve the mechanisms that underly these complex effects and ultimately make projections for the future.

References

Andriuzzi WS, Wall DH. 2017. Responses of belowground communities to large aboveground herbivores: Meta‐analysis reveals biome‐dependent patterns and critical research gaps. Global Change Biology 23:3857-3868. doi: https://doi.org/10.1111/gcb.13675

Chen J, Frank DA. 2020. Herbivores stimulate respiration from labile and recalcitrant soil carbon pools in grasslands of Yellowstone National Park. Land Degradation & Development 31:2620-2634. doi: https://doi.org/10.1002/ldr.3656

Jia S, Wang X, Yuan Z, Lin F, Ye J, Hao Z, Luskin MS. 2018. Global signal of top-down control of terrestrial plant communities by herbivores. Proceedings of the National Academy of Sciences 115:6237-6242. doi: https://doi.org/10.1073/pnas.1707984115

Picon-Cochard C, Vassal N, Martin R, Herfurth D, Note P, Louault F. 2021. Intra and inter-annual climatic conditions have stronger effect than grazing intensity on root growth of permanent grasslands. bioRxiv, 2020.08.23.263137, version 6 peer-reviewed and recommended by PCI Ecology. doi: https://doi.org/10.1101/2020.08.23.263137

Schrama M, Veen GC, Bakker EL, Ruifrok JL, Bakker JP, Olff H. 2013. An integrated perspective to explain nitrogen mineralization in grazed ecosystems. Perspectives in Plant Ecology, Evolution and Systematics 15:32-44. doi: https://doi.org/10.1016/j.ppees.2012.12.001

Sitters J, Venterink HO. 2015. The need for a novel integrative theory on feedbacks between herbivores, plants and soil nutrient cycling. Plant and Soil 396:421-426. doi: https://doi.org/10.1007/s11104-015-2679-y

Sitters J, Wubs EJ, Bakker ES, Crowther TW, Adler PB, Bagchi S, Bakker JD, Biederman L, Borer ET, Cleland EE. 2020. Nutrient availability controls the impact of mammalian herbivores on soil carbon and nitrogen pools in grasslands. Global Change Biology 26:2060-2071. doi: https://doi.org/10.1111/gcb.15023

Speed JD, Austrheim G. 2017. The importance of herbivore density and management as determinants of the distribution of rare plant species. Biological Conservation 205:77-84. doi: https://doi.org/10.1016/j.biocon.2016.11.030

Humans are exploiting biological resources since thousands of years. Exploitation of biological resources has become particularly intense since the beginning of the 20th century and the steep increase in the worldwide human population size. Marine and freshwater fishes are not exception to that rule, and they have been (and continue to be) strongly harvested as a source of proteins for humans. For some species, fishery has been so intense that natural stocks have virtually collapsed in only a few decades. The worst example begin that of the Northwest Atlantic cod that has declined by more than 95% of its historical biomasses in only 20-30 years of intensive exploitation (Frank et al. 2005). These rapid and steep changes in biomasses have huge impacts on the entire ecosystems since species targeted by fisheries are often at the top of trophic chains (Frank et al. 2005). 

Beyond demographic impacts, fisheries also have evolutionary impacts on populations, which can also indirectly alter ecosystems (Uusi-Heikkilä et al. 2015; Palkovacs et al. 2018). Fishermen generally focus on the largest specimens, and hence exert a strong selective pressure against these largest fish (which is called “harvest selection”). There is now ample evidence that harvest selection can lead to rapid evolutionary changes in natural populations toward small individuals (Kuparinen & Festa-Bianchet 2017). These evolutionary changes are of course undesirable from a human perspective, and have attracted many scientific questions. Nonetheless, the consequence of harvest selection is not always observable in natural populations, and there are cases in which no phenotypic change (or on the contrary an increase in mean body size) has been observed after intense harvest pressures. In a conceptual Essay, Edeline and Loeuille (Edeline & Loeuille 2020) propose novel ideas to explain why the evolutionary consequences of harvest selection can be so diverse, and how a cross talk between ecological and evolutionary dynamics can explain patterns observed in natural stocks.

 The general and novel concept proposed by Edeline and Loeuille is actually as old as Darwin’s book; The Origin of Species (Darwin 1859). It is based on the simple idea that natural selection acting on harvested populations can actually be strong, and counter-balance (or on the contrary reinforce) the evolutionary consequence of harvest selection. Although simple, the idea that natural and harvest selection are jointly shaping contemporary evolution of exploited populations lead to various and sometimes complex scenarios that can (i) explain unresolved empirical patterns and (ii) refine predictions regarding the long-term viability of exploited populations. 

The Edeline and Loeuille’s crafty inspiration is that natural selection acting on exploited populations is itself an indirect consequence of harvest (Edeline & Loeuille 2020). They suggest that, by modifying the size structure of populations (a key parameter for ecological interactions), harvest indirectly alters interactions between populations and their biotic environment through competition and predation, which changes the ecological theatre and hence the selective pressures acting back to populations. They named this process “size-dependent eco-evolutionary feedback loops” and develop several scenarios in which these feedback loops ultimately deviate the evolutionary outcome of harvest selection from expectation. The scenarios they explore are based on strong theoretical knowledge, and range from simple ones in which a single species (the harvest species) is evolving to more complex (and realistic) ones in which multiple (e.g. the harvest species and its prey) species are co-evolving.

I will not come into the details of each scenario here, and I will let the readers (re-)discovering the complex beauty of biological life and natural selection. Nonetheless, I will emphasize the importance of considering these eco-evolutionary processes altogether to fully grasp the response of exploited populations. Edeline and Loeuille convincingly demonstrate that reduced body size due to harvest selection is obviously not the only response of exploited fish populations when natural selection is jointly considered (Edeline & Loeuille 2020). On the contrary, they show that –under some realistic ecological circumstances relaxing exploitative competition due to reduced population densities- natural selection can act antagonistically, and hence favour stable body size in exploited populations. Although this seems further desirable from a human perspective than a downsizing of exploited populations, it is actually mere window dressing as Edeline and Loeuille further showed that this response is accompanied by an erosion of the evolvability –and hence a lowest probability of long-term persistence- of these exploited populations.

Humans, by exploiting biological resources, are breaking the relative equilibrium of complex entities, and the response of populations to this disturbance is itself often complex and heterogeneous. In this Essay, Edeline and Loeuille provide –under simple terms- the theoretical and conceptual bases required to improve predictions regarding the evolutionary responses of natural populations to exploitation by humans (Edeline & Loeuille 2020). An important next step will be to generate data and methods allowing confronting the empirical reality to these novel concepts (e.g. (Monk et al. 2021), so as to identify the most likely evolutionary scenarios sustaining biological responses of exploited populations, and hence to set the best management plans for the long-term sustainability of these populations.

References

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