In a recent paper published in PNAS, Fayet et al. [1] reported scarce field observations of two Atlantic puffins (four years apart) apparently scratching their bodies using sticks, which was interpreted by the authors as evidence of tool use in this species. In a short response, Benjamin Farrar [2] raises serious concerns about this interpretation and proposes simpler, more parsimonious, mechanisms explaining the observed behaviour: a textbook case of Morgan's canon.
In virtually all introductory lectures on animal behaviour, students are advised to exercise caution when interpreting empirical data and weighting alternative explanations. We are sometimes prisoner of our assumptions: our desire of beliefs in advanced cognitive skills in non-human species make us more receptive to facts confirming our preconceptions than to simpler, less exciting, interpretations (a phenomenon known as "confirmation bias" in psychology). We must resist the temptation to accept appealing explanations without enough critical thinking. Our students are thus taught to apply the Lloyd Morgan's canon, a variant of one of the most important heuristics in Science, the principle of parsimony or Occam's razor, rephrased by Morgan [3, page 53] in the context of animal behaviour: "In no case may we interpret an action as the outcome of a higher psychical faculty, if it can be interpreted as the outcome of the exercise of one that stands lower in the psychological scale". In absence of evidence to the contrary, one should postulate the simplest cognitive skill consistent with the observed behaviour. While sometimes criticized from an epistemological point of view [4-6], it remains an essential and largely accepted framework of animal cognition. It has repeatedly proved to be a useful guide in the minefield of comparative psychology. Classical ethology questions related to the existence of, for instance, meta-cognition [7], intentionality or problem solving [8] have been convincingly investigated using this principle.
Yet, there is a downside to this conservative approach. Blind reference to Morgan's canon may narrow our theoretical thinking about animal cognition [7,9]. It could be counter-productive to systematically deny advanced cognitive skills in animals. On the contrary, keeping our mind open to unplanned observations, unexpected discoveries, or serendipity [10], and being prepared to accept new hypotheses, sometimes fairly remote from the dominant paradigm, may be a fruitful research strategy. To quote Darwin's famous letter to Alfred Wallace: "I am a firm believer, that without speculation there is no good and original observation" [11]. Brief notes in specialized scientific journals, or even in grey literature (by enthusiast amateur ornithologists, ichthyologists, or entomologists), constitutes a rich array of anecdotal observations. For instance, Sol et al. [12] convincingly compared the innovation propensity across bird species by screening ornithology literature using keywords like 'never reported', 'not seen before', 'first report', 'unusual' or 'novel'. Even if "the plural of anecdote is not data" as the saying goes, such descriptions of novel behaviours, even single-subject observations, are indisputably precious: taxonomic ubiquity of a behaviour is a powerful argument in favour of evolutionary convergence. Of course, a race to the bottom, amplified by the inevitable media hypes around scientific articles questioning human exceptionalism, is another possible scientific trap for behavioural biologists in search of skills characteristic of so-called advanced species, but never described so far in supposedly cognitively simpler organisms. As stated by Franz de Waal [9]: "I have nothing against anecdotes, especially if they have been caught on camera or come from reputable observers who know their animals; but I do view them as a starting point of research, never an end point".
In the case of the two video observations of puffins apparently using sticks as scratching tool, it must be considered as a mere anecdote unless scientists systematically investigate this behaviour. In his constructive criticism of Fayet et al.'s paper, Benjamin Farrar [2] proposes interesting directions of research and testable predictions. A correlation between the background rate of stick picking and the rate of stick preening would indicate that this behaviour was more likely explained by fluke than genuine innovation in this species.

References

[1] Fayet, A. L., Hansen, E. S., and Biro, D. (2020). Evidence of tool use in a seabird. Proceedings of the National Academy of Sciences, 117(3), 1277–1279. doi: 10.1073/pnas.1918060117
[2] Farrar, B. G. (2020). Evidence of tool use in a seabird? PsyArXiv, 463hk, ver. 5 recommended and peer-reviewed by Peer Community In Ecology. doi: 10.31234/osf.io/463hk
[3] Morgan, C. L. (1894). An introduction to comparative psychology. London, UK: Walter Scott, Ltd. Retrieved from https://archive.org/details/introductiontoco00morg/page/53/mode/2up
[4] Meketa, I. (2014). A critique of the principle of cognitive simplicity in comparative cognition. Biology and Philosophy, 29(5), 731–745. doi: 10.1007/s10539-014-9429-z
[5] Fitzpatrick, S. (2017). Against Morgan's Canon. In K. Andrews and J. Beck (Eds.), The Routledge handbook of philosophy of animal minds (pp. 437–447). London, UK: Routledge, Taylor and Francis Group. doi: 10.4324/9781315742250.ch42
[6] Starzak, T. (2017). Interpretations without justification: a general argument against Morgan's Canon. Synthese, 194(5), 1681–1701. doi: 10.1007/s11229-016-1013-4
[7] Arbilly, M., and Lotem, A. (2017). Constructive anthropomorphism: a functional evolutionary approach to the study of human-like cognitive mechanisms in animals. Proceedings of the Royal Society B: Biological Sciences, 284(1865), 20171616. doi: 10.1098/rspb.2017.1616
[8] Taylor, A. H., Knaebe, B., and Gray, R. D. (2012). An end to insight? New Caledonian crows can spontaneously solve problems without planning their actions. Proceedings of the Royal Society B: Biological Sciences, 279(1749), 4977–4981. doi: 10.1098/rspb.2012.1998
[9] de Waal, F. (2016). Are we smart enough to know how smart animals are? New-York, USA: W. W. Norton and Company.
[10] Scheffer, M. (2014). The forgotten half of scientific thinking. Proceedings of the National Academy of Sciences, 111(17), 6119–6119. doi: 10.1073/pnas.1404649111
[11] Darwin, C. R. (1857). Letter to A. R. Wallace, 22 December 1857. Retrieved 30 January 2020, from https://www.darwinproject.ac.uk/letter/DCP-LETT-2192.xml
[12] Sol, D., Lefebvre, L., and Rodríguez-Teijeiro, J. D. (2005). Brain size, innovative propensity and migratory behaviour in temperate Palaearctic birds. Proceedings of the Royal Society B: Biological Sciences, 272(1571), 1433–1441. doi: 10.1098/rspb.2005.3099

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

What predisposes two individuals to form and maintain a relationship is a fundamental question. Using facial recognition to see whether couples' faces change over time to become more and more similar, psychology researchers have concluded that couples tend to be formed from the start between people whose faces are more similar than average [1]. As the saying goes, birds of a feather flock together.

And what about in nature? Are these rules of assembly valid for communities of different species?

In his seminal contribution, Robert MacArthur (1984) wrote ‘To do science is to search for repeated patterns’ [2]. Identifying the mechanisms that govern the arrangement of life is a hot research topic in the field of ecology for decades, and an absolutely essential prerequisite to answer the outstanding question of what shape ecological patterns in multi-species communities such as species-area relationships, relative species abundances, or spatial and temporal turnover of community composition; amid others [3]. To explain ecological patterns in nature, some rely on the concept that every species - through evolutionary processes and the acquisition of a unique set of traits that allow a species to be adapted to its abiotic and biotic environment - occupies a unique niche: Species coexistence comes as the result of niche differentiation [4,5]. Such a view has been challenged by the recognition of the key role of neutral processes [6], however, in which demographic stochasticity contributes to shape multi-species communities and to explain why congener species coexist much more frequently than expected by chance [7,8]. While the niche-based and neutral theories appear seemingly opposed at first sight [9], the dichotomy may be more philosophical than empirical [4,5]. Many examples have come to support that both concepts are not incompatible as they together influence the structure, diversity and functioning of communities [10], and are simply extreme cases of a continuum [11]. From this perspective, extrinsic factors, i.e., environmental heterogeneity, may influence the location of a given community along the niche-neutrality continuum. 

The walk of species in nature is therefore neither random nor ecologically predestined. In microbial assemblages, the co-existence of these two antagonistic mechanisms has been shown both theoretically and empirically. It has been shown that a combination of stabilising (niche) and equalising (neutral) mechanisms was responsible for the existence of groups of coexistent species (clumps) in a phytoplankton rich community [12]. Analysing interannual changes (2003-2009) in the weekly abundance of diatoms and dinoflagellates located in a temperate coastal ecosystem of the Western English Channel, Mutshinda et al. [13] found a mixture of biomass dynamics consistent with the neutrality-niche continuum hypothesis. While niche processes explained the dynamic of phytoplankton functional groups (i.e., diatoms vs. dinoflagellates) in terms of biomass, neutral processes mainly dominated - 50 to 75% of the time - the dynamics at the species level within functional groups [13]. From one endpoint to another, defining the location of a community along the continuum is all matter of scale [4,11].

In their study, testing predictions made by an emergent neutrality model, Graco-Roza et al. [14] provide empirical evidence that neutral and niche processes joined together to shape and drive planktonic communities in a riverine ecosystem. Body size - the 'master trait' - is used here as a discriminant ecological dimension along the niche axis. From their analysis, they not only show that the specific abundance is organised in clumps and gaps along the niche axis, but also reveal that different clumps exist along the river course. They identify two main clumps in body size - with species belonging to three different morphologically-based functional groups - and characterise that among-species differences in biovolume are driven by functional redundancy at the clump level; species functional distinctiveness being related to the relative biovolume of species. By grouping their variables according to seasons (cold-dry vs. warm-wet) or river elevation profile (upper, medium and lower course), they hereby highlight how environmental heterogeneity contributes to shape species assemblages and their dynamics and conclude that emergent neutrality models are a powerful approach to explain species coexistence; and therefore ecological patterns.

References

[1] Tea-makorn PP, Kosinski M (2020) Spouses’ faces are similar but do not become more similar with time. Scientific Reports, 10, 17001. https://doi.org/10.1038/s41598-020-73971-8.

[2] MacArthur RH (1984) Geographical Ecology: Patterns in the Distribution of Species. Princeton University Press.

[3] Vellend M (2020) The Theory of Ecological Communities (MPB-57). Princeton University Press.

[4] Wennekes PL, Rosindell J, Etienne RS (2012) The Neutral—Niche Debate: A Philosophical Perspective. Acta Biotheoretica, 60, 257–271. https://doi.org/10.1007/s10441-012-9144-6.

[5] Gravel D, Guichard F, Hochberg ME (2011) Species coexistence in a variable world. Ecology Letters, 14, 828–839. https://doi.org/10.1111/j.1461-0248.2011.01643.x.

[6] Hubbell SP (2001) The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32). Princeton University Press.

[7] Leibold MA, McPeek MA (2006) Coexistence of the Niche and Neutral Perspectives in Community Ecology. Ecology, 87, 1399–1410. https://doi.org/10.1890/0012-9658(2006)87[1399:COTNAN]2.0.CO;2.

[8] Pielou EC (1977) The Latitudinal Spans of Seaweed Species and Their Patterns of Overlap. Journal of Biogeography, 4, 299–311. https://doi.org/10.2307/3038189.

[9] Holt RD (2006) Emergent neutrality. Trends in Ecology & Evolution, 21, 531–533. https://doi.org/10.1016/j.tree.2006.08.003. 

[10] Scheffer M, Nes EH van (2006) Self-organized similarity, the evolutionary emergence of groups of similar species. Proceedings of the National Academy of Sciences, 103, 6230–6235. https://doi.org/10.1073/pnas.0508024103.

[11] Gravel D, Canham CD, Beaudet M, Messier C (2006) Reconciling niche and neutrality: the continuum hypothesis. Ecology Letters, 9, 399–409. https://doi.org/10.1111/j.1461-0248.2006.00884.x.

[12] Vergnon R, Dulvy NK, Freckleton RP (2009) Niches versus neutrality: uncovering the drivers of diversity in a species-rich community. Ecology Letters, 12, 1079–1090. https://doi.org/10.1111/j.1461-0248.2009.01364.x.

[13] Mutshinda CM, Finkel ZV, Widdicombe CE, Irwin AJ (2016) Ecological equivalence of species within phytoplankton functional groups. Functional Ecology, 30, 1714–1722. https://doi.org/10.1111/1365-2435.12641.

[14] Graco-Roza C, Segura AM, Kruk C, Domingos P, Soininen J, Marinho MM (2021) Clumpy coexistence in phytoplankton: The role of functional similarity in community assembly. bioRxiv, 869966, ver. 6 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/869966

In this article, Saade et al. (2021) investigate how the rate of local extinctions and their spatial distribution affect recolonization dynamics in metacommunities. They use an elegant combination of microcosm experiments with metacommunities of freshwater ciliates and mathematical modelling mirroring their experimental system. Their main findings are (i) that local patch extinctions increase both local (α-) and inter-patch (β-) diversity in a transient way during the recolonization process, (ii) that these effects depend more on the spatial distribution of extinctions (dispersed or clustered) than on their amount, and (iii) that they may spread regionally.
Microcosm experiments are already quite cool just by themselves and have contributed largely to conceptual advances in community ecology (see Fraser and Keddy 1997, or Jessup et al. 2004 for reviews on this topic), but they are here exploited to a whole further level by the fitting of a metapopulation dynamics model. The model allows both to identify the underlying mechanisms most likely to generate the patterns observed (here, competitive interactions) and to assess the robustness of these patterns when considering larger spatial or temporal scales. This release of experimental limitations allows here for the analysis of quantitative metrics of spatial structure, like the distance to the closest patch, which gives an interesting insight into the functional basis of the effect of the spatial distribution of extinctions.

A major strength of this study is that it highlights the importance of considering the spatial structure explicitly. Recent work on ecological networks has shown repeatedly that network structure affects the propagation of pathogens (Badham and Stocker 2010), invaders (Morel-Journel et al. 2019), or perturbation events (Gilarranz et al. 2017). Here, the spatial structure of the metacommunity is a regular grid of patches, but the distribution of extinction events may be either regularly dispersed (i.e., extinct patches are distributed evenly over the grid and are all surrounded by non-extinct patches only) or clustered (all extinct patches are neighbours). This has a direct effect on the neighbourhood of perturbed patches, and because perturbations have mostly local effects, their recovery dynamics are dominated by the composition of this immediate neighbourhood. In landscapes with dispersed extinctions, the neighbourhood of a perturbed patch is not affected by the amount of extinctions, and neither is its recovery time. In contrast, in landscapes with clustered extinctions, the amount of extinctions affects the depth of the perturbed area, which takes longer to recover when it is larger. Interestingly, the spatial distribution of extinctions here is functionally equivalent to differences in connectivity between perturbed and unperturbed patches, which results in contrasted “rescue recovery” and “mixing recovery” regimes as described by Zelnick et al. (2019).
 
Furthermore, this study focuses on local dynamics of competition and short-term, transient patterns that may have been overlooked by more classical, equilibrium-based approaches of dynamical systems of metacommunities. Indeed, in a metacommunity composed of several competitors, early theoretical work demonstrated that species coexistence is possible at the regional scale only, provided that spatial heterogeneity creates spatial variance in fitness or precludes the superior competitor from accessing certain habitat patches (Skellam 1951, Levins 1969). In the spatially homogeneous experimental system of Saade et al., one of the three ciliate species ends up dominating the community at equilibrium. However, following local, one-time extinction events, the community endures a recolonization process in which differences in dispersal may provide temporary spatial niches for inferior competitors. These transient patterns might prove essential to understand and anticipate the resilience of natural systems that are under increasing pressure, and enduring ever more frequent and intense perturbations (IPBES 2019). Spatial autocorrelation in extinction events was previously identified as a risk for stability and persistence of metacommunities (Ruokolainen 2013, Kahilainen et al. 2018). These new results show that autocorrelated perturbations also have longer-lasting effects, which is likely to increase their overall impact on metacommunity dynamics. As spatial and temporal autocorrelation of temperature and extreme climatic events are expected to increase (Di Cecco and Gouthier 2018), studies that investigate how metacommunities respond to the structure of the distribution of perturbations are more necessary than ever.
 
References

Badham J, Stocker R (2010) The impact of network clustering and assortativity on epidemic behaviour. Theoretical Population Biology, 77, 71–75. https://doi.org/10.1016/j.tpb.2009.11.003
 
Di Cecco GJ, Gouhier TC (2018) Increased spatial and temporal autocorrelation of temperature under climate change. Scientific Reports, 8, 14850. https://doi.org/10.1038/s41598-018-33217-0
 
Fraser LH, Keddy P (1997) The role of experimental microcosms in ecological research. Trends in Ecology & Evolution, 12, 478–481. https://doi.org/10.1016/S0169-5347(97)01220-2
 
Gilarranz LJ, Rayfield B, Liñán-Cembrano G, Bascompte J, Gonzalez A (2017) Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science, 357, 199–201. https://doi.org/10.1126/science.aal4122
 
IPBES (2019) Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. https://doi.org/10.5281/zenodo.3553579 
 
Jessup CM, Kassen R, Forde SE, Kerr B, Buckling A, Rainey PB, Bohannan BJM (2004) Big questions, small worlds: microbial model systems in ecology. Trends in Ecology & Evolution, 19, 189–197. https://doi.org/10.1016/j.tree.2004.01.008
 
Kahilainen A, van Nouhuys S, Schulz T, Saastamoinen M (2018) Metapopulation dynamics in a changing climate: Increasing spatial synchrony in weather conditions drives metapopulation synchrony of a butterfly inhabiting a fragmented landscape. Global Change Biology, 24, 4316–4329. https://doi.org/10.1111/gcb.14280

Levins R (1969) Some Demographic and Genetic Consequences of Environmental Heterogeneity for Biological Control1. Bulletin of the Entomological Society of America, 15, 237–240. https://doi.org/10.1093/besa/15.3.237
 
Morel-Journel T, Assa CR, Mailleret L, Vercken E (2019) Its all about connections: hubs and invasion in habitat networks. Ecology Letters, 22, 313–321. https://doi.org/10.1111/ele.13192

Ruokolainen L (2013) Spatio-Temporal Environmental Correlation and Population Variability in Simple Metacommunities. PLOS ONE, 8, e72325. https://doi.org/10.1371/journal.pone.0072325

Saade C, Kefi S, Gougat-Barbera C, Rosenbaum B, Fronhofer EA (2021) Spatial distribution of local patch extinctions drives recovery dynamics in metacommunities. bioRxiv, 2020.12.03.409524, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.12.03.409524
 
Skellam JG (1951) Random Dispersal in Theoretical Populations. Biometrika, 38, 196–218. https://doi.org/10.2307/2332328
 
Zelnik YR, Arnoldi J-F, Loreau M (2019) The three regimes of spatial recovery. Ecology, 100, e02586. https://doi.org/10.1002/ecy.2586

One of the main issues of forest ecosystems is rising tree mortality as a result of extreme weather events (Franklin et al., 1987). Eventually, tree mortality reduces forest biomass (Allen et al., 2010), although its effect on forest ecosystem fluxes seems not lasting too long (Anderegg et al., 2016). This controversy about the negative consequences of tree mortality is joined to the debate about the drivers triggering and the mechanisms accelerating tree decline. For instance, there is still room for discussion about carbon starvation or hydraulic failure determining the decay processes (Sevanto et al., 2014) or about the importance of mortality sources (Reichstein et al., 2013). Therefore, understanding and predicting tree mortality has become one of the challenges for forest ecologists in the last decade, doubling the rate of articles published on the topic (*). Although predicting the responses of ecosystems to environmental change based on the traits of species may seem a simplistic conception of ecosystem functioning (Sutherland et al., 2013), identifying those traits that are involved in the proneness of a tree to die would help to predict how forests will respond to climate threatens.
Modelling tree mortality is complex, involving multiple factors acting simultaneously at different scales, from tree genetics to ecosystem dynamics and from microsite conditions to global climatic events. Therefore, taking into account different approaches to reduce uncertainty of the predictions is needed (Bugmann et al., 2019). Petit-Cailleux et al. (2020) uses statistical and process-based models to detect the main mortality drivers of a drought- and frost-prone beech population. Particularly, they assessed the intra-individual characteristics of the population, that may play a decisive role explaining the differences in tree vulnerability to extreme weather events. Comparing the results of both analytical approaches, they find out several key factors, such as defoliation, leaf phenology and tree size, that were consistent between them. Even more, the process-based model showed the physiological mechanisms that may explain the individual vulnerability, for instance higher loss of hydraulic conductance may increase the mortality risk of trees with early budburst phenology and large stem diameter. The authors also successfully model annual mortality rate with a linear relationship including only three parameters: loss of conductance, biomass of reserves and late frost days.
This valuable study is a good example of the complexity in understanding and predicting tree mortality. The authors carried out the ambitious commitment of studying the inter-annual variation in mortality with 14-year dataset. However, it might be not enough time to control for the dependence of temporal data to soundly model mortality rate. The authors also acknowledge that the use of two approaches increases the knowledge from different perspectives, but at the same time comparing their results is difficult because the parameters used are not identical. Particularly, process-based models tend to consider the same microclimatic conditions for every tree in the population, and may produce inconsistences with statistical models. Alternatively, individual-based modelling might overcome some of the incompatibilities between the approaches (Zhu et al., 2019).

(*) Number (and percentage) of articles found in Web of Sciences after searching (December the 10th, 2020) “tree mortality”: from 163 (0.006%) in 2010 to 412 (0.013%) in 2020.

References

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