A network includes a set of vertices or nodes (e.g., species in an interaction network), and a set of edges or links (e.g., interactions between species). Whether and how networks vary in space and/or time are questions often addressed in ecological research. 

Two ecological networks can differ in several extents: in that species are different in the two networks and establish new interactions (species turnover), or in that species that are present in both networks establish different interactions in the two networks (rewiring). The ecological meaning of changes in network structure is quite different according to whether species turnover or interaction rewiring plays a greater role. Therefore, much attention has been devoted in recent years on quantifying and interpreting the relative changes in network structure due to species turnover and/or rewiring.

Poisot et al. (2012) proposed to partition the global variation in structure between networks, \( \beta_{WN} \) (WN = Whole Network) into two terms: \( \beta_{OS} \) (OS = Only Shared species) and \( \beta_{ST} \) (ST = Species Turnover), such as \( \beta_{WN} = \beta_{OS} + \beta_{ST} \).

The calculation lays on enumerating the interactions between species that are common or not to two networks, as illustrated on Figure 1 for a simple case. Specifically, Poisot et al. (2012) proposed to use a Sorensen type measure of network dissimilarity, i.e., \( \beta_{WN} = \frac{a+b+c}{(2a+b+c)/2} -1=\frac{b+c}{2a+b+c} \) , where \( a \) is the number of interactions shared between the networks, while \( b \) and \( c \) are interaction numbers unique to one and the other network, respectively. \( \beta_{OS} \) is calculated based on the same formula, but only for the subnetworks including the species common to the two networks, in the form \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a_{OS}+b_{OS}+c_{OS}} \) (e.g., Fig. 1). \( \beta_{ST} \) is deduced by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and represents in essence a "dissimilarity in interaction structure introduced by dissimilarity in species composition" (Poisot et al. 2012).

Figure 1. Ecological networks exemplified in Fründ (2021) and discussed in Poisot (2022). a is the number of shared links (continuous lines in right figures), while b+c is the number of edges unique to one or the other network (dashed lines in right figures).

Alternatively, Fründ (2021) proposed to define \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a+b+c} \) and \( \beta_{ST} = \frac{b_{ST}+c_{ST}}{2a+b+c} \), where \( b_{ST}=b-b_{OS} \)  and \( c_{ST}=c-c_{OS} \) , so that the components \( \beta_{OS} \) and \( \beta_{ST} \) have the same denominator. In this way, Fründ (2021) partitioned the count of unique \( b+c=b_{OS}+b_{ST}+c_{ST} \) interactions, so that \( \beta_{OS} \) and \( \beta_{ST} \) sums to \( \frac{b_{OS}+c_{OS}+b_{ST}+c_{ST}}{2a+b+c} = \frac{b+c}{2a+b+c} = \beta_{WN} \). Fründ (2021) advocated that this partition allows a more sensible comparison of \( \beta_{OS} \) and \( \beta_{ST} \), in terms of the number of links that contribute to each component.

For instance, let us consider the networks 1 and 2 in Figure 1 (left panel) such as \( a_{OS}=2 \) (continuous lines in right panel), \( b_{ST} + c_{ST} = 1 \) and \( b_{OS} + c_{OS} = 1 \) (dashed lines in right panel), and thereby \( a = 2 \), \( b+c=2 \), \( \beta_{WN} = 1/3 \). Fründ (2021) measured \( \beta_{OS}=\beta_{ST}=1/6 \) and argued that it is appropriate insofar as it reflects that the number of unique links in the OS and ST components contributing to network dissimilarity (dashed lines) are actually equal. Conversely, the formula of Poisot et al. (2012) yields \( \beta_{OS}=1/5 \), hence \( \beta_{ST} = \frac{1}{3}-\frac{1}{5}=\frac{2}{15}<\beta_{OS} \). Fründ (2021) thus argued that the method of Poisot tends to underestimate the contribution of species turnover.

To clarify and avoid misinterpretation of the calculation of \( \beta_{OS} \) and \( \beta_{ST} \) in Poisot et al. (2012), Poisot (2022) provides a new, in-depth mathematical analysis of the decomposition of \( \beta_{WN} \). Poisot et al. (2012) quantify in \( \beta_{OS} \) the actual contribution of rewiring in network structure for the subweb of common species. Poisot (2022) thus argues that \( \beta_{OS} \) relates only to the probability of rewiring in the subweb, while the definition of \( \beta_{OS} \) by Fründ (2021) is relative to the count of interactions in the global network (considered in denominator), and is thereby dependent on both rewiring probability and species turnover. Poisot (2022) further clarifies the interpretation of \( \beta_{ST} \). \( \beta_{ST} \) is obtained by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and thus represents the influence of species turnover in terms of the relative architectures of the global networks and of the subwebs of shared species. Coming back to the example of Fig.1., the Poisot et al. (2012) formula posits that \( \frac{\beta_{ST}}{\beta_{WN}}=\frac{2/15}{1/3}=2/5 \), meaning that species turnover contributes two-fifths of change in network structure, while rewiring in the subweb of common species contributed three fifths.  Conversely, the approach of Fründ (2021) does not compare the architectures of global networks and of the subwebs of shared species, but considers the relative contribution of unique links to network dissimilarity in terms of species turnover and rewiring. 

Poisot (2022) concludes that the partition proposed in Fründ (2021) does not allow unambiguous ecological interpretation of rewiring. He provides guidelines for proper interpretation of the decomposition proposed in Poisot et al. (2012).

References

Fründ J (2021) Dissimilarity of species interaction networks: how to partition rewiring and species turnover components. Ecosphere, 12, e03653. https://doi.org/10.1002/ecs2.3653

Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361. https://doi.org/10.1111/ele.12002

Poisot T (2022) Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. EcoEvoRxiv Preprints, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/gxhu2

Pheromones are used by many insects to find the opposite sex for mating. Especially for nocturnal mosquitoes it seems logical that such pheromones exist as they can only partly rely on visual cues when flying at night. The males of many mosquito species form swarms and conspecific females fly into these swarms to mate. The two sibling species of malaria mosquitoes Anopheles gambiae s.s. and An. coluzzii coexist and both form swarms consisting of only one species. Although hybrids can be produced, these hybrids are rarely found in nature. In the study presented by Poda and colleagues (2022) it was tested if long-range sex pheromones exist in these two mosquito sibling species.

In a previous study by Mozūraites et al. (2020), five compounds (acetoin, sulcatone, octanal, nonanal and decanal) were identified that induced male swarming and increase mating success. Interestingly these compounds are frequently found in nature and have been shown to play a role in sugar feeding or host finding of An. gambiae. In the recommended study performed by Poda et al. (2022) no evidence of long-range sex pheromones in A. gambiae s.s. and An. coluzzii was found. The discrepancy between the two studies is difficult to explain but some of the methods varied between studies. Mozūraites et al. (2020) for example, collected odours from mosquitoes in small 1l glass bottles, where swarming is questionable, while in the study of Poda et al. (2022) 50 x 40 x 40 cm cages were used and swarming observed, although most swarms are normally larger. On the other hand, some of the analytical techniques used in the Mozūraites et al. (2020) study were more sensitive while others were more sensitive in the Poda et al. (2022) study. Because it is difficult to prove that something does not exist, the authors nicely indicate that “an absence of evidence is not an evidence of absence” (Poda et al., 2022). Nevertheless, recently colonized species were tested in large cage setups where swarming was observed and various methods were used to try to detect sex pheromones. No attraction to the volatile blend from male swarms was detected in an olfactometer, no antenna-electrophysiological response of females to male swarm volatile compounds was detected and no specific male swarm volatile was identified.

This study will open the discussion again if (sex) pheromones play a role in swarming and mating of malaria mosquitoes. Future studies should focus on sensitive real-time volatile analysis in mating swarms in large cages or field settings. In comparison to moths for example that are very sensitive to very specific pheromones and attract from a large distance, such a long-range specific pheromone does not seem to exist in these mosquito species. Acoustic and visual cues have been shown to be involved in mating (Diabate et al., 2003; Gibson and Russell, 2006) and especially at long distances, visual cues are probably important for the detection of these swarms.

References

Diabate A, Baldet T, Brengues C, Kengne P, Dabire KR, Simard F, Chandre F, Hougard JM, Hemingway J, Ouedraogo JB, Fontenille D (2003) Natural swarming behaviour of the molecular M form of Anopheles gambiae. Transactions of The Royal Society of Tropical Medicine and Hygiene, 97, 713–716. https://doi.org/10.1016/S0035-9203(03)80110-4

Gibson G, Russell I (2006) Flying in Tune: Sexual Recognition in Mosquitoes. Current Biology, 16, 1311–1316. https://doi.org/10.1016/j.cub.2006.05.053

Mozūraitis, R., Hajkazemian, M., Zawada, J.W., Szymczak, J., Pålsson, K., Sekar, V., Biryukova, I., Friedländer, M.R., Koekemoer, L.L., Baird, J.K., Borg-Karlson, A.-K., Emami, S.N. (2020) Male swarming aggregation pheromones increase female attraction and mating success among multiple African malaria vector mosquito species. Nature Ecology & Evolution, 4, 1395–1401. https://doi.org/10.1038/s41559-020-1264-9

Poda, S.B., Buatois, B., Lapeyre, B., Dormont, L., Diabate, A., Gnankine, O., Dabire, R.K.,  Roux, O. (2022) No evidence for long-range male sex pheromones in two malaria mosquitoes. bioRxiv, 2020.07.05.187542, ver. 6 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.07.05.187542

The importance of the vertical structure of vegetation cover for the functioning, management and conservation of ecosystems has received particular attention from ecologists in the last decades. Canopy architecture has many implications for light extinction coefficient, temperature variation reduction, self-shading which are all key parameters for the structuring and functioning of different ecosystems such as grasslands [1,2], forests [3,4], phytoplankton communities [5, 6], macroalgal populations [7] and even underwater animal forests such as octocoral communities [8].

This research topic, therefore, benefits from a large body of literature and the facilitative role of self-shadowing is no longer in question. However, it is always puzzling to note that some of the most common ecosystems turn out to be amongst the least known. This is precisely the case of the Fucus serratus communities which are widespread in Northeast Atlantic along the Atlantic coast of Europe from Svalbard to Portugal, as well as Northwest Atlantic & Gulf of St. Lawrence, easily accessible at low tide, but which have comparatively received less attention than more emblematic macro-algal communities such as Laminariales.

The lack of attention paid to these most common Fucales is particularly critical as some species such as F. serratus are proving to be particularly vulnerable to environmental change, leading to a predicted northward retreat from its current southern boundary [9].

In the present study [10], the authors showed the importance of the vegetation cover in resisting tide-induced environmental stresses. The canopy of F. serratus mitigates stress levels experienced in the lower layers during emersion, while various acclimation strategies take over to maintain the photosynthetic apparatus in optimal conditions.

They hereby highlight adaptation mechanisms to the extreme environment represented by the intertidal zone. These adaptation strategies were expected and similar mechanisms had been shown at the cellular level previously [11]. The earliest studies on the subject have shown that the structure of the bottom, the movement of water, and light availability all "influence the distribution of Fucaceae and disturb the regularity of their fine zonation, which itself is caused by the most important factor, desiccation", as Zaneveld states in his review [12]. He observed that the causes of the zonal distribution of marine algae are numerous, and identified several points of interest such as the relative period of emersion, the rapidity of desiccation, the loss of water, and the thickness of the cell walls.

The present study thus highlights the existence of facilitative mechanisms associated with F. serratus canopy and nicely confirms previous work with in situ observations. It also highlights the importance of the vegetative cover in combating desiccation and introduces the dampening effect as a facilitating mechanism.

The effect of the vegetation cover can sometimes even be felt beyond its immediate area of influence. A recent study shows that ground-level ozone is significantly reduced by the combined effects of canopy shading and turbulence [4]. Below the canopy, the light intensity becomes sufficiently low which inhibits ozone formation due to the decrease in the rates of hydroxyl radical formation and the rates of conversion of nitrogen dioxide to nitrogen oxide by photolysis. In addition, reductions in light levels associated with foliage promote ozone-destroying reactions between plant-emitted species, such as nitric oxide and/or alkenes, and ozone itself. The reduction in diffusivity slows the upward transport of surface emitted species, partially decoupling the area under the canopy from the rest of the atmosphere.

By analogy with the work of Makar et al [4], and in the light of the results provided by the authors of this study, one may wonder whether the canopy dampening of F. serratus communities (and other common fucoids widely distributed on our coasts) might not also influence atmospheric chemistry, both at the Earth's surface and in the atmospheric boundary layer. The lack of accumulation of reactive oxygen species under the canopy found by the authors is consistent with this hypothesis and suggests that the damping effect of F. serratus may well have much wider consequences than expected.

References

[1] Jurik TW, Kliebenstein H (2000) Canopy Architecture, Light Extinction and Self-Shading of a Prairie Grass, Andropogon Gerardii. The American Midland Naturalist, 144, 51–65. http://www.jstor.org/stable/3083010

[2] Mitchley J, Willems JH (1995) Vertical canopy structure of Dutch chalk grasslands in relation to their management. Vegetatio, 117, 17–27. https://doi.org/10.1007/BF00033256

[3] Kane VR, Gillespie AR, McGaughey R, Lutz JA, Ceder K, Franklin JF (2008) Interpretation and topographic compensation of conifer canopy self-shadowing. Remote Sensing of Environment, 112, 3820–3832. https://doi.org/10.1016/j.rse.2008.06.001

[4] Makar PA, Staebler RM, Akingunola A, Zhang J, McLinden C, Kharol SK, Pabla B, Cheung P, Zheng Q (2017) The effects of forest canopy shading and turbulence on boundary layer ozone. Nature Communications, 8, 15243. https://doi.org/10.1038/ncomms15243

[5] Shigesada N, Okubo A (1981) Analysis of the self-shading effect on algal vertical distribution in natural waters. Journal of Mathematical Biology, 12, 311–326. https://doi.org/10.1007/BF00276919

[6] Barros MP, Pedersén M, Colepicolo P, Snoeijs P (2003) Self-shading protects phytoplankton communities against H2O2-induced oxidative damage. Aquatic Microbial Ecology, 30, 275–282. https://doi.org/10.3354/ame030275

[7] Ørberg SB, Krause-Jensen D, Mouritsen KN, Olesen B, Marbà N, Larsen MH, Blicher ME, Sejr MK (2018) Canopy-Forming Macroalgae Facilitate Recolonization of Sub-Arctic Intertidal Fauna and Reduce Temperature Extremes. Frontiers in Marine Science, 5. https://doi.org/10.3389/fmars.2018.00332

[8] Nelson H, Bramanti L (2020) From Trees to Octocorals: The Role of Self-Thinning and Shading in Underwater Animal Forests. In: Perspectives on the Marine Animal Forests of the World (eds Rossi S, Bramanti L), pp. 401–417. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-57054-5_12

[9] Jueterbock A, Kollias S, Smolina I, Fernandes JMO, Coyer JA, Olsen JL, Hoarau G (2014) Thermal stress resistance of the brown alga Fucus serratus along the North-Atlantic coast: Acclimatization potential to climate change. Marine Genomics, 13, 27–36. https://doi.org/10.1016/j.margen.2013.12.008

[10] Migné A, Duong G, Menu D, Davoult D, Gévaert F (2021) Dynamics of Fucus serratus thallus photosynthesis and community primary production during emersion across seasons: canopy dampening and biochemical acclimation. HAL, hal-03079617, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://hal.archives-ouvertes.fr/hal-03079617

[11] Lichtenberg M, Kühl M (2015) Pronounced gradients of light, photosynthesis and O2 consumption in the tissue of the brown alga Fucus serratus. New Phytologist, 207, 559–569. https://doi.org/10.1111/nph.13396

[12] Zaneveld JS (1937) The Littoral Zonation of Some Fucaceae in Relation to Desiccation. Journal of Ecology, 25, 431–468. https://doi.org/10.2307/2256204

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

In the article, "Are the more flexible great-tailed grackles also better at behavioral inhibition?", Logan and colleagues (2021) are setting an excellent standard for cognitive research on wild-caught animals. Using a decent sample (N=18) of wild-caught birds, they set out to test the ambiguous link between behavioral flexibility and behavioral inhibition, which is supported by some studies but rejected by others. Where this study is more thorough and therefore also more revealing than most extant research, the authors ran a battery of tests, examining both flexibility (reversal learning and solution switching) and inhibition (go/no go task; detour task; delay of gratification) through multiple different test series. They also -- somewhat accidentally -- performed their experiments and analyses with and without different criteria for correctness (85%, 100%). Their mistakes, assumptions and amendments of plans made during preregistration are clearly stated and this demonstrates the thought-process of the researchers very clearly.

Logan et al. (2021) show that inhibition in great-tailed grackles is a multi-faceted construct, and demonstrate that the traditional go/no go task likely tests a very different aspect of inhibition than the detour task, which was never linked to any of their flexibility measures. Their comprehensive Bayesian analyses held up the results of some of the frequentist statistics, indicating a consistent relationship between flexibility and inhibition, with more flexible individuals also showing better inhibition (in the go/no go task). This same model, combined with inconsistencies in the GLM analyses (depending on the inclusion or exclusion of an outlier), led them to recommend caution in the creation of arbitrary thresholds for "success" in any cognitive tasks. Their accidental longer-term data collection also hinted at patterns of behaviour that shorter-term data collection did not. Of course, researchers have to decide on success criteria in order to conduct experiments, but in the same way that frequentist statistics are acknowledged to have flaws, the setting of success criteria must be acknowledged as inherently arbitrary. Where possible, researchers could reveal novel, biologically salient patterns by continuing beyond the point where a convenient success criterion has been reached. This research also underscores that tests may not be examining the features we expected them to measure, and are highly sensitive to biological and ecological variation between species as well as individual variation within populations.

To me, this study is an excellent argument for pre-registration of research (registered as Logan et al. 2019 and accepted by Vogel 2019), as the authors did not end up cherry-picking only those results or methods that worked. The fact that some of the tests did not "work", but was still examined, added much value to the study. The current paper is a bit densely written because of the comprehensiveness of the research. Some editorial polishing would likely make for more elegant writing. However, the arguments are clear, the results novel, and the questions thoroughly examined. The results are important not only for cognitive research on birds, but are potentially valuable to any cognitive scientist. I recommend this article as excellent food for thought.

References

Logan CJ, McCune K, Johnson-Ulrich Z, Bergeron L, Seitz B, Blaisdell AP, Wascher CAF. (2019) Are the more flexible individuals also better at inhibition? http://corinalogan.com/Preregistrations/g_inhibition.html  In principle acceptance by PCI Ecology of the version on 6 Mar 2019

Logan CJ, McCune KB, MacPherson M, Johnson-Ulrich Z, Rowney C, Seitz B, Blaisdell AP, Deffner D, Wascher CAF (2021) Are the more flexible great-tailed grackles also better at behavioral inhibition? PsyArXiv, ver. 7 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.31234/osf.io/vpc39

Vogel E (2019) Adapting to a changing environment: advancing our understanding of the mechanisms that lead to behavioral flexibility. Peer Community in Ecology, 100016. https://doi.org/10.24072/pci.ecology.100016