Assessing the reliability of the methods we use in actually measuring the intended trait should be one of our first priorities when designing a study – especially when the trait in question is not directly observable and is measured through a proxy. 

This is the case for cognitive traits, which are often quantified through measures of behavioral performance. Behavioral flexibility is of particular interest in the context of great environmental changes that a lot of populations have to experiment. This type of behavioral performance is often measured through reversal learning experiments (Bond 2007). In these experiments, individuals first learn a preference, for example for an object of a certain type of form or color, associated with a reward such as food. The characteristics of the rewarded object then change, and the individuals hence have to learn these new characteristics (to get the reward). The time needed by the individual to make this change in preference has been considered a measure of behavioral flexibility.

Although reversal learning experiments have been widely used, their construct validity to assess behavioral flexibility has not been thoroughly tested. This was the aim of McCune and collaborators' (2023) study, through the test of the repeatability of individual performance within and across contexts of reversal learning, in the great-tailed grackle.

This manuscript presents a post-study of the preregistered study* (Logan et al. 2019) that was peer-reviewed and received an In Principle Recommendation for PCI Ecology (Coulon 2019; the initial preregistration was split into 3 post-studies).
Using 34 great-tailed grackles wild-caught in Tempe, Arizona (USA), the authors tested in aviaries 2 hypotheses:

• First, that the behavioral flexibility measured by reversal learning is repeatable within individuals across sessions of the same experiment;
• Second, that there is repeatability of the measured behavioral flexibility (within individuals) across different types of reversal learning experiments (context).

The first hypothesis was tested by measuring the repeatability of the time needed by individuals to switch color preference in a color reversal learning task (colored tubes), over serial sessions of this task. The second one was tested by measuring the time needed by individuals to switch solutions, within 3 different contexts: (1) colored tubes, (2) plastic and (3) wooden multi-access boxes involving several ways to access food.

Despite limited sample sizes, the results of these experiments suggest that there is both temporal and contextual repeatability of behavioral flexibility performance of great-tailed grackles, as measured by reversal learning experiments.

Those results are a first indication of the construct validity of reversal learning experiments to assess behavioral flexibility. As highlighted by McCune and collaborators, it is now necessary to assess the discriminant validity of these experiments, i.e. checking that a different performance is obtained with tasks (experiments) that are supposed to measure different cognitive abilities.
 
* A pre-registered study is a study in which context, aims, hypotheses and methodologies have been written down as an empirical paper, peer-reviewed and pre-accepted before research is undertaken. Pre-registrations are intended to reduce publication bias and reporting bias.
 
REFERENCES
 
Bond, A. B., Kamil, A. C., & Balda, R. P. (2007). Serial reversal learning and the evolution of behavioral
flexibility in three species of north american corvids (Gymnorhinus cyanocephalus, Nucifraga columbiana,
Aphelocoma californica). Journal of Comparative Psychology, 121 (4), 372. https://doi.org/10.1037/0735-7036.121.4.372

Coulon, A. (2019) Can context changes improve behavioral flexibility? Towards a better understanding of species adaptability to environmental changes. Peer Community in Ecology, 100019. https://doi.org/10.24072/pci.ecology.100019

Logan, CJ, Lukas D, Bergeron L, Folsom M, & McCune, K. (2019).  Is behavioral flexibility related to foraging and social behavior in a rapidly expanding species? In Principle Acceptance by PCI Ecology of the Version on 6 Aug 2019. http://corinalogan.com/Preregistrations/g_flexmanip.html

McCune KB, Blaisdell AP, Johnson-Ulrich Z, Lukas D, MacPherson M, Seitz BM, Sevchik A, Logan CJ (2023) Using repeatability of performance within and across contexts to validate measures of behavioral flexibility. EcoEvoRxiv, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/X2R59K

​​Research about the effects of climate change on ecological communities has been abundant in the last decades. In particular, studies about the effects of climate change on mountain ecosystems have been key for understanding and communicating the consequences of this global phenomenon. Alpine regions show higher increases in warming in comparison to low-altitude ecosystems and this trend is likely to continue. This warming has caused reduced snowfall and/or changes in the duration of snow cover. For example, Notarnicola (2020) reported that 78% of the world’s mountain areas have experienced a snow cover decline since 2000. In the same vein, snow cover has decreased by 10% compared with snow coverage in the late 1960s (Walther et al., 2002) and snow cover duration has decreased at a rate of 5 days/decade (Choi et al., 2010). These changes have impacted the dynamics of high-altitude plant and animal populations. Some impacts are changes in the hibernation of animals, the length of the growing season for plants and the soil microbial composition (Chávez et al. 2021).

Lenzi et al. (2023), give us an excellent study using long-term data on alpine amphibian populations. Authors show how climate change has impacted the reproductive phenology of Bufo bufo, especially the breeding season starts 30 days earlier than ~40 years ago. This earlier breeding is associated with the increasing temperatures and reduced snow cover in these alpine ecosystems. However, these changes did not occur in a linear trend but a marked acceleration was observed until mid-1990s with a later stabilization. Authors associated these nonlinear changes with complex interactions between the global trend of seasonal temperatures and site-specific conditions. 

Beyond the earlier breeding season, changes in phenology can have important impacts on the long-term viability of alpine populations. Complex interactions could involve positive and negative effects like harder environmental conditions for propagules, faster development of juveniles, or changes in predation pressure. This study opens new research opportunities and questions like the urgent assessment of the global impact of climate change on animal fitness. This study provides key information for the conservation of these populations.

References

Chávez RO, Briceño VF, Lastra JA, Harris-Pascal D, Estay SA (2021) Snow Cover and Snow Persistence Changes in the Mocho-Choshuenco Volcano (Southern Chile) Derived From 35 Years of Landsat Satellite Images. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.643850

Choi G, Robinson DA, Kang S (2010) Changing Northern Hemisphere Snow Seasons. Journal of Climate, 23, 5305–5310. https://doi.org/10.1175/2010JCLI3644.1

Lenzi O, Grossenbacher K, Zumbach S, Lüscher B, Althaus S, Schmocker D, Recher H, Thoma M, Ozgul A, Schmidt BR (2022) Four decades of phenology in an alpine amphibian: trends, stasis, and climatic drivers.bioRxiv, 2022.08.16.503739, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.08.16.503739

Notarnicola C (2020) Hotspots of snow cover changes in global mountain regions over 2000–2018. Remote Sensing of Environment, 243, 111781. https://doi.org/10.1016/j.rse.2020.111781

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

The study of animal movement is essential for understanding their behaviour and how ecological or global changes impact their routines [1]. Recent technological advancements have improved the collection of movement data [2], but limited statistical tools have hindered the analysis of such data [3–5]. Animal movement is influenced not only by environmental factors but also by internal knowledge and memory, which are challenging to observe directly [6,7]. Routine movement behaviours and the incorporation of memory into models remain understudied.

Researchers have developed ‘MoveFormer’ [8], a deep learning-based model that predicts future movements based on past context, addressing these challenges and offering insights into the importance of different context lengths and information types. The model has been applied to a dataset of over 1,550 trajectories from various species, and the authors have made the MoveFormer source code available for further research.

Inspired by the step-selection framework and efforts to quantify uncertainty in movement predictions, MoveFormer leverages deep learning, specifically the Transformer architecture, to encode trajectories and understand how past movements influence current and future ones – a critical question in movement ecology. The results indicate that integrating information from a few days to two or three weeks before the movement enhances predictions. The model also accounts for environmental predictors and offers insights into the factors influencing animal movements.

Its potential impact extends to conservation, comparative analyses, and the generalisation of uncertainty-handling methods beyond ecology, with open-source code fostering collaboration and innovation in various scientific domains. Indeed, this method could be applied to analyse other kinds of movements, such as arm movements during tool use [9], pen movements, or eye movements during drawing [10], to better understand anticipation in actions and their intentionality.

References

1.           Méndez, V.; Campos, D.; Bartumeus, F. Stochastic Foundations in Movement Ecology: Anomalous Diffusion, Front Propagation and Random Searches; Springer Series in Synergetics; Springer: Berlin, Heidelberg, 2014; ISBN 978-3-642-39009-8.
https://doi.org/10.1007/978-3-642-39010-4
 
2.           Fehlmann, G.; King, A.J. Bio-Logging. Curr. Biol. 2016, 26, R830-R831.
https://doi.org/10.1016/j.cub.2016.05.033
 
3.           Jacoby, D.M.; Freeman, R. Emerging Network-Based Tools in Movement Ecology. Trends Ecol. Evol. 2016, 31, 301-314.
https://doi.org/10.1016/j.tree.2016.01.011
 
4.           Michelot, T.; Langrock, R.; Patterson, T.A. moveHMM: An R Package for the Statistical Modelling of Animal Movement Data Using Hidden Markov Models. Methods Ecol. Evol. 2016, 7, 1308-1315.
https://doi.org/10.1111/2041-210X.12578
 
5.           Wang, G. Machine Learning for Inferring Animal Behavior from Location and Movement Data. Ecol. Inform. 2019, 49, 69-76.
https://doi.org/10.1016/j.ecoinf.2018.12.002
 
6.           Noser, R.; Byrne, R.W. Change Point Analysis of Travel Routes Reveals Novel Insights into Foraging Strategies and Cognitive Maps of Wild Baboons. Am. J. Primatol. 2014, 76, 399-409.
https://doi.org/10.1002/ajp.22181
 
7.           Fagan, W.F.; Lewis, M.A.; Auger‐Méthé, M.; Avgar, T.; Benhamou, S.; Breed, G.; LaDage, L.; Schlägel, U.E.; Tang, W.; Papastamatiou, Y.P. Spatial Memory and Animal Movement. Ecol. Lett. 2013, 16, 1316-1329.
https://doi.org/10.1111/ele.12165
 
8.           Cífka, O.; Chamaillé-Jammes, S.; Liutkus, A. MoveFormer: A Transformer-Based Model for Step-Selection Animal Movement Modelling. bioRxiv 2023, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.
https://doi.org/10.1101/2023.03.05.531080
 
9.           Ardoin, T.; Sueur, C. Automatic Identification of Stone-Handling Behaviour in Japanese Macaques Using LabGym Artificial Intelligence. 2023, https://doi.org/10.13140/RG.2.2.30465.02402
 
10.         Martinet, L.; Pelé, M. Drawing in Nonhuman Primates: What We Know and What Remains to Be Investigated. J. Comp. Psychol. Wash. DC 1983 2021, 135, 176-184, doi:10.1037/com0000251.
https://doi.org/10.1037/com0000251

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