Species Distribution Models (SDMs) are one of the most commonly used tools to predict where species are, where they may be in the future, and, at times, what are the variables driving this prediction. As such, applying an SDM to a dataset is akin to making a bet: that the known occurrence data are informative, that the resolution of predictors is adequate vis-à-vis the scale at which their impact is expressed, and that the model will adequately capture the shape of the relationships between predictors and predicted occurrence.

In this contribution, Lambert & Virgili (2023) perform a comprehensive assessment of different sources of complications to this process, using replicated simulations of two synthetic species. Their experimental process is interesting, in that both the data generation and the data analysis stick very close to what would happen in "real life". The use of synthetic species is particularly relevant to the assessment of SDM robustness, as they enable the design of species for which the shape of the relationship is given: in short, we know what the model should capture, and can evaluate the model performance against a ground truth that lacks uncertainty.

Any simulation study is limited by the assumptions established by the investigators; when it comes to spatial data, the "shape" of the landscape, both in terms of auto-correlation and in where the predictors are available. Lambert & Virgili (2023) nicely circumvent these issues by simulating synthetic species against the empirical distribution of predictors; in other words, the species are synthetic, but the environment for which the prediction is made is real. This is an important step forward when compared to the use of e.g. neutral landscapes (With 1997), which can have statistical properties that are not representative of natural landscapes (see e.g. Halley et al., 2004).

A striking point in the study by Lambert & Virgili (2023) is that they reveal a deep, indeed deeper than expected, stochasticity in SDMs; whether this is true in all models remains an open question, but does not invalidate their recommendation to the community: the interpretation of outcomes is a delicate exercise, especially because measures that inform on the goodness of the model fit do not capture the predictive quality of the model outputs. This preprint is both a call to more caution, and a call to more curiosity about the complex behavior of SDMs, while also providing a sensible template to perform future analyses of the potential issues with predictive models.

References

Halley, J. M., et al. (2004) “Uses and Abuses of Fractal Methodology in Ecology: Fractal Methodology in Ecology.” Ecology Letters, vol. 7, no. 3, pp. 254–71. https://doi.org/10.1111/j.1461-0248.2004.00568.x.

Lambert, Charlotte, and Auriane Virgili (2023). Data Stochasticity and Model Parametrisation Impact the Performance of Species Distribution Models: Insights from a Simulation Study. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.01.17.524386

With, Kimberly A. (1997) “The Application of Neutral Landscape Models in Conservation Biology. Aplicacion de Modelos de Paisaje Neutros En La Biologia de La Conservacion.” Conservation Biology, vol. 11, no. 5, pp. 1069–80. https://doi.org/10.1046/j.1523-1739.1997.96210.x.

Climate change presents a challenge to many life forms and the resulting loss of biodiversity will critically depend on the ability of organisms to timely respond to a changing environment. Shifts in ecological parameters have repeatedly been attributed to global warming, with the effectiveness of these responses varying among species [1, 2]. Organisms do not only have to face a global increase in mean temperatures, but a complex interplay with another crucial but largely understudied aspect of climate change: thermal fluctuations. Understanding the mechanisms underlying adaptation to thermal fluctuations is thus a timely and critical challenge.
Coral reefs are among the most threaten ecosystems in the context of current global changes [3]. Brener-Raffalli and colleagues [4] provided a very complete study digging into the physiological, symbiont-based and transcriptomic mechanisms underlying response of corals to temperature changes. They used an experimental approach, following the heat stress response of coral colonies from different species of the genus Pocillopora. While the symbiont community composition did not significantly change facing exposure to warmer temperatures, the authors provided evidence for transcriptomic changes especially linked to stress response genes that may underlie plastic responses to heat stress.
The authors furthermore investigated the thermal stress response of corals originating from two sites differing in their natural thermal regimes, and found that they differ in the extent and nature of plastic response, including the expression of gene regulation factors and the basal expression level of some genes. These two sites also differ in a variety of aspects, including the focal coral species, which precludes from concluding about the role of thermal regime adaptation into the differences observed. However, these results still highlight a very interesting and important direction deserving further investigation [5], and point out the importance of variability in thermal stress response among localities [6] that might potentially mediate global warming consequences on coral reefs.

References

[1] Parmesan, C., & Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421(6918), 37–42. doi: 10.1038/nature01286
[2] Menzel, A., Sparks, T. H., Estrella, N., Koch, E., Aasa, A., Ahas, R., … Zust, A. (2006). European phenological response to climate change matches the warming pattern. Global Change Biology, 12(10), 1969–1976. doi: 10.1111/j.1365-2486.2006.01193.x
[3] Bellwood, D. R., Hughes, T. P., Folke, C., & Nyström, M. (2004). Confronting the coral reef crisis. Nature, 429(6994), 827–833. doi: 10.1038/nature02691
[4] Brener-Raffalli, K., Vidal-Dupiol, J., Adjeroud, M., Rey, O., Romans, P., Bonhomme, F., Pratlong, M., Haguenauer, A., Pillot, R., Feuillassier, L., Claereboudt, M., Magalon, H., Gélin, P., Pontarotti, P., Aurelle, D., Mitta, G. and Toulza, E. (2019). Gene expression plasticity and frontloading promote thermotolerance in Pocillopora corals. BioRxiv, 398602, ver 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/398602
[5] Kenkel, Carly D., and Matz, M. V. (2017). Gene expression plasticity as a mechanism of coral adaptation to a variable environment. Nature Ecology and Evolution, 1(1), 0014. doi: 10.1038/s41559-016-0014
[6] Kenkel, C. D., Meyer, E., and Matz, M. V. (2013). Gene expression under chronic heat stress in populations of the mustard hill coral (Porites astreoides) from different thermal environments. Molecular Ecology, 22(16), 4322–4334. doi: 10.1111/mec.12390

Understanding processes at species’ range limits is of paramount importance in an era of global change. For example, the boreal kelp Laminaria digitata, which dominates low intertidal and shallow subtidal rocky reefs in northwestern Europe, is declining in the equatorward portion of its range [1]. In this contribution, Migné and colleagues [2] focus on L. digitata near its southern range limit on the coast of France and use a variety of techniques to paint a complete picture of the physiological responses of the kelp to environmental changes. Importantly, and in contrast to earlier work on the species which focused on subtidal individuals (e.g. [3]), Migné et al. [2] describe responses not only in the most physiologically stressful portion of the species’ range but also in the most stressful portion of its local environment: the upper portion of its zone on the shoreline, where it is periodically exposed to aerial conditions and associated thermal and desiccation stresses.
The authors show that whereas L. digitata possesses mechanisms to protect it from irradiance stress at low tide, these mechanisms are not sufficient to prevent damage to photosynthetic pathways (e.g., reduction in optimal quantum yields of photosystem II). This species experiences severe heat stress associated with mid-day low tides during the summer, and the cumulative damage associated with these stresses is likely associated with the range contraction that is currently underway. Given the important role that L. digitata plays as food and habitat for other organisms, its loss will have cascading impacts on community structure and ecosystem functioning. Understanding the mechanisms underlying these declines is essential to understanding the impacts of climate change on species, communities, and ecosystems.

References

[1] Raybaud, V., Beaugrand, G., Goberville, E., Delebecq, G., Destombe, C., Valero, M., Davoult, D., Morin, P. & Gevaert, F. (2013). Decline in kelp in west Europe and climate. PloS one, 8(6), e66044. doi: 10.1371/journal.pone.0066044
[2] Delebecq, G., Davoult, D., Menu, D., Janquin, M. A., Migné, A., Dauvin, J. C., & Gevaert, F. (2011). In situ photosynthetic performance of Laminaria digitata (Phaeophyceae) during spring tides in Northern Brittany. CBM-Cahiers de Biologie Marine, 52(4), 405. doi: 10.21411/CBM.A.C9EE91F
[3] Migné, A., Delebecq, G., Davoult, D., Spilmont, N., Menu, D., Janquin, M.-A., and Gevaert, F. (2019). Photosynthesis of Laminaria digitata during the immersion and emersion periods of spring tidal cycles during hot, sunny weather. Hal, 01827565, ver. 4 peer-reviewed and recommended by PCI Ecology. hal-01827565

As biodiversity has become a major global concern for a variety of stakeholders, and society in general, assessments of biodiversity trends at all spatial scales have flourished in the past decades. To assess trends, one needs data, and the more precise the data, the more precise the trend. Or, if precision is not perfect, uncertainty in the data must be acknowledged and accounted for. Such considerations have already been raised in ecology, most notably regarding the value of species distribution data to model the current and future distribution of species (Rocchini et al., 2011, Duputié et al., 2014, Tessarolo et al., 2021), leading to serious doubts regarding the value of public databases (Maldonado et al., 2015). And more recently similar issues have been raised regarding databases of species traits (Augustine et al., 2024), emphasizing the importance of good data practice and traceability.

Science is by nature a self-correcting human process, with many steps of the scientific activity prone to errors and misinterpretations. Collation of ecological data, sadly, is proof of this. Spurred by the astonishing results of Hallmann et al. (2017) regarding the decline of insect biomass, and to more precisely answer the question of biodiversity trends in insects and settle an ongoing debate (Cardinale et al., 2018), van Klink et al. (2020, 2021) established the InsectChange database. Several perceptive comments have already been made regarding the possible issues in the methods and interpretations of this study (Desquilbet et al., 2020, Jähnig et al., 2021, Duchenne et al., 2022). However, the biggest issue might have been finally unearthed by Gaume & Desquilbet (2024): with poorly curated data, the InsectChange database is unlikely to support most of the initial claims regarding insect biodiversity trends.

The compilation of errors and inconsistencies present in InsectChange and evinced by Gaume & Desquilbet (2024) is stunning to say the least, with a mix of field and experimental data combined without regard for experimental manipulation of environmental factors, non-standardised transformations of abundances, the use of non-insect taxa to compute insect trends, and inadequate geographical localizations of samplings. I strongly advise all colleagues interested in the study of biodiversity from global databases to consider the points raised by the authors, as it is quite likely that other databases might suffer from the same ailments as well. Reading this paper is also educating and humbling in its own way, since the publication of the original papers based on InsectChange seems to have proceeded without red flags from reviewers or editors. The need for publishing fast results that will make the next buzz, thus obeying the natural selection of bad science (Smaldino and McElreath, 2016), might be the systemic culprit. However, this might also be the opportunity ecology needs to consider the reviewing and curation of data as a crucial step of science quality assessment. To make final assessments, let us proceed with less haste.

References

Augustine, S. P., Bailey-Marren, I., Charton, K. T., Kiel, N. G. & Peyton, M. S. (2024) Improper data practices erode the quality of global ecological databases and impede the progress of ecological research. Global Change Biology, 30, e17116. https://doi.org/10.1111/gcb.17116

Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. (2018) Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biological Conservation, 219, 175-183. https://doi.org/10.1016/j.biocon.2017.12.021

Desquilbet, M., Gaume, L., Grippa, M., Céréghino, R., Humbert, J.-F., Bonmatin, J.-M., Cornillon, P.-A., Maes, D., Van Dyck, H. & Goulson, D. (2020) Comment on “Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances”. Science, 370, eabd8947. https://doi.org/10.1126/science.abd8947

Duchenne, F., Porcher, E., Mihoub, J.-B., Loïs, G. & Fontaine, C. (2022) Controversy over the decline of arthropods: a matter of temporal baseline? Peer Community Journal, 2. https://doi.org/10.24072/pcjournal.131

Duputié, A., Zimmermann, N. E. & Chuine, I. (2014) Where are the wild things? Why we need better data on species distribution. Global Ecology and Biogeography, 23, 457-467. https://doi.org/10.1111/geb.12118

Gaume, L. & Desquilbet, M. (2024) InsectChange: Comment. biorXiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2023.06.17.545310

Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., Stenmans, W., Müller, A., Sumser, H., Hörren, T., Goulson, D. & de Kroon, H. (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLOS ONE, 12, e0185809. https://doi.org/10.1371/journal.pone.0185809

Jähnig, S. C., Baranov, V., Altermatt, F., Cranston, P., Friedrichs-Manthey, M., Geist, J., He, F., Heino, J., Hering, D., Hölker, F., Jourdan, J., Kalinkat, G., Kiesel, J., Leese, F., Maasri, A., Monaghan, M. T., Schäfer, R. B., Tockner, K., Tonkin, J. D. & Domisch, S. (2021) Revisiting global trends in freshwater insect biodiversity. WIREs Water, 8, e1506. https://doi.org/10.1002/wat2.1506

Maldonado, C., Molina, C. I., Zizka, A., Persson, C., Taylor, C. M., Albán, J., Chilquillo, E., Rønsted, N. & Antonelli, A. (2015) Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? Global Ecology and Biogeography, 24, 973-984. https://doi.org/10.1111/geb.12326

Rocchini, D., Hortal, J., Lengyel, S., Lobo, J. M., Jiménez-Valverde, A., Ricotta, C., Bacaro, G. & Chiarucci, A. (2011) Accounting for uncertainty when mapping species distributions: The need for maps of ignorance. Progress in Physical Geography, 35, 211-226. https://doi.org/10.1177/0309133311399491

Smaldino, P. E. & McElreath, R. (2016) The natural selection of bad science. Royal Society Open Science, 3. https://doi.org/10.1098/rsos.160384

Tessarolo, G., Ladle, R. J., Lobo, J. M., Rangel, T. F. & Hortal, J. (2021) Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models. Ecography, 44, 1743-1755. https://doi.org/10.1111/ecog.05793

van Klink, R., Bowler, D. E., Comay, O., Driessen, M. M., Ernest, S. K. M., Gentile, A., Gilbert, F., Gongalsky, K. B., Owen, J., Pe'er, G., Pe'er, I., Resh, V. H., Rochlin, I., Schuch, S., Swengel, A. B., Swengel, S. R., Valone, T. J., Vermeulen, R., Wepprich, T., Wiedmann, J. L. & Chase, J. M. (2021) InsectChange: a global database of temporal changes in insect and arachnid assemblages. Ecology, 102, e03354. https://doi.org/10.1002/ecy.3354

van Klink, R., Bowler, D. E., Gongalsky, K. B., Swengel, A. B., Gentile, A. & Chase, J. M. (2020) Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science, 368, 417-420. https://doi.org/10.1126/science.aax9931

Behavioral flexibility, i.e. the “ability to adapt behavior to new circumstances through packaging information and making it available to other cognitive processes” (Logan et al. 2023), appears as one of the crucial elements of responses of animal species to changing environments. Behavioral flexibility can change within the life of individuals, depending on their experience on the degree of variability and predictability of their surrounding environment. But little is known on the cognitive processes involved in these temporal changes in behavioral flexibility within individuals.

This is what Lukas et al. (2024) investigated very thoroughly, using the framework of serial reversal learning experiments on great-tailed grackles to study different aspects of the question. Behavioral flexibility as involved in serial reversal learning experiments was previously modeled as being made of two primary parameters: the rate of updating associations, phi (i.e. how fast individuals learn the associations between a cue and its associated reward or danger); and the sensitivity to the learned associations, lambda (i.e. how strong do individuals make their choices based on the associations they learned). 

Lukas et al. (2024)* used a Bayesian reinforcement model to infer phi and lambda in individuals going through serial reversal learning experiments, to understand which of these two parameters explains most of the variation in grackle performance in serial reversal learning, how correlated they are, how they can change along time depending on an individual’s experience, how variable they can be among individuals, and whether they can predict performance in other contexts. But beforehand, the authors used an individual-based model to assess the ability of the Bayesian reinforcement model to correctly assess phi and lambda in their experimental design. They also used the Bayesian model to infer the range of values of phi and lambda an individual needs to exhibit to reduce errors in the serial reversal learning experiment.

Among other results, this study shows that in a context of rapidly changing but strongly reliable cues, the variation in the success of grackles is more associated with the rate of updating associations (phi) than the sensitivity to learned associations (lambda). Besides, phi increased within individuals along the serial reversal learning experiment, while lambda only slightly decreased. However, it is very interesting to note that different approaches could be adopted by different individuals through the training, leading them eventually to the same final performance: slightly different combinations of changes in lambda and phi lead to different behaviours but compensate each other in the end in the final success rate. 
This study provides exciting insights into the cognitive processes involved in how changes in behavioral flexibility of individuals can happen in this type of serial learning experiments. But it also offers interesting openings to understand the mechanisms by which behavioral flexibility can change in the wild, helping individuals to cope with rapidly changing environments.
 
* Lukas et al. (2024) 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). 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

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  

Dieter Lukas, Kelsey B. McCune, Aaron P. Blaisdell, Zoe Johnson-Ulrich, Maggie MacPherson, Benjamin M. Seitz, Augustus Sevchik, Corina J. Logan (2024) Bayesian reinforcement learning models reveal how great-tailed grackles improve their behavioral flexibility in serial reversal learning experiments. ecoevoRxiv, ver.4 peer-reviewed and recommended by Peer Community in Ecology https://doi.org/10.32942/osf.io/4ycps