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

Citizen science is becoming an important piece for the acquisition of scientific knowledge in the fields of natural sciences, and particularly in the inventory and monitoring of biodiversity (McKinley et al. 2017). The information generated with the collaboration of citizens has an evident importance in conservation, by providing information on the state of populations and habitats, helping in mitigation and restoration actions, and very importantly contributing to involve society in conservation (Brown and Williams 2019). An obvious advantage of these initiatives is the ability to mobilize human resources on a large territorial scale and in the medium term, which would otherwise be difficult to finance. The resulting increasing information then can be processed with advanced computational techniques (Hochachka et al 2012; Kelling et al. 2015), thus improving our interpretation of the distribution of species. Specifically, the ability to obtain information on a large territorial scale can be integrated into studies based on Species Distribution Models SDMs. One of the common problems with SDMs is that they often work from species occurrences that have been opportunistically recorded, either by professionals or amateurs. A great challenge for data obtained from non-professional citizens, however, remains to ensure its standardization and quality (Kosmala et al. 2016). This requires a clear and effective design, solid volunteer training, and a high level of coordination that turns out to be complex (Brown and Williams 2019). Finally, it is essential to perform a quality validation following scientifically recognized standards, since they are often conditioned by errors and biases in obtaining information (Bird et al. 2014). There are two basic approaches to obtain the necessary data for this validation: getting it from an external source (external validation), or allocating a part of the database itself (internal validation or cross-validation) to this function.
Matutini et al. (2020) in his work 'How citizen science could improve Species Distribution Models and their independent assessment' shows a novel application of the data generated by a citizen science initiative ('Un Dragon dans mon Jardin') by providing an external source for the validation of SDMs, as a tool to construct habitat suitability maps for nine species of amphibians in western France. Importantly, 'Un Dragon dans mon Jardin' contains standardized presence-absence data, the approximation recognized as the most robust (Guisan, et al. 2017). The SDMs to be validated, in turn, were based on opportunistic information obtained by citizens and professionals. The result shows the usefulness of this external data source by minimizing the overestimation of model accuracy that is obtained with cross-validation with the internal evaluation dataset. It also shows the importance of properly filtering the information obtained by citizens by determining the threshold of sampling effort.
The destiny of citizen science is to be integrated into the complex world of science. Supported by the increasing level of the formation of society, it is becoming a fundamental piece in the scientific system dedicated to the study of biodiversity and its conservation. After funding for scientists specialized in the recognition of biodiversity has been cut back, we are seeing a transformation of the activity of these scientists towards the design, coordination, training and verification of programs for the acquisition of field information obtained by citizens. A main goal is that a substantial part of this information will eventually get integrated into the scientific system, and rigorous verification process a fundamental element for such purpose, as shown by Matutini et al. (2020) work.

References

[1] Bird TJ et al. (2014) Statistical solutions for error and bias in global citizen science datasets. Biological Conservation 173: 144-154. doi: 10.1016/j.biocon.2013.07.037
[2] Brown ED and Williams BK (2019) The potential for citizen science to produce reliable and useful information in ecology. Conservation Biology 33: 561-569. doi: 10.1111/cobi.13223
[3] Guisan A, Thuiller W and Zimmermann N E (2017) Habitat Suitability and Distribution Models: With Applications in R. The University of Chicago Press. doi: 10.1017/9781139028271
[4] Hochachka WM, Fink D, Hutchinson RA, Sheldon D, Wong WK and Kelling S (2012) Data-intensive science applied to broad-scale citizen science. Trens Ecol Evol 27: 130-137. doi: 10.1016/j.tree.2011.11.006
[5] Kelling S, Fink D, La Sorte FA, Johnston A, Bruns NE and Hochachka WM (2015) Taking a ‘Big Data’ approach to data quality in a citizen science project. Ambio 44(Supple. 4):S601-S611. doi: 10.1007/s13280-015-0710-4
[6] Kosmala M, Wiggins A, Swanson A and Simmons B (2016) Assessing data quality in citizen science. Front Ecol Environ 14: 551–560. doi: 10.1002/fee.1436
[7] Matutini F, Baudry J, Pain G, Sineau M and Pithon J (2020) How citizen science could improve Species Distribution Models and their independent assessment. bioRxiv, 2020.06.02.129536, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/2020.06.02.129536
[8] McKinley DC et al. (2017) Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation 208:15-28. doi: 10.1016/j.biocon.2016.05.015

Most species are hard to observe, and different methods are required to estimate demographic parameters such as the number of young individuals produced (one measure of breeding success) and survival. In the former case, and in particular for birds of prey, it often relies upon direct observations of breeding pairs on their nests. Two problems can then occur, that some young are missed and therefore the breeding success is underestimated (“false negatives”), but it is also possible that because for example of the nest structure or vegetation surrounding the nest, more young birds than in fact are present are counted (“false positives”). Sollmann et al. (2024) address this problem by using data where the truth is known as each nest was also accessed after climbing the tree, and a hierarchical model accounting for both undercounts and overcounts. Finally, they assess the impact of this correction on projected population size using simulations.

This paper is a solid contribution to the panoply of methods and models that are available for monitoring populations, and has potential applications for many species for which both false positives and false negatives can be a problem. The results on the projected population sizes – showing that for growing populations correcting for bias can lead to large differences in population sizes after a few decades – may seem counterintuitive as population growth rate of long-lived species such as birds of prey is not very sensitive to a change in breeding success (as compared to adult survival). However, one should just be reminded that a small difference in population growth rate may translate to a large difference after many years – for example a growth rate of 1.05 after 50 years mean than population size is multiplied by 11.5, whereas a growth of 1.03 after 50 years mean a multiplication by 4.4, more than twice less individuals. Small differences may matter a lot if they are sustained, and a key aspect of management is to ensure that they are. Of course, management actions having an impact on survival may be more effective, but they might be harder to achieve than for example ensuring that birds of prey breed successfully.

References

Sollmann Rahel, Adenot Nathalie, Spakovszky Péter, Windt Jendrik, Mattsson Brady J. 2024. Accounting for observation biases associated with counts of young when estimating fecundity. bioRxiv, v. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.12.01.569571