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24 Mar 2023
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Rapid literature mapping on the recent use of machine learning for wildlife imagery

Review of machine learning uses for the analysis of images on wildlife

Recommended by based on reviews by Falk Huettmann and 1 anonymous reviewer

In the field of ecology, there is a growing interest in machine (including deep) learning for processing and automatizing repetitive analyses on large amounts of images collected from camera traps, drones and smartphones, among others. These analyses include species or individual recognition and classification, counting or tracking individuals, detecting and classifying behavior. By saving countless times of manual work and tapping into massive amounts of data that keep accumulating with technological advances, machine learning is becoming an essential tool for ecologists. We refer to recent papers for more details on machine learning for ecology and evolution (Besson et al. 2022, Borowiec et al. 2022, Christin et al. 2019, Goodwin et al. 2022, Lamba et al. 2019, Nazir & Kaleem 2021, Perry et al. 2022, Picher & Hartig 2023, Tuia et al. 2022, Wäldchen & Mäder 2018).

In their paper, Nakagawa et al. (2023) conducted a systematic review of the literature on machine learning for wildlife imagery. Interestingly, the authors used a method unfamiliar to ecologists but well-established in medicine called rapid review, which has the advantage of being quickly completed compared to a fully comprehensive systematic review while being representative (Lagisz et al., 2022). Through a rigorous examination of more than 200 articles, the authors identified trends and gaps, and provided suggestions for future work. Listing all their findings would be counterproductive (you’d better read the paper), and I will focus on a few results that I have found striking, fully assuming a biased reading of the paper. First, Nakagawa et al. (2023) found that most articles used neural networks to analyze images, in general through collaboration with computer scientists. A challenge here is probably to think of teaching computer vision to the generations of ecologists to come (Cole et al. 2023). Second, the images were dominantly collected from camera traps, with an increase in the use of aerial images from drones/aircrafts that raise specific challenges. Third, the species concerned were mostly mammals and birds, suggesting that future applications should aim to mitigate this taxonomic bias, by including, e.g., invertebrate species. Fourth, most papers were written by authors affiliated with three countries (Australia, China, and the USA) while India and African countries provided lots of images, likely an example of scientific colonialism which should be tackled by e.g., capacity building and the involvement of local collaborators. Last, few studies shared their code and data, which obviously impedes reproducibility. Hopefully, with the journals’ policy of mandatory sharing of codes and data, this trend will be reversed. 

REFERENCES

Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF (2022) Towards the fully automated monitoring of ecological communities. Ecology Letters, 25, 2753–2775. https://doi.org/10.1111/ele.14123

Borowiec ML, Dikow RB, Frandsen PB, McKeeken A, Valentini G, White AE (2022) Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13, 1640–1660. https://doi.org/10.1111/2041-210X.13901

Christin S, Hervet É, Lecomte N (2019) Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632–1644. https://doi.org/10.1111/2041-210X.13256

Cole E, Stathatos S, Lütjens B, Sharma T, Kay J, Parham J, Kellenberger B, Beery S (2023) Teaching Computer Vision for Ecology. https://doi.org/10.48550/arXiv.2301.02211

Goodwin M, Halvorsen KT, Jiao L, Knausgård KM, Martin AH, Moyano M, Oomen RA, Rasmussen JH, Sørdalen TK, Thorbjørnsen SH (2022) Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook†. ICES Journal of Marine Science, 79, 319–336. https://doi.org/10.1093/icesjms/fsab255

Lagisz M, Vasilakopoulou K, Bridge C, Santamouris M, Nakagawa S (2022) Rapid systematic reviews for synthesizing research on built environment. Environmental Development, 43, 100730. https://doi.org/10.1016/j.envdev.2022.100730

Lamba A, Cassey P, Segaran RR, Koh LP (2019) Deep learning for environmental conservation. Current Biology, 29, R977–R982. https://doi.org/10.1016/j.cub.2019.08.016

Nakagawa S, Lagisz M, Francis R, Tam J, Li X, Elphinstone A, Jordan N, O’Brien J, Pitcher B, Sluys MV, Sowmya A, Kingsford R (2023) Rapid literature mapping on the recent use of machine learning for wildlife imagery. EcoEvoRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.32942/X2H59D

Nazir S, Kaleem M (2021) Advances in image acquisition and processing technologies transforming animal ecological studies. Ecological Informatics, 61, 101212. https://doi.org/10.1016/j.ecoinf.2021.101212

Perry GLW, Seidl R, Bellvé AM, Rammer W (2022) An Outlook for Deep Learning in Ecosystem Science. Ecosystems, 25, 1700–1718. https://doi.org/10.1007/s10021-022-00789-y

Pichler M, Hartig F Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, n/a. https://doi.org/10.1111/2041-210X.14061

Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T (2022) Perspectives in machine learning for wildlife conservation. Nature Communications, 13, 792. https://doi.org/10.1038/s41467-022-27980-y

Wäldchen J, Mäder P (2018) Machine learning for image-based species identification. Methods in Ecology and Evolution, 9, 2216–2225. https://doi.org/10.1111/2041-210X.13075

Rapid literature mapping on the recent use of machine learning for wildlife imageryShinichi Nakagawa, Malgorzata Lagisz, Roxane Francis, Jessica Tam, Xun Li, Andrew Elphinstone, Neil R. Jordan, Justine K. O’Brien, Benjamin J. Pitcher, Monique Van Sluys, Arcot Sowmya, Richard T. Kingsford<p>1. Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, classify animals and their behaviours. Yet, we currently have a very few systematic liter...Behaviour & Ethology, Conservation biologyOlivier GimenezAnonymous2022-10-31 22:05:46 View
16 Sep 2019
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Blood, sweat and tears: a review of non-invasive DNA sampling

Words matter: extensive misapplication of "non-invasive" in describing DNA sampling methods, and proposed clarifying terms

Recommended by based on reviews by 2 anonymous reviewers

The ability to successfully sequence trace quantities of environmental DNA (eDNA) has provided unprecedented opportunities to use genetic analyses to elucidate animal ecology, behavior, and population structure without affecting the behavior, fitness, or welfare of the animal sampled. Hair associated with an animal track in the snow, the shed exoskeleton of an insect, or a swab of animal scat are all examples of non-invasive methods to collect eDNA. Despite the seemingly uncomplicated definition of "non-invasive" as proposed by Taberlet et al. [1], Lefort et al. [2] highlight that its appropriate application to sampling methods in practice is not so straightforward. For example, collecting scat left behind on the forest floor by a mammal could be invasive if feces is used by that species to mark territorial boundaries. Other collection strategies such as baited DNA traps to collect hair, capturing and handling an individual to swab or stimulate emission of a body fluid, or removal of a presumed non essential body part like a feather, fish scale, or even a leg from an insect are often described as "non-invasive" sampling methods. However, such methods cannot be considered truly non-invasive. At a minimum, attracting or capturing and handling an animal to obtain a DNA sample interrupts its normal behavioral routine, but additionally can cause both acute and long-lasting physiological and behavioral stress responses and other effects. Even invertebrates exhibit long-term hypersensitization after an injury, which manifests as heightened vigilance and enhanced escape responses [3-5].
Through an extensive analysis of 380 papers published from 2013-2018, Lefort et al. [2] document the widespread misapplication of the term "non-invasive" to methods used to sample DNA. An astonishing 58% of these papers employed the term incorrectly. A big part of the problem is that "non-invasive" is usually used by authors in the medical or veterinary sense of not breaking the skin or entering the body [6], rather than in the broader, ecological sense of Taberlet et al. [1]. The authors argue that correct use of the term matters, because it may lead naive readers – one can imagine students, policy makers, and the general public – to incorrectly assume a particular method is safe to use in a situation where disturbing the animal could affect experimental results or raise animal welfare concerns. Such assumptions can affect experimental design, as well as interpretations of one's own or others' data.
The importance of the Lefort et al. [2] paper lies in part on the authors' call for the research community to be much more careful when applying the term "non-invasive" to methods of DNA sampling. This call cannot be shrugged off as a minor problem in a few papers – as their literature review demonstrates, "non-invasive" is being applied incorrectly more often than not. The authors recognize that not all DNA sampling must be non-invasive to be useful or ethical. Examples include taking samples for DNA extraction from museum specimens, or opportunistically from carcasses of animals hunted either legally or seized by authorities from poachers. In many cases, there may be no viable non-invasive method to obtain DNA, but a researcher strives to collect samples using methods that, although they may involve taking a sample directly from the animal's body, do not disrupt, or only slightly disrupt behavior, fitness, or welfare of the animal. Thus, the other important contribution by Lefort et al. [2] is to propose the terms "non-disruptive" and "minimally-disruptive" to describe such sampling methods, which are not strictly non-invasive. While gray areas undoubtedly remain, as acknowledged by the authors, answering the call for correct use of "non-invasive" and applying the proposed new terms for certain types of invasive sampling with a focus on level of disruption, will go a long way in limiting misconceptions and misinterpretations caused by the current confusion in terminology.

References

[1] Taberlet P., Waits L. P. and Luikart G. 1999. Noninvasive genetic sampling: look before you leap. Trends Ecol. Evol. 14: 323-327. doi: 10.1016/S0169-5347(99)01637-7
[2] Lefort M.-C., Cruickshank R. H., Descovich K., Adams N. J., Barun A., Emami-Khoyi A., Ridden J., Smith V. R., Sprague R., Waterhouse B. R. and Boyer S. 2019. Blood, sweat and tears: a review of non-invasive DNA sampling. bioRxiv, 385120, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/385120
[3] Khuong T. M., Wang Q.-P., Manion J., Oyston L. J., Lau M.-T., Towler H., Lin Y. Q. and Neely G. G. 2019. Nerve injury drives a heightened state of vigilance and neuropathic sensitization in Drosophila. Science Advances 5: eaaw4099. doi: 10.1126/sciadv.aaw4099
[4] Crook, R. J., Hanlon, R. T. and Walters, E. T. 2013. Squid have nociceptors that display widespread long-term sensitization and spontaneous activity after bodily injury. Journal of Neuroscience, 33(24), 10021-10026. doi: 10.1523/JNEUROSCI.0646-13.2013
[5] Walters E. T. 2018. Nociceptive biology of molluscs and arthropods: evolutionary clues about functions and mechanisms potentially related to pain. Frontiers in Physiololgy 9: doi: 10.3389/fphys.2018.01049
[6] Garshelis, D. L. 2006. On the allure of noninvasive genetic sampling-putting a face to the name. Ursus 17: 109-123. doi: 10.2192/1537-6176(2006)17[109:OTAONG]2.0.CO;2

Blood, sweat and tears: a review of non-invasive DNA samplingMarie-Caroline Lefort, Robert H Cruickshank, Kris Descovich, Nigel J Adams, Arijana Barun, Arsalan Emami-Khoyi, Johnaton Ridden, Victoria R Smith, Rowan Sprague, Benjamin Waterhouse, Stephane Boyer<p>The use of DNA data is ubiquitous across animal sciences. DNA may be obtained from an organism for a myriad of reasons including identification and distinction between cryptic species, sex identification, comparisons of different morphocryptic ...Behaviour & Ethology, Conservation biology, Molecular ecology, ZoologyThomas Wilson Sappington2018-11-30 13:33:31 View
09 Nov 2023
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Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets

Marks lost in action, biased estimations

Recommended by ORCID_LOGO based on reviews by Olivier Gimenez, Devin Johnson and 1 anonymous reviewer

Capture-Mark-Recapture (CMR) data are commonly used to estimate ecological variables such as abundance, survival probability, or transition rates from one state to another (e.g. from juvenile to adult, or migration from one site to another). Many studies have shown how estimations can be affected by neglecting one aspect of the population under study (e.g. the heterogeneity in survival between individuals) or one limit of the methodology itself (e.g. the fact that observers might not detect an individual although it is still alive). Strikingly, very few studies have yet assessed the robustness of one fundamental assumption of all CMR-based inferences: marks are supposed definitive and immutable. If they are not, how are estimations affected? Addressing this issue is the main goal of the paper by Touzalin et al. (2023), and they did a very nice work. But, because the answer is not that simple, it also calls for further investigations.

When and why would mark loss bias estimation? In at least two situations. First, when estimating survival rates: if an individual loses its mark, it will be considered as dead, hence death rates will be overestimated. Second, more subtly, when estimating transition rates: if one individual loses its mark at the specific moment where its state changes, then a transition will be missed in data. The history of the marked individual would then be split into two independent CMR sequences as if there were two different individuals, including one which died.

Touzalin et al. (2023) thoroughly studied these two situations by estimating ecological parameters on 1) well-thought simulated datasets, that cover a large range of possible situations inspired from a nice compilation of hundreds of estimations from fish and bats studies, and 2) on their own bats dataset, for which they had various sources of information about mark losses, i.e. different mark types on the same individuals, including mark based on genotypes, and marks found on the soil in the place where bats lived. Their main findings from the simulated datasets are that there is a general trend for underestimation of survival and transition rates if mark loss is not accounting for in the model, as it would be intuitively expected. However, they also showed from the bats dataset that biases do not show any obvious general trend, suggesting complex interactions between different ecological processes and/or with the estimation procedure itself.

The results by Touzalin et al. (2023) strongly suggest that mark loss should systematically be included in models estimating parameters from CMR data. In addition to adapt the inferential models, the authors also recommend considering either a double marking, or even a single but ‘permanent’ mark such as one based on the genotypes. However, the potential gain of a double marking or of the use of genotypes is still to be evaluated both in theory and practice, and it seems to be not that obvious at first sight. First because double marking can be costly for experimenters but also for the marked animals, especially as several studies showed that marks can significantly affect survival or recapture rates. Second because multiple sources of errors can affect genotyping, which would result in wrong individual assignations especially in populations with low genetic diversity or high inbreeding, or no individual assignation at all, which would increase the occurrence of missing data in CMR datasets. Touzalin et al. (2023) supposed in their paper that there were no genotyping errors, but one can doubt it to be true in most situations. They have now important and interesting other issues to address.

References

Frédéric Touzalin, Eric J. Petit, Emmanuelle Cam, Claire Stagier, Emma C. Teeling, Sébastien J. Puechmaille (2023) Mark loss can strongly bias demographic rates in multi-state models: a case study with simulated and empirical datasets. BioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.03.25.485763

Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasetsFrédéric Touzalin, Eric J. Petit, Emmanuelle Cam, Claire Stagier, Emma C. Teeling, Sébastien J. Puechmaille<p style="text-align: justify;">1. The development of methods for individual identification in wild species and the refinement of Capture-Mark-Recapture (CMR) models over the past few decades have greatly improved the assessment of population demo...Conservation biology, DemographySylvain Billiard2022-04-12 18:49:34 View
12 May 2022
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Riparian forest restoration as sources of biodiversity and ecosystem functions in anthropogenic landscapes

Complex but positive diversity - ecosystem functioning relationships in Riparian tropical forests

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Many ecological drivers can impact ecosystem functionality and multifunctionality, with the latter describing the joint impact of different functions on ecosystem performance and services. It is now generally accepted that taxonomically richer ecosystems are better able to sustain high aggregate functionality measures, like energy transfer, productivity or carbon storage (Buzhdygan 2020, Naeem et al. 2009), and different ecosystem services (Marselle et al. 2021) than those that are less rich. Antonini et al. (2022) analysed an impressive dataset on animal and plant richness of tropical riparian forests and abundances, together with data on key soil parameters. Their work highlights the importance of biodiversity on functioning, while accounting for a manifold of potentially covarying drivers. Although the key result might not come as a surprise, it is a useful contribution to the diversity - ecosystem functioning topic, because it is underpinned with data from tropical habitats. To date, most analyses have focused on temperate habitats, using data often obtained from controlled experiments. 

The paper also highlights that diversity–functioning relationships are complicated. Drivers of functionality vary from site to site and each measure of functioning, including parameters as demonstrated here, can be influenced by very different sets of predictors, often associated with taxonomic and trait diversity. Single correlative comparisons of certain aspects of diversity and functionality might therefore return very different results. Antonini et al. (2022) show that, in general, using 22 predictors of functional diversity, varying predictor subsets were positively associated with soil functioning. Correlational analyses alone cannot resolve the question of causal link. Future studies should therefore focus on inferring precise mechanisms behind the observed relationships, and the environmental constraints on predictor subset composition and strength.

References

Antonini Y, Beirão MV, Costa FV, Azevedo CS, Wojakowski MM, Kozovits AR, Pires MRS, Sousa HC de, Messias MCTB, Fujaco MA, Leite MGP, Vidigal JP, Monteiro GF, Dirzo R (2022) Riparian forest restoration as sources of biodiversity and ecosystem functions in anthropogenic landscapes. bioRxiv, 2021.09.08.459375, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.09.08.459375

Buzhdygan OY, Meyer ST, Weisser WW, Eisenhauer N, Ebeling A, Borrett SR, Buchmann N, Cortois R, De Deyn GB, de Kroon H, Gleixner G, Hertzog LR, Hines J, Lange M, Mommer L, Ravenek J, Scherber C, Scherer-Lorenzen M, Scheu S, Schmid B, Steinauer K, Strecker T, Tietjen B, Vogel A, Weigelt A, Petermann JS (2020) Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands. Nature Ecology & Evolution, 4, 393–405. https://doi.org/10.1038/s41559-020-1123-8

Marselle MR, Hartig T, Cox DTC, de Bell S, Knapp S, Lindley S, Triguero-Mas M, Böhning-Gaese K, Braubach M, Cook PA, de Vries S, Heintz-Buschart A, Hofmann M, Irvine KN, Kabisch N, Kolek F, Kraemer R, Markevych I, Martens D, Müller R, Nieuwenhuijsen M, Potts JM, Stadler J, Walton S, Warber SL, Bonn A (2021) Pathways linking biodiversity to human health: A conceptual framework. Environment International, 150, 106420. https://doi.org/10.1016/j.envint.2021.106420

Naeem S, Bunker DE, Hector A, Loreau M, Perrings C (Eds.) (2009) Biodiversity, Ecosystem Functioning, and Human Wellbeing: An Ecological and Economic Perspective. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:oso/9780199547951.001.0001

Riparian forest restoration as sources of biodiversity and ecosystem functions in anthropogenic landscapesYasmine Antonini, Marina Vale Beirao, Fernanda Vieira Costa, Cristiano Schetini Azevedo, Maria Wojakowski, Alessandra Kozovits, Maria Rita Silverio Pires, Hildeberto Caldas Sousa, Maria Cristina Teixeira Braga Messias, Maria Augusta Goncalves Fuja...<ol> <li style="text-align: justify;">Restoration of tropical riparian forests is challenging, since these ecosystems are the most diverse, dynamic, and complex physical and biological terrestrial habitats. This study tested whether biodiversity ...Biodiversity, Community ecology, Ecological successions, Ecosystem functioning, Terrestrial ecologyWerner Ulrich2021-09-10 10:51:23 View
18 Apr 2024
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The large and central Caligo martia eyespot may reduce fatal attacks by birds: a case study supports the deflection hypothesis in nature

Intimidation or deflection: field experiments in a tropical forest to simultaneously test two competing hypotheses about how butterfly eyespots confer protection against predators

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Eyespots—round or oval spots, usually accompanied by one or more concentric rings, that together imitate vertebrate eyes—are found in insects of at least three orders and in some tropical fishes (Stevens 2005). They are particularly frequent in Lepidoptera, where they occur on wings of adults in many species (Monteiro et al. 2006), and in caterpillars of many others (Janzen et al. 2010). The resemblance of eyespots to vertebrate eyes often extends to details, such as fake « pupils » (round or slit-like) and « eye sparkle » (Blut et al. 2012). Larvae of one hawkmoth species even have fake eyes that appear to blink (Hossie et al. 2013). Eyespots have interested evolutionary biologists for well over a century. While they appear to play a role in mate choice in some adult Lepidoptera, their adaptive significance in adult Lepidoptera, as in caterpillars, is mainly as an anti-predator defense (Monteiro 2015). However, there are two competing hypotheses about the mechanism by which eyespots confer defense against predators. The « intimidation » hypothesis postulates that eyespots intimidate potential predators, startling them and reducing the probability of attack. The « deflection » hypothesis holds that eyespots deflect attacks to parts of the body where attack has relatively little effect on the animal’s functioning and survival. In caterpillars, there is little scope for the deflection hypothesis, because attack on any part of a caterpillar’s body is likely to be lethal. Much observational and some experimental evidence supports the intimidation hypothesis in caterpillars (Hossie & Sherratt 2012). In adult Lepidoptera, however, both mechanisms are plausible, and both have found support (Stevens 2005). The most spectacular examples of intimidation are in butterflies in which eyespots located centrally in hindwings and hidden in the natural resting position are suddenly exposed, startling the potential predator (e.g., Vallin et al. 2005). The most spectacular examples of deflection are seen in butterflies in which eyespots near the hindwing margin combined with other traits give the appearance of a false head (e.g., Chotard et al. 2022; Kodandaramaiah 2011). 

Most studies have attempted to test for only one or the other of these mechanisms—usually the one that seems a priori more likely for the butterfly species being studied. But for many species, particularly those that have neither spectacular startle displays nor spectacular false heads, evidence for or against the two hypotheses is contradictory.  

Iserhard et al. (2024) attempted to simultaneously test both hypotheses, using the neotropical nymphalid butterfly Caligo martia. This species has a large ventral hindwing eyespot, exposed in the insect’s natural resting position, while the rest of the ventral hindwing surface is cryptically coloured. In a previous study of this species, De Bona et al. (2015) presented models with intact and disfigured eyespots on a computer monitor to a European bird species, the great tit (Parus major). The results favoured the intimidation hypothesis. Iserhard et al. (2024) devised experiments presenting more natural conditions, using fairly realistic dummy butterflies, with eyespots manipulated or unmanipulated, exposed to a diverse assemblage of insectivorous birds in nature, in a tropical forest. Using color-printed paper facsimiles of wings, with eyespots present, UV-enhanced, or absent, they compared the frequency of beakmarks on modeling clay applied to wing margins (frequent attacks would support the deflection hypothesis) and (in one of two experiments) on dummies with a modeling-clay body (eyespots should lead to reduced frequency of attack, to wings and body, if birds are intimidated). Their experiments also included dummies without eyespots whose wings were either cryptically coloured (as in unmanipulated butterflies) or not. Their results, although complex, indicate support for the deflection hypothesis: dummies with eyespots were mostly attacked on these less vital parts. Dummies lacking eyespots were less frequently attacked, especially when they were camouflaged. Camouflaged dummies without eyespots were in fact the least frequently attacked of all the models. However, when dummies lacking eyespots were attacked, attacks were usually directed to vital body parts. These results show some of the complexity of estimating costs and benefits of protective conspicuous signals vs. camouflage (Stevens et al. 2008).

Two complementary experiments were conducted. The first used facsimiles with « wings » in a natural resting position (folded, ventral surfaces exposed), but without a modeling-clay « body ». In the second experiment, facsimiles had a modeling-clay « body », placed between the two unfolded wings to make it as accessible to birds as the wings. However, these dummies displayed the ventral surfaces of unfolded wings, an unnatural resting position. The study was thus not able to compare bird attacks to the body vs. wings in a natural resting position. One can understand the reason for this methodological choice, but it is a limitation of the study.

The naturalness of the conditions under which these field experiments were conducted is a strong argument for the biological significance of their results. However, the uncontrolled conditions naturally result in many questions being left open. The butterfly dummies were exposed to at least nine insectivorous bird species. Do bird species differ in their behavioral response to eyespots? Do responses depend on the distance at which a bird first detects the butterfly? Do eyespots and camouflage markings present on the same animal both function, but at different distances (Tullberg et al. 2005)? Do bird responses vary depending on the particular light environment in the places and at the times when they encounter the butterfly (Kodandaramaiah 2011)? Answering these questions under natural, uncontrolled conditions will be challenging, requiring onerous methods, (e.g., video recording in multiple locations over time). The study indicates the interest of pursuing these questions.

References

Blut, C., Wilbrandt, J., Fels, D., Girgel, E.I., & Lunau, K. (2012). The ‘sparkle’ in fake eyes–the protective effect of mimic eyespots in Lepidoptera. Entomologia Experimentalis et Applicata, 143, 231-244. https://doi.org/10.1111/j.1570-7458.2012.01260.x

Chotard, A., Ledamoisel, J., Decamps, T., Herrel, A., Chaine, A.S., Llaurens, V., & Debat, V. (2022). Evidence of attack deflection suggests adaptive evolution of wing tails in butterflies. Proceedings of the Royal Society B, 289, 20220562. https://doi.org/10.1098/rspb.2022.0562

De Bona, S., Valkonen, J.K., López-Sepulcre, A., & Mappes, J. (2015). Predator mimicry, not conspicuousness, explains the efficacy of butterfly eyespots. Proceedings of the Royal Society B, 282, 1806. https://doi.org/10.1098/RSPB.2015.0202

Hossie, T.J., & Sherratt, T.N. (2012). Eyespots interact with body colour to protect caterpillar-like prey from avian predators. Animal Behaviour, 84, 167-173. https://doi.org/10.1016/j.anbehav.2012.04.027

Hossie, T.J., Sherratt, T.N., Janzen, D.H., & Hallwachs, W. (2013). An eyespot that “blinks”: an open and shut case of eye mimicry in Eumorpha caterpillars (Lepidoptera: Sphingidae). Journal of Natural History, 47, 2915-2926. https://doi.org/10.1080/00222933.2013.791935

Iserhard, C.A., Malta, S.T., Penz, C.M., Brenda Barbon Fraga; Camila Abel da Costa; Taiane Schwantz; & Kauane Maiara Bordin (2024). The large and central Caligo martia eyespot may reduce fatal attacks by birds : a case study supports the deflection hypothesis in nature. Zenodo, ver. 1 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.5281/zenodo.10980357

Janzen, D.H., Hallwachs, W., & Burns, J.M. (2010). A tropical horde of counterfeit predator eyes. Proceedings of the National Academy of Sciences, USA, 107, 11659-11665. https://doi.org/10.1073/pnas.0912122107

Kodandaramaiah, U. (2011). The evolutionary significance of butterfly eyespots. Behavioral Ecology, 22, 1264-1271. https://doi.org/10.1093/beheco/arr123

Monteiro, A. (2015). Origin, development, and evolution of butterfly eyespots. Annual Review of Entomology, 60, 253-271. https://doi.org/10.1146/annurev-ento-010814-020942

Monteiro, A., Glaser, G., Stockslager, S., Glansdorp, N., & Ramos, D. (2006). Comparative insights into questions of lepidopteran wing pattern homology. BMC Developmental Biology, 6, 1-13. https://doi.org/10.1186/1471-213X-6-52

Stevens, M. (2005). The role of eyespots as anti-predator mechanisms, principally demonstrated in the Lepidoptera. Biological Reviews, 80, 573–588. https://doi.org/10.1017/S1464793105006810

Stevens, M., Stubbins, C.L., & Hardman C.J. (2008). The anti-predator function of ‘eyespots’ on camouflaged and conspicuous prey. Behavioral Ecology and Sociobiology, 62, 1787-1793. https://doi.org/10.1007/s00265-008-0607-3

Tullberg, B.S., Merilaita, S., & Wiklund, C. (2005). Aposematism and crypsis combined as a result of distance dependence: functional versatility of the colour pattern in the swallowtail butterfly larva. Proceedings of the Royal Society B, 272, 1315-1321. https://doi.org/10.1098/rspb.2005.3079

Vallin, A., Jakobsson, S., Lind, J., & Wiklund, C. (2005). Prey survival by predator intimidation: an experimental study of peacock butterfly defence against blue tits. Proceedings of the Royal Society B, 272, 1203-1207. https://doi.org/10.1098/rspb.2004.3034

The large and central *Caligo martia* eyespot may reduce fatal attacks by birds: a case study supports the deflection hypothesis in natureCristiano Agra Iserhard, Shimene Torve Malta, Carla Maria Penz, Brenda Barbon Fraga, Camila Abel da Costa, Taiane Schwantz, Kauane Maiara Bordin<p>Many animals have colorations that resemble eyes, but the functions of such eyespots are debated. Caligo martia (Godart, 1824) butterflies have large ventral hind wing eyespots, and we aimed to test whether these eyespots act to deflect or to t...Biodiversity, Community ecology, Conservation biology, Life history, Tropical ecologyDoyle Mc Key2023-11-21 15:00:20 View
26 May 2023
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Using repeatability of performance within and across contexts to validate measures of behavioral flexibility

Do reversal learning methods measure behavioral flexibility?

Recommended by ORCID_LOGO based on reviews by Maxime Dahirel and Aparajitha Ramesh

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

Using repeatability of performance within and across contexts to validate measures of behavioral flexibilityMcCune KB, Blaisdell AP, Johnson-Ulrich Z, Lukas D, MacPherson M, Seitz BM, Sevchik A, Logan CJ<p style="text-align: justify;">Research into animal cognitive abilities is increasing quickly and often uses methods where behavioral performance on a task is assumed to represent variation in the underlying cognitive trait. However, because thes...Behaviour & Ethology, Evolutionary ecology, Preregistrations, ZoologyAurélie Coulon2022-08-15 20:56:42 View
14 Jun 2024
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Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese orchards

Uncovering the ecology in big-data by hierarchizing multi-scale environmental effects

Recommended by based on reviews by Kévin Tougeron and Jianqiang Sun

Along with the generalization of open-access practices, large, heterogeneous datasets are becoming increasingly available to ecologists (Farley et al. 2018). While such data offer exciting opportunities for unveiling original patterns and trends, they also raise new challenges regarding how to extract relevant information and actually improve our knowledge of complex ecological systems, beyond purely descriptive correlations (Dietze 2017, Farley et al. 2018).

In this work, Caumette et al. (2024) develop an original ecoinformatics approach to relate multi-scale environmental factors to the temporal dynamics of a major pest in mango orchards. Their method relies on the recent tree-boosting method GPBoost (Sigrist 2022) to hierarchize the influence of environmental factors of heterogeneous nature (e.g., orchard composition and management; landscape structure; climate) on the emergence date of the oriental fruit fly, Bactrocera dorsalis. As boosting methods allows the analysis of high-dimensional data, they are particularly adapted to the exploration of such datasets, to uncover unexpected, potentially complex dependencies between ecological dynamics and multiple environmental factors (Farley et al. 2018). In this article, Caumette et al. (2024) make a special effort to guide the reader step by step through their complex analysis pipeline to make it broadly understandable to the average ecologist, which is no small feat. I particularly welcome this commitment, as making new, cutting-edge analytical methods accessible to a large community of science practitioners with varying degrees of statistical or programming expertise is a major challenge for the future of quantitative ecology. 

The main result of Caumette et al. (2024) is that temperature and humidity conditions both at the local and regional scales are the main predictors of B. dorsalis emergence date, while orchard management practices seem to have relatively little influence. This suggests that favourable climatic conditions may allow the persistence of small populations of B. dorsalis over the dry season, which may then act as a propagule source for early re-infestations. However, as the authors explain, the resulting regression model is not designed for predictive purposes and should not at this stage be used for decision-making in pest management. Its main interest rather resides in identifying potential key factors favoring early infestations of B. dorsalis, and help focusing future experimental field studies on the most relevant levers for integrated pest management in mango orchards.

In a wider perspective, this work also provides a convincing proof-of-concept for the use of boosting methods to identify the most influential factors in large, multivariate datasets in a variety of ecological systems. It is also crucial to keep in mind that the current exponential growth in high-throughput environmental data (Lucivero 2020) could quickly come into conflict with the need to reduce the environmental footprint of research (Mariette et al. 2022). In this context, robust and accessible methods for extracting and exploiting all the information available in already existing datasets might prove essential to a sustainable pursuit of science.

References
 
Caumette C, Diatta P, Piry S, Chapuis M-P, Faye E, Sigrist F, Martin O, Papaïx J, Brévault T, Berthier K. 2024. Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese orchards. bioRxiv 2023.11.10.566583, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.1101/2023.11.10.566583

Dietze MC. 2017. Ecological Forecasting. Princeton University Press
 
Farley SS, Dawson A, Goring SJ, Williams JW. 2018. Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions. BioScience, 68, 563–576, https://doi.org/10.1093/biosci/biy068
 
Lucivero F. 2020. Big Data, Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives. Science and Engineering Ethics 26, 1009–1030. https://doi.org/10.1007/s11948-019-00171-7

Mariette J, Blanchard O, Berné O, Aumont O, Carrey J, Ligozat A-L, Lellouch E, Roche P-E, Guennebaud G, Thanwerdas J, Bardou P, Salin G, Maigne E, Servan S, Ben-Ari T 2022. An open-source tool to assess the carbon footprint of research. Environmental Research: Infrastructure and Sustainability, 2022. https://dx.doi.org/10.1088/2634-4505/ac84a4
 
Sigrist F. 2022. Gaussian process boosting. The Journal of Machine Learning Research, 23, 10565-10610. https://jmlr.org/papers/v23/20-322.html
 

Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of *Bactrocera dorsalis* population growth in Senegalese orchardsCécile Caumette, Paterne Diatta, Sylvain Piry, Marie-Pierre Chapuis, Emile Faye, Fabio Sigrist, Olivier Martin, Julien Papaïx, Thierry Brévault, Karine Berthier<p>Implementing integrated pest management programs to limit agricultural pest damage requires an understanding of the interactions between the environmental variability and population demographic processes. However, identifying key environmental ...Demography, Landscape ecology, Statistical ecologyElodie Vercken2023-12-11 17:02:08 View
30 Jan 2020
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Diapause is not selected as a bet-hedging strategy in insects: a meta-analysis of reaction norm shapes

When to diapause or not to diapause? Winter predictability is not the answer

Recommended by based on reviews by Kévin Tougeron, Md Habibur Rahman Salman and 1 anonymous reviewer

Winter is a harsh season for many organisms that have to cope with food shortage and potentially lethal temperatures. Many species have evolved avoidance strategies. Among them, diapause is a resistance stage many insects use to overwinter. For an insect, it is critical to avoid lethal winter temperatures and thus to initiate diapause before winter comes, while making the most of autumn suitable climatic conditions [1,2]. Several cues can be used to appreciate that winter is coming, including day length and temperature [3]. But climate changes, temperatures rise and become more variable from year to year, which imposes strong pressure upon insect phenology [4]. How can insects adapt to changes in the mean and variance of winter onset?
In this paper, Jens Joschinski and Dries Bonte [5] address this question by using a well conducted meta-analysis of 458 diapause reaction norms obtained from 60 primary studies. They first ask first if insect mean diapause timing is tuned to match winter onset. They further ask if insects adapt to climatic unpredictability through a bet-hedging strategy by playing it safe and avoid risk (conservative bet-hedging) or on the contrary by avoiding to put all their eggs in one basket and spread the risk among their offspring (diversified bet-hedging). From published papers, the authors extracted data on mean diapause timing and information on latitude from which they retrieved day length inducing diapause, the date of winter onset and the day length at winter onset.
They found a positive correlation between latitude and the day length inducing diapause. On the contrary they found positive but (very) weak correlation between the date of winter onset and the date of diapause, thus indicating that diapause timing is not as optimally adapted to local environments as expected, particularly at high latitudes. They only found weak correlations between climate unpredictability and variability in diapause timing, and no correlation between climate unpredictability and deviation from optimal diapause timing. Together, these findings go against the hypothesis that insects use diversified or conservative bet-hedging strategies to cope with uncertainty in climatic conditions.
This is what makes the study thought provoking: the results do not match the theory well. Not because of a lack of data or a narrow scope, but because diapause is a complex trait that is determined by a large array of physiological and ecological factors [3]. Determining what are these factors is of particular interest in the face of the current climate change. This study shows what does not determine the timing of insect diapause. Researchers now know where to look at to improve our understanding of this key aspect of insect adaptation to climatic conditions.

References

[1] Dyck, H. V., Bonte, D., Puls, R., Gotthard, K., and Maes, D. (2015). The lost generation hypothesis: could climate change drive ectotherms into a developmental trap? Oikos, 124(1), 54–61. doi: 10.1111/oik.02066
[2] Gallinat, A. S., Primack, R. B., and Wagner, D. L. (2015). Autumn, the neglected season in climate change research. Trends in Ecology & Evolution, 30(3), 169–176. doi: 10.1016/j.tree.2015.01.004
[3] Tougeron, K. (2019). Diapause research in insects: historical review and recent work perspectives. Entomologia Experimentalis et Applicata, 167(1), 27–36. doi: 10.1111/eea.12753
[4] Bale, J. S., and Hayward, S. a. L. (2010). Insect overwintering in a changing climate. Journal of Experimental Biology, 213(6), 980–994. doi: 10.1242/jeb.037911
[5] Joschinski, J., and Bonte, D. (2020). Diapause is not selected as a bet-hedging strategy in insects: a meta-analysis of reaction norm shapes. BioRxiv, 752881, ver. 3 recommended and peer-reviewed by PCI Ecology. doi: 10.1101/752881

Diapause is not selected as a bet-hedging strategy in insects: a meta-analysis of reaction norm shapesJens Joschinski and Dries BonteMany organisms escape from lethal climatological conditions by entering a resistant resting stage called diapause, and it is essential that this strategy remains optimally timed with seasonal change. Climate change therefore exerts selection press...Maternal effects, Meta-analyses, Phenotypic plasticity, Terrestrial ecologyBastien Castagneyrol2019-09-20 11:47:47 View
12 Aug 2021
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A study on the role of social information sharing leading to range expansion in songbirds with large vocal repertoires: Enhancing our understanding of the Great-Tailed Grackle (Quiscalus mexicanus) alarm call

Does the active vocabulary in Great-tailed Grackles supports their range expansion? New study will find out

Recommended by Jan Oliver Engler based on reviews by Guillermo Fandos and 2 anonymous reviewers

Alarm calls are an important acoustic signal that can decide the life or death of an individual. Many birds are able to vary their alarm calls to provide more accurate information on e.g. urgency or even the type of a threatening predator. According to the acoustic adaptation hypothesis, the habitat plays an important role too in how acoustic patterns get transmitted. This is of particular interest for range-expanding species that will face new environmental conditions along the leading edge. One could hypothesize that the alarm call repertoire of a species could increase in newly founded ranges to incorporate new habitats and threats individuals might face. Hence selection for a larger active vocabulary might be beneficial for new colonizers. Using the Great-Tailed Grackle (Quiscalus mexicanus) as a model species, Samantha Bowser from Arizona State University and Maggie MacPherson from Louisiana State University want to find out exactly that. 

The Great-Tailed Grackle is an appropriate species given its high vocal diversity. Also, the species consists of different subspecies that show range expansions along the northern range edge yet to a varying degree. Using vocal experiments and field recordings the researchers have a high potential to understand more about the acoustic adaptation hypothesis within a range dynamic process. 

Over the course of this assessment, the authors incorporated the comments made by two reviewers into a strong revision of their research plans. With that being said, the few additional comments made by one of the initial reviewers round up the current stage this interesting research project is in. 

To this end, I can only fully recommend the revised research plan and am much looking forward to the outcomes from the author’s experiments, modeling, and field data. With the suggestions being made at such an early stage I firmly believe that the final outcome will be highly interesting not only to an ornithological readership but to every ecologist and biogeographer interested in drivers of range dynamic processes.

References

Bowser, S., MacPherson, M. (2021). A study on the role of social information sharing leading to range expansion in songbirds with large vocal repertoires: Enhancing our understanding of the Great-Tailed Grackle (Quiscalus mexicanus) alarm call. In principle recommendation by PCI Ecology. https://doi.org/10.17605/OSF.IO/2UFJ5. Version 3

A study on the role of social information sharing leading to range expansion in songbirds with large vocal repertoires: Enhancing our understanding of the Great-Tailed Grackle (Quiscalus mexicanus) alarm call Samantha Bowser, Maggie MacPherson<p>The acoustic adaptation hypothesis posits that animal sounds are influenced by the habitat properties that shape acoustic constraints (Ey and Fischer 2009, Morton 2015, Sueur and Farina 2015).Alarm calls are expected to signal important habitat...Biogeography, Biological invasions, Coexistence, Dispersal & Migration, Habitat selection, Landscape ecologyJan Oliver Engler Darius Stiels, Anonymous2020-12-01 18:11:02 View
05 Mar 2019
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Are the more flexible great-tailed grackles also better at inhibition?

Adapting to a changing environment: advancing our understanding of the mechanisms that lead to behavioral flexibility

Recommended by based on reviews by Simon Gingins and 2 anonymous reviewers

Behavioral flexibility is essential for organisms to adapt to an ever-changing environment. However, the mechanisms that lead to behavioral flexibility and understanding what traits makes a species better able to adapt behavior to new environments has been understudied. Logan and colleagues have proposed to use a series of experiments, using great-tailed grackles as a study species, to test four main hypotheses. These hypotheses are centered around exploring the relationship between behavioral flexibility and inhibition in grackles. This current preregistration is a part of a larger integrative research plan examining behavioral flexibility when faced with environmental change. In this part of the project they will examine specifically if individuals that are more flexible are also better at inhibiting: in other words: they will test the assumption that inhibition is required for flexibility.
First, they will test the hypothesis that behavioral flexibility is manipulatable by using a serial reversal learning task. Second, they will test the hypothesis that manipulating behavioral flexibility (improving reversal learning speed through serial reversals using colored tubers) improves flexibility (rule switching) and problem solving in a new context (multi‑access box and serial reversals on a touch screen). Third, they will test the hypothesis that behavioral flexibility within a context is repeatable within individuals, which is important to test if performance is state dependent. Finally, they will test a fourth hypothesis that individuals should converge on an epsilon‑first learning strategy (learn the correct choice after one trial) as they progress through serial reversals. Their innovative approach using three main tasks (delay of gratification, go-no, detour) will allow them to assess different aspects of inhibitory control. They will analyze the results of all three experiments to also assess the utility of these experiments for studying the potential relationship between inhibition and behavioral flexibility.
In their preregistration, Logan and colleagues have proposed to test these hypotheses, each with a set of testable predictions that can be examined with detailed and justified methodologies. They have also provided a comprehensive plan for analyzing the data. All of the reviewers and I agree that this is a very interesting study that has the potential to answer important questions about a critical topic in behavioral ecology: the role of inhibition in the evolution of behavioral flexibility. Given the positive reviews, the comprehensive responses by the PI and her colleagues, and careful revisions, I highly recommend this preregistration.

Are the more flexible great-tailed grackles also better at inhibition?Corina Logan, Kelsey McCune, Zoe Johnson-Ulrich, Luisa Bergeron, Carolyn Rowney, Benjamin Seitz, Aaron Blaisdell, Claudia WascherThis is a PREREGISTRATION. The DOI was issued by OSF and refers to the whole GitHub repository, which contains multiple files. The specific file we are submitting is g_inhibition.Rmd, which is easily accessible at GitHub at https://github.com/cori...Behaviour & Ethology, Preregistrations, ZoologyErin Vogel2018-10-12 18:36:00 View