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Id | Title * | Authors * | Abstract * | Picture * | Thematic fields * ▲ | Recommender | Reviewers | Submission date | |
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24 Mar 2023
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 https://doi.org/10.32942/X2H59DReview of machine learning uses for the analysis of images on wildlifeRecommended by Olivier Gimenez based on reviews by Falk Huettmann and 1 anonymous reviewerIn 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 imagery | Shinichi 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 biology | Olivier Gimenez | Anonymous | 2022-10-31 22:05:46 | View | |
18 Dec 2019
Validating morphological condition indices and their relationship with reproductive success in great-tailed gracklesJennifer M. Berens, Corina J. Logan, Melissa Folsom, Luisa Bergeron, Kelsey B. McCune https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/gcondition.RmdAre condition indices positively related to each other and to fitness?: a test with gracklesRecommended by Marcos Mendez based on reviews by Javier Seoane and Isabel López-RullReproductive succes, as a surrogate of individual fitness, depends both on extrinsic and intrinsic factors [1]. Among the intrinsic factors, resource level or health are considered important potential drivers of fitness but exceedingly difficult to measure directly. Thus, a host of proxies have been suggested, known as condition indices [2]. The question arises whether all condition indices consistently measure the same "inner state" of individuals and whether all of them similarly correlate to individual fitness. In this preregistration, Berens and colleagues aim to answer this question for two common condition indices, fat score and scaled mass index (Fig. 1), using great-tailed grackles as a model system. Although this question is not new, it has not been satisfactorily solved and both reviewers found merit in the attempt to clarify this matter. References [1] Roff, D. A. (2001). Life history evolution. Oxford University Press, Oxford. | Validating morphological condition indices and their relationship with reproductive success in great-tailed grackles | Jennifer M. Berens, Corina J. Logan, Melissa Folsom, Luisa Bergeron, Kelsey B. McCune | Morphological variation among individuals has the potential to influence multiple life history characteristics such as dispersal, migration, reproductive fitness, and survival (Wilder, Raubenheimer, and Simpson (2016)). Theoretically, individuals ... | Behaviour & Ethology, Conservation biology, Demography, Morphometrics, Preregistrations, Zoology | Marcos Mendez | 2019-08-05 20:05:56 | View | ||
16 Sep 2019
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 https://doi.org/10.1101/385120Words matter: extensive misapplication of "non-invasive" in describing DNA sampling methods, and proposed clarifying termsRecommended by Thomas Wilson Sappington based on reviews by 2 anonymous reviewersThe 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]. 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 | Blood, sweat and tears: a review of non-invasive DNA sampling | Marie-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, Zoology | Thomas Wilson Sappington | 2018-11-30 13:33:31 | View | ||
02 Oct 2018
How optimal foragers should respond to habitat changes? On the consequences of habitat conversion.Vincent Calcagno, Frederic Hamelin, Ludovic Mailleret, Frederic Grognard 10.1101/273557Optimal foraging in a changing world: old questions, new perspectivesRecommended by Francois-Xavier Dechaume-Moncharmont based on reviews by Frederick Adler, Andrew Higginson and 1 anonymous reviewerMarginal value theorem (MVT) is an archetypal model discussed in every behavioural ecology textbook. Its popularity is largely explained but the fact that it is possible to solve it graphically (at least in its simplest form) with the minimal amount of equations, which is a sensible strategy for an introductory course in behavioural ecology [1]. Apart from this heuristic value, one may be tempted to disregard it as a naive toy model. After a burst of interest in the 70's and the 80's, the once vivid literature about optimal foraging theory (OFT) has lost its momentum [2]. Yet, OFT and MVT have remained an active field of research in the parasitoidologists community, mostly because the sampling strategy of a parasitoid in patches of hosts and its resulting fitness gain are straightforward to evaluate, which eases both experimental and theoretical investigations [3]. References [1] Fawcett, T. W. & Higginson, A. D. 2012 Heavy use of equations impedes communication among biologists. Proc. Natl. Acad. Sci. 109, 11735–11739. doi: 10.1073/pnas.1205259109 | How optimal foragers should respond to habitat changes? On the consequences of habitat conversion. | Vincent Calcagno, Frederic Hamelin, Ludovic Mailleret, Frederic Grognard | The Marginal Value Theorem (MVT) provides a framework to predict how habitat modifications related to the distribution of resources over patches should impact the realized fitness of individuals and their optimal rate of movement (or patch residen... | Behaviour & Ethology, Dispersal & Migration, Foraging, Landscape ecology, Spatial ecology, Metacommunities & Metapopulations, Theoretical ecology | Francois-Xavier Dechaume-Moncharmont | 2018-03-05 10:42:11 | View | ||
13 Mar 2021
Investigating sex differences in genetic relatedness in great-tailed grackles in Tempe, Arizona to infer potential sex biases in dispersalSevchik, A., Logan, C. J., McCune, K. B., Blackwell, A., Rowney, C. and Lukas, D https://doi.org/10.32942/osf.io/t6behDispersal: from “neutral” to a state- and context-dependent viewRecommended by Emanuel A. Fronhofer based on reviews by 2 anonymous reviewersTraditionally, dispersal has often been seen as “random” or “neutral” as Lowe & McPeek (2014) have put it. This simplistic view is likely due to dispersal being intrinsically difficult to measure empirically as well as “random” dispersal being a convenient simplifying assumption in theoretical work. Clobert et al. (2009), and many others, have highlighted how misleading this assumption is. Rather, dispersal seems to be usually a complex reaction norm, depending both on internal as well as external factors. One such internal factor is the sex of the dispersing individual. A recent review of the theoretical literature (Li & Kokko 2019) shows that while ideas explaining sex-biased dispersal go back over 40 years this state-dependency of dispersal is far from comprehensively understood. Sevchik et al. (2021) tackle this challenge empirically in a bird species, the great-tailed grackle. In contrast to most bird species, where females disperse more than males, the authors report genetic evidence indicating male-biased dispersal. The authors argue that this difference can be explained by the great-tailed grackle’s social and mating-system. Dispersal is a central life-history trait (Bonte & Dahirel 2017) with major consequences for ecological and evolutionary processes and patterns. Therefore, studies like Sevchik et al. (2021) are valuable contributions for advancing our understanding of spatial ecology and evolution. Importantly, Sevchik et al. also lead to way to a more open and reproducible science of ecology and evolution. The authors are among the pioneers of preregistering research in their field and their way of doing research should serve as a model for others. References Bonte, D. & Dahirel, M. (2017) Dispersal: a central and independent trait in life history. Oikos 126: 472-479. doi: https://doi.org/10.1111/oik.03801 Clobert, J., Le Galliard, J. F., Cote, J., Meylan, S. & Massot, M. (2009) Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett.: 12, 197-209. doi: https://doi.org/10.1111/j.1461-0248.2008.01267.x Li, X.-Y. & Kokko, H. (2019) Sex-biased dispersal: a review of the theory. Biol. Rev. 94: 721-736. doi: https://doi.org/10.1111/brv.12475 Lowe, W. H. & McPeek, M. A. (2014) Is dispersal neutral? Trends Ecol. Evol. 29: 444-450. doi: https://doi.org/10.1016/j.tree.2014.05.009 Sevchik, A., Logan, C. J., McCune, K. B., Blackwell, A., Rowney, C. & Lukas, D. (2021) Investigating sex differences in genetic relatedness in great-tailed grackles in Tempe, Arizona to infer potential sex biases in dispersal. EcoEvoRxiv, osf.io/t6beh, ver. 5 peer-reviewed and recommended by Peer community in Ecology. doi: https://doi.org/10.32942/osf.io/t6beh | Investigating sex differences in genetic relatedness in great-tailed grackles in Tempe, Arizona to infer potential sex biases in dispersal | Sevchik, A., Logan, C. J., McCune, K. B., Blackwell, A., Rowney, C. and Lukas, D | <p>In most bird species, females disperse prior to their first breeding attempt, while males remain closer to the place they hatched for their entire lives. Explanations for such female bias in natal dispersal have focused on the resource-defense ... | Behaviour & Ethology, Dispersal & Migration, Zoology | Emanuel A. Fronhofer | 2020-08-24 17:53:06 | View | ||
12 Oct 2019
Investigating the use of learning mechanisms in a species that is rapidly expanding its geographic rangeKelsey McCune, Richard McElreath, Corina Logan http://corinalogan.com/Preregistrations/g_sociallearning.htmlHow would variation in environmental predictability affect the use of different learning mechanisms in a social bird?Recommended by Aliza le Roux based on reviews by Matthew Petelle and 1 anonymous reviewerIn their pre-registered paper [1], McCune and colleagues propose a field-based study of social versus individual learning mechanisms in an avian species (great-tailed grackles) that has been expanding its geographic range. The study forms part of a longer-term project that addresses various aspects of this species’ behaviour and biology, and the experience of the team is clear from the preprint. Assessing variation in learning mechanisms in different sections of the grackles’ distribution range, the researchers will investigate how individual learning and social transmission may impact learning about novel challenges in the environment. Considering that this is a social species, the authors expect both individual learning and social transmission to occur, when groups of grackles encounter new challenges/ opportunities in the wild. This in itself is not a very unusual idea to test [2, 3], but the authors are rigorously distinguishing between imitation, emulation, local enhancement, and social enhancement. Such rigour is certainly valuable in studies of cognition in the wild. References [1] McCune, K. B., McElreath, R., and Logan, C. J. (2019). Investigating the use of learning mechanisms in a species that is rapidly expanding its geographic range. In principle recommendation by Peer Community In Ecology. corinalogan.com/Preregistrations/g_sociallearning.html | Investigating the use of learning mechanisms in a species that is rapidly expanding its geographic range | Kelsey McCune, Richard McElreath, Corina Logan | This is one of many studies planned for our long-term research on the role of behavior and learning in rapid geographic range expansions. Project background: Behavioral flexibility, the ability to change behavior when circumstances change based on... | Behaviour & Ethology, Eco-evolutionary dynamics, Foraging, Preregistrations, Social structure, Spatial ecology, Metacommunities & Metapopulations, Zoology | Aliza le Roux | 2019-07-23 18:45:20 | View | ||
22 Nov 2021
When more competitors means less harvested resourceRecommended by François Munoz based on reviews by Francois Massol, Jeremy Van Cleve and 1 anonymous reviewerIn this paper, Alan R. Rogers (2021) examines the dynamics of foraging strategies for a resource that gains value over time (e.g., ripening fruits), while there is a fixed cost of attempting to forage the resource, and once the resource is harvested nothing is left for other harvesters. For this model, not any pure foraging strategy is evolutionary stable. A mixed equilibrium exists, i.e., with a mixture of foraging strategies within the population, which is still evolutionarily unstable. Nonetheless, Alan R. Rogers shows that for a large number of competitors and/or high harvesting cost, the mixture of strategies remains close to the mixed equilibrium when simulating the dynamics. Surprisingly, in a large population individuals will less often attempt to forage the resource and will instead “go fishing”. The paper also exposes an experiment of the game with students, which resulted in a strategy distribution somehow close to the theoretical mixture of strategies. The economist John F. Nash Jr. (1950) gained the Nobel Prize of economy in 1994 for his game theoretical contributions. He gave his name to the “Nash equilibrium”, which represents a set of individual strategies that is reached whenever all the players have nothing to gain by changing their strategy while the strategies of others are unchanged. Alan R. Rogers shows that the mixed equilibrium in the foraging game is such a Nash equilibrium. Yet it is evolutionarily unstable insofar as a distribution close to the equilibrium can invade. The insights of the study are twofold. First, it sheds light on the significance of Nash equilibrium in an ecological context of foraging strategies. Second, it shows that an evolutionarily unstable state can rule the composition of the ecological system. Therefore, the contribution made by the paper should be most significant to better understand the dynamics of competitive communities and their eco-evolutionary trajectories. References Nash JF (1950) Equilibrium points in n-person games. Proceedings of the National Academy of Sciences, 36, 48–49. https://doi.org/10.1073/pnas.36.1.48 Rogers AR (2021) Beating your Neighbor to the Berry Patch. bioRxiv, 2020.11.12.380311, ver. 8 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.11.12.380311
| Beating your neighbor to the berry patch | Alan R. Rogers | <p style="text-align: justify;">Foragers often compete for resources that ripen (or otherwise improve) gradually. What strategy is optimal in this situation? It turns out that there is no optimal strategy. There is no evolutionarily stable strateg... | Behaviour & Ethology, Evolutionary ecology, Foraging | François Munoz | Erol Akçay, Jorge Peña, Sébastien Lion, François Rousset, Ulf Dieckmann , Troy Day , Corina Tarnita , Florence Debarre , Daniel Friedman , Vlastimil Krivan , Ulf Dieckmann | 2020-12-10 18:38:49 | View | |
10 Jun 2018
A reply to “Ranging Behavior Drives Parasite Richness: A More Parsimonious Hypothesis”Charpentier MJE, Kappeler PM https://doi.org/10.48550/arXiv.1805.08151Does elevated parasite richness in the environment affect daily path length of animals or is it the converse? An answer bringing some new elements of discussionRecommended by Cédric Sueur based on reviews by 2 anonymous reviewersIn 2015, Brockmeyer et al. [1] suggested that mandrills (Mandrillus sphinx) may accept additional ranging costs to avoid heavily parasitized areas. Following this paper, Bicca-Marques and Calegaro-Marques [2] questioned this interpretation and presented other hypotheses. To summarize, whilst Brockmeyer et al. [1] proposed that elevated daily path length may be a consequence of elevated parasite richness, Bicca-Marques and Calegaro-Marques [2] viewed it as a cause. In this current paper, Charpentier and Kappeler [3] respond to some of the criticisms by Bicca-Marques and Calegaro-Marques and discuss the putative parsimony of the two competing scenarios. The manuscript is interesting and focuses on an important question concerning the discussion about the social organization and home range use in wild mandrills. This answer helps to move this debate forward and should stimulate more empirical studies of the role of environmentally-transmitted parasites in shaping ranging and movement patterns of wild vertebrates. Given the elements this paper brings to the topics, it should have been published in American Journal of Primatology, the journal that published the two previous articles. References [1] Brockmeyer, T., Kappeler, P. M., Willaume, E., Benoit, L., Mboumba, S., & Charpentier, M. J. E. (2015). Social organization and space use of a wild mandrill (Mandrillus sphinx) group. American Journal of Primatology, 77(10), 1036–1048. doi: 10.1002/ajp.22439 | A reply to “Ranging Behavior Drives Parasite Richness: A More Parsimonious Hypothesis” | Charpentier MJE, Kappeler PM | <p>In a recent article, Bicca-Marques and Calegaro-Marques [2016] discussed the putative assumptions related to an interpretation we provided regarding an observed positive relationship between weekly averaged parasite richness of a group of mandr... | Behaviour & Ethology, Evolutionary ecology, Foraging, Host-parasite interactions, Spatial ecology, Metacommunities & Metapopulations, Zoology | Cédric Sueur | 2018-05-22 10:59:33 | View | ||
03 Apr 2020
Body temperatures, life history, and skeletal morphology in the nine-banded armadillo (Dasypus novemcinctus)Frank Knight, Cristin Connor, Ramji Venkataramanan, Robert J. Asher https://doi.org/10.17863/CAM.50971Is vertebral count in mammals influenced by developmental temperature? A study with Dasypus novemcinctusRecommended by Mar Sobral based on reviews by Darin Croft and ?Mammals show a very low level of variation in vertebral count, both among and within species, in comparison to other vertebrates [1]. Jordan’s rule for fishes states that the vertebral number among species increases with latitude, due to ambient temperatures during development [2]. Temperature has also been shown to influence vertebral count within species in fish [3], amphibians [4], and birds [5]. However, in mammals the count appears to be constrained, on the one hand, by a possible relationship between the development of the skeleton and the proliferations of cell lines with associated costs (neural malformations, cancer etc., [6]), and on the other by the cervical origin of the diaphragm [7]. References [1] Hautier L, Weisbecker V, Sánchez-Villagra MR, Goswami A, Asher RJ (2010) Skeletal development in sloths and the evolution of mammalian vertebral patterning. Proceedings of the National Academy of Sciences, 107, 18903–18908. doi: 10.1073/pnas.1010335107 | Body temperatures, life history, and skeletal morphology in the nine-banded armadillo (Dasypus novemcinctus) | Frank Knight, Cristin Connor, Ramji Venkataramanan, Robert J. Asher | <p>The nine banded armadillo (*Dasypus novemcinctus*) is the only xenarthran mammal to have naturally expanded its range into the middle latitudes of the USA. It is not known to hibernate, but has been associated with unusually labile core body te... | Behaviour & Ethology, Evolutionary ecology, Life history, Physiology, Zoology | Mar Sobral | 2019-11-22 22:57:31 | View | ||
26 May 2023
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 https://doi.org/10.32942/X2R59KDo reversal learning methods measure behavioral flexibility?Recommended by Aurélie Coulon based on reviews by Maxime Dahirel and Aparajitha RameshAssessing 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).
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. 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 flexibility | McCune 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, Zoology | Aurélie Coulon | 2022-08-15 20:56:42 | View |
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