Marginal 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].
This preprint [4] is in line with the long-established literature on OFT. It follows two theoretical articles [5,6] in which Vincent Calcagno and co-authors assessed the effect of changes in the environmental conditions on optimal foraging strategy. This time, they did not modify the shape of the gain function (describing the diminishing return of the cumulative intake as a function of the residency time in a patch) but the relative frequencies of good and bad patches. At first sight, that sounds like a minor modification of their earlier models. Actually, even the authors initially were fooled by the similarities before spotting the pitfalls. Here, they genuinely point out the erroneous verbal prediction in their previous paper in which some non-trivial effects of the change in patch frequencies have been overlooked. The present study indeed provides a striking example of ecological fallacy, and more specifically of Simpson's paradox which occurs when the aggregation of subgroups modifies the apparent pattern at the scale of the entire population [7,8]. In the case of MVT under constraints of habitat conversion, the increase of the residency times in both bad and good patches can result in a decrease of the average residency time at the level of the population. This apparently counter-intuitive property can be observed, for instance, when the proportion of bad quality patches strongly increases, which increases the probability that the individual forages on theses quickly exploited patches, and thus decreases its average residency time on the long run.
The authors thus put the model on the drawing board again. Proper assessment of the effect of change in the frequency of patch quality is more mathematically challenging than when one considers only changes in the shape of the gain function. The expected gain must be evaluated at the scale of the entire habitat instead of single patch. Overall, this study, which is based on a rigorous formalism, stands out as a warning against too rapid interpretations of theoretical outputs. It is not straightforward to generalize the predictions of previous models without careful evaluating their underlying hypotheses. The devil is in the details: some slight, seemingly minor, adjustments of the assumptions may have some major consequences.
The authors discussed the general conditions leading to changes in residency times or movement rates. Yet, it is worth pointing out again that it would be a mistake to blindly consider these theoretical results as forecasts for the foragers' behaviour in natura. OFT models has for a long time been criticized for sweeping under the carpet the key questions of the evolutionary dynamics and the maintenance of the optimal strategy in a population [9,10]. The distribution of available options is susceptible to change rapidly due to modifications of the environmental conditions or, even more simply, the presence of competitors which continuously remove the best options from the pool of available options [11]. The key point here is that the constant monitoring of available options implies cognitive (neural tissue is one of the most metabolically expensive tissues) and ecological costs: assessment and adjustment to the environmental conditions requires time, energy, and occasional mistakes (cost of naiveté, [12]). While rarely considered in optimal analyses, these costs should severely constraint the evolution of the subtle decision rules. Under rapidly fluctuating conditions, it could be more profitable to maintain a sub-optimal strategy (but performing reasonably well on the long run) than paying the far from negligible costs implied by the pursuit of optimal strategies [13,14]. For instance, in the analysis presented in this preprint, it is striking how close the fitness gains of the plastic and the non-plastic forager are, particularly if one remembers that the last-mentioned cognitive and ecological costs have been neglected in these calculations.
Yet, even if one can arguably question its descriptive value, such models are worth more than a cursory glance. They still have normative value insofar that they provide upper bounds for the response to modifications of the environmental conditions. Such insights are precious to design future experiments on the question. Being able to compare experimentally measured behaviours with the extremes of the null model (stubborn non-plastic forager) and the optimal strategy (only achievable by an omniscient daemon) informs about the cognitive bias or ecological costs experienced by real life foragers. I thus consider that this model, and more generally most OFT models, are still a valuable framework which deserves further examination.

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
[2] Owens, I. P. F. 2006 Where is behavioural ecology going? Trends Ecol. Evol. 21, 356–361. doi: 10.1016/j.tree.2006.03.014
[3] Louâpre, P., Fauvergue, X., van Baaren, J. & Martel, V. 2015 The male mate search: an optimal foraging issue? Curr. Opin. Insect Sci. 9, 91–95. doi: 10.1016/j.cois.2015.02.012
[4] Calcagno, V., Hamelin, F., Mailleret, L., & Grognard, F. (2018). How optimal foragers should respond to habitat changes? On the consequences of habitat conversion. bioRxiv, 273557, ver. 4 peer-reviewed and recommended by PCI Ecol. doi: 10.1101/273557
[5] Calcagno, V., Grognard, F., Hamelin, F. M., Wajnberg, É. & Mailleret, L. 2014 The functional response predicts the effect of resource distribution on the optimal movement rate of consumers. Ecol. Lett. 17, 1570–1579. doi: 10.1111/ele.12379
[6] Calcagno, V., Mailleret, L., Wajnberg, É. & Grognard, F. 2013 How optimal foragers should respond to habitat changes: a reanalysis of the Marginal Value Theorem. J. Math. Biol. 69, 1237–1265. doi: 10.1007/s00285-013-0734-y
[7] Galipaud, M., Bollache, L., Wattier, R., Dechaume-Moncharmont, F.-X. & Lagrue, C. 2015 Overestimation of the strength of size-assortative pairing in taxa with cryptic diversity: a case of Simpson's paradox. Anim. Behav. 102, 217–221. doi: 10.1016/j.anbehav.2015.01.032
[8] Kievit, R. A., Frankenhuis, W. E., Waldorp, L. J. & Borsboom, D. 2013 Simpson's paradox in psychological science: a practical guide. Front. Psychol. 4, 513. doi: 10.3389/fpsyg.2013.00513
[9] Bolduc, J.-S. & Cézilly, F. 2012 Optimality modelling in the real world. Biol. Philos. 27, 851–869. doi: 10.1007/s10539-012-9333-3
[10] Pierce, G. J. & Ollason, J. G. 1987 Eight reasons why optimal foraging theory is a complete waste of time. Oikos 49, 111–118. doi: 10.2307/3565560
[11] Dechaume-Moncharmont, F.-X., Brom, T. & Cézilly, F. 2016 Opportunity costs resulting from scramble competition within the choosy sex severely impair mate choosiness. Anim. Behav. 114, 249–260. doi: 10.1016/j.anbehav.2016.02.019
[12] Snell-Rood, E. C. 2013 An overview of the evolutionary causes and consequences of behavioural plasticity. Anim. Behav. 85, 1004–1011. doi: 10.1016/j.anbehav.2012.12.031
[13] Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E. W., Trimmer, P. C. & McNamara, J. M. 2014 The evolution of decision rules in complex environments. Trends Cogn. Sci. 18, 153–161. doi: 10.1016/j.tics.2013.12.012
[14] Marshall, J. A. R., Trimmer, P. C., Houston, A. I. & McNamara, J. M. 2013 On evolutionary explanations of cognitive biases. Trends Ecol. Evol. 28, 469-473. doi: 10.1016/j.tree.2013.05.013

How long individuals live has a large influence on a number of biological processes, both for the individuals themselves as well as for the populations they live in. For a given species, survival is often summarized in curves showing the probability to survive from one age to the next. However, these curves often hide a large amount of variation in survival. Variation can occur from chance, or if individuals have different genotypes or phenotypes that can influence how long they might live, or if environmental conditions are not the same across time or space. Such spatiotemporal variations in the conditions that individuals experience can lead to complex patterns of evolution (Kokko et al. 2017) but because of the difficulties to obtain the relevant data they have not been studied much in natural populations.
 
In this manuscript, Bastianelli and colleagues (2021) identify which environmental and population conditions are associated with variation in annual survival of blue tits. The analyses are based on an impressive dataset, tracking a total of almost 5500 adults in four populations studied for at least 19 years. The authors describe two core results. First, average annual survival is lower in deciduous forests compared to evergreen forests. The differences in average annual survival between the forest types link with previously described differences, with individuals having larger clutches (Charmantier et al. 2016) and higher aggression (Dubuc-Messier et al. 2017) in the populations where adult survival is lower. Second, there are huge fluctuations from one year to the next in the percentage of individuals surviving which occur similarly in all populations. Even though survival covaried across the four populations, this variation was not associated with any of the local or global climate indices the authors investigated.
 
Studies like these are fundamental to our understanding of population change. They are important from an applied side as they can reveal the sustainability of populations and inform potential management options. On a basic research side, they reveal how evolution operates in populations. Theoretical studies predict that individuals are often not adapted to average conditions they experience, but either selected to balance the extremes they encounter  or to make the best during harsh conditions when it really matters (Lewontin & Cohen 1969).
 
This study also opens the door to new research, highlighting that demographic studies should pay attention to variation in survival and other life history traits. For blue tits specifically, the study shows that in order to understand the demography of populations we need a better mechanistic understanding of the environmental and physiological pressures influencing whether individuals die or not to make predictions whether and how climate or other ecological effects shape variation in survival.
 
References
 
Bastianelli O, Robert A, Doutrelant C, Franceschi C de, Giovannini P, Charmantier A (2021) Identifying drivers of spatio-temporal variation in survival in four blue tit populations. bioRxiv, 2021.01.28.428563, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.1101/2021.01.28.428563

Charmantier A, Doutrelant C, Dubuc-Messier G, Fargevieille A, Szulkin M (2016) Mediterranean blue tits as a case study of local adaptation. Evolutionary Applications, 9, 135–152. https://doi.org/10.1111/eva.12282

Dubuc-Messier G, Réale D, Perret P, Charmantier A (2017) Environmental heterogeneity and population differences in blue tits personality traits. Behavioral Ecology, 28, 448–459. https://doi.org/10.1093/beheco/arw148

Kokko H, Chaturvedi A, Croll D, Fischer MC, Guillaume F, Karrenberg S, Kerr B, Rolshausen G, Stapley J (2017) Can Evolution Supply What Ecology Demands? Trends in Ecology & Evolution, 32, 187–197. https://doi.org/10.1016/j.tree.2016.12.005

Lewontin RC, Cohen D (1969) On Population Growth in a Randomly Varying Environment. Proceedings of the National Academy of Sciences, 62, 1056–1060. https://doi.org/10.1073/pnas.62.4.1056

Understanding the relative importance of species-specific traits and environmental factors in modulating species distributions is an intriguing question in ecology [1]. Both behavioral flexibility (i.e., the ability to change the behavior in changing circumstances) and habitat availability are known to influence the ability of a species to expand its geographic range [2,3]. However, the role of each factor is context and species dependent and more information is needed to understand how these two factors interact. In this pre-registration, Logan et al. [4] explain how they will use Great-tailed grackles (Quiscalus mexicanus), a species with a flexible behavior and a rapid geographic range expansion, to evaluate the relative role of habitat and behavior as drivers of the species’ expansion [4]. The authors present very clear hypotheses, predicted results and also include alternative predictions. The rationales for all the hypotheses are clearly stated, and the methodology (data and analyses plans) are described with detail. The large amount of information already collected by the authors for the studied species during previous projects warrants the success of this study. It is also remarkable that the authors will make all their data available in a public repository, and that the pre-registration in already stored in GitHub, supporting open access and reproducible science. I agree with the three reviewers of this pre-registration about its value and I think its quality has largely improved during the review process. Thus, I am happy to recommend it and I am looking forward to seeing the results.

References

[1] Gaston KJ. 2003. The structure and dynamics of geographic ranges. Oxford series in Ecology and Evolution. Oxford University Press, New York.

[2] Sol D, Lefebvre L. 2000. Behavioural flexibility predicts invasion success in birds introduced to new zealand. Oikos. 90(3): 599–605. https://doi.org/10.1034/j.1600-0706.2000.900317.x

[3] Hanski I, Gilpin M. 1991. Metapopulation dynamics: Brief history and conceptual domain. Biological journal of the Linnean Society. 42(1-2): 3–16. https://doi.org/10.1111/j.1095-8312.1991.tb00548.x

[4] Logan CJ, McCune KB, Chen N, Lukas D. 2020. Implementing a rapid geographic range expansion - the role of behavior and habitat changes (http://corinalogan.com/Preregistrations/gxpopbehaviorhabitat.html) In principle acceptance by PCI Ecology of the version on 16 Dec 2021 https://github.com/corinalogan/grackles/blob/0fb956040a34986902a384a1d8355de65010effd/Files/Preregistrations/gxpopbehaviorhabitat.Rmd.

Ivimey Cook et al. (2023) provide a concise and useful “How to” review code for researchers in the fields of ecology and evolutionary biology, where the systematic review of code is not yet standard practice during the peer review of articles. Consequently, this article is full of tips for authors on how to make their code easier to review. This handy article applies not only to ecology and evolutionary biology, but to many fields that are learning how to make code more reproducible and shareable. Taking this step toward transparency is key to improving research rigor (Brito et al. 2020) and is a necessary step in helping make research trustable by the public (Rosman et al. 2022).

References

Brito, J. J., Li, J., Moore, J. H., Greene, C. S., Nogoy, N. A., Garmire, L. X., & Mangul, S. (2020). Recommendations to enhance rigor and reproducibility in biomedical research. GigaScience, 9(6), giaa056. https://doi.org/10.1093/gigascience/giaa056

Ivimey-Cook, E. R., Pick, J. L., Bairos-Novak, K., Culina, A., Gould, E., Grainger, M., Marshall, B., Moreau, D., Paquet, M., Royauté, R., Sanchez-Tojar, A., Silva, I., Windecker, S. (2023). Implementing Code Review in the Scientific Workflow: Insights from Ecology and Evolutionary Biology. EcoEvoRxiv, ver 5 peer-reviewed and recommended by Peer Community In Ecology. https://doi.org/10.32942/X2CG64

Rosman, T., Bosnjak, M., Silber, H., Koßmann, J., & Heycke, T. (2022). Open science and public trust in science: Results from two studies. Public Understanding of Science, 31(8), 1046-1062. https://doi.org/10.1177/09636625221100686

The loss of biodiversity is an issue of great concern, especially if the extinction of species or the loss of a large number of individuals within populations results in a loss of critical ecosystem services. We know that the most important threat to most species is habitat loss and degradation (Keil et al., 2015; Pimm et al., 2014); the latter can be caused by multiple anthropogenic activities, including pollution, introduction of invasive species and fragmentation (Brook et al., 2008; Scanes, 2018). Roads are a major cause of habitat fragmentation, isolating previously connected populations and being a direct source of mortality for animals that attempt to cross them (Spellberg, 1998).
While most studies have focused on the effect of roads on larger mammals (Bartonička et al., 2018; Litvaitis and Tash, 2008), in recent years many researchers have grown increasingly concerned about the risk of collision between bats and vehicles (Fensome and Mathews, 2016). For example, a recent publication by Medinas et al. (2021) found 509 bat casualties along a 51-km-long transect during a period of 3 years. Their study provides extremely valuable information to asses which factors primarily drive bat mortality on roads, yet it required a substantial investment of time coupled with the difficulty of detecting bat carcasses. Other studies have used acoustic monitoring as a proxy to gauge risk of collision based on estimates of bat density along roads (reviewed in Fensome and Mathews 2016); while the results of such studies are valuable, the number of passes recorded does not necessarily equal collision risk, as many species may simply avoid crossing the roads. Understanding the risk of collisions is of vital importance for adequate planning of road construction, particularly for key sites that harbor threatened bat species or unusually large populations, especially if these are already greatly impacted by other anthropogenic activities (e.g. wind turbines; Kunz et al. 2007) or unusually deadly pathogens (e.g. white-nose syndrome; Blehert et al. 2009).
The study by Roemer et al. (2020) titled “Influence of local landscape and time of year on bat-road collision risks”, is a welcome addition to our understanding of bat collision risk as it employs a more accurate assessment of bat collision risk based on acoustic monitoring and tracking of flight paths. The goal of the study of Roemer and collaborators, which was conducted at 66 study sites in the Mediterranean region, is to provide an assessment of collision risk based on bat activity near roads. They collected a substantial amount of information for several species: more than 30,000 estimated flight trajectories for 21+ species, including Barbastella barbastellus, Myotis spp., Plecotus sp., Rhinolophus ferrumequinum, Miniopterus schreibersii, Pipistrellus spp., Nyctalus leisleri, and others. They assess risk based on estimates of 1) species abundance from acoustic monitoring, 2) direction of flight paths along roads, and 3) bat-vehicle co-occurrence.
Their findings suggest that risk is habitat, species, guild, and season-specific. Roads within forested habitats posed the largest threats for most species, particularly since most flights within these habitats occurred at the zone of collision risk. They also found that bats typically fly parallel to the road axis regardless of habitat type, which they argue supports the idea that bats may use roads as corridors. The results of their study, as expected, also show that the majority of bat passes were detected during summer or autumn, depending on species, yet they provide novel findings of an increase in risky behaviors during autumn, when the number of passes at the zone of collision risk increased significantly. Their results also suggest that mid-range echolocators, a classification that is based on call design and parameters (Frey-Ehrenbold et al., 2013), had a larger portion of flights in the zone at risk, thus potentially making them more susceptible than short and long-range echolocators to collisions with vehicles.
The methods employed by Roemer et al. (2020) could further help us determine how roads pose species and site-specific threats in a diversity of places without the need to invest a significant amount of time locating bat carcasses. Their findings are also important as they could provide valuable information for deciding where new roads should be constructed, particularly if the most vulnerable species are abundant, perhaps due to the presence of important roost sites. They also show how habitats near larger roads could increase threats, providing an important first step for recommendations regarding road construction and maintenance. As pointed out by one reviewer, one possible limitation of the study is that the results are not supported by the identification of carcasses. For example, does an increase in the number of identified flights at the zone of risk really translate into an increase in the number of collisions? Regardless of the latter, the paper’s methods and results are very valuable and provide an important step towards developing additional tools to assess bat-vehicle collision risks.

References

[1] Bartonička T, Andrášik R, Duľa M, Sedoník J, Bíl M (2018) Identification of local factors causing clustering of animal-vehicle collisions. The Journal of Wildlife Management, 82, 940–947. https://doi.org/10.1002/jwmg.21467
[2] Blehert DS, Hicks AC, Behr M, Meteyer CU, Berlowski-Zier BM, Buckles EL, Coleman JTH, Darling SR, Gargas A, Niver R, Okoniewski JC, Rudd RJ, Stone WB (2009) Bat White-Nose Syndrome: An Emerging Fungal Pathogen? Science, 323, 227–227. https://doi.org/10.1126/science.1163874
[3] Brook BW, Sodhi NS, Bradshaw CJA (2008) Synergies among extinction drivers under global change. Trends in Ecology & Evolution, 23, 453–460. https://doi.org/10.1016/j.tree.2008.03.011
[4] Fensome AG, Mathews F (2016) Roads and bats: a meta-analysis and review of the evidence on vehicle collisions and barrier effects. Mammal Review, 46, 311–323. https://doi.org/10.1111/mam.12072
[5] Frey‐Ehrenbold A, Bontadina F, Arlettaz R, Obrist MK (2013) Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices. Journal of Applied Ecology, 50, 252–261. https://doi.org/10.1111/1365-2664.12034
[6] Keil P, Storch D, Jetz W (2015) On the decline of biodiversity due to area loss. Nature Communications, 6, 8837. https://doi.org/10.1038/ncomms9837
[7] Kunz TH, Arnett EB, Erickson WP, Hoar AR, Johnson GD, Larkin RP, Strickland MD, Thresher RW, Tuttle MD (2007) Ecological impacts of wind energy development on bats: questions, research needs, and hypotheses. Frontiers in Ecology and the Environment, 5, 315–324. https://doi.org/10.1890/1540-9295(2007)5[315:EIOWED]2.0.CO;2
[8] Litvaitis JA, Tash JP (2008) An Approach Toward Understanding Wildlife-Vehicle Collisions. Environmental Management, 42, 688–697. https://doi.org/10.1007/s00267-008-9108-4
[9] Medinas D, Marques JT, Costa P, Santos S, Rebelo H, Barbosa AM, Mira A (2021) Spatiotemporal persistence of bat roadkill hotspots in response to dynamics of habitat suitability and activity patterns. Journal of Environmental Management, 277, 111412. https://doi.org/10.1016/j.jenvman.2020.111412
[10] Pimm SL, Jenkins CN, Abell R, Brooks TM, Gittleman JL, Joppa LN, Raven PH, Roberts CM, Sexton JO (2014) The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344. https://doi.org/10.1126/science.1246752
[11] Roemer C, Coulon A, Disca T, Bas Y (2020) Influence of local landscape and time of year on bat-road collision risks. bioRxiv, 2020.07.15.204115, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.07.15.204115
[12] Scanes CG (2018) Chapter 19 - Human Activity and Habitat Loss: Destruction, Fragmentation, and Degradation. In: Animals and Human Society (eds Scanes CG, Toukhsati SR), pp. 451–482. Academic Press. https://doi.org/10.1016/B978-0-12-805247-1.00026-5
[13] Spellerberg I (1998) Ecological effects of roads and traffic: a literature review. Global Ecology & Biogeography Letters, 7, 317–333. https://doi.org/10.1046/j.1466-822x.1998.00308.x