Submit a preprint

Direct submissions to PCI Ecology from bioRxiv.org are possible using the B2J service

Latest recommendationsrsstwitter

IdTitle * Authors * Abstract * Picture * Thematic fields * RecommenderReviewersSubmission date
21 Dec 2020
article picture

Influence of local landscape and time of year on bat-road collision risks

Assessing bat-vehicle collision risks using acoustic 3D tracking

Recommended by ORCID_LOGO based on reviews by Mark Brigham and ?

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

Influence of local landscape and time of year on bat-road collision risksCharlotte Roemer, Aurélie Coulon, Thierry Disca, and Yves Bas<p>Roads impact bat populations through habitat loss and collisions. High quality habitats particularly increase bat mortalities on roads, yet many questions remain concerning how local landscape features may influence bat behaviour and lead to hi...Behaviour & Ethology, Biodiversity, Conservation biology, Human impact, Landscape ecologyGloriana Chaverri2020-07-20 10:56:29 View
03 Feb 2023
article picture

The role of climate change and niche shifts in divergent range dynamics of a sister-species pair

Drivers of range expansion in a pair of sister grackle species

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

The spatial distribution of a species is driven by both biotic and abiotic factors that may change over time (Soberón & Nakamura, 2009; Paquette & Hargreaves, 2021).  Therefore, species ranges are dynamic, especially in humanized landscapes where changes occur at high speeds (Sirén & Morelli, 2020). The distribution of many species is being reduced because of human impacts; however, some species are expanding their distributions, even over their niche (Lustenhouwer & Parker, 2022). One of the factors that may lead to a geographic niche expansion is behavioral flexibility (Mikhalevich et al., 2017), but the mechanisms determining range expansion through behavioral changes are not fully understood. 

The PCI Ecology study by Summers et al. (2023) uses a very large database on the current and historic distribution of two species of grackles that have shown different trends in their distribution. The great-tailed grackle has largely expanded its range over the 20th century, while the range of the boat-tailed grackle has remained very similar. They take advantage of this differential response in the distribution of the two species and run several analyses to test whether it was a change in habitat availability, in the realized niche, in habitat connectivity or in in the other traits or conditions that previously limited the species range, what is driving the observed distribution of the species. The study finds a change in the niche of great-tailed grackle, consistent with the high behavioral flexibility of the species.

The two reviewers and I have seen a lot of value in this study because 1) it addresses a very timely question, especially in the current changing world; 2) it is a first step to better understanding if behavioral attributes may affect species’ ability to change their niche; 3) it contrasts the results using several complementary statistical analyses, reinforcing their conclusions; 4) it is based on the preregistration Logan et al (2021), and any deviations from it are carefully explained and justified in the text and 5) the limitations of the study have been carefully discussed. It remains to know if the boat-tailed grackle has more limited behavioral flexibility than the great-tailed grackle, further confirming the results of this study.
 
References

Logan CJ, McCune KB, Chen N, Lukas D (2021) Implementing a rapid geographic range expansion - the role of behavior and habitat changes. http://corinalogan.com/Preregistrations/gxpopbehaviorhabitat.html

Lustenhouwer N, Parker IM (2022) Beyond tracking climate: Niche shifts during native range expansion and their implications for novel invasions. Journal of Biogeography, 49, 1481–1493. https://doi.org/10.1111/jbi.14395

Mikhalevich I, Powell R, Logan C (2017) Is behavioural flexibility evidence of cognitive complexity? How evolution can inform comparative cognition. Interface Focus, 7, 20160121. https://doi.org/10.1098/rsfs.2016.0121

Paquette A, Hargreaves AL (2021) Biotic interactions are more often important at species’ warm versus cool range edges. Ecology Letters, 24, 2427–2438. https://doi.org/10.1111/ele.13864

Sirén APK, Morelli TL (2020) Interactive range-limit theory (iRLT): An extension for predicting range shifts. Journal of Animal Ecology, 89, 940–954. https://doi.org/10.1111/1365-2656.13150

Soberón J, Nakamura M (2009) Niches and distributional areas: Concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106, 19644–19650. https://doi.org/10.1073/pnas.0901637106

Summers JT, Lukas D, Logan CJ, Chen N (2022) The role of climate change and niche shifts in divergent range dynamics of a sister-species pair. EcoEvoRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/879pe

The role of climate change and niche shifts in divergent range dynamics of a sister-species pairJeremy Summers, Dieter Lukas, Corina J. Logan, Nancy Chen<p>---This is a POST-STUDY manuscript for the PREREGISTRATION, which received in principle acceptance in 2020 from Dr. Sebastián González (reviewed by Caroline Nieberding, Tim Parker, and Pizza Ka Yee Chow; <a href="https://doi.org/10.24072/pci.ec...Behaviour & Ethology, Biogeography, Dispersal & Migration, Human impact, Landscape ecology, Preregistrations, Species distributionsEsther Sebastián González2022-05-26 20:07:33 View
06 Oct 2020
article picture

Does space use behavior relate to exploration in a species that is rapidly expanding its geographic range?

Explore and move: a key to success in a changing world?

Recommended by based on reviews by Joe Nocera, Marion Nicolaus and Laure Cauchard

Changes in the spatial range of many species are one of the major consequences of the profound alteration of environmental conditions due to human activities. Some species expand, sometimes spectacularly during invasions; others decline; some shift. Because these changes result in local biodiversity loss (whether local species go extinct or are replaced by colonizing ones), understanding the factors driving spatial range dynamics appears crucial to predict biodiversity dynamics. Identifying the factors that shape individual movement is a main step towards such understanding. The study described in this preregistration (McCune et al. 2020) falls within this context by testing possible links between individual exploration behaviour and movements related to daily space use in an avian study model currently rapidly expanding, the great-tailed grackle (Quiscalus mexicanus).

Movement and exploration: which direction(s) for the link between exploration and dispersal?
Individuals are known to differ in their tendency to explore the environment (Réale et al. 2007; Wolf and Weissing 2012) and therefore in their motivation to move. Accordingly, exploration has been shown to relate to dispersal behaviour, i.e. movements between breeding sites (Dingemanse et al. 2003, Le Galliard et al. 2011, Rasmussen and Belk 2012; reviews in Cote et al. 2010, Ronce et al. 2012). Yet, the mechanisms underlying this link often remain unclear, due to the correlative nature of the data. A classical assumption is that dispersers may benefit from a high capacity to explore, allowing them to familiarize quicker with their new environment once reached, thus alleviating dispersal costs (Bonte et al. 2012). The association between dispersal and exploration would in this case result from selection for this combination of traits (Ronce et al. 2012), even though dispersal event itself may be independent from (and precede the effect of) exploration behaviour. Alternatively (but not exclusively), dispersal may simply be the final outcome of longer movements by individuals exploring larger ranges (Badyaev et al. 1996, Schliehe-Diecks et al. 2012). In the absence of easy ways to manipulate dispersal behaviour, on the one hand, and exploration tendency, on the other hand, investigating detailed, small-scale individual movements in relation to exploration should thus shed light on which processes may yield the observed relations between exploration as an individual personality trait and large-scale, long-term movements, such as dispersal, underlying species range dynamics.
In this project, the exploration behaviour of grackles will be measured in controlled conditions using standardized tests in captivity (McCune et al. 2019) before individuals are released and their daily space use behaviour will then be measured using remote tracking over long time periods (McCune et al. 2020). Importantly, these coupled measures will be obtained for individuals captured in three different populations: within the historical range of the species, in the middle of its expanding range and at the edge of the range (McCune et al. 2020). Therefore, the project will test (i) whether daily space use of individuals is linked to their intrinsic exploration tendency and (ii) whether space use differs between individuals from different populations along the expanding range. The preregistration echoes a complementary project by the same team that will focus on exploration and test (iii) whether exploration tendency differs between individuals from these different populations. Taken together, these three analyses will therefore provide solid background information to assess the role of exploration in the individuals’ decisions leading to movement and range dynamics in this species.
As underlined in the preregistration, previous studies addressing the links between individual exploration behaviour and movements have mostly focused on dispersal. A first type of studies have (as will be done here) measured exploration behaviour of individuals, often in captivity (Dingemanse et al. 2003, Korsten et al. 2013) but also in the wild (Rasmussen and Belk 2012, Debeffe et al. 2013), and related these measures to subsequent dispersal behaviour. The (often implicit) underlying assumption is that more exploratory individuals will be more likely to move further, explore different habitats and thus end up breeding farther than less explorative ones. In other words, exploration tendency precedes and drives dispersal. Sometimes, exploratory behaviour is measured on individuals of known dispersal status, i.e. after the dispersal event (Hoset et al. 2011), in which case selection for certain exploration phenotypes among dispersers may already have occurred. Besides this first approach, another type of studies have measured ‘exploration’ behaviour under the form of prospecting movements of individuals and linked these movements to subsequent dispersal (often in the context of habitat selection). While these studies were in the past based on direct thus potentially biased observations (Reed et al. 1999), they now rely more and more on technological advances using (miniaturized) remote tracking devices (Ponchon et al. 2013) that provide far more complete and unbiased movement data, and sometimes also complementary measures of individuals’ internal state. In this case, the implicit assumption is that individuals prospecting farther and/or in more habitat patches will be more likely to settle in a site located farther away from their departure site, because of a more exhaustive sampling of possible sites allowing individuals to identify higher-quality sites (Badyaev et al. 1996). In other words, exploration tendency would not directly lead to higher movements or longer distances, but would allow individuals to optimize their habitat choice among more numerous options, thus leading to an increased dispersal probability or distance; the relation between exploration and dispersal would thus be indirect. Prospecting studies address more closely the underlying mechanisms of movement; however, they cannot easily separate intrinsic individual exploratory tendency from the prospecting movements themselves, with potential feedback effects of the information already gathered on future exploration of other sites or patches, thus on subsequent movements.
By focusing on individual daily space use movements as a mechanistic approach to understand large-scale movements potentially involved in colonization and range expansion, the grackle study described in this preregistration (McCune et al. 2020) will thus contribute to bridge the knowledge gaps between exploration and dispersal. By linking exploration measures obtained from a battery of standardized tests conducted in controlled conditions to individual daily space use and movements recorded in the wild, the grackle project is set in between previous studies addressing the links between exploration and dispersal: it will document exploration in a separate and independent context with respect to the movements themselves, and it will use a mechanistic view of detailed movements by the same individuals in the wild to explore potential implications for dispersal and range expansion. Testing differences between the three study populations over the species range will indeed inform about potential large-scale, population implications of among-individual variation in the link between exploration and movements. Because this study will only measure already settled adult individuals whose previous history is unknown, there will nevertheless be no direct possible exploration of the link with either previous or subsequent dispersal behaviour. Thus, the potential links studied here relate more directly to post-dispersal benefits of exploration for an optimal exploitation of the new environment. Yet, if exploration is a life-long personality trait linked to daily movement patterns, it may also relate to natal dispersal movements in young individuals.

Evolutionary and conservation perspectives
If the results of the project reveal that exploration tendency and daily space use movements are indeed linked, and that individuals from populations across the species range differ in these traits, new questions will emerge. A first question would be whether such among-individual differences are at the origin of range expansion or rather one of its consequences since, again, we deal with correlative data here. In other words, individuals may differ in exploration tendency, and this may confer them different ability to move around, find and colonize new habitats; or individuals may show differences in exploration following arrival in a new habitat, either because more explorative individuals gain fitness benefits and are thus selected, or because of behavioural plasticity and post-colonization adjustment of exploration behaviour when facing new ecological and social conditions in the new environment. Another open question relates to the link between daily space use and dispersal: is dispersal a by-product of higher daily movements that allow individuals to discover new favorable places where to settle? Exploring this link could involve measuring just fledged individuals before natal dispersal occurs and/or individuals chosen according to their own dispersal history, and this would then imply long-term population monitoring as an efficient (but constraining) tool to address such questions. Finally, assessing the fitness consequences of the link between exploration and space use behaviour, and whether these consequences differ between populations along the range expansion, would also be needed to understand the contribution of this link to the invasion success of this species.
The study model chosen for this project is a rapidly expanding species. Importantly, however, and as emphasized in the preregistration, documenting links between exploration and daily space use patterns as well as differences between populations with different trajectories can provide crucial information in general to understand population persistence in response to global climate and landscape changes, both regarding invasion ability or extinction risk. The information should be key to assess the probability that a species may decline, persist or expand in studies addressing biodiversity and community dynamics in a changing world.

References

Badayev, A. V., Martin, T. E and Etges, W. J. 1996. Habitat sampling and habitat selection by female wild turkeys: ecological correlates and reproductive consequences. Auk 113: 636-646. doi: https://doi.org/10.2307/4088984
Bonte, D. et al. 2012. Costs of dispersal. Biological Reviews 87: 290-312. doi: https://doi.org/10.1111/j.1469-185X.2011.00201.x
Cote, J., Clobert, J., Brodin, T., Fogarty, S. and Sih, A. 2010. Personality-dependent dispersal: characterization, ontogeny and consequences for spatially structured populations. Philosophical Transactions of the Royal Society B 365: 4065-4576. doi: https://doi.org/10.1098/rstb.2010.0176
Debeffe, L., Morellet, N., Cargnelutti, B., Lourtet, B., Coulon, A., Gaillard, J.-M., Bon, R. and Hewison A. J. M. 2013. Exploration as a key component of natal dispersal: dispersers explore more than philopatric individuals in roe deer. Animal Behaviour 86: 143-151. doi: https://doi.org/10.1016/j.anbehav.2013.05.005
Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. and Drent, P. J. 2003. Natal dispersal and personalities in great tits (Parus major). Proceedings of the Royal Society B 270: 741-747. doi: https://doi.org/10.1098/rspb.2002.2300
Hoset, K. S., Ferchaud, A.-L., Dufour, F., Mersch, D., Cote, J. and Le Galliard, J.-F. 2011. Natal dispersal correlates with behavioral traits that are not consistent across early life stages. Behavioral Ecology 22: 176–183. doi: https://doi.org/10.1093/beheco/arq188
Korsten, P., van Overveld, T., Adriaensen, F. and Matthysen, E. 2013. Genetic integration of local dispersal and exploratory behaviour in a wild bird. Nature Communications 4: 2362. doi: https://doi.org/10.1038/ncomms3362
Le Galliard, J.-F., Rémy, A., Ims, R. A. and Lambin, X. 2011. Patterns and processes of dispersal behaviour in arvicoline rodents. Molecular Ecology 21: 505-523. doi: https://doi.org/10.1111/j.1365-294X.2011.05410.x
McCune K, Ross C, Folsom M, Bergeron L, Logan CJ. 2020. Does space use behavior relate to exploration in a species that is rapidly expanding its geographic range? http://corinalogan.com/Preregistrations/gspaceuse.html In principle acceptance by PCI Ecology of the version on 23 Sep 2020 https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/gspaceuse.Rmd.
McCune K, MacPherson M, Rowney C, Bergeron L, Folsom M, Logan CJ. 2019. Is behavioral flexibility linked with exploration, but not boldness, persistence, or motor diversity? (http://corinalogan.com/Preregistrations/gexploration.html) In principle acceptance by PCI Ecology of the version on 27 Mar 2019 https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/gexploration.Rmd
Ponchon, A., Grémillet, D., Doligez, B., Chambert, T., Tveraa, T., González-Solís, J. and Boulinier, T. 2013. Tracking prospecting movements involved in breeding habitat selection: insights, pitfalls and perspectives. Methods in Ecology and Evolution 4: 143-150. doi: https://doi.org/10.1111/j.2041-210x.2012.00259.x
Rasmussen, J. E. and Belk, M. C. 2012. Dispersal behavior correlates with personality of a North American fish. Current Zoology 58: 260–270. doi: https://doi.org/10.1093/CZOOLO%2F58.2.260
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. and Dingemanse, N. J. 2007. Integrating animal temperament within ecology and evolution. Biological Reviews 82: 291-318. doi: https://doi.org/10.1111/j.1469-185x.2007.00010.x
Reed, J. M., Boulinier, T., Danchin, E. and Oring, L. W. 1999. Informed dispersal: prospecting by birds for breeding sites. Current Ornithology 15: 189-259. doi: https://doi.org/10.1007/978-1-4757-4901-4_5
Ronce, O. and Clobert, J. 2012. Dispersal syndromes. pp. 119-138 In Dispersal Ecology and Evolution (eds. Clobert, J., Baguette, M., Benton, T. G. and Bullock, J. M.), pp. 119-138. Oxford University Press.
Schliehe-Diecks, S., Eberle, M. and Kappeler, P. M. 2012. Walk the line - dispersal movements of gray mouse lemurs (Microcebus murinus). Behavioral Ecology and Sociobiology 66: 1175-1185. doi: https://dx.doi.org/10.1007%2Fs00265-012-1371-y
Wolf, M. and Weissing, F. J. 2012. Animal personalities: consequences for ecology and evolution. Trends in Ecology and Evolution 27: 452-461. doi: https://doi.org/10.1016/j.tree.2012.05.001

Does space use behavior relate to exploration in a species that is rapidly expanding its geographic range?Kelsey B. McCune, Cody Ross, Melissa Folsom, Luisa Bergeron, Corina LoganGreat-tailed grackles (Quiscalus mexicanus) are rapidly expanding their geographic range (Wehtje 2003). Range expansion could be facilitated by consistent behavioural differences between individuals on the range edge and those in other parts of th...Behaviour & Ethology, Biological invasions, Conservation biology, Habitat selection, Phenotypic plasticity, Preregistrations, Spatial ecology, Metacommunities & MetapopulationsBlandine Doligez2019-09-30 19:27:40 View
06 Oct 2020
article picture

Implementing a rapid geographic range expansion - the role of behavior and habitat changes

The role of behavior and habitat availability on species geographic expansion

Recommended by ORCID_LOGO based on reviews by Caroline Marie Jeanne Yvonne Nieberding, Pizza Ka Yee Chow, Tim Parker and 1 anonymous reviewer

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.

Implementing a rapid geographic range expansion - the role of behavior and habitat changesLogan CJ, McCune KB, Chen N, Lukas D<p>It is generally thought that behavioral flexibility, the ability to change behavior when circumstances change, plays an important role in the ability of a species to rapidly expand their geographic range (e.g., Lefebvre et al. (1997), Griffin a...Behaviour & Ethology, Biological invasions, Dispersal & Migration, Foraging, Habitat selection, Human impact, Phenotypic plasticity, Preregistrations, ZoologyEsther Sebastián GonzálezAnonymous, Caroline Marie Jeanne Yvonne Nieberding, Tim Parker2020-05-14 11:18:57 View
08 Aug 2020
article picture

Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator

While the quoll’s away, the mice will play… and the seeds will pay

Recommended by based on reviews by 2 anonymous reviewers

A predator can strongly influence the demography of its prey, which can have profound carryover effects on the trophic network; so-called density-mediated indirect interactions (DMII; Werner and Peacor 2003; Schmitz et al. 2004; Trussell et al. 2006). Furthermore, a novel predator can alter the phenotypes of its prey for traits that will change prey foraging efficiency. These trait-mediated indirect interactions may in turn have cascading effects on the demography and features of the basal resources consumed by the intermediate consumer (TMIII; Werner and Peacor 2003; Schmitz et al. 2004; Trussell et al. 2006), but very few studies have looked for these effects (Trusell et al. 2006). The study “Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator”, by Jolly et al. (2020) is therefore a much-needed addition to knowledge in this field. The authors have profited from a rare introduction of Northern quolls (Dasyurus hallucatus) on an Australian island, to examine both the density-mediated and trait-mediated indirect interactions with grassland melomys (Melomys burtoni) and the vegetation of their woodland habitat.
Jolly et al. (2020) compared melomys populations in four quoll-invaded and three quoll-free sites on the same island. Using capture-mark-recapture methods, they found a lower survival and decreased population size in quoll-invaded sites compared to quoll-free sites. Although they acknowledge that this decline could be attributable to either the direct effects of the predator or to a wildfire that occurred early in the experiment in the quoll-invaded sites, the authors argue that the wildfire alone cannot explain all of their results.
Beyond demographic effects, Jolly et al. (2020) also examined risk taking, foraging behaviour, and predator avoidance in melomys. Quoll presence was first associated with a strong decrease in risk taking in melomys, but the difference disappeared over the three years of study, indicating a possible adjustment by the prey. In quoll-invaded sites, though, melomys continued to be more neophobic than in the quoll-free sites throughout the study. Furthermore, in a seed (i.e. wheat) removal experiment, Jolly et al. (2020) measured how melomys harvested seeds in the presence or absence of predator scents. In both quoll-invaded and quoll-free sites, melomys density increased seed harvest efficiency. Melomys also removed less seeds in quoll-invaded sites than in quoll-free sites, supporting both the DMII and TMII hypotheses. However, in the quoll-invaded sites only, melomys foraged less on predator-scented seed patches than on unscented ones, trading foraging efficiency for an increased safety against predators, and this effect increased across the years. This last result indicates that predators can indirectly influence seed consumption through the trade-off between foraging and predator avoidance, strongly supporting the TMII hypothesis.
Ideally, the authors would have run a nice before-after, impact-control design, but nature does not always allow for ideal experimental designs. Regardless, the results of such an “experiment in the wild” predation study are still valuable, as they are very rare (Trussell et al. 2006), and they provide crucial information on the direct and indirect interactions along a trophic cascade. Furthermore, the authors have effectively addressed any concerns about potential confounding factors, and thus have a convincing argument that their results represent predator-driven demographic and behavioural changes.
One important question remains from an evolutionary ecology standpoint: do the responses of melomys to the presence of quolls represent phenotypically plastic changes or rapid evolutionary changes caused by novel selection pressures? Classically, TMII are assumed to be mostly caused by phenotypic plasticity (Werner and Peacor 2003), and this might be the case when the presence of the predator is historical. Phenotypic plasticity allows quick and reversible adjustments of the prey population to changes in the predator density. When the predator population declines, such rapid phenotypic changes can be reversed, reducing the cost associated with anti-predator behaviour (e.g., lower foraging efficiency) in the absence of predators. In the case of a novel predator, however, short-term evolutionary responses by the prey may play role in the TMII, as they would allow a phenotypic shift in prey’s traits along the trade-off between foraging efficiency and anti-predator response that will probably more advantageous over the longer term, if the predator does not disappear. The authors state that they could not rule out one or the other of these hypotheses. However, future work estimating the relative importance of phenotypic plasticity and evolutionary changes in the quoll-melomys system would be valuable. Phenotypic selection analysis, for example, by estimating the link between survival and the traits measured, might help test for a fitness advantage to altered behaviour in the presence of a predator. Common garden experiments, comparing the quoll-invaded and the quoll-free melomys populations, might also provide information on any potential evolutionary changes caused by predation. More work could also analyse the potential effects on the seed populations. Not only might the reduction in seed predation have consequences on the landscape in the future, as the authors mention, but it may also mean that the seeds themselves could be subject to novel selection pressures, which may affect their phenology, physiology or life history. Off course, the authors will have to switch from wheat to a more natural situation, and evaluate the effects of changes in the melomys population on the feature of the local vegetation and the ecosystem.
Finally, the authors have not yet found that the observed changes in the traits have translated into a demographic rebound for melomys. Here again, I can see an interesting potential for further studies. Should we really expect an evolutionary rescue (Bell and Gonzalez 2009) in this system? Alternatively, should the changes in behaviour be accompanied by permanent changes in life history, such as a slower pace-of-life (Réale et al. 2010) that could possibly lead to lower melomys density?
This paper provides nice in natura evidence for density- and trait-mediated indirect interactions hypotheses. I hope it will be the first of a long series of work on this interesting quoll-melomys system, and that the authors will be able to provide more information on the eco-evolutionary consequences of a novel predator on a trophic network.

References

-Bell G, Gonzalez A (2009) Evolutionary rescue can prevent extinction following environmental change. Ecology letters, 12(9), 942-948. https://doi.org/10.1111/j.1461-0248.2009.01350.x
-Jolly CJ, Smart AS, Moreen J, Webb JK, Gillespie GR, Phillips BL (2020) Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator. bioRxiv, 856997, ver. 6 peer-reviewed and recommended by PCI Ecology. https://doi.org/ 10.1101/856997
-Matassa C, Ewanchuk P, Trussell G (2018) Cascading effects of a top predator on intraspecific competition at intermediate and basal trophic levels. Functional Ecology, 32(9), 2241-2252. https://doi.org/10.1111/1365-2435.13131
-Réale D, Garant D, Humphries MM, Bergeron P, Careau V, Montiglio PO (2010) Personality and the emergence of the pace-of-life syndrome concept at the population level. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1560), 4051-4063. https://doi.org/10.1098/rstb.2010.0208
-Schmitz O, Krivan V, Ovadia O (2004) Trophic cascades: the primacy of trait‐mediated indirect interactions. Ecology Letters 7(2), 153-163. https://doi.org/10.1111/j.1461-0248.2003.00560.x
-Trussell G, Ewanchuk P, Matassa C (2006). Habitat effects on the relative importance of trait‐ and density‐mediated indirect interactions. Ecology Letters, 9(11), 1245-1252. https://doi.org/10.1111/j.1461-0248.2006.00981.x
-Werner EE, Peacor SD (2003) A review of trait‐mediated indirect interactions in ecological communities. Ecology, 84(5), 1083-1100. https://doi.org/10.1890/0012-9658(2003)084[1083:AROTII]2.0.CO;2

Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predatorChris J Jolly, Adam S Smart, John Moreen, Jonathan K Webb, Graeme R Gillespie and Ben L Phillips<p>The arrival of novel predators can trigger trophic cascades driven by shifts in prey numbers. Predators also elicit behavioural change in prey populations, via phenotypic plasticity and/or rapid evolution, and such changes may also contribute t...Behaviour & Ethology, Biological invasions, Evolutionary ecology, Experimental ecology, Foraging, Herbivory, Population ecology, Terrestrial ecology, Tropical ecologyDenis Réale2019-11-27 21:39:44 View
15 Jun 2020
article picture

Investigating the rare behavior of male parental care in great-tailed grackles

Studying a rare behavior in a polygamous bird: male parental care in great-tailed grackles

Recommended by based on reviews by Matthieu Paquet and André C Ferreira

The Great-tailed grackle (Quiscalus mexicanus) is a polygamous bird species that is originating from Central America and rapidly expanding its geographic range toward the North, and in which females were long thought to be the sole nest builders and caretakers of the young. In their pre-registration [1], Folsom and collaborators report repeated occurrences of male parental care and develop hypotheses aiming at better understanding the occurrence and the fitness consequences of this very rarely observed male behavior. They propose to assess if male parental care correlates with the circulating levels of several relevant hormones, increases offspring survival, is a local adaptation, and is a mating strategy, in surveying three populations located in Arizona (middle of the geographic range expansion), California (northern edge of the geographic range), and in Central America (core of the range). This study is part of a 5-year bigger project.
Both reviewers and I strongly value Folsom and collaborators’ commitment to program a study, in natural field conditions, of a rare, yet likely evolutionary-important behavior, namely parental care by males of the great-tailed grackle. Yet, we all also recognized that it is a risky endeavor, and as a consequence, we wondered about the authors’ ability to reach a sufficient sample size to statistically test (all) hypotheses and predictions with enough confidence (e.g. risk of type I errors, also known as false positives).
Folsom and collaborators acknowledged these limitations in their pre-registration. (i) They made the exploratory nature of their research work clear to readers. (ii) They adapted their analysis plan in running prior power analyses and in focusing on effect sizes (estimates and confidence intervals). (iii) Last and not least, Folsom and collaborators clearly exposed a priori hypotheses, their associated predictions and alternatives, and ranked the latter based on their plausibility according to knowledge in the current and other study systems. Developing theory about male parental care behavior more generally with regard to a polygamous species that is rapidly expanding its geographic range and that is considered not to provide male parental care is without any doubt an added value to this study.
In summary, while this study will likely be insufficient to fully understand male parental care behavior of great-tailed grackles, it is much needed because it will definitely allow rejecting some predictions (e.g., if this behavior is present in all the studied populations, it would be common across range against expectation; finding only one male providing care to an unrelated offspring would lead to reject the prediction that males only care for their own offspring) and thus it will help laying the foundation of future research directions.
I strongly support the pre-registration system and thank all the contributors for producing a fruitful discussion throughout the review process, which in fine improved the clarity and logic of this pre-registration. Given the positive and encouraging reviews, the detailed and thorough answers to all comments by Folsom and collaborators, and their satisfactory and interesting revisions, I am happy to recommend this pre-registration and I look forward to seeing its outcomes.

References

[1] Folsom MA, MacPherson M, Lukas D, McCune KB, Bergeron L, Bond A, Blackwell A, Rowney C, Logan CJ. 2020. Investigating the rare behavior of male parental care in great-tailed grackles. corinalogan.com/Preregistrations/gmalecare.html In principle acceptance by PCI Ecology of the version on 15 June 2020 corinalogan/grackles/blob/master/Files/Preregistrations/gmalecare.Rmd.

Investigating the rare behavior of male parental care in great-tailed gracklesFolsom MA, MacPherson M, Lukas D, McCune KB, Bergeron L, Bond A, Blackwell A, Rowney C, Logan CJThis is a PREREGISTRATION submitted for pre-study peer review. Our planned data collection START DATE is May 2020, therefore it would be ideal if the peer review process could be completed before then. Abstract: Great-tailed grackles (Quiscalus...Behaviour & Ethology, Biological invasions, Preregistrations, ZoologyMarie-Jeanne Holveck2019-12-05 17:38:47 View
12 Jan 2022
article picture

No Evidence for Long-range Male Sex Pheromones in Two Malaria Mosquitoes

The search for sex pheromones in malaria mosquitoes

Recommended by based on reviews by Marcelo Lorenzo and 1 anonymous reviewer

Pheromones are used by many insects to find the opposite sex for mating. Especially for nocturnal mosquitoes it seems logical that such pheromones exist as they can only partly rely on visual cues when flying at night. The males of many mosquito species form swarms and conspecific females fly into these swarms to mate. The two sibling species of malaria mosquitoes Anopheles gambiae s.s. and An. coluzzii coexist and both form swarms consisting of only one species. Although hybrids can be produced, these hybrids are rarely found in nature. In the study presented by Poda and colleagues (2022) it was tested if long-range sex pheromones exist in these two mosquito sibling species.

In a previous study by Mozūraites et al. (2020), five compounds (acetoin, sulcatone, octanal, nonanal and decanal) were identified that induced male swarming and increase mating success. Interestingly these compounds are frequently found in nature and have been shown to play a role in sugar feeding or host finding of An. gambiae. In the recommended study performed by Poda et al. (2022) no evidence of long-range sex pheromones in A. gambiae s.s. and An. coluzzii was found. The discrepancy between the two studies is difficult to explain but some of the methods varied between studies. Mozūraites et al. (2020) for example, collected odours from mosquitoes in small 1l glass bottles, where swarming is questionable, while in the study of Poda et al. (2022) 50 x 40 x 40 cm cages were used and swarming observed, although most swarms are normally larger. On the other hand, some of the analytical techniques used in the Mozūraites et al. (2020) study were more sensitive while others were more sensitive in the Poda et al. (2022) study. Because it is difficult to prove that something does not exist, the authors nicely indicate that “an absence of evidence is not an evidence of absence” (Poda et al., 2022). Nevertheless, recently colonized species were tested in large cage setups where swarming was observed and various methods were used to try to detect sex pheromones. No attraction to the volatile blend from male swarms was detected in an olfactometer, no antenna-electrophysiological response of females to male swarm volatile compounds was detected and no specific male swarm volatile was identified.

This study will open the discussion again if (sex) pheromones play a role in swarming and mating of malaria mosquitoes. Future studies should focus on sensitive real-time volatile analysis in mating swarms in large cages or field settings. In comparison to moths for example that are very sensitive to very specific pheromones and attract from a large distance, such a long-range specific pheromone does not seem to exist in these mosquito species. Acoustic and visual cues have been shown to be involved in mating (Diabate et al., 2003; Gibson and Russell, 2006) and especially at long distances, visual cues are probably important for the detection of these swarms.

References

Diabate A, Baldet T, Brengues C, Kengne P, Dabire KR, Simard F, Chandre F, Hougard JM, Hemingway J, Ouedraogo JB, Fontenille D (2003) Natural swarming behaviour of the molecular M form of Anopheles gambiae. Transactions of The Royal Society of Tropical Medicine and Hygiene, 97, 713–716. https://doi.org/10.1016/S0035-9203(03)80110-4

Gibson G, Russell I (2006) Flying in Tune: Sexual Recognition in Mosquitoes. Current Biology, 16, 1311–1316. https://doi.org/10.1016/j.cub.2006.05.053

Mozūraitis, R., Hajkazemian, M., Zawada, J.W., Szymczak, J., Pålsson, K., Sekar, V., Biryukova, I., Friedländer, M.R., Koekemoer, L.L., Baird, J.K., Borg-Karlson, A.-K., Emami, S.N. (2020) Male swarming aggregation pheromones increase female attraction and mating success among multiple African malaria vector mosquito species. Nature Ecology & Evolution, 4, 1395–1401. https://doi.org/10.1038/s41559-020-1264-9

Poda, S.B., Buatois, B., Lapeyre, B., Dormont, L., Diabate, A., Gnankine, O., Dabire, R.K.,  Roux, O. (2022) No evidence for long-range male sex pheromones in two malaria mosquitoes. bioRxiv, 2020.07.05.187542, ver. 6 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.07.05.187542

No Evidence for Long-range Male Sex Pheromones in Two Malaria MosquitoesSerge Bèwadéyir Poda, Bruno Buatois, Benoit Lapeyre, Laurent Dormont, Abdoulaye Diabaté, Olivier Gnankiné, Roch K. Dabiré, Olivier Roux<p style="text-align: justify;">Cues involved in mate seeking and recognition prevent hybridization and can be involved in speciation processes. In malaria mosquitoes, females of the two sibling species <em>Anopheles gambiae</em> s.s. and <em>An. ...Behaviour & Ethology, Chemical ecologyNiels Verhulst2021-04-26 12:28:36 View
28 Feb 2023
article picture

Acoustic cues and season affect mobbing responses in a bird community

Two common European songbirds elicit different community responses with their mobbing calls

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Many bird species participate in mobbing in which individuals approach a predator while producing conspicuous vocalizations (Magrath et al. 2014). Mobbing is interesting to behavioral ecologists because of the complex array of costs of benefits. Costs range from the obvious risk of approaching a predator while drawing that predator’s attention to the more mundane opportunity costs of taking time away from other activities, such as foraging. Benefits may involve driving the predator to leave, teaching relatives to recognize predators, signaling quality to conspecifics, or others. An added layer of complexity in this system comes from the inter-specific interactions that often occur among different mobbing species (Magrath et al. 2014).

This study by Salis et al. (2023) explored the responses of a local bird community to mobbing calls produced by individuals of two common mobbing species in European forests, coal tits, and crested tits. Not only did they compare responses to these two different species, they assessed the impact of the number of mobbing individuals on the stimulus recordings, and they did so at two very different times of the year with different social contexts for the birds involved, winter (non-breeding) and spring (breeding). The experiment was well-designed and highly powered, and the authors tested and confirmed an important assumption of their design, and thus the results are convincing. It is clear that members of the local bird community responded differently to the two different species, and this result raises interesting questions about why these species differed in their tendency to attract additional mobbers. For instance, are species that recruit more co-mobbers more effective at recruiting because they are more reliable in their mobbing behavior (Magrath et al. 2014), more likely to reciprocate (Krams and Krama, 2002), or for some other reason? Hopefully this system, now of proven utility thanks to the current study, will be useful for following up on hypotheses such as these. Other convincing results, such as the higher rate of mobbing response in winter than in spring, also merit following up with further work.

Finally, their observation that playback of vocalizations of multiple individuals often elicited a more mobbing response that the playback of vocalizations of a single individual are interesting and consistent with other recent work indicating that groups of mobbers recruit more additional mobbers than do single mobbers (Dutour et al. 2021). However, as acknowledged in the manuscript, the design of the current study did not allow a distinction between the effect of multiple individuals signaling versus an effect of a stronger stimulus. Thus, this last result leaves the question of the effect of mobbing group size in these species open to further study.

REFERENCES

Dutour M, Kalb N, Salis A, Randler C (2021) Number of callers may affect the response to conspecific mobbing calls in great tits (Parus major). Behavioral Ecology and Sociobiology, 75, 29. https://doi.org/10.1007/s00265-021-02969-7

Krams I, Krama T (2002) Interspecific reciprocity explains mobbing behaviour of the breeding chaffinches, Fringilla coelebs. Proceedings of the Royal Society of London. Series B: Biological Sciences, 269, 2345–2350. https://doi.org/10.1098/rspb.2002.2155

Magrath RD, Haff TM, Fallow PM, Radford AN (2015) Eavesdropping on heterospecific alarm calls: from mechanisms to consequences. Biological Reviews, 90, 560–586. https://doi.org/10.1111/brv.12122

Salis A, Lena JP, Lengagne T (2023) Acoustic cues and season affect mobbing responses in a bird community. bioRxiv, 2022.05.05.490715, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.05.05.490715

Acoustic cues and season affect mobbing responses in a bird communityAmbre Salis, Jean Paul Lena, Thierry Lengagne<p>Heterospecific communication is common for birds when mobbing a predator. However, joining the mob should depend on the number of callers already enrolled, as larger mobs imply lower individual risks for the newcomer. In addition, some ‘communi...Behaviour & Ethology, Community ecology, Social structureTim Parker2022-05-06 09:29:30 View
20 Jun 2024
article picture

Spider mites collectively avoid plants with cadmium irrespective of their frequency or the presence of competitors

We are better together: Spider mites running away from Cadmium contaminated plants make better decisions collectively than individually

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Hyperaccumulator plants can concentrate heavy metals present on the soil in their tissues, avoiding their toxic effects and potentially discouraging herbivores (Martens & Boyd, 1994). But not all herbivores are necessarily discouraged, and access to locally abundant resources with low interspecific competition from other herbivores, can affect feeding choices. Godinho et al. performed a series of controlled laboratorial trials to evaluate if herbivores (spider mites) avoid tomato plants with high concentrations of Cadmium under alternative scenarios, namely: the presence/absence of conspecifics, the presence/absence of a competitor species (a congeneric mite), and the relative abundance of contaminated plants.

They found that when looking for plants to lay their eggs, individual spider-mites (females) do not seem to discriminate between plants with or without cadmium, despite a significantly lower performance on the former. However, they consistently chose plants without Cadmium in set-ups where 200 mites are faced with this decision together. This preference was consistent and independent from the relative abundance of cadmium-free plants, but only when mites do this decision collectively. In addition, this preference was stronger than that for plants where interspecific competition was lower, with mites preferring to face high competition from congeneric herbivores than laying their eggs on Cadmium contaminated plants. 

Taken together these experiments suggest that aggregation is a key mechanism by which spider mites can avoid metal contaminated plants. As good research often does, these experiments open several important questions that will need to be addressed in the future. In particular, it will be important to clarify what are the sensorial and behavioural mechanisms that allow this decision/outcome when spider mites make this choice collectively but lead to a different outcome (no choice) when they face this decision alone. Additionally, it will be interesting to explore the potentially adaptive (or non-adaptive) consequences of this behaviour in terms of individual and inclusive fitness. One thing seems certain: both the abiotic and the biotic context can affect spider mite choices, and both need to be considered to advance our understanding about the trade-offs between plant defence mechanisms and associated herbivore decisions and fitness. 

References

Martens, S. N., & Boyd, R. S. (1994). The ecological significance of nickel hyperaccumulation: a plant chemical defense. Oecologia, 98(3–4), 379–384. https://doi.org/10.1007/BF00324227

Godinho, D. P., I. Fragata, M. C. de la Masseliere, S. Magalhaes 2024 Spider mites collectively avoid plants with cadmium irrespective of their frequency or the presence of competitors. bioRxiv, ver. 4, peer-reviewed and recommended by PCI Ecology 2023.08.17.553707. https://doi.org/10.1101/2023.08.17.553707

 

Spider mites collectively avoid plants with cadmium irrespective of their frequency or the presence of competitorsDiogo Prino Godinho*, Ines Fragata*, Maud Charlery de la Masseliere, Sara Magalhaes<p>1. Plants can accumulate heavy metals from polluted soils on their shoots and use this to defend themselves against herbivory. One possible strategy for herbivores to cope with the reduction in performance imposed by heavy metal accumulation in...Behaviour & Ethology, Competition, Habitat selection, HerbivoryRuben Heleno2023-11-09 11:52:58 View
24 Mar 2023
article picture

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