In most vertebrate species, the period of life spanning from departure from the growing site until reaching a more advanced life stage (immature or adult) is critical. During this period, juveniles are often highly vulnerable because they have not reached the morphological, physiological and behavioural maturity levels of adults yet and are therefore at high risk of mortality, e.g. through starvation, depredation or competition (e.g. Marchetti & Price 1989, Wunderle 1991, Naef-Daenzer & Grüebler 2016). In line with this, juvenile survival is most often far lower than adult survival (e.g. Wooller et al. 1992). In species with parental care, juveniles have to acquire behavioural independence from their parents and possibly establish their own territory during this period of life. Very often, this is also the period that is least well-known in the life cycle (Cox et al. 2014, Naef-Daenzer & Grüebler 2016) because of reduced accessibility to individuals and/or adoption of low conspicuous behaviours. Therefore, our understanding of how juveniles acquire typical adult behaviours and how this progressively increases their survival prospects is still very limited (Naef-Daenzer & Grüebler 2016), and questions such as the length of this transition period or the cognitive (e.g. learning, memorization) mechanisms involved remain largely unresolved. This is particularly true regarding the acquisition of independent foraging behaviour (Marchetti & Price 1989).

Because direct observations of juvenile behaviours are usually very difficult except in specific situations or at the cost of an enormous effort, the use of remote tracking devices can be particularly appealing in this context (e.g. Ponchon et al. 2013, Kays et al. 2015). Over the past decades, technical advances have allowed the monitoring of not only individuals’ movements at both large and small spatial scales but also their activities and behaviours based on different parameters recording e.g. speed of movement or diving depth (Whitford & Klimley 2019). Device miniaturization has in particular allowed smaller species to be equipped and/or longer periods of time to be monitored (e.g. Naef-Daenzer et al. 2005). This has opened up whole fields of research, and has been particularly used on marine seabirds. In these species, individuals are most often inaccessible when at sea, representing most of the time outside (and even within) the breeding season, and the life cycle of these long-lived species can include an extended immature period (up to many years) during which most of them will remain unseen, until they come back as breeders or pre-breeders (e.g. Wooller et al. 1992, Oro & Martínez-Abraín 2009). Survival has been found to increase gradually with age in these species before reaching high values characteristic of the adult stage. However, the mechanisms underlying this increase are still to be deciphered.

The study by Delord et al. (2023) builds upon the hypothesis that juveniles gradually learn foraging techniques and movement strategies, improving their foraging efficiency, as previous data on flight parameters seemed to show in different long-lived bird species. Yet, these previous studies obtained data over a limited period of time, i.e. a few months at best. Whether these data could capture the whole dynamics of the progressive acquisition of foraging and movement skills can only be assessed by measuring behaviour over a longer time period and comparing it to similar data in adults, to account for seasonal variation in relation to both resource availability and energetic demands, e.g. due to molt.

The present study (Delord et al. 2023) addresses these questions by taking advantage of longer-lasting recordings of the location and activity of juvenile, immature and adult birds obtained simultaneously to investigate changes over time in juvenile behaviour and thereby provide hints about how young progressively acquire foraging skills. This study is performed on Amsterdam albatrosses, a highly endangered long-lived sea bird, with obvious conservation issues (Thiebot et al. 2015). The results show progressive changes in foraging effort over the first two months after departure from the birth colony, but large differences remain between life stages over a much longer time frame. They also reveal strong variations between sexes and over time in the year. Overall, this study, therefore, confirms the need for very long-term data to be collected in order to address the question of progressive behavioural maturation and associated survival consequences in such species with strongly deferred maturity. Ideally, the same individuals should be monitored over different life stages, from the juvenile period up to adulthood, but this would require further technical development to release the issue of powering duration limitation.

As reviewers emphasized in the first review round, one main challenge now remains to ascertain the outcome of the observed behavioural changes in foraging behaviour: we expect them to reflect improvement in foraging skills and thus performance of juveniles over time, but this would need to be tested. Collecting data on foraging efficiency is yet another challenge, that future technical developments may also help overcome. Importantly also, data were available only for individuals that could be caught again because the tracking device had to be retrieved from the bird. Here, a substantial fraction of the loggers (one-fifth) could not be found again (Delord et al. 2023). To what extent the birds for which no data could be obtained are a random sample of the equipped birds would also need to be assessed. The further development of remote tracking techniques allowing data to be downloaded from a long distance should help further exploration of behavioural ontogeny of juveniles while maturing and its survival consequences. Because the maturation process explored here is likely to show very different characteristics (e.g. timing and speed) in smaller / shorter-lived species (see Cox et al. 2014, Naef-Daenzer & Grüebler 2016), the development of miniaturization is also expected to allow further investigation of post-fledging behavioural maturation in a wider range of bird species. Our understanding of this crucial life phase in different types of species should thus continue to progress in the coming years.

References

Cox W. A., Thompson F. R. III, Cox A. S. & Faaborg J. 2014. Post-fledging survival in passerine birds and the value of post-fledging studies to conservation. Journal of Wildlife Management, 78: 183-193. https://doi.org/10.1002/jwmg.670

Delord K., Weimerskirch H. & Barbraud C. 2023. The challenges of independence: ontogeny of at-sea behaviour in a long-lived seabird. bioRxiv, ver. 6 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.10.23.465439

Kays R., Crofoot M. C., Jetz W. & Wikelski M. 2015. Terrestrial animal tracking as an eye on life and planet. Science, 348 (6240). https://doi.org/10.1126/science.aaa2478

Marchetti K: & Price T. 1989. Differences in the foraging of juvenile and adult birds: the importance of developmental constraints. Biological Reviews, 64: 51-70. https://doi.org/10.1111/j.1469-185X.1989.tb00638.x

Naef-Daenzer B., Fruh D., Stalder M., Wetli P. & Weise E. 2005. Miniaturization (0.2 g) and evaluation of attachment techniques of telemetry transmitters. The Journal of Experimental Biology, 208: 4063–4068. https://doi.org/10.1242/jeb.01870

Naef-Daenzer B. & Grüebler M. U. 2016. Post-fledging survival of altricial birds: ecological determinants and adaptation. Journal of Field Ornithology, 87: 227-250. https://doi.org/10.1111/jofo.12157

Oro D. & Martínez-Abraín A. 2009. Ecology and behavior of seabirds. Marine Ecology, pp.364-389.

Ponchon A., Grémillet D., Doligez B., Chambert T., Tveera T., Gonzàles-Solìs J & Boulinier T. 2013. Tracking prospecting movements involved in breeding habitat selection: insights, pitfalls and perspectives. Methods in Ecology and Evolution, 4: 143-150. https://doi.org/10.1111/j.2041-210x.2012.00259.x

Thiebot J.-B., Delord K., Barbraud C., Marteau C. & Weimerskirch H. 2015. 167 individuals versus millions of hooks: bycatch mitigation in longline fisheries underlies conservation of Amsterdam albatrosses. Aquatic Conservation 26: 674-688. https://doi.org/10.1002/aqc.2578

Whitford M & Klimley A. P. An overview of behavioral, physiological, and environmental sensors used in animal biotelemetry and biologging studies. Animal Biotelemetry, 7: 26. https://doi.org/10.1186/s40317-019-0189-z

Wooller R.D., Bradley J. S. & Croxall J. P. 1992. Long-term population studies of seabirds. Trends in Ecology and Evolution, 7: 111-114. https://doi.org/10.1016/0169-5347(92)90143-y

Wunderle J. M. 1991. Age-specific foraging proficiency in birds. Current Ornithology, 8: 273-324.

Mimicry between species has long attracted the attention of scientists. Over a century ago, Bates first proposed that palatable species should gain a benefit by resembling unpalatable species (Bates 1862). Not long after, Müller suggested that there could also be a mutual advantage for two unpalatable species to mimic one another to reduce predator error (Müller 1879). These forms of mimicry, Batesian and Müllerian, are now widely studied, providing broad insights into behaviour, ecology and evolution.

Numerous taxa, including both invertebrates and vertebrates, show examples of Batesian or Müllerian mimicry. Bees and wasps provide a particularly interesting case due to the differences in defence between females and males of the same species. While both males and females may display warning colours, only females can sting and inject venom to cause pain and allow escape from predators. Therefore, males are palatable mimics and can resemble females of their own species or females of another species (dual sex-limited mimicry). This asymmetry in defence could have impacts on both population structure and community assembly, yet research into mimicry largely focuses on systems without sex differences.

Here, Boutin and colleagues (2023) use a differential equations model to explore the effect of mimicry on population structure and community assembly for sex-limited defended species. Specifically, they address three questions, 1) how do female noxiousness and sex-ratio influence the extinction risk of a single species?; 2) what is the effect of mimicry on species co-existence? and 3) how does dual sex-limited mimicry influence species co-existence? Their results reveal contexts in which populations with undefended males can persist, the benefit of Müllerian mimicry for species coexistence and that dual sex-limited mimicry can have a destabilising impact on species coexistence.

The results not only contribute to our understanding of how mimicry is maintained in natural systems but also demonstrate how changes in relative abundance or population structure of one species could impact another species. Further insight into the population and community dynamics of insects is particularly important given the current population declines (Goulson 2019; Seibold et al 2019).

References

Bates, H. W. 1862. Contributions to the insect fauna of the Amazon Valley, Lepidoptera: Heliconidae. Trans. Linn. Soc. Lond. 23:495- 566. https://doi.org/10.1111/j.1096-3642.1860.tb00146.x

Boutin, M., Costa, M., Fontaine, C., Perrard, A., Llaurens, V. 2022 Influence of sex-limited mimicry on extinction risk in Aculeata: a theoretical approach. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.10.21.513153

Goulson, D. 2019. The insect apocalypse, and why it matters. Curr. Biol. 29: R967-R971. https://doi.org/10.1016/j.cub.2019.06.069

Müller, F. 1879. Ituna and Thyridia; a remarkable case of mimicry in butterflies. Trans. Roy. Entom. Roc. 1879:20-29.

Seibold, S., Gossner, M. M., Simons, N. K., Blüthgen, N., Müller, J., Ambarlı, D., ... & Weisser, W. W. 2019. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature, 574: 671-674. https://doi.org/10.1038/s41586-019-1684-3

The secretion of extracellular polymeric substances (EPS) enables microorganisms to shape and interact with their environment [1]. EPS support cell adhesion and motility, offer protection from unfavorable conditions, and facilitate nutrient acquisition and transfer between microorganisms [2]. EPS production and consumption thus control the formation and structural organization of biofilms [3]. However, in marine environments, our understanding of the sources and composition of EPS is limited.
 
In this study, Hubas et al. [4] compare the carbon and nitrogen isotope ratios in EPS with the carbon isotope ratios of fatty acid biomarkers to identify the main EPS producers in intertidal sediments. The authors find pronounced differences in the diversity, composition, isotope signatures, and production/consumption dynamics of EPS between muddy and sandy environments. While the contribution of diatoms was highest in the bound fraction of EPS in muddy environments, diatom contribution was highest in the colloidal fraction of EPS in sandy environments. These differences between sites likely reflect the functional differences in EPS dynamics of epipelic and episammic sediment communities.
 
Taken together, the innovative approach of the authors provides insights into the diversity and origin of EPS in microphytobenthic communities and highlights the importance of different microbial groups in EPS production. These findings are vital for understanding EPS dynamics in microbial interactions and their role in the functioning of coastal ecosystems.

References

1. Flemming, H.-C. (2016) EPS-then and now. Microorganisms 4, 41 https://doi.org/10.3390/microorganisms4040041
2. Wolfaardt, G.M. et al. (1999) Function of EPS. In Microbial Extracellular Polymeric Substances, pp. 171–200, Springer Berlin Heidelberg https://doi.org/10.1007/978-3-642-60147-7
3. Flemming, H.-C. et al. (2007) The EPS matrix: the “house of biofilm cells.” J. Bacteriol. 189, 7945–7947 https://doi.org/10.1128/jb.00858-07
4. Hubas, C. et al. (2022) Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.12.02.516908

Tick-borne pathogens are considered as one of the major threats to public health – Lyme borreliosis being a well-known example of such disease. Global change – from climate change to changes in land use or invasive species – is playing a role in the increased risk associated with these pathogens. An important aspect of our knowledge of ticks and their associated pathogens is seasonality – one component being the phenology of within-year tick occurrences. This is important both in terms of health risk – e.g., when is the risk of encountering ticks high – and ecological understanding, as tick dynamics may depend on the weather as well as different hosts with their own dynamics and habitat use.

Hoch et al. (2023) provide a detailed data set on the phenology of one species of tick, Ixodes ricinus, in 6 different locations of France. Whereas relatively cool sites showed a clear peak in spring-summer, warmer sites showed in addition relatively high occurrences in fall-winter, with a minimum in late summer-early fall. Such results add robust data to the existing evidence of the importance of local climatic patterns for explaining tick phenology.

Recent analyses have shown that the phenology of Lyme borreliosis has been changing in northern Europe in the last 25 years, with seasonal peaks in cases occurring now 6 weeks earlier (Goren et al. 2023). The study by Hoch et al. (2023) is of too short duration to establish temporal changes in phenology (“only” 8 years, 2014-2021, see also Wongnak et al 2021 for some additional analyses; given the high year-to-year variability in weather, establishing phenological changes often require longer time series), and further work is needed to get better estimates of these changes and relate them to climate, land-use, and host density changes. Moreover, the phenology of ticks may also be related to species-specific tick phenology, and different tick species do not respond to current changes in identical ways (see for example differences between the two Ixodes species in Finland; Uusitalo et al. 2022). An efficient surveillance system requires therefore an adaptive monitoring design with regard to the tick species as well as the evolving causes of changes.

References

Goren, A., Viljugrein, H., Rivrud, I. M., Jore, S., Bakka, H., Vindenes, Y., & Mysterud, A. (2023). The emergence and shift in seasonality of Lyme borreliosis in Northern Europe. Proceedings of the Royal Society B: Biological Sciences, 290(1993), 20222420. https://doi.org/10.1098/rspb.2022.2420

Hoch, T., Madouasse, A., Jacquot, M., Wongnak, P., Beugnet, F., Bournez, L., . . . Agoulon, A. (2023). Seasonality of host-seeking Ixodes ricinus nymph abundance in relation to climate. bioRxiv, ver.4 peer-reviewed and recommended by Peer Community In Ecology. https://doi.org/10.1101/2022.07.25.501416

Uusitalo, R., Siljander, M., Lindén, A., Sormunen, J. J., Aalto, J., Hendrickx, G., . . . Vapalahti, O. (2022). Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland. Parasites & Vectors, 15(1), 310. https://doi.org/10.1186/s13071-022-05410-8

Wongnak, P., Bord, S., Jacquot, M., Agoulon, A., Beugnet, F., Bournez, L., . . . Chalvet-Monfray, K. (2022). Meteorological and climatic variables predict the phenology of Ixodes ricinus nymph activity in France, accounting for habitat heterogeneity. Scientific Reports, 12(1), 7833. https://doi.org/10.1038/s41598-022-11479-z

Scale is a big topic in ecology [1]. Environmental variation happens at particular scales. The typical scale at which organisms disperse is species-specific, but, as a first approximation, an ensemble of similar species, for instance, trees, could be considered to share a typical dispersal scale. Finally, characteristic spatial scales of species interactions are, in general, different from the typical scales of dispersal and environmental variation. Therefore, conceptually, we can distinguish these three characteristic spatial scales associated with three different processes: species selection for a given environment (E), dispersal (D), and species interactions (I), respectively.  

From the famous species-area relation to the spatial distribution of biomass and species richness, the different macro-ecological patterns we usually study emerge from an interplay between dispersal and local interactions in a physical environment that constrains species establishment and persistence in every location. To make things even more complicated, local environments are often modified by the species that thrive in them, which establishes feedback loops.  It is usually assumed that local interactions are short-range in comparison with species dispersal, and dispersal scales are typically smaller than the scales at which the environment varies (I < D < E, see [2]), but this should not always be the case. 

The authors of this paper [2] relax this typical assumption and develop a theoretical framework to study how diversity and ecosystem functioning are affected by different relations between the typical scales governing interactions, dispersal, and environmental variation. This is a huge challenge. First, diversity and ecosystem functioning across space and time have been empirically characterized through a wide variety of macro-ecological patterns. Second, accommodating local interactions, dispersal and environmental variation and species environmental preferences to model spatiotemporal dynamics of full ecological communities can be done also in a lot of different ways. One can ask if the particular approach suggested by the authors is the best choice in the sense of producing robust results, this is, results that would be predicted by alternative modeling approaches and mathematical analyses [3]. The recommendation here is to read through and judge by yourself.  

The main unusual assumption underlying the model suggested by the authors is non-local species interactions. They introduce interaction kernels to weigh the strength of the ecological interaction with distance, which gives rise to a system of coupled integro-differential equations. This kernel is the key component that allows for control and varies the scale of ecological interactions. Although this is not new in ecology [4], and certainly has a long tradition in physics ---think about the electric or the gravity field, this approach has been widely overlooked in the development of the set of theoretical frameworks we have been using over and over again in community ecology, such as the Lotka-Volterra equations or, more recently, the metacommunity concept [5].

In Physics, classic fields have been revised to account for the fact that information cannot travel faster than light. In an analogous way, a focal individual cannot feel the presence of distant neighbors instantaneously. Therefore, non-local interactions do not exist in ecological communities. As the authors of this paper point out, they emerge in an effective way as a result of non-random movements, for instance, when individuals go regularly back and forth between environments (see [6], for an application to infectious diseases), or even migrate between regions. And, on top of this type of movement, species also tend to disperse and colonize close (or far) environments. Individual mobility and dispersal are then two types of movements, characterized by different spatial-temporal scales in general. Species dispersal, on the one hand, and individual directed movements underlying species interactions, on the other, are themselves diverse across species, but it is clear that they exist and belong to two distinct categories. 

In spite of the long and rich exchange between the authors' team and the reviewers, it was not finally clear (at least, to me and to one of the reviewers) whether the model for the spatio-temporal dynamics of the ecological community (see Eq (1) in [2]) is only presented as a coupled system of integro-differential equations on a continuous landscape for pedagogical reasons, but then modeled on a discrete regular grid for computational convenience. In the latter case, the system represents a regular network of local communities,  becomes a system of coupled ODEs, and can be numerically integrated through the use of standard algorithms. By contrast,  in the former case, the system is meant to truly represent a community that develops on continuous time and space, as in reaction-diffusion systems. In that case, one should keep in mind that numerical instabilities can arise as an artifact when integrating both local and non-local spatio-temporal systems. Spatial patterns could be then transient or simply result from these instabilities. Therefore, when analyzing spatiotemporal integro-differential equations, special attention should be paid to the use of the right numerical algorithms. The authors share all their code at https://zenodo.org/record/5543191, and all this can be checked out. In any case, the whole discussion between the authors and the reviewers has inherent value in itself, because it touches on several limitations and/or strengths of the author's approach,  and I highly recommend checking it out and reading it through.

Beyond these methodological issues, extensive model explorations for the different parameter combinations are presented. Several results are reported, but, in practice, what is then the main conclusion we could highlight here among all of them?  The authors suggest that "it will be difficult to manage landscapes to preserve biodiversity and ecosystem functioning simultaneously, despite their causative relationship", because, first, "increasing dispersal and interaction scales had opposing
effects" on these two patterns, and, second, unexpectedly, "ecosystems attained the highest biomass in scenarios which also led to the lowest levels of biodiversity". If these results come to be fully robust, this is, they pass all checks by other research teams trying to reproduce them using alternative approaches, we will have to accept that we should preserve biodiversity on its own rights and not because it enhances ecosystem functioning or provides particular beneficial services to humans. 

References

[1] Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73:1943–1967. https://doi.org/10.2307/1941447

[2] Yuval R. Zelnik, Matthieu Barbier, David W. Shanafelt, Michel Loreau, Rachel M. Germain. 2023. Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.1101/2021.10.11.463913

[3] Baron, J. W. and Galla, T. 2020. Dispersal-induced instability in complex ecosystems. Nature Communications  11, 6032. https://doi.org/10.1038/s41467-020-19824-4

[4] Cushing, J. M. 1977. Integrodifferential equations and delay models in population dynamics 
 Springer-Verlag, Berlin. https://doi.org/10.1007/978-3-642-93073-7

[5] M. A. Leibold, M. Holyoak, N. Mouquet, P. Amarasekare, J. M. Chase, M. F. Hoopes, R. D. Holt, J. B. Shurin, R. Law, D. Tilman, M. Loreau, A. Gonzalez. 2004. The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters, 7(7): 601-613. https://doi.org/10.1111/j.1461-0248.2004.00608.x

[6] M. Pardo-Araujo, D. García-García, D. Alonso, and F. Bartumeus. 2023. Epidemic thresholds and human mobility. Scientific reports 13 (1), 11409. https://doi.org/10.1038/s41598-023-38395-0