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

Whether environmental conditions –in particular energy and water availability– are sufficient to account for species richness gradients (e.g. Currie 1991), or the effects of other biotic and historical or regional factors need to be considered as well (e.g. Ricklefs 1987), was the subject of debate during the 1990s and 2000s (e.g. Francis & Currie 2003; Hawkins et al. 2003, 2006; Currie et al. 2004; Ricklefs 2004). The metabolic theory of ecology (Brown et al. 2004) provided a solid and well-rooted theoretical support for the preponderance of energy as the main driver for richness variations. As any good piece of theory, it provided testable predictions about the sign and shape (i.e. slope) of the relationship between temperature –a key aspect of ambient energy– and species richness. However, these predictions were not supported by empirical evaluations (e.g. Kreft & Jetz 2007; Algar et al. 2007; Hawkins et al. 2007a), as the effects of a myriad of other environmental gradients, regional factors and evolutionary processes result in a wide variety of richness–temperature responses across different groups and regions (Hawkins et al. 2007b; Hortal et al. 2008). So, in a textbook example of how good theoretical work helps advancing science even if proves to be (partially) wrong, the evaluation of this aspect of the metabolic theory of ecology led to current understanding that, while species richness does respond to current climatic conditions, many other ecological, evolutionary and historical factors do modify such response across scales (see, e.g., Ricklefs 2008; Hawkins 2008; D’Amen et al. 2017). And the kinetic model linking mean annual temperature and species richness (Allen et al. 2002; Brown et al. 2004) was put aside as being, perhaps, another piece of the puzzle of the origin of current diversity gradients.

Segovia (2021) puts together an elegant way of reinvigorating this part of the metabolic theory of ecology. He uses quantile regressions to model just the upper parts of the relationship between species richness and mean annual temperature, rather than modelling its central tendency through the classical linear regression family of methods –as was done in the past. This assumes that the baseline effect of ambient energy does produce the negative linear relationship between richness and temperature predicted by the kinetic model (Allen et al. 2002), but also that this effect only poses an upper limit for species richness, and the effects of other factors may result in lower levels of species co-occurrence, thus producing a triangular rather than linear relationship. The results of Segovia’s simple and elegant analytical design show unequivocally that the predictions of the kinetic model become progressively more explanatory towards the upper quartiles of the relationship between species richness and temperature along over 10,000 tree local inventories throughout the Americas, reaching over 70% of explanatory power for the upper 5% of the relationship (i.e. the 95% quantile). This confirms to a large extent his reformulation of the predictions of the kinetic model. 

Further, the neat study from Segovia (2021) also provides evidence confirming that the well-known spatial non-stationarity in the richness–temperature relationship (see Cassemiro et al. 2007) also applies to its upper-bound segment. Both the explanatory power and the slope of the relationship in the 95% upper quantile vary widely between biomes, reaching values similar to the predictions of the kinetic model only in cold temperate environments ­–precisely where temperature becomes more important than water availability as a constrain to plant life (O’Brien 1998; Hawkins et al. 2003). Part of these variations are indeed related with changes in water deficit and number of frost days along the XXth Century, as shown by the residuals of this paper (Segovia 2021) and a more detailed separate study (Segovia et al. 2020). This pinpoints the importance of the relative balance between water and energy as two of the main climatic factors constraining species diversity gradients, confirming the value of hypotheses that date back to Humboldt’s work (see Hawkins 2001, 2008). There is however a significant amount of unexplained variation in Segovia’s analyses, in particular in the progressive departure of the predictions of the kinetic model as we move towards the tropics, or downwards along the lower quantiles of the richness–temperature relationship. This calls for a deeper exploration of the factors that modify the baseline relationship between richness and energy, opening a new avenue for the macroecological investigation of how different forces and processes shape up geographical diversity gradients beyond the mere energetic constrains imposed by the basal limitations of multicellular life on Earth.

References

Algar, A.C., Kerr, J.T. and Currie, D.J. (2007) A test of Metabolic Theory as the mechanism underlying broad-scale species-richness gradients. Global Ecology and Biogeography, 16, 170-178. doi: https://doi.org/10.1111/j.1466-8238.2006.00275.x

Allen, A.P., Brown, J.H. and Gillooly, J.F. (2002) Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science, 297, 1545-1548. doi: https://doi.org/10.1126/science.1072380

Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M. and West, G.B. (2004) Toward a metabolic theory of ecology. Ecology, 85, 1771-1789. doi: https://doi.org/10.1890/03-9000

Cassemiro, F.A.d.S., Barreto, B.d.S., Rangel, T.F.L.V.B. and Diniz-Filho, J.A.F. (2007) Non-stationarity, diversity gradients and the metabolic theory of ecology. Global Ecology and Biogeography, 16, 820-822. doi: https://doi.org/10.1111/j.1466-8238.2007.00332.x

Currie, D.J. (1991) Energy and large-scale patterns of animal- and plant-species richness. The American Naturalist, 137, 27-49. doi: https://doi.org/10.1086/285144

Currie, D.J., Mittelbach, G.G., Cornell, H.V., Field, R., Guegan, J.-F., Hawkins, B.A., Kaufman, D.M., Kerr, J.T., Oberdorff, T., O'Brien, E. and Turner, J.R.G. (2004) Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecology Letters, 7, 1121-1134. doi: https://doi.org/10.1111/j.1461-0248.2004.00671.x

D'Amen, M., Rahbek, C., Zimmermann, N.E. and Guisan, A. (2017) Spatial predictions at the community level: from current approaches to future frameworks. Biological Reviews, 92, 169-187. doi: https://doi.org/10.1111/brv.12222

Francis, A.P. and Currie, D.J. (2003) A globally consistent richness-climate relationship for Angiosperms. American Naturalist, 161, 523-536. doi: https://doi.org/10.1086/368223

Hawkins, B.A. (2001) Ecology's oldest pattern? Trends in Ecology & Evolution, 16, 470. doi: https://doi.org/10.1016/S0169-5347(01)02197-8 

Hawkins, B.A. (2008) Recent progress toward understanding the global diversity gradient. IBS Newsletter, 6.1, 5-8. https://escholarship.org/uc/item/8sr2k1dd

Hawkins, B.A., Field, R., Cornell, H.V., Currie, D.J., Guégan, J.-F., Kaufman, D.M., Kerr, J.T., Mittelbach, G.G., Oberdorff, T., O'Brien, E., Porter, E.E. and Turner, J.R.G. (2003) Energy, water, and broad-scale geographic patterns of species richness. Ecology, 84, 3105-3117. doi: https://doi.org/10.1890/03-8006

Hawkins, B.A., Diniz-Filho, J.A.F., Jaramillo, C.A. and Soeller, S.A. (2006) Post-Eocene climate change, niche conservatism, and the latitudinal diversity gradient of New World birds. Journal of Biogeography, 33, 770-780. doi: https://doi.org/10.1111/j.1365-2699.2006.01452.x

Hawkins, B.A., Albuquerque, F.S., Araújo, M.B., Beck, J., Bini, L.M., Cabrero-Sañudo, F.J., Castro Parga, I., Diniz-Filho, J.A.F., Ferrer-Castán, D., Field, R., Gómez, J.F., Hortal, J., Kerr, J.T., Kitching, I.J., León-Cortés, J.L., et al. (2007a) A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients. Ecology, 88, 1877-1888. doi:10.1890/06-1444.1. doi: https://doi.org/10.1890/06-1444.1

Hawkins, B.A., Diniz-Filho, J.A.F., Bini, L.M., Araújo, M.B., Field, R., Hortal, J., Kerr, J.T., Rahbek, C., Rodríguez, M.Á. and Sanders, N.J. (2007b) Metabolic theory and diversity gradients: Where do we go from here? Ecology, 88, 1898–1902. doi: https://doi.org/10.1890/06-2141.1

Hortal, J., Rodríguez, J., Nieto-Díaz, M. and Lobo, J.M. (2008) Regional and environmental effects on the species richness of mammal assemblages. Journal of Biogeography, 35, 1202–1214. doi: https://doi.org/10.1111/j.1365-2699.2007.01850.x

Kreft, H. and Jetz, W. (2007) Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences USA, 104, 5925-5930. doi: https://doi.org/10.1073/pnas.0608361104

O'Brien, E. (1998) Water-energy dynamics, climate, and prediction of woody plant species richness: an interim general model. Journal of Biogeography, 25, 379-398. doi: https://doi.org/10.1046/j.1365-2699.1998.252166.x

Ricklefs, R.E. (1987) Community diversity: Relative roles of local and regional processes. Science, 235, 167-171. doi: https://doi.org/10.1126/science.235.4785.167

Ricklefs, R.E. (2004) A comprehensive framework for global patterns in biodiversity. Ecology Letters, 7, 1-15. doi: https://doi.org/10.1046/j.1461-0248.2003.00554.x

Ricklefs, R.E. (2008) Disintegration of the ecological community. American Naturalist, 172, 741-750. doi: https://doi.org/10.1086/593002

Segovia, R.A. (2021) Temperature predicts the maximum tree-species richness and water and frost shape the residual variation. bioRxiv, 836338, ver. 4 peer-reviewed and recommended by Peer community in Ecology. doi: https://doi.org/10.1101/836338

Segovia, R.A., Pennington, R.T., Baker, T.R., Coelho de Souza, F., Neves, D.M., Davis, C.C., Armesto, J.J., Olivera-Filho, A.T. and Dexter, K.G. (2020) Freezing and water availability structure the evolutionary diversity of trees across the Americas. Science Advances, 6, eaaz5373. doi: https://doi.org/10.1126/sciadv.aaz5373

European farmland bird populations continue declining at an alarming rate, and some species require urgent action to avoid their demise (Silva et al. 2024). While factors such as climate change and urbanization also play an important role in driving the decline of farmland bird populations, its main driver seems to be linked with agricultural intensification (Rigal et al. 2023). Besides increased pesticide and fertilizer use, agricultural intensification often results in the homogenization of agricultural landscapes through the removal of seminatural linear features such as hedgerows, field margins, and grassy strips that can be beneficial for biodiversity. These features may be particularly important during the breeding season, when breeding farmland birds can benefit from patches of denser vegetation to conceal nests and improve breeding success. It is both important and timely to understand how landscape management can help to address the ongoing decline of farmland birds by identifying specific actions that can boost breeding success.

Perrot et al. 2023 contribute to this effort by exploring how red-legged partridges use linear features in an intensive agricultural landscape during the breeding season. Through a combination of targeted fieldwork and GPS tracking, the authors highlight patterns in home range size and habitat selection that provide insights for landscape management. Specifically, their results suggest that birds have smaller range sizes in the vicinity of traffic routes and seminatural features structured by both herbaceous and woody cover. Furthermore, they show that breeding birds tend to choose linear elements with herbaceous cover whereas non-breeders prefer linear elements with woody cover, underlining the importance of accounting for the needs of both breeding and non-breeding birds. In particular, the authors stress the importance of providing additional vegetation elements such as hedges, grassy strips or embankments in order to increase landscape heterogeneity. These landscape elements are usually found in the vicinity of linear infrastructures such as roads and tracks, but it is important they are available also in separate areas to avoid the risk of bird collision and the authors provide specific recommendations towards this end. Overall, this is an important study with clear recommendations on how to improve landscape management for these farmland birds.

References

Perrot, C., Séranne, L., Berceaux, A., Noel, M., Arroyo, B., & Bacon, L. (2023) "Use of linear features by red-legged partridges in an intensive agricultural landscape: implications for landscape management in farmland." bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.
https://doi.org/10.1101/2023.07.27.550774
 
Rigal, S., Dakos, V., Alonso, H., Auniņš, A., Benkő, Z., Brotons, L., ... & Devictor, V. (2023) "Farmland practices are driving bird population decline across Europe." Proceedings of the National Academy of Sciences 120.21: e2216573120.
https://doi.org/10.1073/pnas.2216573120
 
Silva, J. P., Gameiro, J., Valerio, F., & Marques, A. T. (2024) "Portugal's farmland bird crisis requires action." Science 383.6679: 157-157.
https://doi.org/10.1126/science.adn1390

Are you an ecologist who uses a computer or know someone that does? Even if your research doesn’t rely heavily on advanced computational techniques, it likely hasn’t escaped your attention that computers are increasingly being used to analyse field data and make predictions about the consequences of environmental change. So before artificial intelligence and robots take over from scientists, now is great time to read about how experts think computers could make your life easier and lead to innovations in ecological research. In “Data-based, synthesis-driven: setting the agenda for computational ecology”, Poisot and colleagues [1] provide a brief history of computational ecology and offer their thoughts on how computational thinking can help to bridge different types of ecological knowledge. In this wide-ranging article, the authors share practical strategies for realising three main goals: (i) tighter integration of data and models to make predictions that motivate action by practitioners and policy-makers; (ii) closer interaction between data-collectors and data-users; and (iii) enthusiasm and aptitude for computational techniques in future generations of ecologists. The key, Poisot and colleagues argue, is for ecologists to “engage in meaningful dialogue across disciplines, and recognize the currencies of their collaborations.” Yes, this is easier said than done. However, the journey is much easier with a guide and when everyone involved serves to benefit not only from the eventual outcome, but also the process.

References

[1] Poisot, T., Labrie, R., Larson, E., & Rahlin, A. (2018). Data-based, synthesis-driven: setting the agenda for computational ecology. BioRxiv, 150128, ver. 4 recommended and peer-reviewed by PCI Ecology. doi: 10.1101/150128

Parasitism requires parasites and hosts to meet and is therefore conditioned by their respective dispersal abilities. While dispersal has been studied in a number of wild vertebrates (including in relation to infection risk), we still have poor knowledge of the movements of their parasites. Yet we know that many parasites, and in particular vectors transmitting pathogens from host to host, possess the ability to move actively during at least part of their lives.
So... how far does a vector go – and is this reflected in the population structure of the pathogens they transmit? This is the question addressed by Rataud et al. [1], who provide the first attempt at using capture-mark-recapture to estimate not only functional dispersal, but also detection probability and survival in a wild parasite that is also a vector for other pathogens.
The authors find that (i) functional dispersal of soft ticks within a gull colony is very limited. Moreover, they observe unexpected patterns: (ii) experimental displacement of ticks does not induce homing behaviour, and (iii) despite lower survival, tick dispersal was lower in nests not containing hosts than in successful nests.
These results contrast with expectations based on the distribution of infectious agents. Low tick dispersal within the colony, combined with host territoriality during breeding and high site fidelity between years should result in a spatially structured distribution of infectious agents carried by ticks. This is not the case here. One possible explanation could be that soft ticks live for much longer than a breeding season, and that they disperse at other times of year to a larger extent than usually assumed.
This study represents one chapter of a story that will likely keep unfolding. It raises fascinating questions, and illustrates the importance of basic knowledge of parasite ecology and behaviour to better understand pathogen dynamics in the wild.

References
[1] Rataud A., Dupraz M., Toty C., Blanchon T., Vittecoq M., Choquet R. & McCoy K.D. (2019). Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite. Zenodo, 2592114. Ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.5281/zenodo.2592114