Urbanisation modifies species’ habitats affecting their density, distribution, fitness, and behaviour with knock-on effects for their parasites’ abundance and transmission (Bradley & Altizer 2007). A meta-analysis found that changes in resource provisioning due to anthropogenic change can have both positive and negative effects on parasite infection in wildlife populations, but that feeding on urban waste had an effect of reducing infection, especially for helminths and protozoa (Becker, Streicker & Altizer 2015). Another study found that urbanisation reduced ectoparasite load in birds, but had no effect on endoparasites or avian flu (Reid et al. 2024). These changes may be due to novel diets reducing transmission via predation upon trophic hosts (Becker, Streicker & Altizer 2015) or behavioural, leading to more time available to preen (Reid et al. 2024). Less is known about how urbanisation affects invertebrates (but see Lewthwaite et al., 2024) and their parasites. This is important considering that invertebrates are often intermediate hosts of, and/or vector other parasites.

Recent work has found that snails and slugs can trap nematodes in their shells to prevent infection (Rae 2017). This newly discovered resistance mechanism reveals that the shell serves an immune defence function. It also provides a record of nematode exposure and documents incidences of resistance to infection as the trapped nematode becomes fixed onto the shell surface (Rae 2017). Dahirel and co-authors exploit this to investigate whether snail-nematode interactions change in response to increasing levels of urbanisation (Dahirel et al. 2024).

They explore whether the proportion of Cepaea nemoralis snails with trapped nematodes in their shell changes across an urbanisation gradient. They also explore whether different phenotypic snail traits, notably shell size, colour, band number and fusion explain the likelihood of having trapped nematodes in their shells. An increase in urbanisation was associated with a decrease in the proportion of snails with trapped nematodes in their shells. At the same time larger shells were more likely to have trapped nematodes, but this effect did not change across the urbanisation gradient. 

The authors discuss that reduced nematode encapsulation in urban environments may be due to lower encounter rate due to either fewer nematodes in urban environments, changes in snail behaviour reducing exposure, or alternatively that urban snails were less resistant to nematode infection. 

It will be interesting to investigate how this resistance mechanism is related to other forms of snail immunity and whether high rates of nematode encapsulation are an indicator of high resistance or high exposure. This will enable nematode trapping to be used as a marker to indicate environments and/or snail populations harbouring high levels of parasitism and further exploitation of museum collections to understand host-parasite interactions in the past (Rae 2017).

References

Becker, D.J., Streicker, D.G. & Altizer, S. (2015) Linking anthropogenic resources to wildlife-pathogen dynamics: a review and meta-analysis. Ecol Lett, 18, 483-495. https://doi.org/10.1111/ele.12428

Bradley, C.A. & Altizer, S. (2007) Urbanization and the ecology of wildlife diseases. Trends Ecol Evol, 22, 95-102. https://doi.org/10.1016/j.tree.2006.11.001

Maxime Dahirel, Hannah Reyné, Katrien De Wolf, Dries Bonte (2024) Urban Cepaea nemoralis snails are less likely to have nematodes trapped within their shells. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.03.07.583959

Lewthwaite, J.M.M., Baiotto, T.M., Brown, B.V., Cheung, Y.Y., Baker, A.J., Lehnen, C., McGlynn, T.P., Shirey, V., Gonzalez, L., Hartop, E., Kerr, P.H., Wood, E. & Guzman, L.M. (2024) Drivers of arthropod biodiversity in an urban ecosystem. Sci Rep, 14, 390. https://doi.org/10.1038/s41598-023-50675-3

Rae, R. (2017) The gastropod shell has been co-opted to kill parasitic nematodes. Sci Rep, 7, 4745. https://doi.org/10.1038/s41598-017-04695-5

Reid, R., Capilla-Lasheras, P., Haddou, Y., Boonekamp, J. & Dominoni, D.M. (2024) The impact of urbanization on health depends on the health metric, life stage and level of urbanization: a global meta-analysis on avian species. Proc Biol Sci, 291, 20240617. https://doi.org/10.1098/rspb.2024.0617

Understanding why and how species coexist in local communities is one of the central questions in ecology. There is general agreement that species distribution and coexistence are determined by a number of key mechanisms, including the environmental requirements of species, dispersal, evolutionary constraints, resource availability and selection, metapopulation dynamics, and biotic interactions (e.g. Soberón & Nakamura 2009; Colwell & Rangel 2009; Ricklefs 2015). These factors are however intricately intertwined in a scale-structured fashion (Hortal et al. 2010; D’Amen et al. 2017), making it particularly difficult to tease apart the effects of each one of them. This could be addressed by the novel field of Joint Species Distribution Modelling (JSDM; Okasvainen & Abrego 2020), as it allows assessing the effects of several sets of factors and the co-occurrence and/or covariation in abundances of potentially interacting species at the same time (Pollock et al. 2014; Ovaskainen et al. 2016; Dormann et al. 2018). However, the development of JSDM has been hampered by the general lack of good-quality detailed data on species co-occurrences and abundances (see Hortal et al. 2015).

Facon et al. (2021) use a particularly large compilation of field surveys to study the abundance and co-occurrence of Tephritidae fruit flies in c. 400 orchards, gardens and natural areas throughout the island of Réunion. Further, they combine such information with lab data on their host-selection fundamental niche (i.e. in the absence of competitors), codifying traits of female choice and larval performances in 21 host species. They use Poisson Log-Normal models, a type of mixed model that allows one to jointly model the random effects associated with all species, and retrieve the covariations in abundance that are not explained by environmental conditions or differences in sampling effort. Then, they use a series of models to evaluate the effects on these matrices of ecological covariates (date, elevation, habitat, climate and host plant), species interactions (by comparing with a constrained residual variance-covariance matrix) and the species’ host-selection fundamental niches (through separate models for each fly species).

The eight Tephritidae species inhabiting Réunion include both generalists and specialists in Solanaceae and Cucurbitaceae with a known history of interspecific competition. Facon et al. (2021) use a comprehensive JSDM approach to assess the effects of different factors separately and altogether. This allows them to identify large effects of plant hosts and the fundamental host-selection niche on species co-occurrence, but also to show that ecological covariates and weak –though not negligible– species interactions are necessary to account for all residual variance in the matrix of joint species abundances per site. Further, they also find evidence that the fitness per host measured in the lab has a strong influence on the abundances in each host plant in the field for specialist species, but not for generalists. Indeed, the stronger effects of competitive exclusion were found in pairs of Cucurbitaceae specialist species. However, these analyses fail to provide solid grounds to assess why generalists are rarely found in Cucurbitaceae and Solanaceae. Although they argue that this may be due to Connell’s (1980) ghost of competition past (past competition that led to current niche differentiation), further data on the evolutionary history of these fruit flies is needed to assess this hypothesis.

Finding evidence for the effects of competitive interactions on species’ occurrences and spatial distributions is often difficult, perhaps because these effects occur over longer time scales than the ones usually studied by ecologists (Yackulic 2017). The work by Facon and colleagues shows that weak effects of competition can be detected also at the short ecological timescales that determine coexistence in local communities, under the virtuous combination of good-quality data and sound analytical designs that account for several aspects of species’ niches, their biotopes and their joint population responses. This adds a new dimension to the application of Hutchinson’s (1978) niche framework to understand the spatial dynamics of species and communities (see also Colwell & Rangel 2009), although further advances to incorporate dispersal-driven metacommunity dynamics (see, e.g., Ovaskainen et al. 2016; Leibold et al. 2017) are certainly needed. Nonetheless, this work shows the potential value of in-depth analyses of species coexistence based on combining good-quality field data with well-thought out JSDM applications. If many studies like this are conducted, it is likely that the uprising field of Joint Species Distribution Modelling will improve our understanding of the hierarchical relationships between the different factors affecting species coexistence in ecological communities in the near future.

References

Colwell RK, Rangel TF (2009) Hutchinson’s duality: The once and future niche. Proceedings of the National Academy of Sciences, 106, 19651–19658. https://doi.org/10.1073/pnas.0901650106

Connell JH (1980) Diversity and the Coevolution of Competitors, or the Ghost of Competition Past. Oikos, 35, 131–138. https://doi.org/10.2307/3544421

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

Dormann CF, Bobrowski M, Dehling DM, Harris DJ, Hartig F, Lischke H, Moretti MD, Pagel J, Pinkert S, Schleuning M, Schmidt SI, Sheppard CS, Steinbauer MJ, Zeuss D, Kraan C (2018) Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecology and Biogeography, 27, 1004–1016. https://doi.org/10.1111/geb.12759

Facon B, Hafsi A, Masselière MC de la, Robin S, Massol F, Dubart M, Chiquet J, Frago E, Chiroleu F, Duyck P-F, Ravigné V (2021) Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of community structure in fruit flies. bioRxiv, 2020.12.07.414326. ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.1101/2020.12.07.414326

Hortal J, de Bello F, Diniz-Filho JAF, Lewinsohn TM, Lobo JM, Ladle RJ (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46, 523–549. https://doi.org/10.1146/annurev-ecolsys-112414-054400

Hortal J, Roura‐Pascual N, Sanders NJ, Rahbek C (2010) Understanding (insect) species distributions across spatial scales. Ecography, 33, 51–53. https://doi.org/10.1111/j.1600-0587.2009.06428.x

Hutchinson, G.E. (1978) An introduction to population biology. Yale University Press, New Haven, CT.

Leibold MA, Chase JM, Ernest SKM (2017) Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology, 98, 909–919. https://doi.org/10.1002/ecy.1697

Ovaskainen O, Abrego N (2020) Joint Species Distribution Modelling: With Applications in R. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108591720

Ovaskainen O, Roy DB, Fox R, Anderson BJ (2016) Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution, 7, 428–436. https://doi.org/10.1111/2041-210X.12502

Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5, 397–406. https://doi.org/10.1111/2041-210X.12180

Ricklefs RE (2015) Intrinsic dynamics of the regional community. Ecology Letters, 18, 497–503. https://doi.org/10.1111/ele.12431

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

Yackulic CB (2017) Competitive exclusion over broad spatial extents is a slow process: evidence and implications for species distribution modeling. Ecography, 40, 305–313. https://doi.org/10.1111/ecog.02836

To observe, characterise, identify, understand, predict... This is the approach that researchers follow every day. This sequence is tirelessly repeated as the biological model, the targeted ecosystem and/or the experimental, environmental or modelling conditions change. This way of proceeding is essential in a world of rapid change in response to the frenetic pace of intensifying pressures and forcings that impact ecosystems. To better understand our Earth and the dynamics of its components, to map ecosystems and diversity patterns, and to identify changes, humanity had to demonstrate inventiveness and defy gravity. 

Gustave Hermite and Georges Besançon were the first to launch aloft balloons equipped with radio transmitters, making possible the transmission of meteorological data to observers in real time [1]. The development of aviation in the middle of the 20th century constituted a real leap forward for the frequent acquisition of aerial observations, leading to a significant improvement in weather forecasting models. The need for systematic collection of data as holistic as possible – an essential component for the observation of complex biological systems - has resulted in pushing the limits of technological prowess. 

The conquest of space and the concurrent development of satellite observations has largely contributed to the collection of a considerable mass of data, placing our Earth under the "macroscope" - a concept introduced to ecology in the early 1970s by Howard T. Odum (see [2]), and therefore allowing researchers to move towards a better understanding of ecological systems, deterministic and stochastic patterns … with the ultimate goal of improving management actions [2,3]. Satellite observations have been carried out for nearly five decades now [3] and have greatly contributed to a better qualitative and quantitative understanding of the functioning of our planet, its diversity, its climate... and to a better anticipation of possible future changes (e.g., [4-7]).

This access to rich and complex sources of information, for which both spatial and temporal resolutions are increasingly fine, results in the implementation of increasingly complex computation-based analyses, in order to meet the need for a better understanding of ecological mechanisms and processes, and their possible changes. Steven Levitt stated that "Data is one of the most powerful mechanisms for telling stories". This is so true … Data should not be used as a guide to thinking and a critical judgment at each stage of the data exploitation process should not be neglected. 

This is what Mahood et al. [8] rightly remind us in their article "Ten simple rules for working with high-resolution remote sensing data" in which they provide the fundamentals to consider when working with data of this nature, a still underutilized resource in several topics, such as conservation biology [3]. In this unconventional article, presented in a pedagogical way, the authors remind different generations of readers how satellite data should be handled and processed. The authors aim to make the readers aware of the most frequent pitfalls encouraging them to use data adapted to their original question, the most suitable tools/methods/procedures, to avoid methodological overkill, and to ensure both ethical use of data and transparency in the research process. While access to high-resolution data is increasingly easy thanks to the implementation of dedicated platforms [4], and because of the development of easy-to-use processing software and pipelines, it is important to take the time to recall some of the essential rules and guidelines for managing them, from new users with little or no experience who will find in this article the recommendations, resources and advice necessary to start exploiting remote sensing data, to more experienced researchers.

References

[1] Jeannet P, Philipona R, and Richner H (2016). 8 Swiss upper-air balloon soundings since 1902. In: Willemse S, Furger M (2016) From weather observations to atmospheric and climate sciences in Switzerland: Celebrating 100 years of the Swiss Society for Meteorology. vdf Hochschulverlag AG. 

[2] Odum HT (2007) Environment, Power, and Society for the Twenty-First Century: The Hierarchy of Energy. Columbia University Press.

[3] Boyle SA, Kennedy CM, Torres J, Colman K, Pérez-Estigarribia PE, Sancha NU de la (2014) High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology. PLOS ONE, 9, e86908. https://doi.org/10.1371/journal.pone.0086908

[4] Le Traon P-Y, Antoine D, Bentamy A, Bonekamp H, Breivik LA, Chapron B, Corlett G, Dibarboure G, DiGiacomo P, Donlon C, Faugère Y, Font J, Girard-Ardhuin F, Gohin F, Johannessen JA, Kamachi M, Lagerloef G, Lambin J, Larnicol G, Le Borgne P, Leuliette E, Lindstrom E, Martin MJ, Maturi E, Miller L, Mingsen L, Morrow R, Reul N, Rio MH, Roquet H, Santoleri R, Wilkin J (2015) Use of satellite observations for operational oceanography: recent achievements and future prospects. Journal of Operational Oceanography, 8, s12–s27. https://doi.org/10.1080/1755876X.2015.1022050

[5] Turner W, Rondinini C, Pettorelli N, Mora B, Leidner AK, Szantoi Z, Buchanan G, Dech S, Dwyer J, Herold M, Koh LP, Leimgruber P, Taubenboeck H, Wegmann M, Wikelski M, Woodcock C (2015) Free and open-access satellite data are key to biodiversity conservation. Biological Conservation, 182, 173–176. https://doi.org/10.1016/j.biocon.2014.11.048

[6] Melet A, Teatini P, Le Cozannet G, Jamet C, Conversi A, Benveniste J, Almar R (2020) Earth Observations for Monitoring Marine Coastal Hazards and Their Drivers. Surveys in Geophysics, 41, 1489–1534. https://doi.org/10.1007/s10712-020-09594-5

[7] Zhao Q, Yu L, Du Z, Peng D, Hao P, Zhang Y, Gong P (2022) An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. Remote Sensing, 14, 1863. https://doi.org/10.3390/rs14081863

[8] Mahood AL, Joseph MB, Spiers A, Koontz MJ, Ilangakoon N, Solvik K, Quarderer N, McGlinchy J, Scholl V, Denis LS, Nagy C, Braswell A, Rossi MW, Herwehe L, Wasser L, Cattau ME, Iglesias V, Yao F, Leyk S, Balch J (2021) Ten simple rules for working with high resolution remote sensing data. OSFpreprints, ver. 6 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.31219/osf.io/kehqz

Understanding how livestock management is a source of social stress and disturbances for cattle is an important question with potential applications for animal welfare programs and sustainable development. In their article, Sosa and colleagues [1] first propose to evaluate the effects of individual characteristics on dyadic social relationships and on the social dynamics of four groups of cattle. Using network analyses, the authors provide an interesting and complete picture of dyadic interactions among groupmates. Although shown elsewhere, the authors demonstrate that individuals that are close in age and close in rank form stronger dyadic associations than other pairs. Second, the authors take advantage of some transfers of animals between groups -for management purposes- to assess how these transfers affect the social dynamics of groupmates. Their central finding is that the identity of transferred animals is a key-point. In particular, removing offspring strongly destabilizes the social relationships of mothers while adding a bull into a group also profoundly impacts female-female social relationships, as social networks before and after transfer of these key-animals are completely different. In addition, individuals, especially the young ones, that are transferred without familiar conspecifics take more time to socialize with their new group members than individuals transferred with familiar groupmates, generating a potential source of stress. Interestingly, the authors end up their article with some thoughts on the implications of their findings for animal welfare and ethics. This study provides additional evidence that empirical science has a major role to play in providing recommendations regarding societal questions such as livestock management and animal wellbeing.

References

[1] Sosa, S., Pelé, M., Debergue, E., Kuntz, C., Keller, B., Robic, F., Siegwalt-Baudin, F., Richer, C., Ramos, A., & Sueur C. (2018). Impact of group management and transfer on individual sociality in Highland cattle (Bos Taurus). arXiv:1805.11553v4 [q-bio.PE] peer-reviewed and recommended by PCI Ecol. https://arxiv.org/abs/1805.11553v4

In this study by Blouet, Bramanti, and Guizen (2022), the authors aim to tackle a long-standing data gap regarding research on marine benthic communities found on artificial reefs. The study is well thought out, and should serve as an important reference on this topic going forward.
Artificial reefs (ARs) are increasingly deployed in coastal waters around the world in order to reduce pressure on fisheries or to enhance fisheries stocks, via providing a hard substrate and complex shapes that induce the development of benthic communities, which together with the shape of the ARs themselves can provide areas for fish species to live. Much research has documented the effects of ARs on fish abundance and diversity, and documented over the short-term the benthic communities that settle and grow on ARs. However, there is a clear data gap on longer-term (e.g. greater than 10 years) trends of benthic communities on ARs. As well, any study on ARs must also account for the shape(s) of the ARs themselves, as there are numerous designs deployed, and also consider the depth of the ARs, and the age of the ARs.
The authors used the extensive ARs deployed in the Gulf of Lion in the northwestern Mediterranean to examine the effects of AR shape, depth, age (time since deployment), and location, both at local and wider regional scales, specifically examining the presence and absence of five marine species; 2 gorgonian octocorals, 1 ascidian, 1 annelid, and 1 bryozoan. Results indicate that location influenced the benthic communities above all other factors, suggesting the importance of considering the geographic location in future AR deployment and management of communities. The authors theorize that larval supply processes are important in shaping the observed patterns.
I conclude that this is an important report on AR ecology for several reasons. Firstly, the authors collected data from a variety of benthic species, including species that are habitat-forming but unfortunately perhaps not as focused on as more commercially important species. Secondly, by utilizing ARs deployed from as far back as the mid-1980s, the authors have generated longer-term information on benthic communities on ARs than what is commonly seen in the literature. Finally, the authors should be commended for their clever and hard work to incorporate all of the various factors into their analyses, and elucidating the importance of location. In fairness, this last point represents the only true limitation of the paper, as some of the statistical analyses were limited due to the small numbers of ARs fitting certain categories, and thereby limiting some of the conclusions. Still, it is very rare that a marine experimental ecologist would be in charge of AR deployment designs for 40 years, and the authors cannot be faulted for this shortcoming over which they had no control. On the contrary, the fact that the authors have performed this important work in the face of potentially limited analyses should be recognized. Marine ecology is often strongly limited by a lack of past data. In order to move past this impediment, more excellent work like the current paper is needed, conducted in a wider variety of ecosystems. I hope Blouet et al. (2022) can serve as a template for future work on a wider scale.
 
Reference

Blouet S, Bramanti L, Guizien K (2022) Artificial reefs geographical location matters more than shape, age and depth for sessile invertebrate colonization in the Gulf of Lion (NorthWestern Mediterranean Sea). bioRxiv, 2021.10.08.463669, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.10.08.463669