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

In the recent years, morphometrics, the quantitative description of shape and its covariation [1] gained considerable momentum in evolutionary ecology. Using the form of organisms to describe, classify and try to understand their diversity can be traced back at least to Aristotle. More recently, two successive revolutions rejuvenated this idea [1–3]: first, a proper mathematical refoundation of the theory of shape, then a technical revolution in the apparatus able to acquire raw data. By using a feature extraction method and planning its massive use on data acquired by aerial drones, the study by Lacaux and colleagues [4] retraces this curse of events.
The radial symmetry of Aurelia spp. jelly fish, a common species complex, is affected by stress and more largely by environmental variations, such as pollution exposition. Aurelia spp. normally present four gonads so that the proportion of non-tetramerous individuals in a population has been proposed as a biomarker [5,6].
In this study, the authors implemented the Hough transform to largely automate the detection of the gonads in Aurelia spp. Such use of the Hough transform, a long-used approach to identify shapes through edge detection, is new to morphometrics. Here, the Aurelia spp. gonads are identified as ellipses from which aspect descriptors can be derived, and primarily counted and thus can be used to quantify the proportion of individuals presenting body plans disorders.

The sample sizes studied here were too low to allow finer-grained ecophysiological investigations. That being said, the proof-of-concept is convincing and this paper paths the way for an operational and innovative approach to the ecological monitoring of sensible aquatic ecosystems.

References

[1] Kendall, D. G. (1989). A survey of the statistical theory of shape. Statistical Science, 87-99. doi: https://doi.org/10.1214/ss/1177012589
[2] Rohlf, F. J., and Marcus, L. F. (1993). A revolution morphometrics. Trends in ecology & evolution, 8(4), 129-132. doi: https://doi.org/10.1016/0169-5347(93)90024-J
[3] Adams, D. C., Rohlf, F. J., and Slice, D. E. (2004). Geometric morphometrics: ten years of progress following the ‘revolution’. Italian Journal of Zoology, 71(1), 5-16. doi: https://doi.org/10.1080/11250000409356545
[4] Lacaux, C., Desolneux, A., Gadreaud, J., Martin-Garin, B. and Thiéry, A. (2020) Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.). bioRxiv, 2020.03.11.986984, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. doi: https://doi.org/10.1101/2020.03.11.986984
[5] Gershwin, L. A. (1999). Clonal and population variation in jellyfish symmetry. Journal of the Marine Biological Association of the United Kingdom, 79(6), 993-1000. doi: https://doi.org/10.1017/S0025315499001228
[6] Gadreaud, J., Martin-Garin, B., Artells, E., Levard, C., Auffan, M., Barkate, A.-L. and Thiéry, A. (2017) The moon jellyfish as a new bioindicator: impact of silver nanoparticles on the morphogenesis. In: Mariottini GL, editor. Jellyfish: ecology, distribution patterns and human interactions. Nova Science Publishers; 2017. pp. 277–292.

Species Distribution Models (SDMs) are one of the most commonly used tools to predict where species are, where they may be in the future, and, at times, what are the variables driving this prediction. As such, applying an SDM to a dataset is akin to making a bet: that the known occurrence data are informative, that the resolution of predictors is adequate vis-à-vis the scale at which their impact is expressed, and that the model will adequately capture the shape of the relationships between predictors and predicted occurrence.

In this contribution, Lambert & Virgili (2023) perform a comprehensive assessment of different sources of complications to this process, using replicated simulations of two synthetic species. Their experimental process is interesting, in that both the data generation and the data analysis stick very close to what would happen in "real life". The use of synthetic species is particularly relevant to the assessment of SDM robustness, as they enable the design of species for which the shape of the relationship is given: in short, we know what the model should capture, and can evaluate the model performance against a ground truth that lacks uncertainty.

Any simulation study is limited by the assumptions established by the investigators; when it comes to spatial data, the "shape" of the landscape, both in terms of auto-correlation and in where the predictors are available. Lambert & Virgili (2023) nicely circumvent these issues by simulating synthetic species against the empirical distribution of predictors; in other words, the species are synthetic, but the environment for which the prediction is made is real. This is an important step forward when compared to the use of e.g. neutral landscapes (With 1997), which can have statistical properties that are not representative of natural landscapes (see e.g. Halley et al., 2004).

A striking point in the study by Lambert & Virgili (2023) is that they reveal a deep, indeed deeper than expected, stochasticity in SDMs; whether this is true in all models remains an open question, but does not invalidate their recommendation to the community: the interpretation of outcomes is a delicate exercise, especially because measures that inform on the goodness of the model fit do not capture the predictive quality of the model outputs. This preprint is both a call to more caution, and a call to more curiosity about the complex behavior of SDMs, while also providing a sensible template to perform future analyses of the potential issues with predictive models.

References

Halley, J. M., et al. (2004) “Uses and Abuses of Fractal Methodology in Ecology: Fractal Methodology in Ecology.” Ecology Letters, vol. 7, no. 3, pp. 254–71. https://doi.org/10.1111/j.1461-0248.2004.00568.x.

Lambert, Charlotte, and Auriane Virgili (2023). Data Stochasticity and Model Parametrisation Impact the Performance of Species Distribution Models: Insights from a Simulation Study. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.01.17.524386

With, Kimberly A. (1997) “The Application of Neutral Landscape Models in Conservation Biology. Aplicacion de Modelos de Paisaje Neutros En La Biologia de La Conservacion.” Conservation Biology, vol. 11, no. 5, pp. 1069–80. https://doi.org/10.1046/j.1523-1739.1997.96210.x.

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

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