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

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.

Agricultural field margin plants, often referred to as “spontaneous” species, are key for the stabilization of several social-ecological processes related to crop production such as pollination or pest control (Tamburini et al. 2020). Because of its beneficial function, increasing the diversity of field margin flora becomes as important as crop diversity in process-based agricultures such as agroecology. Contrary, supply-dependent intensive agricultures produce monocultures and homogenized environments that might benefit their productivity, which generally includes the control or elimination of the field margin flora (Emmerson et al. 2016, Aligner 2018). Considering that different agricultural practices are produced by (and produce) different territories (Moore 2020) and that they are also been shaped by current climate change, we urgently need to understand how agricultural intensification constrains the potential of territories to develop agriculture more resilient to such change (Altieri et al., 2015). Thus, studies unraveling how agricultural practices' effects on agricultural field margin flora interact with those of climate change is of main importance, as plant strategies better adapted to such social-ecological processes may differ.        
 
In this vein, the study of Poinas et al. (2024) can be considered a key contribution. It exemplifies how agricultural intensification practiced in the context of climate change can constrain the potential of agricultural field margin flora to cope with climatic variations. The authors found that the incidence of plant strategies better adapted to climate change (conservative/stress-tolerant and Mediterranean species) increased with higher temperatures and lower soil moisture, and with lower intensity of margin management. In contrast, the incidence of ruderal species decreased with climate change. Thus, increasing or even maintaining current levels of agricultural intensification may affect the potential of French agriculture to move to sustainable process-based agricultures because of the reduction of plant diversity, particularly of vegetation better adapted to climate change. 
 
By using an impressive dataset spanning 9 years and 555 agricultural margins in continental France, Poinas et al. (2024) investigated temporal changes in climatic variables (temperature and soil moisture), agricultural practices (herbicide and fertilizers quantity, the frequency of margin mowing or grinding), plant taxonomical and functional diversity, plant strategies (Grime 1977, 1988) and relationships between these temporal changes. Temporal changes in plant strategies were associated with those observed in climatic variables and agricultural practices. Even such associations seem to be mediated by spatial changes, as described in the supplementary material and in their most recent article (Poinas et al. 2023), changes in climatic variables registered in a decade shaped plant strategies and therefore the diversity and functional potential of agricultural field margins. These results are clearly synthesized in Figures 6 and 7 of the present contribution.
 
As shown by Poinas et al. (2024), in the context of climate change, decreasing agricultural intensification will produce more diverse agricultural field margins by promoting the persistence of plant species better adapted to higher temperatures and lower soil moisture. Thus, adopting other agricultural practices (e.g., agroforestry, agroecology) will produce territories with a higher potential to move to sustainable processes-based agricultures that may better cope with climate change by harboring higher biocultural diversity (Altieri et al. 2015).

References

Alignier, A., 2018. Two decades of change in a field margin vegetation metacommunity as a result of field margin structure and management practice changes. Agric., Ecosyst. & Environ., 251, 1–10. https://doi.org/10.1016/j.agee.2017.09.013 

Altieri, M.A., Nicholls, C.I., Henao, A., Lana, M.A., 2015. Agroecology and the design of climate change-resilient farming systems. Agron. Sustain. Dev. 35, 869–890. https://doi.org/10.1007/s13593-015-0285-2

Emmerson, M., Morales, M. B., Oñate, J. J., Batary, P., Berendse, F., Liira, J., Aavik, T., Guerrero, I., Bommarco, R., Eggers, S., Pärt, T., Tscharntke, T., Weisser, W., Clement, L. & Bengtsson, J. (2016). How agricultural intensification affects biodiversity and ecosystem services. In Adv. Ecol. Res. 55, 43-97. https://doi.org/10.1016/bs.aecr.2016.08.005

Grime, J. P., 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. The American Naturalist, 111(982), 1169–1194. https://doi.org/10.1086/283244

Grime, J. P., 1988. The C-S-R model of primary plant strategies—Origins, implications and tests. In L. D. Gottlieb & S. K. Jain, Plant Evolutionary Biology (pp. 371–393). Springer Netherlands. https://doi.org/10.1007/978-94-009-1207-6_14

Moore, J., 2020. El capitalismo en la trama de la vida (Capitalism in The Web of Life). Traficantes de sueños, Madrid, Spain. 

Poinas, I., Fried, G., Henckel, L., & Meynard, C. N., 2023. Agricultural drivers of field margin plant communities are scale-dependent. Bas. App. Ecol. 72, 55-63. https://doi.org/10.1016/j.baae.2023.08.003

Poinas, I., Meynard, C. N., Fried, G., 2024. Functional trade-offs: exploring the temporal response of field margin plant communities to climate change and agricultural practices, bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.03.03.530956

Tamburini, G., Bommarco, R., Wanger, T.C., Kremen, C., Van Der Heijden, M.G., Liebman, M., Hallin, S., 2020. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715. https://doi.org/10.1126/sciadv.aba1715

The output of a community model depends on how you set its parameters. Thus, analyses of specific parameter settings hardwire the results to specific ecological scenarios. Because more general answers are often of interest, one tradition is to give models a statistical treatment: one summarizes how model parameters vary across species, and then predicts how changing the summary, instead of the individual parameters themselves, would change model output. Arguably the best-known example is the work initiated by May, showing that the properties of a community matrix, encoding effects species have on each other near their equilibrium, determine stability (1,2). More recently, this statistical treatment has also been applied to one of community ecology’s more prickly and slippery subjects: community assembly, which deals with the question “Given some regional species pool, which species will be able to persist together at some local ecosystem?”. Summaries of how species grow and interact in this regional pool predict the fraction of survivors and their relative abundances, the kind of dynamics, and various kinds of stability (3,4). One common characteristic of such statistical treatments is the assumption of disorder: if species do not interact in too structured ways, simple and therefore powerful predictions ensue that often stand up to scrutiny in relatively ordered systems. 
 
In their recent preprint, Miller, Clenet, et al. (5) subscribe to this tradition and consider tractable assembly scenarios (6) to study the outcome of assembly in a metacommunity. They recover a result of remarkable simplicity: roughly half of the species pool makes it into the final assemblage. Their vehicle is Tilman’s classic metacommunity model (7), where colonization rates are traded off with competitive ability. More precisely, in this model, one ranks species according to their colonization rate and attributes a greater competitive strength to lower-ranked species, which makes competition strictly hierarchical and thus departs from the disorder usually imposed by statistical approaches. The authors then leverage the simplicity of the species interaction network implied by this recursive setting to analytically probe how many species survive assembly. This turns out to be a fixed fraction that is distributed according to a Binomial with a mean of 0.5. While these results should not be extrapolated beyond the system at hand (4), they are important for two reasons. First, they imply that, within the framework of metacommunities driven by competition-colonization tradeoffs, richer species pools will produce richer communities: there is no upper bound on species richness, other than the one set by the raw material available for assembly. Second, this conclusion does not rely on simulation or equation solving and is, therefore, a hopeful sign of the palatability of the problem, if formalized in the right way. Their paper then shows that varying some of the settings does not change the main conclusion: changing how colonization rates distribute across species, and therefore the nature of the tradeoff, or the order with which species invade seems not to disrupt the big picture. Only when invaders are created “de novo” during assembly, a scenario akin to “de novo” mutation, a smaller fraction of species will survive assembly. 
 
As always, logical extensions of this study involve complicating the model and then looking if the results stay on par. The manuscript cites switching to other kinds of competition-colonization tradeoffs, and the addition of spatial heterogeneity as two potential avenues for further research. While certainly of merit, alternative albeit more bumpy roads would encompass models with radically different behavior. Most notably, one wonders how priority effects would play out. The current analysis shows that different invasion orders always lead to the same final composition, and therefore the same final species richness, confirming earlier results from models with similar structures (6). In models with priority effects, different invasion orders will surely lead to different compositions at the end. However, if one only cares about how many (and not which) species survive, it is unsure how much priority effects will qualitatively affect assembly. Because priority effects are varied in their topological manifestation (8), an important first step will be to evaluate which kinds of priority effects are compliant with formal analysis. 
 
References
 
1. May, R. M. (1972). Will a Large Complex System be Stable? Nature 238, 413–414. https://doi.org/10.1038/238413a0

2. Allesina, S. & Tang, S. (2015). The stability–complexity relationship at age 40: a random matrix perspective. Population Ecology, 57, 63–75. https://doi.org/10.1007/s10144-014-0471-0

3. Bunin, G. (2016). Interaction patterns and diversity in assembled ecological communities. Preprint at http://arxiv.org/abs/1607.04734.

4. Barbier, M., Arnoldi, J.-F., Bunin, G. & Loreau, M. (2018). Generic assembly patterns in complex ecological communities. Proceeding of the National Academy of Sciences, 115, 2156–2161. https://doi.org/10.1073/pnas.1710352115

5. Miller, Z. R., Clenet, M., Libera, K. D., Massol, F. & Allesina, S. (2023). Coexistence of many species under a random competition-colonization trade-off. bioRxiv 2023.03.23.533867, ver 3 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2023.03.23.533867

6. Serván, C. A. & Allesina, S. (2021). Tractable models of ecological assembly. Ecology Letters, 24, 1029–1037. https://doi.org/10.1111/ele.13702

7. Tilman, D. (1994). Competition and Biodiversity in Spatially Structured Habitats. Ecology, 75, 2–16. https://doi.org/10.2307/1939377

8. Song, C., Fukami, T. & Saavedra, S. (2021). Untangling the complexity of priority effects in multispecies communities. Ecolygy Letters, 24, 2301–2313. https://doi.org/10.1111/ele.13870

While many species' populations are declining, primarily due to human-related impacts (McKnee et al., 2014), certain species have thrived by utilizing human-influenced environments, leading to their population expansion (Muñoz & Real, 2006). In this context, the capacity to adapt and modify behaviors in response to new surroundings is believed to play a crucial role in facilitating species' spread to novel areas (Duckworth & Badyaev, 2007). For example, an increase in innovative behaviors within recently established communities could aid in discovering previously untapped food resources, while a decrease in exploration might reduce the likelihood of encountering dangers in unfamiliar territories (e.g., Griffin et al., 2016). To investigate the contribution of these behaviors to rapid range expansions, it is essential to directly measure and compare behaviors in various populations of the species.

The study conducted by Logan et al. (2023) aims to comprehend the role of behavioral changes in the range expansion of great-tailed grackles (Quiscalus mexicanus). To achieve this, the researchers compared the prevalence of specific behaviors at both the expansion's edge and its middle. Great-tailed grackles were chosen as an excellent model due to their behavioral adaptability, rapid geographic expansion, and their association with human-modified environments. The authors carried out a series of experiments in captivity using wild-caught individuals, following a detailed protocol. The study successfully identified differences in two of the studied behavioral traits: persistence (individuals participated in a larger proportion of trials) and flexibility variance (a component of the species' behavioral flexibility, indicating a higher chance that at least some individuals in the population could be more flexible). Notably, individuals at the edge of the population exhibited higher values of persistence and flexibility, suggesting that these behavioral traits might be contributing factors to the species' expansion. Overall, the study by Logan et al. (2023) is an excellent example of the importance of behavioral flexibility and other related behaviors in the process of species' range expansion and the significance of studying these behaviors across different populations to gain a better understanding of their role in the expansion process.

Finally, it is important to underline that this study is part of a pre-registration that received an In Principle Recommendation in PCI Ecology (Sebastián-González 2020) where objectives, methodology, and expected results were described in detail. The authors have identified any deviation from the original pre-registration and thoroughly explained the reasons for their deviations, which were very clear. 

References

Duckworth, R. A., & Badyaev, A. V. (2007). Coupling of dispersal and aggression facilitates the rapid range expansion of a passerine bird. Proceedings of the National Academy of Sciences, 104(38), 15017-15022. https://doi.org/10.1073/pnas.0706174104

Griffin, A.S., Guez, D., Federspiel, I., Diquelou, M., Lermite, F. (2016). Invading new environments: A mechanistic framework linking motor diversity and cognition to establishment success. Biological Invasions and Animal Behaviour, 26e46. https://doi.org/10.1017/CBO9781139939492.004

Logan, C. J., McCune, K., LeGrande-Rolls, C., Marfori, Z., Hubbard, J., Lukas, D. 2023. Implementing a rapid geographic range expansion - the role of behavior changes. EcoEvoRxiv, ver. 3 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.32942/X2N30J

McKee, J. K., Sciulli, P. W., Fooce, C. D., & Waite, T. A. (2004). Forecasting global biodiversity threats associated with human population growth. Biological Conservation, 115(1), 161-164. https://doi.org/10.1016/S0006-3207(03)00099-5

Muñoz, A. R., & Real, R. (2006). Assessing the potential range expansion of the exotic monk parakeet in Spain. Diversity and Distributions, 12(6), 656-665. https://doi.org/10.1111/j.1472-4642.2006.00272.x

Sebastián González, E. (2020) The role of behavior and habitat availability on species geographic expansion. Peer Community in Ecology, 100062. https://doi.org/10.24072/pci.ecology.100062. Reviewers: Caroline Nieberding, Tim Parker, and Pizza Ka Yee Chow.