Researchers do not live in a vacuum, and the social context we live in affects how we do science. On one hand, increased competition for scarce funding creates the wrong incentives to do fast analysis, leading sometimes to poorly checked results that accumulate errors (Fraser et al. 2018). On the other hand, the actual challenges the world faces require more than ever robust scientific evidence that can be used to tackle the current rapid human-induced environmental change. Moreover, scientists' credibility is at stake at this moment where the global flow of information can be politically manipulated, and accessing reliable sources of information is paramount for society. At the crossroads of these challenges is scientific reproducibility. Making our results transparent and reproducible ensures that no perverse incentives can compromise our findings, that results can be reliably applied to solve relevant problems, and that we regain societal credibility in the scientific process. Unfortunately, in ecology and evolution, we are still far from publishing open, transparent, and reproducible papers (Maitner et al. 2024). Understanding which factors promote increased use of good practices regarding reproducibility is hence very welcome.

Sanchez-Tojar and colleagues (2025) conducted a (reproducible) analysis of code and data-sharing practices (a cornerstone of scientific reproducibility) in journals with and without explicit policies regarding data and code deposition. The gist is that having policies in place increases data and code sharing. Doing science about how we do science (meta-science) is important to understand which actions drive our behavior as scientists. This paper highlights that in the absence of strong societal or personal incentives to share code and data, clear policies can catalyze this process. However, in my opinion, policies are a needed first step to consolidate a more permanent change in researchers' behavior regarding reproducible science, but policies alone will not be enough to fix the problem if we do not change also the cultural values around how we publish science. Appealing to inner values, and recognizing science needs to be reproducible to ensure potential errors are easily spotted and corrected requires a deep cultural change. 

References

Fraser, Hannah, Tim Parker, Shinichi Nakagawa, Ashley Barnett, and Fiona Fidler. "Questionable research practices in ecology and evolution." PloS one 13, no. 7 (2018): e0200303. https://doi.org/10.1371/journal.pone.0200303

Maitner, Brian, Paul Efren Santos Andrade, Luna Lei, Jamie Kass, Hannah L. Owens, George CG Barbosa, Brad Boyle et al. "Code sharing in ecology and evolution increases citation rates but remains uncommon." Ecology and Evolution 14, no. 8 (2024): e70030.   https://doi.org/10.1002/ece3.70030

Alfredo Sánchez-Tójar, Aya Bezine, Marija Purgar, Antica Culina (2025) Code-sharing policies are associated with increased reproducibility potential of ecological findings. EcoEvoRxiv, ver.4 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.32942/X21S7H

Islands may represent just a small fraction (6.67%) of the planet’s land but they host a disproportionate 20% of the world’s biodiversity. Yet islands are highly vulnerable to human-induced change. Out of all IUCN Red list species, almost half of them are found on islands (Russell and Kueffer, 2019) while from the approximately 800 known extinctions that have occurred since the European expansion around the world, 75% have occurred on islands (IUCN, 2017).
 
Vulnerability is defined as “the degree to which a system is likely to experience harm due to exposure to a hazard” (Fuessel, 2007). It is meaningful to express vulnerability let s say of a population or species to a specific threat, pressure, or stress (like for instance the highly studied species vulnerability to climate change (Pacifici et al., 2015). Vulnerability is typically made up of three components: exposure (the extent of stress or threat that the species encounters and is projected to encounter), sensitivity (the ability of a species to persist under a given stress or threat), adaptation (the ability of the species to adapt to changes in a given stress or threat). 
 
When thinking of these three components, it becomes quickly evident that island biodiversity should be “naturally” vulnerable to global change stress (Frankham et al., 2002). First, it is hard to escape for insular species compared to mainland ones meaning that they cannot avoid exposure. Second, insular species are highly sensitive to any stress and stochastic events given their high specialisation due to their endemism. Third, insular species are less likely to adapt to new threats due to their small population sizes and naturally fragmented distribution ranges that both decrease their genetic diversity (aka adaptation potential). Thus, estimating the vulnerability of insular species is an important step towards better management and mitigation of their risk to extinction to ongoing global change. But an assessment framework designed for insular species is currently lacking.
 
Bellard and colleagues (Bellard et al., 2025) contribution is exactly addressing this objective. The authors present an adapted framework aimed to quantify the vulnerability of terrestrial insular biota by incorporating the idiosyncrasies of island biota: the island syndrome (ie the idiosyncratic evolutionary outcomes that arise in insular environments), the isolated nature of islands, and their high levels of endemism. It is the consequences of these three features that the authors highlight on expanding their insular vulnerability assessment. More in detail, Bellard et al (2025) build on existing vulnerability frameworks that are not specific to island ecosystems by focusing on the inclusion of multiple threats and enlarging the dimensions of diversity (taxonomic, functional and phylogenetic diversity). In that sense, this work stands out as it delivers a missing framework specific for island biodiversity, without minimising its potential as an extension on existing mainland (not island) vulnerability assessments.
 
The framework consists of 5 steps: 1) define the scope of the vulnerability assessment in terms of spatial and temporal extent, relevant threats, and studied biota; 2) determine the markers of exposure, sensitivity, and adaptive capacity; 3) compute measures of vulnerability and its components; and 4) conduct an uncertainty analysis to improve the vulnerability assessment. Step 5 is basically the use of the actual vulnerability assessment for practical conservation action and policy, and the authors are showing (Box 2 in Bellard et al (2025)) how their proposed vulnerability assessment could make the link to what is actually developed for (ie identifying which species are most vulnerable and what drives their vulnerability).
 
No doubt there is a growing number of literature on the design and application of biodiversity vulnerability assessments. Yet, this contribution is making the case for a special treatment of island biodiversity vulnerability assessments, while also providing a rather complete reading to a newcomer into vulnerability assessment frameworks.
 

References

Bellard Céline, Marino Clara, Butt Nathalie, Fernández-Palacios José María, Rigal François, Robuchon Marine, Lenoir Jonathan, Irl Severin, Benítez-López Ana, Capdevila Pol, Zhu G, Caetano Gabriel, Denelle Pierre, Philippe-Lesaffre Martin, Schipper Aafke, M Foden Wendy, Kissling W. Daniel, Leclerc Camille (2025) A framework to quantify the vulnerability of insular biota to global change. HAL, ver.3 peer-reviewed and recommended by PCI Ecology https://hal.science/hal-04550966

Frankham, R., Briscoe, D. A., and Ballou, J. D. (2002). Introduction to Conservation Genetics. Cambridge University Press.Fuessel, H.-M. (2007). Vulnerability: A generally applicable conceptual framework for climate change research. Glob. Environ. Change 17, 155–167. https://doi.org/10.1016/j.gloenvcha.2006.05.002

IUCN (2017). IUCN 2017 : International Union for Conservation of Nature annual report 2017. Available at: https://iucn.org/resources/annual-reports/iucn-2017-international-union-conservation-nature-annual-report-2017 (Accessed March 10, 2025).

Pacifici, M., Foden, W. B., Visconti, P., Watson, J. E. M., Butchart, S. H. M., Kovacs, K. M., et al. (2015). Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215–224. https://doi.org/10.1038/nclimate2448

Russell, J. C., and Kueffer, C. (2019). Island Biodiversity in the Anthropocene. Annu. Rev. Environ. Resour. 44, 31–60. https://doi.org/10.1146/annurev-environ-101718-033245

Community structure is determined by the regional species pool – which for simplicity can be assumed to be filtered through dispersal limitations, abiotic conditions, and species coexistence mechanisms (Cornell & Harrison 2014). This filtering involves macroecological constraints, such as energy and space availability, and assembly rules that determine species composition (Diamond 1975; Weiher & Keddy 1995; Guisan & Rahbek 2011; Hortal et al. 2012). But also by a series of processes that determine species distributions across scales, including biogeographical and stochastic processes (e.g., large-scale dispersal and occupancy dynamics within the landscape) and deterministic niche-based responses to abiotic and biotic conditions, which interact across scales (Soberón 2010; Hortal et al. 2010; Brousseau et al. 2018). These processes collectively determine the persistence of species assemblages within communities. It follows that, to understand the processes determining the structure of these communities it is necessary to combine methods analyse the effects of drivers acting on both species distributions and community responses.

Van de Walle et al. (2025) take this integrative approach. The final revised version of their work combines multivariate techniques (in this case a RDA) and Joint SDMs to model the small-scale distribution and structure of the invertebrate communities inhabiting a series of coastal dunes in Southern England, France, Belgium and the Netherlands. The paper builds upon well-designed stratified field surveys, which allow them to identify variations at different scales, from geographical to local. These high-quality field data, together with the combination of different modelling techniques, allows them to identify both a clear biogeographical zonation in the structure of these communities, and the existence of a series of neat responses of species to the spatial structure and vigour of the tussocks created by the marram grass fixing the sand dunes. Their models also include the body size, feeding guild and phylogenetic relationships between co-occurring species, although their effects are smaller compared to those of biogeographical differences –which, arguably, are determined by differences in the species pool of each dune system, and species responses to the microhabitat conditions created by the tussocks. They can however identify a trade-off between generalist and specialist species within each community.

Note that here I'm using model in the sense of tools for understanding and explaining complex ecological systems, as advocated by Levins (1966). Which is precisely what Van de Walle et al. (2025) do here. By combining techniques tailored to model species distributions and community-level responses, they (we) gain a much improved understanding of how both species pools and the spatial structure of habitats determine the composition of ecological communities. Importantly, Van de Walle et al. (2025) use this knowledge to obtain key insights about how to manage and restore these endangered habitats, thereby proving the value of this kind of integrative approaches.

References

Brousseau, P.-M., Gravel, D., & Handa, I. T. (2018). On the development of a predictive functional trait approach for studying terrestrial arthropods. Journal of Animal Ecology, 87(5), 1209–1220. https://doi.org/10.1111/1365-2656.12834

Cornell, H. V., & Harrison, S. P. (2014). What are species pools and when are they important? Annual Review of Ecology, Evolution, and Systematics, 45(1), 45–67. http://dx.doi.org/10.1146/annurev-ecolsys-120213-091759

Diamond, J. M. (1975). Assembly of species communities. In M. L. Cody & J. M. Diamond (Eds.), Ecology and Evolution of Communities (pp. 342–444). Harvard University Press.

Guisan, A., & Rahbek, C. (2011). SESAM – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. Journal of Biogeography, 38(8), 1433–1444. https://doi.org/10.1111/j.1365-2699.2011.02550.x

Hortal, J., Roura-Pascual, N., Sanders, N. J., & Rahbek, C. (2010). Understanding (insect) species distributions across spatial scales. Ecography, 33(1). https://doi.org/10.1111/j.1600-0587.2009.06428.x

Hortal, J., de Marco, P., Santos, A. M. C., & Diniz-Filho, J. A. F. (2012). Integrating biogeographical processes and local community assembly. Journal of Biogeography, 39(4). https://doi.org/10.1111/j.1365-2699.2012.02684.x

Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54, 421–431.

Soberón, J. (2010). Niche and area of distribution modeling: A population ecology perspective. Ecography, 33(1), 159–167. https://doi.org/10.1111/j.1600-0587.2009.06074.x 

van de Walle, R., Dahirel, M., Langeraert, W., Benoit, D., Vantieghem, P., Vandegehuchte, M. L., Massol, F., & Bonte, D. (2025). Drivers of plant-associated invertebrate community structure in West-European coastal dunes. BioRxiv, 2024.06.24.600350, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.06.24.600350

Weiher, E., & Keddy, P. A. (1995). Assembly rules, null models, and trait dispersion: New questions from old patterns. Oikos, 74(1), 159–164. https://doi.org/10.2307/3545686

As mankind develops increasingly efficient and productive methods of agriculture and food production, we have reached a point where intensive agriculture threatens several aspects of life on Earth, negatively affecting biodiversity, carbon, nitrogen and phosphorus cycles and water reservoirs, while producing considerable amounts of greenhouse gases (Krebs and Bach, 2018). There was a need to develop farming methods that were friendly to both nature and people, producing good quality, healthy food without destroying the environment. The idea of permaculture, a concept of sustainable agriculture based on methods learned directly from nature, originated in the 1960s, invented and developed by Bruce Charles Mollison and David Holmgren (Mollison and Holmgren 1979, Mollison et al. 1991, Holmgren 2002). Although the idea of permaculture has attracted scientific interest, the representation in published studies is unbalanced in favour of positive ecological and sociological effects, with much less presence of rigorous experimental testing (Ferguson and Lovell 2014, Reiff et al. 2024a).

Reiff et al. (2024b) provided the first large-scale empirical evidence of permaculture production outcomes for Central Europe. Based on results from 11 commercial permaculture sites, situated mostly in Germany but also in Switzerland and Luxembourg, the authors found that food production from permaculture sites was on average comparable to that from conventional and organic agriculture. The authors were very thorough in pointing out the issues that could potentially affect their results and which need further testing.

Among these, the authors highlight the considerable variability between the 11 sites studied, which may suggest that different permacultures should differ in details according to their specificity - an interesting issue that definitely requires further study. The other factor that the authors point out that could have influenced the results and led to an underestimation of the real potential is the age of the permaculture sites. The sites from the study were relatively young, and their potential can be expected to increase with time.

It is important to note that the results are mostly applicable to vegetables, as vegetable production accounted for 94% of production in the permaculture sites (followed by tree crops, 6%, and soft fruit production, 0.5%). There is therefore a need to include other types of crops produced in further studies of this type.

To date, the results informing permaculture food production are urgently needed and should cover the potentially wide range of geographical regions and crops produced. The results of Reiff et al. (2025) show that rigorous testing of this issue is demanding, but the authors provide a very sound "road map" of further steps.      

Literature:

Ferguson R. S. and Lovell S. T. 2014. Permaculture for agroecology: design, movement, practice, and worldview. A review. Agronomy for Sustainable Development 34, 251-274. https://doi.org/10.1007/s13593-013-0181-6

Holmgren D. 2002. Permaculture: Principles & Pathways Beyond Sustainability. Holmgren Design Services, pp. 320.

Krebs J. and Bach S. 2018. Permaculture – scientific evidence of principles for the agroecological design of farming systems. Sustainability 10, 3218, https://doi.org/10.3390/su10093218

Mollison B. C. and Holmgren D. 1979. Permaculture One: A Perennial Agricultural System for Human Settlements. Tagari Publications, pp. 136.

Mollison B. C., Slay, R. M. and Jeeves A. 1991. Introduction to permaculture. Tagari Publications, pp. 198.

Reiff J., Jungkunst H. F., Mauser K. M., Kampel S., Regending S., Rösch V., Zaller J. G. and Entling M. H. 2024a. Permaculture enhances carbon stocks, soil quality and biodiversity in Central Europe. Communications Earth & Environment 5, 305. https://doi.org/10.1038/s43247-024-01405-8

Reiff J., Jungkunst H. F., Antes N. and Entling M. H. 2024b. Crop productivity of Central European Permaculture is within the range of organic and conventional agriculture. bioRxiv, ver.2 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2024.09.09.611985

Seagrasses, particularly Zostera species, have been the subject of numerous studies due to their widespread distribution across the globe (Short et al., 2007), yet they have been in decline for several years as a result of global environmental changes (Touchette, 2007). While case studies and regional studies have been conducted, there remains a lack of information on how to scale these findings, particularly because of the heterogeneous nature of these habitats.

In their study, Lacoste et al. examine the ecosystem trajectories of two Zostera species along a regional gradient spanning sites in the English Channel, the Atlantic Ocean, and the Mediterranean Sea. Their research is based on a recently published database, which offers valuable insights for comparing with other studies and serves as a resource for addressing future questions (Lacoste et al., 2024). This underscores the need for a global database to facilitate the integration of functional responses across studies, thus advancing our understanding of Zostera ecology on a larger scale. The multi-trait approach employed in their study provides a comprehensive view of population dynamics over a 1.5-year period, covering different seasons.

Such studies highlight the complex responses of Zostera populations when considering environmental, seasonal, and geographical heterogeneity. Understanding these dynamics raises important questions about modeling, particularly in relation to the development of a more global database as previously mentioned.

However, the review process has pointed out that the environmental data should be further refined to more rigorously support the presented results. Some statistical analyses could also benefit from improvements to ensure clearer and more explicit conclusions. These concerns are related to the challenges of sampling, the time required for such efforts, and the need to account for spatiotemporal variability. This study could serve as a foundational step for advancing our understanding of Zostera population dynamics on a global scale. In my opinion, despite the large ongoing scientific efforts, upscaling remains one of the major challenges for functional ecologists (Wood et al., 2024), particularly when plant habitats exhibit the kind of heterogeneity seen in Zostera, as demonstrated by Lacoste et al. in their work.

References

Élise Lacoste, Aurélien Boyé, Aline Blanchet-Aurigny, Nicolas Desroy, Isabelle Auby, Touria Bajjouk, Constance Bourdier, Nicolas Cimiterra, Céline Cordier, Amélia Curd, Lauriane Derrien, Élodie Foucault, Jean-Dominique Gaffet, Florian Ganthy, Loic Rigouin, Claire Rollet, Laura Soissons, Aurélien Tancray, Vincent Ouisse (2024) Regional and local variability in the morphometric traits of two emblematic seagrass species (Zostera marina and Zostera noltei) along the French coast. Zenodo, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.5281/zenodo.10427767

Lacoste, E., Ouisse, V., Nicolas, D., Allano, L., Auby, I., Bajjouk, T., Bourdier, C., Caisey, X., de Casamajor, M.-N., Cimiterra, N., Cordier, C., Curd, A., Derrien, L., Droual, G., Dubois, F. S., Foucault, E., Foveau, A., Gaffet, J.-D., Ganthy, F., … Blanchet-Aurigny, A. (2024). A dataset of Zostera marina and Zostera noltei structure and functioning in four sites along the French coast over a period of 18 months. https://doi.org/10.5281/zenodo.14174128

Short, F., Carruthers, T., Dennison, W., & Waycott, M. (2007). Global seagrass distribution and diversity : A bioregional model. Journal of Experimental Marine Biology and Ecology, 350(1), 3‑20. https://doi.org/10.1016/j.jembe.2007.06.012

Touchette, B. W. (2007). The biology and ecology of seagrasses. Journal of Experimental Marine Biology and Ecology, 350(1), 1‑2. https://doi.org/10.1016/j.jembe.2007.06.013

Wood, G. V., Filbee-Dexter, K., Coleman, M. A., Valckenaere, J., Aguirre, J. D., Bentley, P. M., Carnell, P., Dawkins, P. D., Dykman, L. N., Earp, H. S., Ennis, L. B., Francis, P., Franco, J. N., Hayford, H., Lamb, J. B., Ling, S. D., Layton, C., Lis, E., Masters, B., … Wernberg, T. (2024). Upscaling marine forest restoration : Challenges, solutions and recommendations from the Green Gravel Action Group. Frontiers in Marine Science, 11. https://doi.org/10.3389/fmars.2024.1364263