Habitat complexity is an important mediator of processes spanning levels of biological organization from organisms to ecosystems (Shumway et al. 2007, Soukup et al. 2022). This complexity, which can be biogenic (e.g., foundation species; Bracken et al. 2007, Ellison 2019) or abiotic (e.g., substrate rugosity; Kovalenko et al. 2012), shapes processes ranging from individual foraging behavior (Michel and Adams 2009) to species’ interactions to food-web structure and biogeochemical rates (Langellotto and Denno 2006, Larsen et al. 2021, Soukup et al. 2022). For example, in the presence of simulated aquatic vegetation, predatory diving beetle larvae shift from active foraging to sit-and-wait predation, reducing activity and prey encounter rates (Michel and Adams 2009).

In this contribution, Edeline et al. (2023) present a detailed perspective on the role of habitat complexity in shaping populations of a freshwater fish (medaka, Oryzias latipes, Adrianichthyidae), including survival, age-class diversity, population growth rate, and density-dependence in the stock-recruitment relationship associated with changes in carrying capacity. Importantly, changes in these population demographic attributes and rates were associated with the role of habitat complexity in mitigating cannibalism – consumption of juvenile O. latipes by conspecific adults. Whereas this is not unexpected – Langelotto and Denno (2006) showed that habitat complexity reduces cannibalism in wolf spiders – the careful work of Edeline et al. (2023) to link changes in habitat complexity to multiple population-level attributes provides a uniquely detailed description of the role of submerged aquatic vegetation in mediating population diversity (e.g., higher age-class diversity) and productivity (e.g., population growth rate).

In many ways, this work by Edeline et al. (2023) provides population-level parallels to perspectives on the role of habitat complexity in determining community-level diversity and productivity. Structurally complex habitats, such as those provided by foundation species (Bracken et al. 2007, Ellison 2019) and substrate heterogeneity (Fairchild et al. 2024), are associated with higher species diversity and abundance at the community level. Edeline et al. (2023) extend these perspectives to the population level, highlighting the importance of habitat complexity across levels of biological organization. Their work highlights within-population diversity and interactions, including cannibalism and competition, illustrating often-neglected aspects of food-web complexity (Polis and Strong 1996).

References

Matthew E. S. Bracken, Barry E. Bracken, Laura Rogers-Bennett (2007) Species diversity and foundation species: potential indicators of fisheries yields and marine ecosystem functioning. California Cooperative Oceanic Fisheries Investigations Reports 48: 82-91. https://calcofi.org/downloads/publications/calcofireports/v48/Vol_48_Bracken.pdf

Eric Edeline, Yoann Bennevault, David Rozen-Rechels (2023) Habitat structural complexity increases age-class coexistence and population growth rate through relaxed cannibalism in a freshwater fish. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://www.biorxiv.org/content/10.1101/2023.07.18.549540v4

Aaron M. Ellison (2019) Foundation species, non-trophic interactions, and the value of being common. iScience 13: 254-68. https://doi.org/10.1016/j.isci.2019.02.020

Tom P. Fairchild, Bettina Walter, Joshua J. Mutter, John N. Griffin. (2024) Topographic heterogeneity triggers complementary cascades that enhance ecosystem multifunctionality. Ecology 105: e4434. https://doi.org/10.1002/ecy.4434

Katya E. Kovalenko, Sidinei M. Thomaz, Danielle M. Warfe (2012) Habitat complexity: approaches and future directions. Hydrobiologia 685: 1-17. https://doi.org/10.1007/s10750-011-0974-z

Gail A. Langellotto, Robert F. Denno. (2006) Refuge from cannibalism in complex-structured habitats: implications for the accumulation of invertebrate predators. Ecological Entomology 31: 575-81. https://doi.org/10.1111/j.1365-2311.2006.00816.x

Annegret Larsen, Joshua R. Larsen, Stuart N. Lane (2021) Dam builders and their works: beaver influences on the structure and Function of river corridor hydrology, geomorphology, biogeochemistry and ecosystems. Earth-Science Reviews 218: 103623. https://doi.org/10.1016/j.earscirev.2021.103623

Matt J. Michel, Melinda M. Adams. (2009) Differential effects of structural complexity on predator foraging behavior. Behavioral Ecology: 313-17. https://doi.org/10.1093/beheco/arp005

Gary A. Polis, Donald R. Strong (1996) Food web complexity and community dynamics. American Naturalist 147: 813-46. https://doi.org/10.1086/285880

Caroly A. Shumway, Hans A. Hofmann, Adam P. Dobberfuhl (2007) Quantifying habitat complexity in aquatic ecosystems. Freshwater Biology 52: 1065-76. https://doi.org/10.1111/j.1365-2427.2007.01754.x.

Pavel R. Soukup, Joacim Näslund, Johan Höjesjö, David S. Boukal (2022) From individuals to communities: habitat complexity affects all levels of organization in aquatic environments. Wiley Interdisciplinary Reviews: Water 9: e1575.  https://doi.org/10.1002/wat2.1575

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