Citizen science is becoming an important piece for the acquisition of scientific knowledge in the fields of natural sciences, and particularly in the inventory and monitoring of biodiversity (McKinley et al. 2017). The information generated with the collaboration of citizens has an evident importance in conservation, by providing information on the state of populations and habitats, helping in mitigation and restoration actions, and very importantly contributing to involve society in conservation (Brown and Williams 2019). An obvious advantage of these initiatives is the ability to mobilize human resources on a large territorial scale and in the medium term, which would otherwise be difficult to finance. The resulting increasing information then can be processed with advanced computational techniques (Hochachka et al 2012; Kelling et al. 2015), thus improving our interpretation of the distribution of species. Specifically, the ability to obtain information on a large territorial scale can be integrated into studies based on Species Distribution Models SDMs. One of the common problems with SDMs is that they often work from species occurrences that have been opportunistically recorded, either by professionals or amateurs. A great challenge for data obtained from non-professional citizens, however, remains to ensure its standardization and quality (Kosmala et al. 2016). This requires a clear and effective design, solid volunteer training, and a high level of coordination that turns out to be complex (Brown and Williams 2019). Finally, it is essential to perform a quality validation following scientifically recognized standards, since they are often conditioned by errors and biases in obtaining information (Bird et al. 2014). There are two basic approaches to obtain the necessary data for this validation: getting it from an external source (external validation), or allocating a part of the database itself (internal validation or cross-validation) to this function.
Matutini et al. (2020) in his work 'How citizen science could improve Species Distribution Models and their independent assessment' shows a novel application of the data generated by a citizen science initiative ('Un Dragon dans mon Jardin') by providing an external source for the validation of SDMs, as a tool to construct habitat suitability maps for nine species of amphibians in western France. Importantly, 'Un Dragon dans mon Jardin' contains standardized presence-absence data, the approximation recognized as the most robust (Guisan, et al. 2017). The SDMs to be validated, in turn, were based on opportunistic information obtained by citizens and professionals. The result shows the usefulness of this external data source by minimizing the overestimation of model accuracy that is obtained with cross-validation with the internal evaluation dataset. It also shows the importance of properly filtering the information obtained by citizens by determining the threshold of sampling effort.
The destiny of citizen science is to be integrated into the complex world of science. Supported by the increasing level of the formation of society, it is becoming a fundamental piece in the scientific system dedicated to the study of biodiversity and its conservation. After funding for scientists specialized in the recognition of biodiversity has been cut back, we are seeing a transformation of the activity of these scientists towards the design, coordination, training and verification of programs for the acquisition of field information obtained by citizens. A main goal is that a substantial part of this information will eventually get integrated into the scientific system, and rigorous verification process a fundamental element for such purpose, as shown by Matutini et al. (2020) work.

References

[1] Bird TJ et al. (2014) Statistical solutions for error and bias in global citizen science datasets. Biological Conservation 173: 144-154. doi: 10.1016/j.biocon.2013.07.037
[2] Brown ED and Williams BK (2019) The potential for citizen science to produce reliable and useful information in ecology. Conservation Biology 33: 561-569. doi: 10.1111/cobi.13223
[3] Guisan A, Thuiller W and Zimmermann N E (2017) Habitat Suitability and Distribution Models: With Applications in R. The University of Chicago Press. doi: 10.1017/9781139028271
[4] Hochachka WM, Fink D, Hutchinson RA, Sheldon D, Wong WK and Kelling S (2012) Data-intensive science applied to broad-scale citizen science. Trens Ecol Evol 27: 130-137. doi: 10.1016/j.tree.2011.11.006
[5] Kelling S, Fink D, La Sorte FA, Johnston A, Bruns NE and Hochachka WM (2015) Taking a ‘Big Data’ approach to data quality in a citizen science project. Ambio 44(Supple. 4):S601-S611. doi: 10.1007/s13280-015-0710-4
[6] Kosmala M, Wiggins A, Swanson A and Simmons B (2016) Assessing data quality in citizen science. Front Ecol Environ 14: 551–560. doi: 10.1002/fee.1436
[7] Matutini F, Baudry J, Pain G, Sineau M and Pithon J (2020) How citizen science could improve Species Distribution Models and their independent assessment. bioRxiv, 2020.06.02.129536, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/2020.06.02.129536
[8] McKinley DC et al. (2017) Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation 208:15-28. doi: 10.1016/j.biocon.2016.05.015

Some biological hypotheses are widely popular, so much so that we tend to forget their original lack of success. This is particularly true for hypotheses with catchy names. The ‘Ghost of competition past’ is part of the title of a paper by the great ecologist, JH Connell, one of the many losses of 2020 (Connell 1980). The hypothesis states that, even though we may not detect competition in current populations, their traits and distributions may be shaped by past competition events. Although this hypothesis has known a great success in the ecological literature, the original paper actually ends with “I will no longer be persuaded by such invoking of "the Ghost of Competition Past"”. Similarly, the hypothesis that mothers of herbivores choose host plants where their offspring will have a higher fitness was proposed by John Jaenike in 1978 (Jaenike 1978), and later coined the ‘mother knows best’ hypothesis. The hypothesis was readily questioned or dismissed: “Mother doesn't know best” (Courtney and Kibota 1990), or “Does mother know best?” (Valladares and Lawton 1991), but remains widely popular. It thus seems that catchy names (and the intuitive ideas behind them) have a heuristic value that is independent from the original persuasion in these ideas and the accumulation of evidence that followed it.

The paper by Castagneryol et al. (2021) analyses the preference-performance relationship in the box tree moth (BTM) Cydalima perspectalis, after defoliation of their host plant, the box tree, by conspecifics. It thus has bearings on the two previously mentioned hypotheses. Specifically, they created an artificial population of potted box trees in a greenhouse, in which 60 trees were infested with BTM third instar larvae, whereas 61 were left uninfested. One week later, these larvae were removed and another three weeks later, they released adult BTM females and recorded their host choice by counting egg clutches laid by these females on the plants. Finally, they evaluated the effect of previously infested vs uninfested plants on BTM performance by measuring the weight of third instar larvae that had emerged from those eggs.  

This experimental design was adopted because BTM is a multivoltine species. When the second generation of BTM arrives, plants have been defoliated by the first generation and did not fully recover. Indeed, Castagneryol et al. (2021) found that larvae that developed on previously infested plants were much smaller than those developing on uninfested plants, and the same was true for the chrysalis that emerged from those larvae. This provides unequivocal evidence for the existence of a ghost of competition past in this system. However, the existence of this ghost still does not result in a change in the distribution of BTM, precisely because mothers do not know best: they lay as many eggs on plants previously infested than on uninfested plants. 

The demonstration that the previous presence of a competitor affects the performance of this herbivore species confirms that ghosts exist. However, whether this entails that previous (interspecific) competition shapes species distributions, as originally meant, remains an open question. Species phenology may play an important role in exposing organisms to the ghost, as this time-lagged competition may have been often overlooked. It is also relevant to try to understand why mothers don’t care in this, and other systems. One possibility is that they will have few opportunities to effectively choose in the real world, due to limited dispersal or to all plants being previously infested. 

References

Castagneyrol, B., Halder, I. van, Kadiri, Y., Schillé, L. and Jactel, H. (2021) Host-mediated, cross-generational intraspecific competition in a herbivore species. bioRxiv, 2020.07.30.228544, ver. 5 peer-reviewed and recommended by PCI Ecology. doi: https://doi.org/10.1101/2020.07.30.228544

Connell, J. H. (1980). Diversity and the coevolution of competitors, or the ghost of competition past. Oikos, 131-138. doi: https://doi.org/10.2307/3544421

Courtney, S. P. and Kibota, T. T. (1990) in Insect-plant interactions (ed. Bernays, E.A.) 285-330.

Jaenike, J. (1978). On optimal oviposition behavior in phytophagous insects. Theoretical population biology, 14(3), 350-356. doi: https://doi.org/10.1016/0040-5809(78)90012-6

Valladares, G., and Lawton, J. H. (1991). Host-plant selection in the holly leaf-miner: does mother know best?. The Journal of Animal Ecology, 227-240. doi: https://doi.org/10.2307/5456

Along with the generalization of open-access practices, large, heterogeneous datasets are becoming increasingly available to ecologists (Farley et al. 2018). While such data offer exciting opportunities for unveiling original patterns and trends, they also raise new challenges regarding how to extract relevant information and actually improve our knowledge of complex ecological systems, beyond purely descriptive correlations (Dietze 2017, Farley et al. 2018).

In this work, Caumette et al. (2024) develop an original ecoinformatics approach to relate multi-scale environmental factors to the temporal dynamics of a major pest in mango orchards. Their method relies on the recent tree-boosting method GPBoost (Sigrist 2022) to hierarchize the influence of environmental factors of heterogeneous nature (e.g., orchard composition and management; landscape structure; climate) on the emergence date of the oriental fruit fly, Bactrocera dorsalis. As boosting methods allows the analysis of high-dimensional data, they are particularly adapted to the exploration of such datasets, to uncover unexpected, potentially complex dependencies between ecological dynamics and multiple environmental factors (Farley et al. 2018). In this article, Caumette et al. (2024) make a special effort to guide the reader step by step through their complex analysis pipeline to make it broadly understandable to the average ecologist, which is no small feat. I particularly welcome this commitment, as making new, cutting-edge analytical methods accessible to a large community of science practitioners with varying degrees of statistical or programming expertise is a major challenge for the future of quantitative ecology. 

The main result of Caumette et al. (2024) is that temperature and humidity conditions both at the local and regional scales are the main predictors of B. dorsalis emergence date, while orchard management practices seem to have relatively little influence. This suggests that favourable climatic conditions may allow the persistence of small populations of B. dorsalis over the dry season, which may then act as a propagule source for early re-infestations. However, as the authors explain, the resulting regression model is not designed for predictive purposes and should not at this stage be used for decision-making in pest management. Its main interest rather resides in identifying potential key factors favoring early infestations of B. dorsalis, and help focusing future experimental field studies on the most relevant levers for integrated pest management in mango orchards.

In a wider perspective, this work also provides a convincing proof-of-concept for the use of boosting methods to identify the most influential factors in large, multivariate datasets in a variety of ecological systems. It is also crucial to keep in mind that the current exponential growth in high-throughput environmental data (Lucivero 2020) could quickly come into conflict with the need to reduce the environmental footprint of research (Mariette et al. 2022). In this context, robust and accessible methods for extracting and exploiting all the information available in already existing datasets might prove essential to a sustainable pursuit of science.

References
 
Caumette C, Diatta P, Piry S, Chapuis M-P, Faye E, Sigrist F, Martin O, Papaïx J, Brévault T, Berthier K. 2024. Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese orchards. bioRxiv 2023.11.10.566583, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.1101/2023.11.10.566583

Dietze MC. 2017. Ecological Forecasting. Princeton University Press
 
Farley SS, Dawson A, Goring SJ, Williams JW. 2018. Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions. BioScience, 68, 563–576, https://doi.org/10.1093/biosci/biy068
 
Lucivero F. 2020. Big Data, Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives. Science and Engineering Ethics 26, 1009–1030. https://doi.org/10.1007/s11948-019-00171-7

Mariette J, Blanchard O, Berné O, Aumont O, Carrey J, Ligozat A-L, Lellouch E, Roche P-E, Guennebaud G, Thanwerdas J, Bardou P, Salin G, Maigne E, Servan S, Ben-Ari T 2022. An open-source tool to assess the carbon footprint of research. Environmental Research: Infrastructure and Sustainability, 2022. https://dx.doi.org/10.1088/2634-4505/ac84a4
 
Sigrist F. 2022. Gaussian process boosting. The Journal of Machine Learning Research, 23, 10565-10610. https://jmlr.org/papers/v23/20-322.html
 

A core challenge facing ecologists is to work through an ever-increasing amount of data. The accelerating decline in biodiversity worldwide, mounting pressure of anthropogenic impacts, and increasing demand for actionable indicators to guide effective policy means that monitoring will only intensify, and rely on tools that can generate even more information (Gonzalez et al., 2023). How, then, do we handle this new volume and diversity of data?

This is the question Royaux et al. (2024) are tackling with their contribution. By introducing both a conceptual ("How should we think about our work?") and an operational ("Here is a tool to do our work with") framework, they establish a series of best practices for the analysis of ecological data.

It is easy to think about best practices in ecological data analysis in its most proximal form: is it good statistical practice? Is the experimental design correct? These have formed the basis of many recommendations over the years (see e.g. Popovic et al., 2024, for a recent example). But the contribution of Royaux et al. focuses on a different part of the analysis pipeline: the computer science (and software engineering) aspect of it.

As data grows in volume and complexity, the code needed to handle it follows the same trend. It is not a surprise, therefore, to see that the demand for programming skills in ecologists has doubled recently (Feng et al., 2020), prompting calls to make computational literacy a core component of undergraduate education (Farrell & Carrey, 2018). But beyond training, an obvious way to make computational analysis ecological data more reliable and effective is to build better tools. This is precisely what Royaux et al. have achieved.

They illustrate their approach through their experience building Galaxy-Ecology, a computing environment for ecological analysis: by introducing a clear taxonomy of computing concepts (data exploration, pre-processing, analysis, representation), with a hierarchy between them (formatting, data correction, anonymization), they show that we can think about the pipeline going from data to results in a way that is more systematized, and therefore more prone to generalization.

We may buckle at the idea of yet another ontology, or yet another framework, for our work, but I am convinced that the work of Royaux et al. is precisely what our field needs. Because their levels of atomization (their term for the splitting of complex pipelines into small, single-purpose tasks) are easy to understand, and map naturally onto tasks that we already perform, it is likely to see wide adoption. Solving the big, existential challenges of monitoring and managing biodiversity at the global scale requires the adoption of good practices, and a tool like Galaxy-Ecology goes a long way towards this goal.

References

Farrell, K.J., and Carey, C.C. (2018). Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses. Ecol. Evol. 8, 7744-7751.
https://doi.org/10.1002/ece3.4363

Feng, X., Qiao, H., and Enquist, B. (2020). Doubling demands in programming skills call for ecoinformatics education. Frontiers in Ecology and the Environment 18, 123-124.
https://doi.org/10.1002/fee.2179
 
Gonzalez, A., Vihervaara, P., Balvanera, P., Bates, A.E., Bayraktarov, E., Bellingham, P.J., Bruder, A., Campbell, J., Catchen, M.D., Cavender-Bares, J., et al. (2023). A global biodiversity observing system to unite monitoring and guide action. Nat. Ecol. Evol., 1-5. 
https://doi.org/10.1038/s41559-023-02171-0
 
Popovic, G., Mason, T.J., Drobniak, S.M., Marques, T.A., Potts, J., Joo, R., Altwegg, R., Burns, C.C.I., McCarthy, M.A., Johnston, A., et al. (2024). Four principles for improved statistical ecology. Methods Ecol. Evol. 15, 266-281.
https://doi.org/10.1111/2041-210X.14270
 
Coline Royaux, Jean-Baptiste Mihoub, Marie Jossé, Dominique Pelletier, Olivier Norvez, Yves Reecht, Anne Fouilloux, Helena Rasche, Saskia Hiltemann, Bérénice Batut, Marc Eléaume, Pauline Seguineau, Guillaume Massé, Alan Amossé, Claire Bissery, Romain Lorrilliere, Alexis Martin, Yves Bas, Thimothée Virgoulay, Valentin Chambon, Elie Arnaud, Elisa Michon, Clara Urfer, Eloïse Trigodet, Marie Delannoy, Gregoire Loïs, Romain Julliard, Björn Grüning, Yvan Le Bras (2024) Guidance framework to apply best practices in ecological data analysis: Lessons learned from building Galaxy-Ecology. EcoEvoRxiv, ver.3 peer-reviewed and recommended by PCI Ecology. 
https://doi.org/10.32942/X2G033

The study titled “General mechanisms for a top-down origin of the predator-prey power law” provides a fresh perspective on the classic predator-prey biomass relationship often observed in ecological communities. Traditionally, predator-prey dynamics have been examined through a bottom-up lens, where prey biomass and energy availability dictate predator populations. However, this study, which instead explores the possibility of a top-down origin for predator-prey power laws, offers a new dimension to our understanding of ecosystem regulation and raises questions about how predator-driven interactions might influence biomass scaling laws independently of prey abundance.

Ecologists have long noted that ecosystems often exhibit sublinear scaling between predator and prey biomasses. This pattern implies that predator biomass does not increase proportionally with prey biomass but at a slower rate, leading to a power-law relationship. Traditional explanations, such as those discussed by Peters (1983) and McGill (2006), have linked this to bottom-up processes, suggesting that increases in prey availability support, but do not fully translate to, larger predator populations due to energy losses in the trophic cascade. However, these explanations assume prey abundance as the principal driver. This new work raises an intriguing question: could density-dependent predator interactions, such as competition and interference, be equally or more important in creating this observed power law?

The authors hypothesized that density-dependent predator interactions might independently control predator biomass, even when prey is abundant. To test this, they combined predator and prey biomass dynamics equation based on a modified Lotka-Volterra model with agent-based models (ABMs) on a spatial grid, simulating predator-prey populations under varying environmental gradients and density-dependent conditions. These models allowed them to incorporate predator-specific factors, such as intraspecific competition (predator self-regulation) and predation interference, offering a quantitative framework to observe whether these top-down dynamics could indeed explain the observed biomass scaling independently of prey population changes.

Their results show that density-dependent predator dynamics, particularly at high predator densities, can yield sublinear scaling in predator-prey biomass relationships. This aligns well with empirical data, such as African mammalian ecosystems where predators seem to self-regulate under high prey availability by competing amongst themselves rather than expanding in direct proportion to prey biomass. Such findings support a shift from bottom-up perspectives to a model where top-down processes drive population regulation and biomass scaling.

I think that the work by Mazzarisi and collaborators (2024) offers a thought-provoking twist on predator-prey dynamics and suggests that our traditional frameworks may benefit from a broader, more predator-centered focus.

References

1. Onofrio Mazzarisi, Matthieu Barbier, Matteo Smerlak (2024) General mechanisms for a top-down origin of the predator-prey power law. bioRxiv, ver.2 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.04.04.588057

2. Peters, R. H. (1986). The ecological implications of body size (Vol. 2). Cambridge university press.

3. McGill, B. J. (2006). “A renaissance in the study of abundance.” Science, 314(5801), 770-772. https://doi.org/10.1126/science.1134920