Biodiversity monitoring increasingly relies on modern technologies such as sensor networks and environmental DNA. These high-throughput methods allow biodiversity assessments with unprecedented detail and are especially useful to detect rare and secretive species that are otherwise difficult to observe with traditional survey-based methods. False negatives through imperfect detection are a typical problem in survey data and depend on intrinsic characteristics of the species, site characteristics of the survey site as well as survey characteristics (Guillera 2017). While imperfect detection might be reduced in modern sensor data and eDNA data, also these types of data are by no means error-free and may bare other challenges. In particular, the bioinformatics and image classification approaches used for species identification from these data can induce a higher rate of false positives than would be expected in expert-based survey data (Hartig et al. 2024).

Occupancy models (or occupancy-detection models) have been widely used to map species distributions by fitting a hierarchical model that estimates the paramaters of both the species-environment relationship and an observation submodel. They account for false negatives by inferring detectability from the detection history of a survey location, for example from replicate visits or multiple observers (Guillera 2017). These basic occupancy-detection models assume no false positive errors in the data. Other authors have proposed extensions for false positives that typically rely on unambiguous (known truth) information for some sites or observations (Chambert et al. 2015).

In their preprint, Monchy et al. (2024) propose an extension of classic occupancy models that considers a two-step observation process modelling the detection probability at occupied sites and the associated identification probability, separated into the true positive identification rate and the true negative identification rate. Using a simulation approach, the authors compare the effectiveness of a frequentist (maximum likelihood-based) and Bayesian approach for parameter estimation and identifiability, and additionally test the effectiveness of different priors (from non-informative to highly informative). Results of the maximum-likelihood approach indicated biased parameter estimates and identifiability problems. In the Bayesian approach, inclusion of prior information greatly reduces biases in parameter estimates, especially in detection and positive identification rate.

Importantly, informative priors for the identification process are a by-product of the classifiers that are developed for processing the eDNA data or sensor data. For example, species identification from acoustic sensors is based on image classifiers trained on labelled bird song spectrograms (Kahl et al. 2021) and as part of the evaluation of the classifier, the true positive rate (sensitivity) is routinely being estimated and could thus be readily used in occupancy models accounting for false positives. Thus, the approach proposed by Monchy et al. (2024) is not only highly relevant for biodiversity assessments based on novel sensor and eDNA data but also provides very practical solutions that do not require additional unambiguous data but recycle data that are already available in the processing pipeline. Applying their framework to real-world data will help reducing biases in biodiversity assessments and through improved understanding of the detection process it could also help optimising the design of sensor networks.

References

Thierry Chambert,  David A. W. Miller,  James D. Nichols (2015), Modeling false positive detections in species occurrence data under different study designs. Ecology, 96: 332-339. https://doi.org/10.1890/14-1507.1

Gurutzeta Guillera-Arroita (2017) Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities. Ecography, 40: 281-295. https://doi.org/10.1111/ecog.02445

Florian Hartig, Nerea Abrego, Alex Bush, Jonathan M. Chase, Gurutzeta Guillera-Arroita, Mathew A. Leibold,  Otso Ovaskainen, Loïc Pellissier, Maximilian Pichler, Giovanni Poggiato, Laura Pollock, Sara Si-Moussi, Wilfried Thuiller, Duarte S. Viana, David I. Warton, Damaris Zurell D, Douglas W. Yu (2024) Novel community data in ecology - properties and prospects. Trends in Ecology & Evolution, 39: 280-293. https://doi.org/10.1016/j.tree.2023.09.017

Stefan Kahl, Connor M. Wood, Maximilian Eibl, Holger Klinck (2021) BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61: 101236. https://doi.org/10.1016/j.ecoinf.2021.101236

Célian Monchy, Marie-Pierre Etienne, Olivier Gimenez (2024) Using informative priors to account for identifiability issues in occupancy models with identification errors. bioRxiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.05.07.592917

In the preprint "From Fear to Food: Predation Risk Shapes Deer Behaviour, Their Resources, and Forest Vegetation", Martin et al. provide a comprehensive examination of the intricate interplay between predation risk, deer behavior, and forest ecosystems. The study offers notable insights into the "ecology of fear," as it takes advantage of an extensive dataset that reflects decades of dedicated research effort. The authors’ approach combines behavioral ecology, plant community analysis, and stable isotope studies, making this work a significant contribution to our understanding of complex ecological phenomena.

One of the most striking aspects of this study is the scale and richness of the dataset. The authors used data collected over multiple decades, spanning various experimental contexts, including islands with and without predators, hunting, and culling histories. These datasets are invaluable, as such long-term, geographically diverse studies are rare. The inclusion of both behavioral observations (e.g., flight initiation distances) and ecological outcomes (e.g., vegetation recovery) underscores the effort to provide a holistic understanding of these ecological interactions.

The results are not only scientifically robust but also conceptually significant. They challenge simplistic assumptions about predator-prey relationships by illustrating how both the presence and absence of predation risk can have lasting effects on ecosystems. For example, the findings that culling restores vegetation but creates behavioral shifts in deer populations emphasize the complexity of ecological restoration efforts. These results invite further exploration into how behavioral adaptations to predation risk may alter long-term ecosystem trajectories.

In conclusion, Martin et al.'s preprint represents a significant advancement in understanding predator-prey interactions and their cascading effects on ecosystems. The study’s comprehensive dataset and integrative approach provide a model for future research in ecological and behavioral sciences. It is a commendable contribution to the field, with implications for both theoretical ecology and practical conservation.

References

Jean-Louis Martin, Simon Chamaillé-Jammes, Anne Salomon, Devana Veronica Gomez Pourroy, Mathilde Schlaeflin, Soizic Le Saout, Annick Lucas, Ilham Bentaleb, Simon Chollet, Jake Pattison, Soline Martin-Blangy , Anthony J. Gaston (2024) From fear to food: predation risk shapes deer behaviour, their resources and forest vegetation . HAL, ver.6 peer-reviewed and recommended by PCI Ecology https://hal.science/hal-04381108v5

Estimating population parameters is critical for analysis and management of wildlife populations. Drawing inference at the population level requires a robust sampling scheme and information about the representativeness of the studied population (Williams et al. 2002). In their textbook, Williams et al. (see chapter 5, 2002) listed several sampling issues, including both temporal and spatial heterogeneity and especially imperfect detection. Several methods, either sampling-based or model-based can be used to circumvent these issues.

In their paper, Kissling et al. (2024) addressed the case of the Kittlitz’s murrelet (Brachyramphus brevirostris), a cryptic ice-associated seabird, combining spatial variation in its distribution, temporal variation in breeding propensity, imperfect detection and logistical challenges to access the breeding area. The Kittlitz’s murrelet is thus the perfect species to illustrate common issues and logistical difficulties to implement a standard sampling scheme. 

The authors proposed a modelling framework unifying several datasets from different surveys to extract information on each step of the detection process: the spatial match between the targeted population and the sampled population, the probability of presence in the sample area, the probability of availability given presence in the sample area and finally, the probability of detection given presence and availability. All these components were part of the framework to estimate abundance and trend for this species. 

They took advantage of a radiotracking survey during several years to inform spatial match and probability of presence. They performed a behavioural experiment to assess the probability of availability of murrelets given it was present in sampling area, and they used a conventional distance-sampling boat survey to estimate detection of individuals. This is worth noting that the most variable components were the probability of presence in the sample area, with a global mean of 0.50, and the probability of detection given presence and availability ranging from 0.49 to 0.77. The estimated trend for Kittlitz’s murrelet was negative and all the information gathered in this study will be useful for future conservation plan. 

Coupling a decomposition of the detection process with different data sources was the key to solve problems raised by such “difficult” species, and the paper of Kissling et al. (2024) is a good way to follow for other species, allowing to inform the detection components for the targeted species - and also for our global understanding of detection process, and to infer about the temporal trend of species of conservation concern. 

References

Williams, B. K., Nichols, J. D., and Conroy, M. J. (2002). Analysis and management of animal populations. Academic Press.

Michelle L. Kissling, Paul M. Lukacs, Kelly Nesvacil, Scott M. Gende, Grey W. Pendleton (2024) Using multiple datasets to account for misalignment between statistical and biological populations for abundance estimation. EcoEvoRxiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2W03T

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

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