Different sources of uncertainty are known to affect our ability to predict ecological dynamics (Petchey et al. 2015). However, the consequences of uncertainty on prediction biases have been less investigated, especially when predictions are scaled up to higher levels of organisation as is commonly done in ecology for instance. The study of Orr et al. (2020) addresses this issue. It shows that, in complex systems, the uncertainty of unbiased predictions at a lower level of organisation (e.g. species level) leads to a bias towards underestimation of change at higher level of organisation (e.g. ecosystem level). This bias is strengthened by larger uncertainty and by higher dimensionality of the system.
This general result has large implications for many fields of science, from economics to energy supply or demography. In ecology, as discussed in this study, these results imply that the uncertainty of predictions of species’ change increases the probability of underestimation of changes of diversity and stability at community and ecosystem levels, especially when species richness is high. The uncertainty of predictions of species’ change also increases the probability of underestimation of change when multiple ecosystem functions are considered at once, or when the combined effect of multiple stressors is considered.
The consequences of species diversity on ecosystem functions and stability have received considerable attention during the last decades (e.g. Cardinale et al. 2012, Kéfi et al. 2019). However, since the bias towards underestimation of change increases with species diversity, future studies will need to investigate how the general statistical effect outlined by Orr et al. might affect our understanding of the well-known relationships between species diversity and ecosystem functioning and stability in response to perturbations.

References

Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Larigauderie A, Srivastava DS, Naeem S (2012) Biodiversity loss and its impact on humanity. Nature, 486, 59–67. https://doi.org/10.1038/nature11148
Kéfi S, Domínguez‐García V, Donohue I, Fontaine C, Thébault E, Dakos V (2019) Advancing our understanding of ecological stability. Ecology Letters, 22, 1349–1356. https://doi.org/10.1111/ele.13340
Orr JA, Piggott JJ, Jackson A, Arnoldi J-F (2020) Why scaling up uncertain predictions to higher levels of organisation will underestimate change. bioRxiv, 2020.05.26.117200. https://doi.org/10.1101/2020.05.26.117200
Petchey OL, Pontarp M, Massie TM, Kéfi S, Ozgul A, Weilenmann M, Palamara GM, Altermatt F, Matthews B, Levine JM, Childs DZ, McGill BJ, Schaepman ME, Schmid B, Spaak P, Beckerman AP, Pennekamp F, Pearse IS (2015) The ecological forecast horizon, and examples of its uses and determinants. Ecology Letters, 18, 597–611. https://doi.org/10.1111/ele.12443

Are biological invasions driven by a few pioneers, running ahead of their conspecifics? Or are these pioneers constantly being caught up by, and folded into, the larger flux of propagules from the established populations behind them?
In ecology and beyond, these two scenarios are known as "pulled" and "pushed" fronts, and they come with different expectations. In a pushed front, invasion speed is not just a matter of how good individuals are at dispersing and settling new locations. It becomes a collective, density-dependent property of population fluxes. And in particular, it can depend on the equilibrium abundance of the established populations inside the range, i.e. the species’ carrying capacity K, factoring in its abiotic environment and biotic interactions.
This realization is especially important because it can flip around our expectations about which species expand fast, and how to manage them. We tend to think of initial colonization and long-term abundance as two independent axes of variation among species or indeed as two ends of a spectrum, in the classic competition-colonization tradeoff [1]. When both play into invasion speed, good dispersers might not outrun good competitors. This is useful knowledge, whether we want to contain an invasion or secure a reintroduction.
In their study "When higher carrying capacities lead to faster propagation", Haond et al [2] combine mathematical analysis, Individual-Based simulations and experiments to show that various mechanisms can cause pushed fronts, whose speed increases with the carrying capacity K of the species. Rather than focus on one particular angle, the authors endeavor to demonstrate that this qualitative effect appears again and again in a variety of settings.
It is perhaps surprising that this notable and general connection between K and invasion speed has managed to garner so little fame in ecology. A large fraction of the literature employs the venerable Fisher-KPP reaction-diffusion model, which combines local logistic growth with linear diffusion in space. This model has prompted both considerable mathematical developments [3] and many applications to modelling real invasions [4]. But it only allows pulled fronts, driven by the small populations at the edge of a species range, with a speed that depends only on their initial growth rate r.
This classic setup is, however, singular in many ways. Haond et al [2] use it as a null model, and introduce three mechanisms or factors that each ensure a role of K in invasion speed, while giving less importance to the pioneers at the border.
Two factors, the Allee effect and demographic stochasticity, make small edge populations slower to grow or less likely to survive. These two factors are studied theoretically, and to make their claims stronger, the authors stack the deck against K. When generalizing equations or simulations beyond the null case, it is easy to obtain functional forms where the parameter K does not only play the role of equilibrium carrying capacity, but also affects dynamical properties such as the maximum or mean growth rate. In that case, it can trivially change the propagation speed, without it meaning anything about the role of established populations behind the front. Haond et al [2] avoid this pitfall by disentangling these effects, at the cost of slightly more peculiar expressions, and show that varying essentially nothing but the carrying capacity can still impact the speed of the invasion front.
The third factor, density-dependent dispersal, makes small populations less prone to disperse. It is well established empirically and theoretically that various biological mechanisms, from collective organization to behavioral switches, can prompt organisms in denser populations to disperse more, e.g. in such a way as to escape competition [5]. The authors demonstrate how this effect induces a link between carrying capacity and invasion speed, both theoretically and in a dispersal experiment on the parasitoid wasp, Trichogramma chilonis.
Overall, this study carries a simple and clear message, supported by valuable contributions from different angles. Although some sections are clearly written for the theoretical ecology crowd, this article has something for everyone, from the stray physicist to the open-minded manager. The collaboration between theoreticians and experimentalists, while not central, is worthy of note. Because the narrative of this study is the variety of mechanisms that can lead to the same qualitative effect, the inclusion of various approaches is not a gimmick, but helps drive home its main message. The work is fairly self-contained, although one could always wish for further developments, especially in the direction of more quantitative testing of these mechanisms.
In conclusion, Haond et al [2] effectively convey the widely relevant message that, for some species, invading is not just about the destination, it is about the many offspring one makes along the way.

References

[1] Levins, R., & Culver, D. (1971). Regional Coexistence of Species and Competition between Rare Species. Proceedings of the National Academy of Sciences, 68(6), 1246–1248. doi: 10.1073/pnas.68.6.1246
[2] Haond, M., Morel-Journel, T., Lombaert, E., Vercken, E., Mailleret, L., & Roques, L. (2018). When higher carrying capacities lead to faster propagation. BioRxiv, 307322. doi: 10.1101/307322
[3] Crooks, E. C. M., Dancer, E. N., Hilhorst, D., Mimura, M., & Ninomiya, H. (2004). Spatial segregation limit of a competition-diffusion system with Dirichlet boundary conditions. Nonlinear Analysis: Real World Applications, 5(4), 645–665. doi: 10.1016/j.nonrwa.2004.01.004
[4] Shigesada, N., & Kawasaki, K. (1997). Biological Invasions: Theory and Practice. Oxford University Press, UK.
[5] Matthysen, E. (2005). Density-dependent dispersal in birds and mammals. Ecography, 28(3), 403–416. doi: 10.1111/j.0906-7590.2005.04073.x

Assessing the reliability of the methods we use in actually measuring the intended trait should be one of our first priorities when designing a study – especially when the trait in question is not directly observable and is measured through a proxy. 

This is the case for cognitive traits, which are often quantified through measures of behavioral performance. Behavioral flexibility is of particular interest in the context of great environmental changes that a lot of populations have to experiment. This type of behavioral performance is often measured through reversal learning experiments (Bond 2007). In these experiments, individuals first learn a preference, for example for an object of a certain type of form or color, associated with a reward such as food. The characteristics of the rewarded object then change, and the individuals hence have to learn these new characteristics (to get the reward). The time needed by the individual to make this change in preference has been considered a measure of behavioral flexibility.

Although reversal learning experiments have been widely used, their construct validity to assess behavioral flexibility has not been thoroughly tested. This was the aim of McCune and collaborators' (2023) study, through the test of the repeatability of individual performance within and across contexts of reversal learning, in the great-tailed grackle.

This manuscript presents a post-study of the preregistered study* (Logan et al. 2019) that was peer-reviewed and received an In Principle Recommendation for PCI Ecology (Coulon 2019; the initial preregistration was split into 3 post-studies).
Using 34 great-tailed grackles wild-caught in Tempe, Arizona (USA), the authors tested in aviaries 2 hypotheses:

• First, that the behavioral flexibility measured by reversal learning is repeatable within individuals across sessions of the same experiment;
• Second, that there is repeatability of the measured behavioral flexibility (within individuals) across different types of reversal learning experiments (context).

The first hypothesis was tested by measuring the repeatability of the time needed by individuals to switch color preference in a color reversal learning task (colored tubes), over serial sessions of this task. The second one was tested by measuring the time needed by individuals to switch solutions, within 3 different contexts: (1) colored tubes, (2) plastic and (3) wooden multi-access boxes involving several ways to access food.

Despite limited sample sizes, the results of these experiments suggest that there is both temporal and contextual repeatability of behavioral flexibility performance of great-tailed grackles, as measured by reversal learning experiments.

Those results are a first indication of the construct validity of reversal learning experiments to assess behavioral flexibility. As highlighted by McCune and collaborators, it is now necessary to assess the discriminant validity of these experiments, i.e. checking that a different performance is obtained with tasks (experiments) that are supposed to measure different cognitive abilities.
 
* A pre-registered study is a study in which context, aims, hypotheses and methodologies have been written down as an empirical paper, peer-reviewed and pre-accepted before research is undertaken. Pre-registrations are intended to reduce publication bias and reporting bias.
 
REFERENCES
 
Bond, A. B., Kamil, A. C., & Balda, R. P. (2007). Serial reversal learning and the evolution of behavioral
flexibility in three species of north american corvids (Gymnorhinus cyanocephalus, Nucifraga columbiana,
Aphelocoma californica). Journal of Comparative Psychology, 121 (4), 372. https://doi.org/10.1037/0735-7036.121.4.372

Coulon, A. (2019) Can context changes improve behavioral flexibility? Towards a better understanding of species adaptability to environmental changes. Peer Community in Ecology, 100019. https://doi.org/10.24072/pci.ecology.100019

Logan, CJ, Lukas D, Bergeron L, Folsom M, & McCune, K. (2019).  Is behavioral flexibility related to foraging and social behavior in a rapidly expanding species? In Principle Acceptance by PCI Ecology of the Version on 6 Aug 2019. http://corinalogan.com/Preregistrations/g_flexmanip.html

McCune KB, Blaisdell AP, Johnson-Ulrich Z, Lukas D, MacPherson M, Seitz BM, Sevchik A, Logan CJ (2023) Using repeatability of performance within and across contexts to validate measures of behavioral flexibility. EcoEvoRxiv, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/X2R59K

Two fields of research have grown considerably over the past twenty years: the investigation of human-wildlife conflicts (e.g. see Treves & Santiago-Ávila 2020), and multispecies occupancy modelling (Devarajan et al. 2020). In their recent study, Lauret et al. (2023) combined both in an elegant methodological framework, applied to the study of the co-occurrence of fishing activities and bottlenose dolphins in the French Mediterranean.

A common issue with human-wildlife conflicts (and, in particular, fishery by-catch) is that data is often only available from those conflicts or interactions, limiting the validity of the predictions (Kuiper et al. 2022). Lauret et al. use independent data sources informing the occurrence of fishing vessels and dolphins, combined in a Bayesian multispecies occupancy model where vessels are "the other species". I particularly enjoyed that approach, as integration of human activities in ecological models can be extremely complex, but can also translate in phenomena that can be captured as one would of individuals of a species, as long as the assumptions are made clearly. Here, the model is made more interesting by accounting for environmental factors (seabed depth) borrowing an approach from Generalized Additive Models in the Bayesian framework. While not pretending to provide (yet) practical recommendations to help conserve bottlenose dolphins (and other wildlife conflicts), this study and the associated code are a promising step in that direction.

REFERENCES

Devarajan, K., Morelli, T.L. & Tenan, S. (2020), Multi-species occupancy models: review, roadmap, and recommendations. Ecography, 43: 1612-1624. https://doi.org/10.1111/ecog.04957

Kuiper, T., Loveridge, A.J. and Macdonald, D.W. (2022), Robust mapping of human–wildlife conflict: controlling for livestock distribution in carnivore depredation models. Anim. Conserv., 25: 195-207. https://doi.org/10.1111/acv.12730

Lauret V, Labach H, David L, Authier M, & Gimenez O (2023) Using integrated multispecies occupancy models to map co-occurrence between bottlenose dolphins and fisheries in the Gulf of Lion, French Mediterranean Sea. Ecoevoarxiv, ver. 2 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.32942/osf.io/npd6u

Treves, A. & Santiago-Ávila, F.J. (2020). Myths and assumptions about human-wildlife conflict and coexistence. Conserv. Biol. 34, 811–818.  https://doi.org/10.1111/cobi.13472

In the face of worldwide declines in biodiversity, evaluating the effectiveness of conservation practices is an absolute necessity. Protected Areas (PA) are a key tool for conservation, and the question “Are PA effective” has been on many a research agenda, as the introduction to this preprint will no doubt convince you. A challenge we face is that, until now, few studies have been explicitly designed to evaluate PA, and despite the rise of meta-analyses on the topic, our capacity to quantify their effect on biodiversity remains limited.
This study by Cazalis et al. [1] uses the rich dataset of the North-American Breeding Bird Survey and a sound paired design to investigate how PA change bird assemblages. The methodological care brought to the study in itself is worth the read, and the results are insightful. I will not spoil too much by revealing here that things are “complicated”, and that effects – or lack thereof – depend on the type of ecosystem, and the type of species considered.
If you are interested in conservation, bird communities, species life-history, or like beautiful plots: go and read it.

References

[1] Cazalis, V., Belghali, S., & Rodrigues, A. S. (2019). Using a large-scale biodiversity monitoring dataset to test the effectiveness of protected areas at conserving North-American breeding birds. bioRxiv, 433037, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/433037