Animal behavior, like other kinds of phenotypic plasticity, is crucial for survival and reproduction in environments that vary over space and time. Behaviors themselves may need to be flexible when the distributions of environmental conditions themselves change; for example, short-term weather patterns and long-term climate conditions are changing due to human activity, and behavioral flexibility will likely be key to some population persisting during these changes and others going extinct. Thus, measuring this flexibility is key to understanding which species may be resilient to climate change.

Measuring behavioral flexibility is tricky as different studies may define and measure it differently and yet other studies may measure similar kinds of flexibility yet call them different things. This so-called "jingle-jangle" issue suggests that studies can more robustly measure a behavioral trait when they use multiple behavioral tests. An additional issue is that measuring behavioral traits that vary between individuals due to genetic or development effects, often referred to as "personality" traits, requires that those trait differences be repeatable across time and between individuals. 

With these issues in mind, McCune et al. (2019) presented a preregistration for experiments using a population of great-tailed grackles to investigate how behavioral flexibility relates to other important traits that are known to vary across individuals including exploratory behavior, boldness, and persistence and motor diversity in accessing a new food source. Behavioral flexibility is measured by the rate of learning a color associated with reward after first learning an association with a different color. That preregistration was recommended by PCI Ecology (Van Cleve, 2019). Now, McCune et al. (2025) present results from this study and find that only exploration of a novel environment and persistence were statistically repeatable behaviors. Both behaviors were not significantly correlated with behavioral flexibility. However, grackles that were trained to perform better in the color reversal learning task were more exploratory. This association between behavioral flexibility and exploratory tendencies may have evolved in grackles to help them survive in new environments, which they have proven very capable of doing as they have expanded their range in North American over the last 140 years (Wehtje, 2003).

There are a few key features of McCune et al. (2025) to merit recommendation. First, the authors are intent on demonstrating careful behavioral research practices including, as evidenced above, preregistering their hypotheses and predictions and making available both the preregistered and final analysis code. Second, the study demonstrates a thorough attempt to address two aspects that bedevil behavioral research, namely the "jingle-jangle" issue and repeatability of traits across individuals. Even after measuring multiple features of boldness, exploratory, persistence, McCune et al. (2025) find that only a subset of the measured behaviors are repeatable and only a subset of those are associated with behavioral flexibility. This suggests that only thorough studies like McCune et al. (2025) can start to probe difficult to measure behavioral associations that may be key to understanding how species will respond to our changing world.

References

McCune K, Lukas D,  MacPherson M, Logan CJ (2025) Behavioral flexibility is related to exploration, but not boldness, persistence or motor diversity. EcoEvoRxiv, ver.2 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2H33F

Van Cleve, J. (2019) Probing behaviors correlated with behavioral flexibility. PCI Ecology, 100020. https://doi.org/10.24072/pci.ecology.100020

McCune K, Rowney C, Bergeron L, Logan CJ. (2019) Is behavioral flexibility linked with exploration, but not boldness, persistence, or motor diversity? (http://corinalogan.com/Preregistrations/g_exploration.html) In principle acceptance by PCI Ecology of the version on 27 Mar 2019

Wehtje, W. (2003) The range expansion of the great-tailed grackle (Quiscalus mexicanus Gmelin) in North America since 1880. Journal of Biogeography 30:1593–1607 https://doi.org/10.1046/j.1365-2699.2003.00970.x.

Biodiversity loss can have important consequences for ecosystem functions, as exemplified by a large body of literature spanning at least three decades [1–3]. While connections between species diversity and ecosystem functions are now well-defined and understood, the importance of diversity within species is more elusive. Despite a surge in theoretical work on how intraspecific diversity can affect coexistence in simple community types [4,5], not much is known about how intraspecific diversity drives ecosystem processes in more complex community types. One particular challenge is that intraspecific diversity can be expressed as observable variation of functional traits, or instead subsist as genetic variation of which the consequences for ecosystem processes are harder to grasp.
Raffard et al. [6] examined how intraspecific biodiversity loss in a consumer fish changes species diversity at lower trophic levels and ecosystem processes in pond mesocosms. An interesting feature of this experiment is that it crosses functional and genetic intraspecific diversity. To do so, Raffard and colleagues measured and genotyped European minnow (P. phoxinus) individuals sampled from streams across southern France. Combining these collected specimens into experimental ponds allowed them to control functional (population variance of body size) and genetic intraspecific richness (number of genotypes).
Effects on minnow biomass production were mostly small; biomass was significantly reduced only when lowering both functional and genetic richness. However, the consequences for lower trophic levels (zooplankton and macroinvertebrates) were more pronounced and – importantly – not intuitive. For instance, the macroinvertebrate community was less species-diverse at higher minnow functional richness. If minnows with different body sizes would be the main regulator factors [7] explaining macroinvertebrate interactions, one would expect a more diverse set of minnow body sizes (i.e. higher functional minnow richness) to permit higher instead of lower macroinvertebrate richness. At the same time, the macroinvertebrate community was more species-diverse at higher minnow genotype richness, which could indicate unobserved minnow traits determining macroinvertebrate diversity more than the usual suspects (functional consumer richness). Such unobserved traits could be behavioral traits, allowing for resource partitioning among fish.
The consequences of functional minnow diversity loss on zooplankton diversity were negative, as expected in case body size differences among fish would facilitate coexistence of their zooplankton prey, as explained above. However, this was only the case when genetic diversity was high, suggesting nonstraightforward interactive effects of observed and non-observed traits on prey diversity.
The effects of functional and genetic minnow diversity loss on invertebrate (macroinvertebrates and zooplankton) abundance were more consistent than for invertebrate diversity. This suggests again nonstraightforward relationships in this experimental ecosystem, but now between invertebrate diversity and abundance. When using abundance as a proxy for an ecosystem process (which the authors did not), this result illustrates that biodiversity loss in multitrophic communities can have consequences that are challenging to interpret, let alone predict [8,9]. Path analyses showed how the observed changes of invertebrate diversity and abundance co-determined decomposition, a key ecosystem function. These path analyses had highest explanatory power show when including both kinds of intraspecific diversity.
Taken together, the results by Raffard and colleagues suggest that genetic consumer richness can drive species diversity of connected trophic levels and ecosystem processes with similar magnitude as functional diversity. Indeed, the effects of genetic consumer richness were shown to be so strong as to compensate or exacerbate the loss of observed functional richness. The exact mechanisms explaining these effects remain to be identified, however. The possibility that fish grazing by fish with different (observed or not observed) traits regulates coexistence among invertebrate prey, for instance, would depend on how strong fish consumption feeds back on prey growth during a 30-week experiment. As the authors indicate, detailed studies on resource partitioning among consumers (e.g. using stable isotope labelling) can shed light on these matters. Doing so may address a more fundamental question, which is if the mechanisms linking intraspecific diversity to function are different from those linking interspecific diversity to function, and at what time scales.

References

[1] Tilman D, Downing JA (1994) Biodiversity and stability in grasslands. Nature, 367, 363–365. https://doi.org/10.1038/367363a0
[2] 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
[3] De Laender F, Rohr JR, Ashauer R, Baird DJ, Berger U, Eisenhauer N, Grimm V, Hommen U, Maltby L, Meliàn CJ, Pomati F, Roessink I, Radchuk V, Brink PJV den (2016) Reintroducing Environmental Change Drivers in Biodiversity–Ecosystem Functioning Research. Trends in Ecology & Evolution, 31, 905–915. https://doi.org/10.1016/j.tree.2016.09.007
[4] Hart SP, Schreiber SJ, Levine JM (2016) How variation between individuals affects species coexistence. Ecology Letters, 19, 825–838. https://doi.org/10.1111/ele.12618
[5] Barabás G, D’Andrea R (2016) The effect of intraspecific variation and heritability on community pattern and robustness. Ecology Letters, 19, 977–986. https://doi.org/10.1111/ele.12636
[6] Raffard A, Cucherousset J, Montoya JM, Richard M, Acoca-Pidolle S, Poésy C, Garreau A, Santoul F, Blanchet S (2020) Intraspecific diversity loss in a predator species alters prey community structure and ecosystem functions. bioRxiv, 2020.06.10.144337, ver. 3 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2020.06.10.144337
[7] Pásztor L, Botta-Dukát Z, Magyar G, Czárán T, Meszéna G. Theory-Based Ecology: A Darwinian approach. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199577859.001.0001
[8] Binzer A, Guill C, Rall BC, Brose U (2016) Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food-web structure. Global Change Biology, 22, 220–227. https://doi.org/10.1111/gcb.13086
[9] Schwarz B, Barnes AD, Thakur MP, Brose U, Ciobanu M, Reich PB, Rich RL, Rosenbaum B, Stefanski A, Eisenhauer N (2017) Warming alters energetic structure and function but not resilience of soil food webs. Nature Climate Change, 7, 895–900. https://doi.org/10.1038/s41558-017-0002-z

In the field of ecology, there is a growing interest in machine (including deep) learning for processing and automatizing repetitive analyses on large amounts of images collected from camera traps, drones and smartphones, among others. These analyses include species or individual recognition and classification, counting or tracking individuals, detecting and classifying behavior. By saving countless times of manual work and tapping into massive amounts of data that keep accumulating with technological advances, machine learning is becoming an essential tool for ecologists. We refer to recent papers for more details on machine learning for ecology and evolution (Besson et al. 2022, Borowiec et al. 2022, Christin et al. 2019, Goodwin et al. 2022, Lamba et al. 2019, Nazir & Kaleem 2021, Perry et al. 2022, Picher & Hartig 2023, Tuia et al. 2022, Wäldchen & Mäder 2018).

In their paper, Nakagawa et al. (2023) conducted a systematic review of the literature on machine learning for wildlife imagery. Interestingly, the authors used a method unfamiliar to ecologists but well-established in medicine called rapid review, which has the advantage of being quickly completed compared to a fully comprehensive systematic review while being representative (Lagisz et al., 2022). Through a rigorous examination of more than 200 articles, the authors identified trends and gaps, and provided suggestions for future work. Listing all their findings would be counterproductive (you’d better read the paper), and I will focus on a few results that I have found striking, fully assuming a biased reading of the paper. First, Nakagawa et al. (2023) found that most articles used neural networks to analyze images, in general through collaboration with computer scientists. A challenge here is probably to think of teaching computer vision to the generations of ecologists to come (Cole et al. 2023). Second, the images were dominantly collected from camera traps, with an increase in the use of aerial images from drones/aircrafts that raise specific challenges. Third, the species concerned were mostly mammals and birds, suggesting that future applications should aim to mitigate this taxonomic bias, by including, e.g., invertebrate species. Fourth, most papers were written by authors affiliated with three countries (Australia, China, and the USA) while India and African countries provided lots of images, likely an example of scientific colonialism which should be tackled by e.g., capacity building and the involvement of local collaborators. Last, few studies shared their code and data, which obviously impedes reproducibility. Hopefully, with the journals’ policy of mandatory sharing of codes and data, this trend will be reversed. 

REFERENCES

Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF (2022) Towards the fully automated monitoring of ecological communities. Ecology Letters, 25, 2753–2775. https://doi.org/10.1111/ele.14123

Borowiec ML, Dikow RB, Frandsen PB, McKeeken A, Valentini G, White AE (2022) Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13, 1640–1660. https://doi.org/10.1111/2041-210X.13901

Christin S, Hervet É, Lecomte N (2019) Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632–1644. https://doi.org/10.1111/2041-210X.13256

Cole E, Stathatos S, Lütjens B, Sharma T, Kay J, Parham J, Kellenberger B, Beery S (2023) Teaching Computer Vision for Ecology. https://doi.org/10.48550/arXiv.2301.02211

Goodwin M, Halvorsen KT, Jiao L, Knausgård KM, Martin AH, Moyano M, Oomen RA, Rasmussen JH, Sørdalen TK, Thorbjørnsen SH (2022) Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook†. ICES Journal of Marine Science, 79, 319–336. https://doi.org/10.1093/icesjms/fsab255

Lagisz M, Vasilakopoulou K, Bridge C, Santamouris M, Nakagawa S (2022) Rapid systematic reviews for synthesizing research on built environment. Environmental Development, 43, 100730. https://doi.org/10.1016/j.envdev.2022.100730

Lamba A, Cassey P, Segaran RR, Koh LP (2019) Deep learning for environmental conservation. Current Biology, 29, R977–R982. https://doi.org/10.1016/j.cub.2019.08.016

Nakagawa S, Lagisz M, Francis R, Tam J, Li X, Elphinstone A, Jordan N, O’Brien J, Pitcher B, Sluys MV, Sowmya A, Kingsford R (2023) Rapid literature mapping on the recent use of machine learning for wildlife imagery. EcoEvoRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.32942/X2H59D

Nazir S, Kaleem M (2021) Advances in image acquisition and processing technologies transforming animal ecological studies. Ecological Informatics, 61, 101212. https://doi.org/10.1016/j.ecoinf.2021.101212

Perry GLW, Seidl R, Bellvé AM, Rammer W (2022) An Outlook for Deep Learning in Ecosystem Science. Ecosystems, 25, 1700–1718. https://doi.org/10.1007/s10021-022-00789-y

Pichler M, Hartig F Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, n/a. https://doi.org/10.1111/2041-210X.14061

Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T (2022) Perspectives in machine learning for wildlife conservation. Nature Communications, 13, 792. https://doi.org/10.1038/s41467-022-27980-y

Wäldchen J, Mäder P (2018) Machine learning for image-based species identification. Methods in Ecology and Evolution, 9, 2216–2225. https://doi.org/10.1111/2041-210X.13075

Tick-borne diseases are an important burden on public health all over the globe, making accurate forecasts of tick population a key ingredient in a successful public health strategy. Over long time scales, tick populations can undergo complex dynamics, as they are sensitive to many non-linear effects due to the complex relationships between ticks and the relevant (numerical) features of their environment.

But luckily, capturing complex non-linear responses is a task that machine learning thrives on. In this contribution, Manley et al. (2023) explore the use of Gradient Boosted Trees to predict the distribution (presence/absence) and abundance of ticks across New York state.

This is an interesting modelling challenge in and of itself, as it looks at the same ecological question as an instance of a classification problem (presence/absence) or of a regression problem (abundance). In using the same family of algorithm for both, Manley et al. (2023) provide an interesting showcase of the versatility of these techniques. But their article goes one step further, by setting up a multi-class categorical model that estimates jointly the presence and abundance of a population. I found this part of the article particularly elegant, as it provides an intermediate modelling strategy, in between having two disconnected models for distribution and abundance, and having nested models where abundance is only predicted for the present class (see e.g. Boulangeat et al., 2012, for a great description of the later).

One thing that Manley et al. (2023) should be commended for is their focus on opening up the black box of machine learning techniques. I have never believed that ML models are more inherently opaque than other families of models, but the focus in this article on explainable machine learning shows how these models might, in fact, bring us closer to a phenomenological understanding of the mechanisms underpinning our observations.

There is also an interesting discussion in this article, on the rate of false negatives in the different models that are being benchmarked. Although model selection often comes down to optimizing the overall quality of the confusion matrix (for distribution models, anyway), depending on the type of information we seek to extract from the model, not all types of errors are created equal. If the purpose of the model is to guide actions to control vectors of human pathogens, a false negative (predicting that the vector is absent at a site where it is actually present) is a potentially more damaging outcome, as it can lead to the vector population (and therefore, potentially, transmission) increasing unchecked.

Boulangeat I, Gravel D, Thuiller W. Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances: The role of dispersal and biotic interactions in explaining species distributions and abundances. Ecol Lett. 2012;15: 584-593.
https://doi.org/10.1111/j.1461-0248.2012.01772.x

Manley W, Tran T, Prusinski M, Brisson D. (2023) Modeling tick populations: An ecological test case for gradient boosted trees. bioRxiv, 2023.03.13.532443, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.03.13.532443

Across the temporal expanse of history, the impact of human activities on global landscapes has manifested as a complex interplay of ecological alterations. From the advent of early agricultural practices to the successive waves of industrialization characterizing the 18th and 19th centuries, anthropogenic forces have exerted profound and enduring transformations upon Earth's ecosystems. Indeed, by 2017, more than 80% of the terrestrial biosphere was transformed by human populations and land use, and just 19% remains as wildlands (Ellis et al. 2021).
 
Urbanization engenders profound alterations in environmental conditions, exerting substantial impacts on biological communities. The expansion of built infrastructure, modification of land use patterns, and the introduction of impervious surfaces and habitat fragmentation are key facets of urbanization (Faeth et al. 2011). These alterations generate biodiversity loss, changes in the composition of biological communities, disruptions in access and availability of food and nutrients, and a loss of efficiency in the immune system's control of infections, etc. (Reyes et al. 2013).
 
In this study, Caizergues et al. (2023) investigated the prevalence and diversity of avian malaria parasites (Plasmodium/Haemoproteus sp. and Leucocytozoon sp.) in great tits (Parus major) living across an urbanization gradient. The study reveals nuanced patterns of avian malaria prevalence and lineage diversity in great tits across urban and non-urban environments. While overall parasite diversity remains consistent, there are marked differences in prevalence between life stages and habitats. They observed a high prevalence in adult birds (from 95% to 100%), yet lower prevalence in fledglings (from 0% to 38%). Notably, urban nestlings exhibit higher parasite prevalence than their non-urban counterparts, suggesting a potential link between early malaria infection and the urban heat island effect. This finding underscores the importance of considering both spatial and temporal aspects of urbanization in understanding disease dynamics. Parasite lineages were not habitat-specific. The results suggest a potential parasitic burden in more urbanized areas, with a marginal but notable effect of nest-level urbanization on Plasmodium prevalence. This challenges the common perception of lower parasitic prevalence in urban environments and highlights the need for further investigation into the factors influencing parasite prevalence at finer spatial scales.
 
The discussion emphasizes the significance of examining vector distributions, abundance, and diversity in urban areas, which may be influenced by ecological niches and the presence of suitable habitats such as marshes. The identification of habitat-specific Haemosporidian lineages, particularly those occurring more frequently in urban areas, raises intriguing questions about the factors influencing parasite diversity. The presence of rare lineages in urban environments, such as AFR065, DELURB4, and YWT4, suggests a potential connection between urban bird communities and specific parasite strains.
 
Future research should empirically demonstrate these relationships to enhance our understanding of urban parasitology. This finding has broader implications for wildlife epidemiology, especially when introducing or keeping exotic wildlife in contact with native species. The study highlights the importance of considering not only the prevalence but also the specific lineages of parasites in understanding the dynamics of avian malaria in urban and non-urban habitats. This preprint contributes valuable insights to the ongoing discourse on the intricate interplay between ecological repercussions of human-induced changes (urbanization), biological communities, and the prevalence of vector-borne diseases.
 
References

Caizergues AE, Robira B, Perrier C, Jeanneau M, Berthomieu A, Perret S, Gandon S, Charmantier A (2023) Cities as parasitic amplifiers? Malaria prevalence and diversity in great tits along an urbanization gradient. bioRxiv, 2023.05.03.539263, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.05.03.539263

Ellis EC, Gauthier N, Klein Goldewijk K, Bliege Bird R, Boivin N, Díaz S, Fuller DQ, Gill JL, Kaplan JO, Kingston N, Locke H, McMichael CNH, Ranco D, Rick TC, Shaw MR, Stephens L, Svenning JC, Watson JEM. People have shaped most of terrestrial nature for at least 12,000 years. Proc Natl Acad Sci U S A. 2021 Apr 27;118(17):e2023483118. https://doi.org/10.1073/pnas.2023483118. 

Faeth  SH, Bang  C, Saari  S (2011) Urban biodiversity: Patterns and mechanisms. Ann N Y Acad Sci 1223:69–81. https://doi.org/10.1111/j.1749-6632.2010.05925.x

Faeth  SH, Bang  C, Saari  S (2011) Urban biodiversity: Patterns and mechanisms. Ann N Y Acad Sci 1223:69–81. https://doi.org/10.1111/j.1749-6632.2010.05925.x

Reyes  R, Ahn  R, Thurber  K, Burke  TF (2013) Urbanization and Infectious Diseases: General Principles, Historical Perspectives, and Contemporary Challenges. Challenges Infect Dis 123. https://doi.org/10.1007/978-1-4614-4496-1_4