The hyperdiverse tropical forests have long fascinated ecologists because the fact that so many species persist at a low density at a local scale remains hard to explain. Both niche-based and neutral hypotheses have been tested, primarily based on analyzing the taxonomic composition of tropical forest plots (Janzen 1970; Hubbell 2001). Studies of the functional and phylogenetic structure of tropical tree communities have further aimed to better assess the importance of niche-based processes. For instance, Baraloto et al. (2012) found that co-occurring species were functionally and phylogenetically more similar in a neotropical forest, suggesting a role of environmental filtering. Likewise, Schmitt et al. (2021) found the influence of environmental filtering on the functional composition of an Indian rainforest. Yet these studies evidenced non-random trait-environment association based on the composition of assemblages only (in terms of occurrences and abundances). A major challenge remains to further address whether and how tree performance varies among species and individuals in tropical forests.

Functional traits are related to components of individual fitness (Violle et al. 2007). Recently, more and more emphasis has been put on examining the relationship between functional trait values and demographic parameters (Salguero-Gómez et al. 2018), in order to better understand how functional trait values determine species population dynamics and abundances in assemblages. Fortunel et al. (2018) found an influence of functional traits on species growth variation related to topography, and less clearly to neighborhood density (crowding). Poorter et al. (2018) observed 44% of trait variation within species in a neotropical forest. Although individual trait values would be expected to be better predictors of performance than average values measured at the species level, Poorter et al still found a poor relationship.

Schmitt et al. (2023) examined how abiotic conditions and biotic interactions (considering neighborhood density) influenced the variation of individual potential tree growth, in a tropical forest plot located in French Guiana. They also considered the link between species-averaged values of growth potential and functional traits. Schmitt et al. (2023) found substantial variation in growth potential within species, that functional traits explained 40% of the variation of species-averaged growth and, noticeably, that the taxonomic structure (used as random effect in their model) explained a third of the variation in individual growth.

Although functional traits of roots, wood and leaves could predict a significant part of species growth potential, much variability of tree growth occurred within species. Intraspecific trait variation can thus be huge in response to changing abiotic and biotic contexts across individuals. The information on phylogenetic relationships can still provide a proxy of the integrated phenotypic variation that is under selection across the phylogeny, and determine a variation in growth strategies among individuals. The similarity of the phylogenetic structure suggests a joint selection of these growth strategies and related functional traits during events of convergent evolution. Baraloto et al. (2012) already noted that phylogenetic distance can be a proxy of niche overlap in tropical tree communities. Here, Schmitt et al. further demonstrate that evolutionary heritage is significantly related to individual growth variation, and plead for better acknowledging this role in future studies.

While the role of fitness differences in tropical tree community dynamics remained to be assessed, the present study provides new evidence that individual growth does vary depending on evolutionary relationships, which can reflect the roles of selection and adaptation on growth strategies. Therefore, investigating both the influence of functional traits and phylogenetic relationships on individual performance remains a promising avenue of research, for functional and community ecology in general.

REFERENCES

Baraloto, Christopher, Olivier J. Hardy, C. E. Timothy Paine, Kyle G. Dexter, Corinne Cruaud, Luke T. Dunning, Mailyn-Adriana Gonzalez, et al. 2012. « Using functional traits and phylogenetic trees to examine the assembly of tropical tree communities ». Journal of Ecology, 100: 690‑701.
https://doi.org/10.1111/j.1365-2745.2012.01966.x
 
Fortunel Claire, Lasky Jesse R., Uriarte María, Valencia Renato, Wright S.Joseph, Garwood Nancy C., et Kraft Nathan J. B. 2018. « Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest ». Ecology, 99(10): 2272-2283. https://doi.org/10.1002/ecy.2441
 
Hubbell, S. P. 2001. The Unified Neutral Theory of Biodiversity and Biogeography. 1 vol. Princeton and Oxford: Princeton University Press. https://www.jstor.org/stable/j.ctt7rj8w
 
Janzen, Daniel H. 1970. « Herbivores and the number of tree species in tropical forests ». American Naturalist, 104(940): 501-528. https://doi.org/10.1086/282687
 
Poorter, Lourens, Carolina V. Castilho, Juliana Schietti, Rafael S. Oliveira, et Flávia R. C. Costa. 2018. « Can traits predict individual growth performance? A test in a hyperdiverse tropical forest ». New Phytologist, 219 (1): 109‑21. https://doi.org/10.1111/nph.15206
 
Salguero-Gómez, Roberto, Cyrille Violle, Olivier Gimenez, et Dylan Childs. 2018. « Delivering the promises of trait-based approaches to the needs of demographic approaches, and vice versa ». Functional Ecology, 32 (6): 1424‑35. https://doi.org/10.1111/1365-2435.13148
 
Schmitt, Sylvain, Valérie Raevel, Maxime Réjou‐Méchain, Narayanan Ayyappan, Natesan Balachandran, Narayanan Barathan, Gopalakrishnan Rajashekar, et François Munoz. 2021. « Canopy and understory tree guilds respond differently to the environment in an Indian rainforest ». Journal of Vegetation Science, e13075. https://doi.org/10.1111/jvs.13075
 
Sylvain Schmitt, Bruno Hérault, et Géraldine Derroire. 2023. « High intraspecific growth variability despite strong evolutionary heritage in a neotropical forest ». bioRxiv, 2022.07.27.501745, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.07.27.501745
 
Violle, C., M. L. Navas, D. Vile, E. Kazakou, C. Fortunel, I. Hummel, et E. Garnier. 2007. « Let the concept of trait be functional! » Oikos, 116(5), 882-892. https://doi.org/10.1111/j.0030-1299.2007.15559.x

Understanding the distribution of species on earth is one of the fundamental challenges in ecology and evolution. For a long time, this challenge has mainly been addressed from a correlative point of view with a focus on abiotic factors determining a species abiotic niche (classical bioenvelope models; [1]). It is only recently that researchers have realized that behaviour and especially plasticity in behaviour may play a central role in determining species ranges and their dynamics [e.g., 2-5]. Blaisdell et al. propose to take this even one step further and to analyse how behavioural flexibility and possibly associated causal cognition impacts range dynamics.
The current preregistration is integrated in an ambitious long-term research plan that aims at addressing the above outlined question and focuses specifically on investigating whether more behaviourally flexible individuals are better at deriving causal inferences. The model system the authors plan on using are Great-tailed Grackles which have expanded their range into North America during the last century. The preregistration by Blaisdell et al. is a great example of the future of scientific research: it includes conceptual models, alternative hypotheses and testable predictions along with a sound sampling and analysis plan and embraces the principles of Open Science. Overall, the research the authors propose is fascinating and of highest relevance, as it aims at bridging scales from the microscopic mechanisms that underlie animal behaviour to macroscopic, macroecological consequences (see also [3]). I am very much looking forward to the results the authors will report.

References
[1] Elith, J. & Leathwick, J. R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40: 677-697. doi: 10.1146/annurev.ecolsys.110308.120159
[2] Kubisch, A.; Degen, T.; Hovestadt, T. & Poethke, H. J. (2013) Predicting range shifts under global change: the balance between local adaptation and dispersal. Ecography 36: 873-882. doi: 10.1111/j.1600-0587.2012.00062.x
[3] Keith, S. A. & Bull, J. W. (2017) Animal culture impacts species' capacity to realise climate-driven range shifts. Ecography, 40: 296-304. doi: 10.1111/ecog.02481
[4] Sullivan, L. L.; Li, B.; Miller, T. E.; Neubert, M. G. & Shaw, A. K. (2017) Density dependence in demography and dispersal generates fluctuating invasion speeds. Proc. Natl. Acad. Sci. USA, 114: 5053-5058. doi: 10.1073/pnas.1618744114
[5] Fronhofer, E. A.; Nitsche, N. & Altermatt, F. (2017) Information use shapes the dynamics of range expansions into environmental gradients. Glob. Ecol. Biogeogr. 26: 400-411. doi: 10.1111/geb.12547

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

A network includes a set of vertices or nodes (e.g., species in an interaction network), and a set of edges or links (e.g., interactions between species). Whether and how networks vary in space and/or time are questions often addressed in ecological research. 

Two ecological networks can differ in several extents: in that species are different in the two networks and establish new interactions (species turnover), or in that species that are present in both networks establish different interactions in the two networks (rewiring). The ecological meaning of changes in network structure is quite different according to whether species turnover or interaction rewiring plays a greater role. Therefore, much attention has been devoted in recent years on quantifying and interpreting the relative changes in network structure due to species turnover and/or rewiring.

Poisot et al. (2012) proposed to partition the global variation in structure between networks, \( \beta_{WN} \) (WN = Whole Network) into two terms: \( \beta_{OS} \) (OS = Only Shared species) and \( \beta_{ST} \) (ST = Species Turnover), such as \( \beta_{WN} = \beta_{OS} + \beta_{ST} \).

The calculation lays on enumerating the interactions between species that are common or not to two networks, as illustrated on Figure 1 for a simple case. Specifically, Poisot et al. (2012) proposed to use a Sorensen type measure of network dissimilarity, i.e., \( \beta_{WN} = \frac{a+b+c}{(2a+b+c)/2} -1=\frac{b+c}{2a+b+c} \) , where \( a \) is the number of interactions shared between the networks, while \( b \) and \( c \) are interaction numbers unique to one and the other network, respectively. \( \beta_{OS} \) is calculated based on the same formula, but only for the subnetworks including the species common to the two networks, in the form \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a_{OS}+b_{OS}+c_{OS}} \) (e.g., Fig. 1). \( \beta_{ST} \) is deduced by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and represents in essence a "dissimilarity in interaction structure introduced by dissimilarity in species composition" (Poisot et al. 2012).

Figure 1. Ecological networks exemplified in Fründ (2021) and discussed in Poisot (2022). a is the number of shared links (continuous lines in right figures), while b+c is the number of edges unique to one or the other network (dashed lines in right figures).

Alternatively, Fründ (2021) proposed to define \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a+b+c} \) and \( \beta_{ST} = \frac{b_{ST}+c_{ST}}{2a+b+c} \), where \( b_{ST}=b-b_{OS} \)  and \( c_{ST}=c-c_{OS} \) , so that the components \( \beta_{OS} \) and \( \beta_{ST} \) have the same denominator. In this way, Fründ (2021) partitioned the count of unique \( b+c=b_{OS}+b_{ST}+c_{ST} \) interactions, so that \( \beta_{OS} \) and \( \beta_{ST} \) sums to \( \frac{b_{OS}+c_{OS}+b_{ST}+c_{ST}}{2a+b+c} = \frac{b+c}{2a+b+c} = \beta_{WN} \). Fründ (2021) advocated that this partition allows a more sensible comparison of \( \beta_{OS} \) and \( \beta_{ST} \), in terms of the number of links that contribute to each component.

For instance, let us consider the networks 1 and 2 in Figure 1 (left panel) such as \( a_{OS}=2 \) (continuous lines in right panel), \( b_{ST} + c_{ST} = 1 \) and \( b_{OS} + c_{OS} = 1 \) (dashed lines in right panel), and thereby \( a = 2 \), \( b+c=2 \), \( \beta_{WN} = 1/3 \). Fründ (2021) measured \( \beta_{OS}=\beta_{ST}=1/6 \) and argued that it is appropriate insofar as it reflects that the number of unique links in the OS and ST components contributing to network dissimilarity (dashed lines) are actually equal. Conversely, the formula of Poisot et al. (2012) yields \( \beta_{OS}=1/5 \), hence \( \beta_{ST} = \frac{1}{3}-\frac{1}{5}=\frac{2}{15}<\beta_{OS} \). Fründ (2021) thus argued that the method of Poisot tends to underestimate the contribution of species turnover.

To clarify and avoid misinterpretation of the calculation of \( \beta_{OS} \) and \( \beta_{ST} \) in Poisot et al. (2012), Poisot (2022) provides a new, in-depth mathematical analysis of the decomposition of \( \beta_{WN} \). Poisot et al. (2012) quantify in \( \beta_{OS} \) the actual contribution of rewiring in network structure for the subweb of common species. Poisot (2022) thus argues that \( \beta_{OS} \) relates only to the probability of rewiring in the subweb, while the definition of \( \beta_{OS} \) by Fründ (2021) is relative to the count of interactions in the global network (considered in denominator), and is thereby dependent on both rewiring probability and species turnover. Poisot (2022) further clarifies the interpretation of \( \beta_{ST} \). \( \beta_{ST} \) is obtained by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and thus represents the influence of species turnover in terms of the relative architectures of the global networks and of the subwebs of shared species. Coming back to the example of Fig.1., the Poisot et al. (2012) formula posits that \( \frac{\beta_{ST}}{\beta_{WN}}=\frac{2/15}{1/3}=2/5 \), meaning that species turnover contributes two-fifths of change in network structure, while rewiring in the subweb of common species contributed three fifths.  Conversely, the approach of Fründ (2021) does not compare the architectures of global networks and of the subwebs of shared species, but considers the relative contribution of unique links to network dissimilarity in terms of species turnover and rewiring. 

Poisot (2022) concludes that the partition proposed in Fründ (2021) does not allow unambiguous ecological interpretation of rewiring. He provides guidelines for proper interpretation of the decomposition proposed in Poisot et al. (2012).

References

Fründ J (2021) Dissimilarity of species interaction networks: how to partition rewiring and species turnover components. Ecosphere, 12, e03653. https://doi.org/10.1002/ecs2.3653

Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361. https://doi.org/10.1111/ele.12002

Poisot T (2022) Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. EcoEvoRxiv Preprints, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/gxhu2

Urban ecology, the study of ecological systems in our increasingly urbanized world, is crucial to planning and redesigning cities to enhance ecosystem services (Kremer et al. 2016), human health and well-being and further conservation goals (Dallimer et al. 2012). Urban trees are a crucial component of urban streets and parks that provide shade and cooling through evapotranspiration (Fung and Jim 2019), improve air quality (Lai and Kontokosta 2019), help control storm water (Johnson and Handel 2016), and conserve wildlife (Herrmann et al. 2012; de Andrade et al. 2020).
Ideally, management of urban forests strikes a balance between maintaining the health of urban trees while retaining those organisms, such as herbivores, that connect a tree to the urban ecosystem. Herbivory by arthropods can substantially affect tree growth and reproduction (Whittaker and Warrington 1985), and so understanding factors that influence herbivory in urban forests is important to effective management. At the same time, herbivorous arthropods are important as key components of urban bird diets (Airola and Greco 2019) and provide a backyard glimpse at forest ecosystems in an increasingly built environment (Pearse 2019). Maintenance of arthropod predators may be one way to retain arthropods in urban forests while keeping detrimental outbreaks of herbivores in check. In “Insect herbivory on urban trees: Complementary effects of tree neighbors and predation” Stemmelen and colleagues (Stemmelen et al. 2020) use a clever sampling design to show that insect herbivory decreases as the diversity of neighboring trees increased. By placing artificial larvae out on trees, they provide evidence that increased predation in higher diversity urban forest patches might drive patterns in herbivory. The paper also demonstrates the importance of tree species identity in determining leaf herbivory.
The implications of this research for urban foresters is that deliberately planting diverse urban forests will help manage insect herbivores and should thus improve tree health. Potential knock-on effects could be seen for the ecosystem services provided by urban forests. While it might be tempting to simply plant more of the species that are subject to low current rates of herbivory, other research on the long-term vulnerability of monocultures to attack by specialist pathogens and herbivores (Tooker and Frank 2012) cautions against such an approach. Furthermore, the importance of urban forest insects to birds, including migrating birds, argues for managing urban forests more holistically (Greco and Airola 2018).
Stemmelen et al. (2020) used an observational approach focused on urban forests in Montreal, Canada in their research. Their findings suggest follow-up research focused on a broader cross-section of urban forests across latitudes, as well as experimental research. Experiments could, for example, exclude avian predators with netting (e.g. (Marquis and Whelan 1994)) to evaluate the relative importance of birds to managing urban insects on trees, as well as the flip side of that equation, the important to birds of insects on urban trees.
In summary, Stemmelen and colleague’s manuscript illustrates clever sampling and use of observational data to infer broader ecological patterns. It is worth reading to better understand the role of diversity in driving plant-insect community interactions and given the implications of the findings for sustainable long-term management of urban forests.

References

Airola, D. and Greco, S. (2019). Birds and oaks in California’s urban forest. Int. Oaks, 30, 109–116.
de Andrade, A.C., Medeiros, S. and Chiarello, A.G. (2020). City sloths and marmosets in Atlantic forest fragments with contrasting levels of anthropogenic disturbance. Mammal Res., 65, 481–491. doi: https://doi.org/10.1007/s13364-020-00492-0
Dallimer, M., Irvine, K.N., Skinner, A.M.J., Davies, Z.G., Rouquette, J.R., Maltby, L.L., et al. (2012). Biodiversity and the Feel-Good Factor: Understanding Associations between Self-Reported Human Well-being and Species Richness. Bioscience, 62, 47–55. doi: https://doi.org/10.1525/bio.2012.62.1.9
Fung, C.K.W. and Jim, C.Y. (2019). Microclimatic resilience of subtropical woodlands and urban-forest benefits. Urban For. Urban Green., 42, 100–112. doi: https://doi.org/10.1016/j.ufug.2019.05.014
Greco, S.E. and Airola, D.A. (2018). The importance of native valley oaks (Quercus lobata) as stopover habitat for migratory songbirds in urban Sacramento, California, USA. Urban For. Urban Green., 29, 303–311. doi: https://doi.org/10.1016/j.ufug.2018.01.005
Herrmann, D.L., Pearse, I.S. and Baty, J.H. (2012). Drivers of specialist herbivore diversity across 10 cities. Landsc. Urban Plan., 108, 123–130. doi: https://doi.org/10.1016/j.landurbplan.2012.08.007
Johnson, L.R. and Handel, S.N. (2016). Restoration treatments in urban park forests drive long-term changes in vegetation trajectories. Ecol. Appl., 26, 940–956. doi: https://doi.org/10.1890/14-2063
Kremer, P., Hamstead, Z., Haase, D., McPhearson, T., Frantzeskaki, N., Andersson, E., et al. (2016). Key insights for the future of urban ecosystem services research. Ecol. Soc., 21: 29. doi: http://doi.org/10.5751/ES-08445-210229
Lai, Y. and Kontokosta, C.E. (2019). The impact of urban street tree species on air quality and respiratory illness: A spatial analysis of large-scale, high-resolution urban data. Heal. Place, 56, 80–87. doi: https://doi.org/10.1016/j.healthplace.2019.01.016
Marquis, R.J. and Whelan, C.J. (1994). Insectivorous birds increase growth of white oak through consumption of leaf-chewing insects. Ecology, 75, 2007–2014. doi: https://doi.org/10.2307/1941605
Pearse, I.S. (2019). Insect herbivores on urban native oak trees. Int. Oaks, 30, 101–108.
Stemmelen, A., Paquette, A., Benot, M.-L., Kadiri, Y., Jactel, H. and Castagneyrol, B. (2020) Insect herbivory on urban trees: Complementary effects of tree neighbours and predation. bioRxiv, 2020.04.15.042317, ver. 5 peer-reviewed and recommended by PCI Ecology. doi: https://doi.org/10.1101/2020.04.15.042317
Tooker, J. F., and Frank, S. D. (2012). Genotypically diverse cultivar mixtures for insect pest management and increased crop yields. J. Appl. Ecol., 49(5), 974-985. doi: https://doi.org/10.1111/j.1365-2664.2012.02173.x
Whittaker, J.B. and Warrington, S. (1985). An experimental field study of different levels of insect herbivory induced By Formica rufa predation on Sycamore (Acer pseudoplatanus) III. Effects on Tree Growth. J. Appl. Ecol., 22, 797. doi: https://doi.org/10.2307/2403230