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

In this article, Saade et al. (2021) investigate how the rate of local extinctions and their spatial distribution affect recolonization dynamics in metacommunities. They use an elegant combination of microcosm experiments with metacommunities of freshwater ciliates and mathematical modelling mirroring their experimental system. Their main findings are (i) that local patch extinctions increase both local (α-) and inter-patch (β-) diversity in a transient way during the recolonization process, (ii) that these effects depend more on the spatial distribution of extinctions (dispersed or clustered) than on their amount, and (iii) that they may spread regionally.
Microcosm experiments are already quite cool just by themselves and have contributed largely to conceptual advances in community ecology (see Fraser and Keddy 1997, or Jessup et al. 2004 for reviews on this topic), but they are here exploited to a whole further level by the fitting of a metapopulation dynamics model. The model allows both to identify the underlying mechanisms most likely to generate the patterns observed (here, competitive interactions) and to assess the robustness of these patterns when considering larger spatial or temporal scales. This release of experimental limitations allows here for the analysis of quantitative metrics of spatial structure, like the distance to the closest patch, which gives an interesting insight into the functional basis of the effect of the spatial distribution of extinctions.

A major strength of this study is that it highlights the importance of considering the spatial structure explicitly. Recent work on ecological networks has shown repeatedly that network structure affects the propagation of pathogens (Badham and Stocker 2010), invaders (Morel-Journel et al. 2019), or perturbation events (Gilarranz et al. 2017). Here, the spatial structure of the metacommunity is a regular grid of patches, but the distribution of extinction events may be either regularly dispersed (i.e., extinct patches are distributed evenly over the grid and are all surrounded by non-extinct patches only) or clustered (all extinct patches are neighbours). This has a direct effect on the neighbourhood of perturbed patches, and because perturbations have mostly local effects, their recovery dynamics are dominated by the composition of this immediate neighbourhood. In landscapes with dispersed extinctions, the neighbourhood of a perturbed patch is not affected by the amount of extinctions, and neither is its recovery time. In contrast, in landscapes with clustered extinctions, the amount of extinctions affects the depth of the perturbed area, which takes longer to recover when it is larger. Interestingly, the spatial distribution of extinctions here is functionally equivalent to differences in connectivity between perturbed and unperturbed patches, which results in contrasted “rescue recovery” and “mixing recovery” regimes as described by Zelnick et al. (2019).
 
Furthermore, this study focuses on local dynamics of competition and short-term, transient patterns that may have been overlooked by more classical, equilibrium-based approaches of dynamical systems of metacommunities. Indeed, in a metacommunity composed of several competitors, early theoretical work demonstrated that species coexistence is possible at the regional scale only, provided that spatial heterogeneity creates spatial variance in fitness or precludes the superior competitor from accessing certain habitat patches (Skellam 1951, Levins 1969). In the spatially homogeneous experimental system of Saade et al., one of the three ciliate species ends up dominating the community at equilibrium. However, following local, one-time extinction events, the community endures a recolonization process in which differences in dispersal may provide temporary spatial niches for inferior competitors. These transient patterns might prove essential to understand and anticipate the resilience of natural systems that are under increasing pressure, and enduring ever more frequent and intense perturbations (IPBES 2019). Spatial autocorrelation in extinction events was previously identified as a risk for stability and persistence of metacommunities (Ruokolainen 2013, Kahilainen et al. 2018). These new results show that autocorrelated perturbations also have longer-lasting effects, which is likely to increase their overall impact on metacommunity dynamics. As spatial and temporal autocorrelation of temperature and extreme climatic events are expected to increase (Di Cecco and Gouthier 2018), studies that investigate how metacommunities respond to the structure of the distribution of perturbations are more necessary than ever.
 
References

Badham J, Stocker R (2010) The impact of network clustering and assortativity on epidemic behaviour. Theoretical Population Biology, 77, 71–75. https://doi.org/10.1016/j.tpb.2009.11.003
 
Di Cecco GJ, Gouhier TC (2018) Increased spatial and temporal autocorrelation of temperature under climate change. Scientific Reports, 8, 14850. https://doi.org/10.1038/s41598-018-33217-0
 
Fraser LH, Keddy P (1997) The role of experimental microcosms in ecological research. Trends in Ecology & Evolution, 12, 478–481. https://doi.org/10.1016/S0169-5347(97)01220-2
 
Gilarranz LJ, Rayfield B, Liñán-Cembrano G, Bascompte J, Gonzalez A (2017) Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science, 357, 199–201. https://doi.org/10.1126/science.aal4122
 
IPBES (2019) Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. https://doi.org/10.5281/zenodo.3553579 
 
Jessup CM, Kassen R, Forde SE, Kerr B, Buckling A, Rainey PB, Bohannan BJM (2004) Big questions, small worlds: microbial model systems in ecology. Trends in Ecology & Evolution, 19, 189–197. https://doi.org/10.1016/j.tree.2004.01.008
 
Kahilainen A, van Nouhuys S, Schulz T, Saastamoinen M (2018) Metapopulation dynamics in a changing climate: Increasing spatial synchrony in weather conditions drives metapopulation synchrony of a butterfly inhabiting a fragmented landscape. Global Change Biology, 24, 4316–4329. https://doi.org/10.1111/gcb.14280

Levins R (1969) Some Demographic and Genetic Consequences of Environmental Heterogeneity for Biological Control1. Bulletin of the Entomological Society of America, 15, 237–240. https://doi.org/10.1093/besa/15.3.237
 
Morel-Journel T, Assa CR, Mailleret L, Vercken E (2019) Its all about connections: hubs and invasion in habitat networks. Ecology Letters, 22, 313–321. https://doi.org/10.1111/ele.13192

Ruokolainen L (2013) Spatio-Temporal Environmental Correlation and Population Variability in Simple Metacommunities. PLOS ONE, 8, e72325. https://doi.org/10.1371/journal.pone.0072325

Saade C, Kefi S, Gougat-Barbera C, Rosenbaum B, Fronhofer EA (2021) Spatial distribution of local patch extinctions drives recovery dynamics in metacommunities. bioRxiv, 2020.12.03.409524, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.12.03.409524
 
Skellam JG (1951) Random Dispersal in Theoretical Populations. Biometrika, 38, 196–218. https://doi.org/10.2307/2332328
 
Zelnik YR, Arnoldi J-F, Loreau M (2019) The three regimes of spatial recovery. Ecology, 100, e02586. https://doi.org/10.1002/ecy.2586

Sexual coercion can be defined as the use by a male of force, or threat of force, which increases the chances that a female will mate with him at a time when she is likely to be fertile, and/or decrease the chances that she will mate with other males, at some cost to the female (Smuts & Smuts 1993). It has been evidenced in a wide range of species and may play an important role in the evolution of sexual conflict and social systems. However, identifying sexual coercion in natural systems can be particularly challenging. Notably, while male behaviour may have immediate consequences on mating success (“harassment”), the mating benefits may be delayed in time (“intimidation”), and in such cases, evidencing coercion requires detailed temporal data at the individual level. Moreover, in some species male aggressive behaviours may be subtle or rare and hence hardly observed, yet still have important effects on female mating probability and fitness. Therefore, investigating the occurrence and consequences of sexual coercion in such species is particularly relevant but studying it in a statistically robust way is likely to require a considerable amount of time spent observing individuals.

In this paper, Smit et al. (2022) test three clear predictions of the sexual coercion hypothesis in a natural population of Mandrills, where severe male aggression towards females is rare: (1) male aggression is more likely on sexually receptive females than on females in other reproductive states, (2) receptive females are more likely to be injured and (3) male aggression directed towards females is positively related to subsequent probability of copulation between those dyads. They also tested an alternative hypothesis, the “aggressive male phenotype” under which the correlation between male aggression towards females and subsequent mating could be statistically explained by male overall aggressivity. In agreement with the three predictions of the sexual coercion hypothesis, (1) male aggression was on average 5 times more likely, and (2) injuries twice as likely, to be observed on sexually receptive females than on females in other reproductive states and (3) copulation between males and sexually receptive females was twice more likely to be observed when aggression by this male was observed on the female before sexual receptivity. There was no support for the aggressive male hypothesis.

The reviewers and I were highly positive about this study, notably regarding the way it is written and how the predictions are carefully and clearly stated, tested, interpreted, and discussed.

This study is a good illustration of a case where some behaviours may not be common or obvious yet have strong effects and likely important consequences and thus be clearly worth studying. More generally, it shows once more the importance of detailed long-term studies at the individual level for our understanding of the ecology and evolution of wild populations.

It is also a good illustration of the challenges faced, when comparing the likelihood of contrasting hypotheses means we need to alter sample sizes and/or the likelihood to observe at all some behaviours. For example, observing copulation within minutes after aggression (and therefore, showing statistical support for “harassment”) is inevitably less likely than observing copulations on the longer-term (and therefore showing statistical support for “intimidation”, when of course effort is put into recording such behavioural data on the long-term). Such challenges might partly explain some apparently intriguing results. For example, why are swollen females more aggressed by males if only aggression before the swollen period seems associated with more chances of mating? Here, the authors systematically provide effect sizes (and confidence intervals) and often describe the effects in an intuitive biological way (e.g., “Swollen females were, on average, about five times more likely to become injured”). This clearly helps the reader to not merely compare statistical significances but also the biological strengths of the estimated effects and the uncertainty around them. They also clearly acknowledge limits due to sample size when testing the harassment hypothesis, yet they provide precious information on the probability of observing mating (a rare behaviour) directly after aggression (already a rare behaviour!), that is, 3 times out of 38 aggressions observed between a male and a swollen female. Once again, this highlights how important it is to be able to pursue the enormous effort put so far into closely and continuously monitoring this wild population.

Finally, this study raises exciting new questions, notably regarding to what extent females exhibit “counter-strategies” in response to sexual coercion, notably whether there is still scope for female mate choice under such conditions, and what are the fitness consequences of these dynamic conflicting sexual interactions. No doubt these questions will sooner than later be addressed by the authors, and I am looking forward to reading their upcoming work.

References

Smit N, Baniel A, Roura-Torres B, Amblard-Rambert P, Charpentier MJE, Huchard E (2022) Sexual coercion in a natural mandrill population. bioRxiv, 2022.02.07.479393, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.02.07.479393

Smuts BB, Smuts R w. (1993) Male Aggression and Sexual Coercion of Females in Nonhuman Primates and Other Mammals: Evidence and Theoretical Implications. In: Advances in the Study of Behavior (eds Slater PJB, Rosenblatt JS, Snowdon CT, Milinski M), pp. 1–63. Academic Press. https://doi.org/10.1016/S0065-3454(08)60404-0

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

Climate change is accelerating (IPCC 2022), and so applies ever stronger selective pressures on biodiversity (Segan et al. 2016). Possible responses include range shifts or adaptations to new climatic conditions (Bellard et al. 2012), but there is still much uncertainty about the extent of most species' adaptive capacities and the impact of extreme climatic events.
 
The pine processionary is a major pest of pine trees in the Mediterranean area. It is notably one of the few species for which a clear link between recent climate change and its northward expansion has been established (Battisti et al. 2005), and as such is often considered as globally benefitting from climate change. However, recent results show a retraction of its range at the southern limit (Bourougaaoui et al. 2021), exposed to high warming (+1.4°C in Tunisia since 1901 as opposed to +1.12°C on average in the Northern hemisphere) and extreme summer temperature events (Verner et al. 2013). Thus, it is possible that the species' adaptive abilities are being challenged at the southern limit of its native range by the magnitude of observed climate change.
 
In this work, Bourougaaoui et al. (2022) investigate how climate change over the last 30 years has impacted the reproductive success of the pine processionary moth in Tunisia. A major methodological interest of this study is that they used data both from historical collections and from recent samplings, which raised a challenge for running a longitudinal analysis as sampling locations differed between the two periods. By applying a grouping method to local climatic data, the authors were able to define several large climatic clusters within the country, and analyze long-term data from different sites within the same clusters. They find that both fecundity and hatching rate decreased over the period, while at the same time both the average temperature increased and climate variability increased. One of the main conclusions is that recurrent episodes of extreme heat during summer might have a larger impact than the long-term increase of average temperature, which strongly echoes how the intensification of weather extremes is currently proving one of the most important dimensions of climate change.
 
However, a most interesting hypothesis also arises from the analysis of the differences between climatic clusters: preexisting adaptations to heat, for instance, phenological shifts that allow the most sensitive stages to develop earlier in the season before the extreme heat events are most likely to occur, might actually reduce impacts in the historically warmest areas. Thus the greatest climate vulnerability might not always stand where one expects it.
 
References

Battisti A, Stastny M, Netherer S, Robinet C, Schopf A, Roques A, Larsson S (2005) Expansion of Geographic Range in the Pine Processionary Moth Caused by Increased Winter Temperatures. Ecological Applications, 15, 2084–2096. https://doi.org/10.1890/04-1903

Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecology Letters, 15, 365–377. https://doi.org/10.1111/j.1461-0248.2011.01736.x

Bourougaaoui A, Ben Jamâa ML, Robinet C (2021) Has North Africa turned too warm for a Mediterranean forest pest because of climate change? Climatic Change, 165, 46. https://doi.org/10.1007/s10584-021-03077-1

Bourougaaoui A, Robinet C, Jamaa MLB, Laparie M (2022) Effects of climate warming on the pine processionary moth at the southern edge of its range: a retrospective analysis on egg survival in Tunisia. bioRxiv, 2021.08.17.456665, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.08.17.456665

IPCC. 2022. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. In Press.

Segan DB, Murray KA, Watson JEM (2016) A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Global Ecology and Conservation, 5, 12–21. https://doi.org/10.1016/j.gecco.2015.11.002

Verner D (2013) Tunisia in a Changing Climate : Assessment and Actions for Increased Resilience and Development. World Bank, Washington, DC. https://doi.org/10.1596/978-0-8213-9857-9