Ecology is the study of the distribution of organisms in space and time and their interactions. As such, there is a tradition of studies relating abiotic environmental conditions to species distribution, while another one is concerned by the effects of consumers on the abundance of their resources.  Interestingly, joining the dots appears more difficult than it would suggest: eluding the effect of species interactions on distribution remains one of the greatest challenges to elucidate nowadays (Kissling et al. 2012). Theory suggests that yes, species interactions such as predation and competition should influence range limits (Godsoe et al. 2017), but the common intuition among many biogeographers remains that over large areas such as regions and continents, environmental drivers like temperature and precipitation overwhelm their local effects. Answering this question is of primary importance in the context where species are moving around with climate warming.  Inconsistencies in food web structure may arise with asynchronized movements of consumers and their resources, leading to a major disruption in regulation and potentially ecosystem functioning. Solving this problem, however, remains very challenging because we have to rely on observational data since experiments are hard to perform at the biogeographical scale. 

The study of St-Gelais is an interesting step forward to solve this problem. Their main objective was to assess the strength of the association between phytoplankton and zooplankton communities at a large spatial scale, looking at the spatial covariation of both taxonomic and functional composition. To do so, they undertook a massive survey of more than 100 lakes across three regions of the boreal region of Québec. Species and functional composition were recorded, along with a set of abiotic variables. Classic community ecology at this point. The difficulty they faced was to disentangle the multiple causal relationships involved in the distribution of both trophic levels. Teasing apart bottom-up and top-down forces driving the assembly of plankton communities using observational data is not an easy task. On the one hand, both trophic levels could respond to variations in temperature, nutrient availability and dissolved organic carbon. The interpretation is fairly straightforward if the two levels respond to different factors, but the situation is much more complicated when they do respond similarly. There are potentially three possible underlying scenarios. First, the phyto and zooplankton communities may share the same environmental requirements, thereby generating a joint distribution over gradients such as temperature and nutrient availability. Second, the abiotic environment could drive the distribution of the phytoplankton community, which would then propagate up and influence the distribution of the zooplankton community. Alternatively, the abiotic environment could constrain the distribution of the zooplankton, which could then affect the one of phytoplankton. In addition to all of these factors, St-Gelais et al also consider that dispersal may limit the distribution, well aware of previous studies documenting stronger dispersal limitations for zooplankton communities. 

Unfortunately, there is not a single statistical approach that could be taken from the shelf and used to elucidate drivers of co-distribution. Joint species distribution was once envisioned as a major step forward in this direction (Warton et al. 2015), but there are several limits preventing the direct interpretation that co-occurrence is linked to interactions (Blanchet et al. 2020). Rather, St-Gelais used a variety of multivariate statistics to reveal the structure in their observational data. First, using a Procrustes analysis (a method testing if the spatial variation of one community is correlated to the structure of another community), they found a significant correlation between phytoplankton and zooplankton communities, indicating a taxonomic coupling between the groups. Interestingly, this observation was maintained for functional composition only when interaction-related traits were considered. At this point, these results strongly suggest that interactions are involved in the correlation, but it's hard to decipher between bottom-up and top-down perspectives. A complementary analysis performed with a constrained ordination, per trophic level, provided complementary pieces of information. First observation was that only functional variation was found to be related to the different environmental variables, not taxonomic variation. Despite that trophic levels responded to water quality variables, spatial autocorrelation was more important for zooplankton communities and the two layers appear to respond to different variables. 

It is impossible with those results to formulate a strong conclusion about whether grazing influence the co-distribution of phytoplankton and zooplankton communities. That's the mere nature of observational data. While there is a strong spatial association between them, there are also diverging responses to the different environmental variables considered. But the contrast between taxonomic and functional composition is nonetheless informative and it seems that beyond the idiosyncrasies of species composition, trait distribution may be more informative and general. Perhaps the most original contribution of this study is the hierarchical approach to analyze the data, combined with the simultaneous analysis of taxonomic and functional distributions. Having access to a vast catalog of multivariate statistical techniques, a careful selection of analyses helps revealing key features in the data, rejecting some hypotheses and accepting others. Hopefully, we will see more and more of such multi-trophic approaches to distribution because it is now clear that the factors driving distribution are much more complicated than anticipated in more traditional analyses of community data. Biodiversity is more than a species list, it is also all of the interactions between them, influencing their distribution and abundance (Jordano 2016).

References

Blanchet FG, Cazelles K, Gravel D (2020) Co-occurrence is not evidence of ecological interactions. Ecology Letters, 23, 1050–1063. https://doi.org/10.1111/ele.13525

Godsoe W, Jankowski J, Holt RD, Gravel D (2017) Integrating Biogeography with Contemporary Niche Theory. Trends in Ecology & Evolution, 32, 488–499. https://doi.org/10.1016/j.tree.2017.03.008

Jordano P (2016) Chasing Ecological Interactions. PLOS Biology, 14, e1002559. https://doi.org/10.1371/journal.pbio.1002559

Kissling WD, Dormann CF, Groeneveld J, Hickler T, Kühn I, McInerny GJ, Montoya JM, Römermann C, Schiffers K, Schurr FM, Singer A, Svenning J-C, Zimmermann NE, O’Hara RB (2012) Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. Journal of Biogeography, 39, 2163–2178. https://doi.org/10.1111/j.1365-2699.2011.02663.x

St-Gelais NF, Vogt RJ, Giorgio PA del, Beisner BE (2021) The taxonomic and functional biogeographies of phytoplankton and zooplankton communities across boreal lakes. bioRxiv, 373332, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.1101/373332

Warton DI, Blanchet FG, O’Hara RB, Ovaskainen O, Taskinen S, Walker SC, Hui FKC (2015) So Many Variables: Joint Modeling in Community Ecology. Trends in Ecology & Evolution, 30, 766–779. https://doi.org/10.1016/j.tree.2015.09.007

Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, Dormann CF, Forchhammer MC, Grytnes J-A, Guisan A, Heikkinen RK, Høye TT, Kühn I, Luoto M, Maiorano L, Nilsson M-C, Normand S, Öckinger E, Schmidt NM, Termansen M, Timmermann A, Wardle DA, Aastrup P, Svenning J-C (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88, 15–30. https://doi.org/10.1111/j.1469-185X.2012.00235.x

Sexual segregation appears in many taxa and can have important ecological, evolutionary and conservation implications. Sexual segregation can take two forms: either the two sexes specialise in different habitats but share the same area (habitat segregation), or they occupy the same habitat but form separate, unisex groups (social segregation) [1,2]. Segregation would have evolved as a way to avoid, or at least, reduce intersexual competition.
Testing whether social or habitat segregation is at play necessitates the use of combined approaches to determine the spatial scale at which segregation occurs. This enterprise is even more challenging when studying marine species, which travel over long distances to reach their foraging areas. This is what Barbraud et al. [3] have endeavoured on the snow petrel (Pagodroma nivea), a sexually dimorphic, polar seabird. Studying sexual segregation at sea requires tools for indirect measures of habitat use and foraging tactics. During the incubation period, in a colony based at Pointe Geologie, Adelie land, East Antarctica, the team has equipped birds with GPS loggers to analyse habitat use and foraging behaviour. It has also compared short-, mid-, and long-term stable isotopic profiles, from plasma, blood cells, and feather samples, respectively.
Barbraud et al. [3] could not detect any evidence for sexual segregation in space use. Furthermore, the two sexes showed similar δ13C profiles, illustrating similar foraging latitudes, and indicating no sexual segregation at large spatial scales. Snow petrels all forage exclusively in the sea ice environment formed over the deep Antarctic continental shelf. The authors, however, found other forms of segregation: males consistently foraged at higher sea ice concentrations than females. Males also fed on higher trophic levels than females. Therefore, male and female snow petrels segregate at a smaller spatial scale, and use different foraging tactics and diet specialisations. Females also took shorter foraging trips than males, with higher mass gain that strongly benefit from higher sea ice concentration. Mass gain in males increased with the length of their foraging trip at sea ice areas.
The authors conclude that high sea ice concentration offers the most favourable foraging habitat for snow petrels, and thus that intersexual competition may drive females away from high sea ice areas. This study shows that combining information from different tools provides an elegant way of isolating the potential factors driving sexual segregation and the spatial scales at which it occurs.

References

[1] Conradt, L. (2005). Definitions, hypotheses, models and measures in the study of animal segregation. In Sexual segregation in vertebrates: ecology of the two sexes (Ruckstuhl K.E. and Neuhaus, P. eds). Cambridge University Press, Cambridge, United Kingdom. Pp:11–34.
[2] Ruckstuhl, K. E. (2007). Sexual segregation in vertebrates: proximate and ultimate causes. Integrative and Comparative Biology, 47(2), 245-257. doi: 10.1093/icb/icm030
[3] Barbraud, C., Delord, K., Kato, A., Bustamante, P., & Cherel, Y. (2018). Sexual segregation in a highly pagophilic and sexually dimorphic marine predator. bioRxiv, 472431, ver. 3 peer-reviewed and recommended bt PCI Ecology. doi: 10.1101/472431

Polygyny is assumed to have led to the evolution of strong sexual size dimorphism (SSD) in mammals, males often being heavier or showing more developed armaments than females (Weckerly 1998; Loison et al. 1999; Pérez‐Barbería et al. 2002).  SSD generally increases with the degree of polygyny of the species. However, the degree of SSD, and particularly of sexual mass dimorphism, is not fixed for each species, and differences exist between populations (Blanckenhorn et al. 2006; Cox & Calsbeek 2010) or even between seasons within populations (Rughetti & Festa‐Bianchet 2011).

In this study, Hewison et al. propose that studying seasonal variation in sexual mass dimorphism and how this can be affected by winter harshness and latitude allows us to better assess the energetic costs associated with the eco-evolutionary constraints acting on each sex. To achieve their goal, Hewison et al. use a formidable, long-term dataset of over 7,000 individuals, in five roe deer populations (Capreolus capreolus), from south-west France and Sweden.

According to the authors, sexual mass dimorphism should be at its lowest in early spring in this species due to a stronger trade-off between antler growth and body weight maintenance in males over winter than in females. Furthermore, harsher conditions, varying both in time and space (i.e., Sweden vs. France), should increase winter weight loss, and thus, mass change differences between the sexes should be stronger and show more variation in Sweden than in France.

Their results support their hypotheses. In the two Swedish populations, males lost more mass than females. In the three French populations, males maintain their body mass while females gain some over the winter. Because of these sex-dependent loss/gain in body mass, sexual dimorphism was stronger early in the winter and null at the onset of spring. Furthermore, sexual dimorphism was stronger in southern than in northern populations. In France, males weighed about 10% more than females, while they weighed about 5% more in Sweden. Roe deer, however, do not show any dimorphism early in the spring, when males start defending their territory. 

The authors also found more variation in mass change among years in Swedish than in French roe deer, suggesting a stronger effect of winter severity on the dynamics of mass change in northern than in southern populations. The authors interpret the decrease in sexual dimorphism throughout the winter by the fact that, during this period, the energetic cost paid by males associated with the growth of their antlers and the effort of establishing their mating territory. They thus attribute the greater mass change in males to the competitive allocation of resources to antler growth or body mass. They also discuss the low probability that such sex differences in mass change could be caused by females’ gestation in this species.

Interestingly, Hewison et al. found that individual differences represented more than 70% of the total variation in body mass, and the low estimated among-individual variance in slopes with time might indicate that, despite a lower SSD, selection pressures on body mass can still be maintained at times when body mass may play an important role, such as in spring with territorial defense or later during mating (Vanpé et al. 2010). 

I recommend this article because it produces strong results, which show, without a shadow of a doubt, sex differences in their seasonal mass changes, resulting in a marked seasonal variation in SSD. The differences observed between southern and northern populations confirm the idea that the severity of the winters endured by these populations acts as a constraint on the deer's patterns of mass change. I hope this study will encourage more examinations of how eco-evolutionary constraints affect the sexual size dimorphism.

References

Blanckenhorn, W. U., Stillwell, R. C., Young, K. A., Fox, C. W., & Ashton, K. G. (2006). When Rensch meets Bergmann: does sexual size dimorphism change systematically with latitude? Evolution, 60(10), 2004-2011. https://doi.org/10.1554/06-110.1

Cox, R. M., & Calsbeek, R. (2010). Sex-specific selection and intraspecific variation in sexual size dimorphism. Evolution, 64(3), 798-809. https://doi.org/10.1111/j.1558-5646.2009.00851.x

Hewison M, Bonnot N, Gaillard JM, Kjellander P, Lemaitre J-F, Morellet N. and Pellerin M (2024) Body mass change over winter is consistently sex-specific across roe deer (Capreolus capreolus) populations. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2022.09.09.507329

Loison, A., Gaillard, J. M., Pélabon, C., & Yoccoz, N. G. (1999). What factors shape sexual size dimorphism in ungulates? Evolutionary Ecology Research, 1(5), 611-633. https://www.evolutionary-ecology.com/issues/v01n05/jjar1019.pdf

Pérez‐Barbería, F. J., Gordon, I. J., & Pagel, M. (2002). The origins of sexual dimorphism in body size in ungulates. Evolution, 56(6), 1276-1285. https://doi.org/10.1111/j.0014-3820.2002.tb01438.x

Rughetti, M., & Festa‐Bianchet, M. (2011). Seasonal changes in sexual size dimorphism in northern chamois. Journal of Zoology, 284(4), 257-264. https://doi.org/10.1111/j.1469-7998.2011.00800.x

Vanpé, C., Gaillard, J. M., Kjellander, P., Liberg, O., Delorme, D., & Hewison, A. M. (2010). Assessing the intensity of sexual selection on male body mass and antler length in roe deer Capreolus capreolus: is bigger better in a weakly dimorphic species? Oikos, 119(9), 1484-1492. https://doi.org/10.1111/j.1600-0706.2010.18312.x

Weckerly, F. W. (1998). Sexual-size dimorphism: influence of mass and mating systems in the most dimorphic mammals. Journal of Mammalogy, 79(1), 33-52. https://doi.org/10.2307/1382840

Scale is a big topic in ecology [1]. Environmental variation happens at particular scales. The typical scale at which organisms disperse is species-specific, but, as a first approximation, an ensemble of similar species, for instance, trees, could be considered to share a typical dispersal scale. Finally, characteristic spatial scales of species interactions are, in general, different from the typical scales of dispersal and environmental variation. Therefore, conceptually, we can distinguish these three characteristic spatial scales associated with three different processes: species selection for a given environment (E), dispersal (D), and species interactions (I), respectively.  

From the famous species-area relation to the spatial distribution of biomass and species richness, the different macro-ecological patterns we usually study emerge from an interplay between dispersal and local interactions in a physical environment that constrains species establishment and persistence in every location. To make things even more complicated, local environments are often modified by the species that thrive in them, which establishes feedback loops.  It is usually assumed that local interactions are short-range in comparison with species dispersal, and dispersal scales are typically smaller than the scales at which the environment varies (I < D < E, see [2]), but this should not always be the case. 

The authors of this paper [2] relax this typical assumption and develop a theoretical framework to study how diversity and ecosystem functioning are affected by different relations between the typical scales governing interactions, dispersal, and environmental variation. This is a huge challenge. First, diversity and ecosystem functioning across space and time have been empirically characterized through a wide variety of macro-ecological patterns. Second, accommodating local interactions, dispersal and environmental variation and species environmental preferences to model spatiotemporal dynamics of full ecological communities can be done also in a lot of different ways. One can ask if the particular approach suggested by the authors is the best choice in the sense of producing robust results, this is, results that would be predicted by alternative modeling approaches and mathematical analyses [3]. The recommendation here is to read through and judge by yourself.  

The main unusual assumption underlying the model suggested by the authors is non-local species interactions. They introduce interaction kernels to weigh the strength of the ecological interaction with distance, which gives rise to a system of coupled integro-differential equations. This kernel is the key component that allows for control and varies the scale of ecological interactions. Although this is not new in ecology [4], and certainly has a long tradition in physics ---think about the electric or the gravity field, this approach has been widely overlooked in the development of the set of theoretical frameworks we have been using over and over again in community ecology, such as the Lotka-Volterra equations or, more recently, the metacommunity concept [5].

In Physics, classic fields have been revised to account for the fact that information cannot travel faster than light. In an analogous way, a focal individual cannot feel the presence of distant neighbors instantaneously. Therefore, non-local interactions do not exist in ecological communities. As the authors of this paper point out, they emerge in an effective way as a result of non-random movements, for instance, when individuals go regularly back and forth between environments (see [6], for an application to infectious diseases), or even migrate between regions. And, on top of this type of movement, species also tend to disperse and colonize close (or far) environments. Individual mobility and dispersal are then two types of movements, characterized by different spatial-temporal scales in general. Species dispersal, on the one hand, and individual directed movements underlying species interactions, on the other, are themselves diverse across species, but it is clear that they exist and belong to two distinct categories. 

In spite of the long and rich exchange between the authors' team and the reviewers, it was not finally clear (at least, to me and to one of the reviewers) whether the model for the spatio-temporal dynamics of the ecological community (see Eq (1) in [2]) is only presented as a coupled system of integro-differential equations on a continuous landscape for pedagogical reasons, but then modeled on a discrete regular grid for computational convenience. In the latter case, the system represents a regular network of local communities,  becomes a system of coupled ODEs, and can be numerically integrated through the use of standard algorithms. By contrast,  in the former case, the system is meant to truly represent a community that develops on continuous time and space, as in reaction-diffusion systems. In that case, one should keep in mind that numerical instabilities can arise as an artifact when integrating both local and non-local spatio-temporal systems. Spatial patterns could be then transient or simply result from these instabilities. Therefore, when analyzing spatiotemporal integro-differential equations, special attention should be paid to the use of the right numerical algorithms. The authors share all their code at https://zenodo.org/record/5543191, and all this can be checked out. In any case, the whole discussion between the authors and the reviewers has inherent value in itself, because it touches on several limitations and/or strengths of the author's approach,  and I highly recommend checking it out and reading it through.

Beyond these methodological issues, extensive model explorations for the different parameter combinations are presented. Several results are reported, but, in practice, what is then the main conclusion we could highlight here among all of them?  The authors suggest that "it will be difficult to manage landscapes to preserve biodiversity and ecosystem functioning simultaneously, despite their causative relationship", because, first, "increasing dispersal and interaction scales had opposing
effects" on these two patterns, and, second, unexpectedly, "ecosystems attained the highest biomass in scenarios which also led to the lowest levels of biodiversity". If these results come to be fully robust, this is, they pass all checks by other research teams trying to reproduce them using alternative approaches, we will have to accept that we should preserve biodiversity on its own rights and not because it enhances ecosystem functioning or provides particular beneficial services to humans. 

References

[1] Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73:1943–1967. https://doi.org/10.2307/1941447

[2] Yuval R. Zelnik, Matthieu Barbier, David W. Shanafelt, Michel Loreau, Rachel M. Germain. 2023. Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.1101/2021.10.11.463913

[3] Baron, J. W. and Galla, T. 2020. Dispersal-induced instability in complex ecosystems. Nature Communications  11, 6032. https://doi.org/10.1038/s41467-020-19824-4

[4] Cushing, J. M. 1977. Integrodifferential equations and delay models in population dynamics 
 Springer-Verlag, Berlin. https://doi.org/10.1007/978-3-642-93073-7

[5] M. A. Leibold, M. Holyoak, N. Mouquet, P. Amarasekare, J. M. Chase, M. F. Hoopes, R. D. Holt, J. B. Shurin, R. Law, D. Tilman, M. Loreau, A. Gonzalez. 2004. The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters, 7(7): 601-613. https://doi.org/10.1111/j.1461-0248.2004.00608.x

[6] M. Pardo-Araujo, D. García-García, D. Alonso, and F. Bartumeus. 2023. Epidemic thresholds and human mobility. Scientific reports 13 (1), 11409. https://doi.org/10.1038/s41598-023-38395-0

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

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

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

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

References

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

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

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

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

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