Coastal lagoons are among the most productive natural ecosystems on Earth. These relatively closed basins are important habitats and nursery for endemic and endangered species and are extremely vulnerable to nutrient input from the surrounding catchment; therefore, they are highly susceptible to anthropogenic influence, pollution and invasion (Pérez-Ruzafa et al., 2019). In general, coastal lagoons exhibit great spatial and temporal variability in their physicochemical water characteristics due to the sporadic mixing of freshwater with marine influx. One of the alternatives for monitoring communities or target species in aquatic ecosystems is the environmental DNA (eDNA), since overcomes some limitations from traditional methods and enables the investigation of multiple species from a single sample (Thomsen and Willerslev, 2015). In coastal lagoons, where the water turbidity is highly variable, there is a major challenge for monitoring the eDNA because filtering turbid water to obtain the eDNA is problematic (filters get rapidly clogged, there is organic and inorganic matter accumulation, etc.). 

The study by Turba et al. (2023) analyzes different ways of dealing with eDNA sampling and processing in turbid waters and sediments of coastal lagoons, and offers guidelines to obtain unbiased results from the subsequent sequencing using 12S (fish) and 16S (Bacteria and Archaea) universal primers. They analyzed the effect on taxa detection of: i) freezing or not prior to filtering; ii) freezing prior to centrifugation to obtain a sample pellet; and iii) using frozen sediment samples as a proxy of what happens in the water. The authors propose these different alternatives (freeze, do not freeze, sediment sampling) because they consider that they are the easiest to carry out. They found that freezing before filtering using a 3 µm pore size filter had no effects on community composition for either primer, and therefore it is a worthwhile approach for comparison of fish, bacteria and archaea in this kind of system. However, significantly different bacterial community composition was found for sediment compared to water samples. Also, in sediment samples the replicates showed to be more heterogeneous, so the authors suggest increasing the number of replicates when using sediment samples. Something that could be a concern with the study is that the rarefaction curves based on sequencing effort for each protocol did not saturate in any case, this being especially evident in sediment samples. The authors were aware of this, used the slopes obtained from each curve as a measure of comparison between samples and considering that the sequencing depth did not meet their expectations, they managed to achieve their goal and to determine which protocol is the most promising for eDNA monitoring in coastal lagoons. Although there are details that could be adjusted in relation to this item, I consider that the approach is promising for this type of turbid system.

References

Pérez-Ruzafa A, Campillo S, Fernández-Palacios JM, García-Lacunza A, García-Oliva M, Ibañez H, Navarro-Martínez PC, Pérez-Marcos M, Pérez-Ruzafa IM, Quispe-Becerra JI, Sala-Mirete A, Sánchez O, Marcos C (2019) Long-Term Dynamic in Nutrients, Chlorophyll a, and Water Quality Parameters in a Coastal Lagoon During a Process of Eutrophication for Decades, a Sudden Break and a Relatively Rapid Recovery. Frontiers in Marine Science, 6. https://doi.org/10.3389/fmars.2019.00026

Thomsen PF, Willerslev E (2015) Environmental DNA – An emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation, 183, 4–18. https://doi.org/10.1016/j.biocon.2014.11.019

Turba R, Thai GH, Jacobs DK (2023) Different approaches to processing environmental DNA samples in turbid waters have distinct effects for fish, bacterial and archaea communities. bioRxiv, 2022.06.17.495388, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.06.17.495388

Whether environmental conditions –in particular energy and water availability– are sufficient to account for species richness gradients (e.g. Currie 1991), or the effects of other biotic and historical or regional factors need to be considered as well (e.g. Ricklefs 1987), was the subject of debate during the 1990s and 2000s (e.g. Francis & Currie 2003; Hawkins et al. 2003, 2006; Currie et al. 2004; Ricklefs 2004). The metabolic theory of ecology (Brown et al. 2004) provided a solid and well-rooted theoretical support for the preponderance of energy as the main driver for richness variations. As any good piece of theory, it provided testable predictions about the sign and shape (i.e. slope) of the relationship between temperature –a key aspect of ambient energy– and species richness. However, these predictions were not supported by empirical evaluations (e.g. Kreft & Jetz 2007; Algar et al. 2007; Hawkins et al. 2007a), as the effects of a myriad of other environmental gradients, regional factors and evolutionary processes result in a wide variety of richness–temperature responses across different groups and regions (Hawkins et al. 2007b; Hortal et al. 2008). So, in a textbook example of how good theoretical work helps advancing science even if proves to be (partially) wrong, the evaluation of this aspect of the metabolic theory of ecology led to current understanding that, while species richness does respond to current climatic conditions, many other ecological, evolutionary and historical factors do modify such response across scales (see, e.g., Ricklefs 2008; Hawkins 2008; D’Amen et al. 2017). And the kinetic model linking mean annual temperature and species richness (Allen et al. 2002; Brown et al. 2004) was put aside as being, perhaps, another piece of the puzzle of the origin of current diversity gradients.

Segovia (2021) puts together an elegant way of reinvigorating this part of the metabolic theory of ecology. He uses quantile regressions to model just the upper parts of the relationship between species richness and mean annual temperature, rather than modelling its central tendency through the classical linear regression family of methods –as was done in the past. This assumes that the baseline effect of ambient energy does produce the negative linear relationship between richness and temperature predicted by the kinetic model (Allen et al. 2002), but also that this effect only poses an upper limit for species richness, and the effects of other factors may result in lower levels of species co-occurrence, thus producing a triangular rather than linear relationship. The results of Segovia’s simple and elegant analytical design show unequivocally that the predictions of the kinetic model become progressively more explanatory towards the upper quartiles of the relationship between species richness and temperature along over 10,000 tree local inventories throughout the Americas, reaching over 70% of explanatory power for the upper 5% of the relationship (i.e. the 95% quantile). This confirms to a large extent his reformulation of the predictions of the kinetic model. 

Further, the neat study from Segovia (2021) also provides evidence confirming that the well-known spatial non-stationarity in the richness–temperature relationship (see Cassemiro et al. 2007) also applies to its upper-bound segment. Both the explanatory power and the slope of the relationship in the 95% upper quantile vary widely between biomes, reaching values similar to the predictions of the kinetic model only in cold temperate environments ­–precisely where temperature becomes more important than water availability as a constrain to plant life (O’Brien 1998; Hawkins et al. 2003). Part of these variations are indeed related with changes in water deficit and number of frost days along the XXth Century, as shown by the residuals of this paper (Segovia 2021) and a more detailed separate study (Segovia et al. 2020). This pinpoints the importance of the relative balance between water and energy as two of the main climatic factors constraining species diversity gradients, confirming the value of hypotheses that date back to Humboldt’s work (see Hawkins 2001, 2008). There is however a significant amount of unexplained variation in Segovia’s analyses, in particular in the progressive departure of the predictions of the kinetic model as we move towards the tropics, or downwards along the lower quantiles of the richness–temperature relationship. This calls for a deeper exploration of the factors that modify the baseline relationship between richness and energy, opening a new avenue for the macroecological investigation of how different forces and processes shape up geographical diversity gradients beyond the mere energetic constrains imposed by the basal limitations of multicellular life on Earth.

References

Algar, A.C., Kerr, J.T. and Currie, D.J. (2007) A test of Metabolic Theory as the mechanism underlying broad-scale species-richness gradients. Global Ecology and Biogeography, 16, 170-178. doi: https://doi.org/10.1111/j.1466-8238.2006.00275.x

Allen, A.P., Brown, J.H. and Gillooly, J.F. (2002) Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science, 297, 1545-1548. doi: https://doi.org/10.1126/science.1072380

Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M. and West, G.B. (2004) Toward a metabolic theory of ecology. Ecology, 85, 1771-1789. doi: https://doi.org/10.1890/03-9000

Cassemiro, F.A.d.S., Barreto, B.d.S., Rangel, T.F.L.V.B. and Diniz-Filho, J.A.F. (2007) Non-stationarity, diversity gradients and the metabolic theory of ecology. Global Ecology and Biogeography, 16, 820-822. doi: https://doi.org/10.1111/j.1466-8238.2007.00332.x

Currie, D.J. (1991) Energy and large-scale patterns of animal- and plant-species richness. The American Naturalist, 137, 27-49. doi: https://doi.org/10.1086/285144

Currie, D.J., Mittelbach, G.G., Cornell, H.V., Field, R., Guegan, J.-F., Hawkins, B.A., Kaufman, D.M., Kerr, J.T., Oberdorff, T., O'Brien, E. and Turner, J.R.G. (2004) Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecology Letters, 7, 1121-1134. doi: https://doi.org/10.1111/j.1461-0248.2004.00671.x

D'Amen, M., Rahbek, C., Zimmermann, N.E. and Guisan, A. (2017) Spatial predictions at the community level: from current approaches to future frameworks. Biological Reviews, 92, 169-187. doi: https://doi.org/10.1111/brv.12222

Francis, A.P. and Currie, D.J. (2003) A globally consistent richness-climate relationship for Angiosperms. American Naturalist, 161, 523-536. doi: https://doi.org/10.1086/368223

Hawkins, B.A. (2001) Ecology's oldest pattern? Trends in Ecology & Evolution, 16, 470. doi: https://doi.org/10.1016/S0169-5347(01)02197-8 

Hawkins, B.A. (2008) Recent progress toward understanding the global diversity gradient. IBS Newsletter, 6.1, 5-8. https://escholarship.org/uc/item/8sr2k1dd

Hawkins, B.A., Field, R., Cornell, H.V., Currie, D.J., Guégan, J.-F., Kaufman, D.M., Kerr, J.T., Mittelbach, G.G., Oberdorff, T., O'Brien, E., Porter, E.E. and Turner, J.R.G. (2003) Energy, water, and broad-scale geographic patterns of species richness. Ecology, 84, 3105-3117. doi: https://doi.org/10.1890/03-8006

Hawkins, B.A., Diniz-Filho, J.A.F., Jaramillo, C.A. and Soeller, S.A. (2006) Post-Eocene climate change, niche conservatism, and the latitudinal diversity gradient of New World birds. Journal of Biogeography, 33, 770-780. doi: https://doi.org/10.1111/j.1365-2699.2006.01452.x

Hawkins, B.A., Albuquerque, F.S., Araújo, M.B., Beck, J., Bini, L.M., Cabrero-Sañudo, F.J., Castro Parga, I., Diniz-Filho, J.A.F., Ferrer-Castán, D., Field, R., Gómez, J.F., Hortal, J., Kerr, J.T., Kitching, I.J., León-Cortés, J.L., et al. (2007a) A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients. Ecology, 88, 1877-1888. doi:10.1890/06-1444.1. doi: https://doi.org/10.1890/06-1444.1

Hawkins, B.A., Diniz-Filho, J.A.F., Bini, L.M., Araújo, M.B., Field, R., Hortal, J., Kerr, J.T., Rahbek, C., Rodríguez, M.Á. and Sanders, N.J. (2007b) Metabolic theory and diversity gradients: Where do we go from here? Ecology, 88, 1898–1902. doi: https://doi.org/10.1890/06-2141.1

Hortal, J., Rodríguez, J., Nieto-Díaz, M. and Lobo, J.M. (2008) Regional and environmental effects on the species richness of mammal assemblages. Journal of Biogeography, 35, 1202–1214. doi: https://doi.org/10.1111/j.1365-2699.2007.01850.x

Kreft, H. and Jetz, W. (2007) Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences USA, 104, 5925-5930. doi: https://doi.org/10.1073/pnas.0608361104

O'Brien, E. (1998) Water-energy dynamics, climate, and prediction of woody plant species richness: an interim general model. Journal of Biogeography, 25, 379-398. doi: https://doi.org/10.1046/j.1365-2699.1998.252166.x

Ricklefs, R.E. (1987) Community diversity: Relative roles of local and regional processes. Science, 235, 167-171. doi: https://doi.org/10.1126/science.235.4785.167

Ricklefs, R.E. (2004) A comprehensive framework for global patterns in biodiversity. Ecology Letters, 7, 1-15. doi: https://doi.org/10.1046/j.1461-0248.2003.00554.x

Ricklefs, R.E. (2008) Disintegration of the ecological community. American Naturalist, 172, 741-750. doi: https://doi.org/10.1086/593002

Segovia, R.A. (2021) Temperature predicts the maximum tree-species richness and water and frost shape the residual variation. bioRxiv, 836338, ver. 4 peer-reviewed and recommended by Peer community in Ecology. doi: https://doi.org/10.1101/836338

Segovia, R.A., Pennington, R.T., Baker, T.R., Coelho de Souza, F., Neves, D.M., Davis, C.C., Armesto, J.J., Olivera-Filho, A.T. and Dexter, K.G. (2020) Freezing and water availability structure the evolutionary diversity of trees across the Americas. Science Advances, 6, eaaz5373. doi: https://doi.org/10.1126/sciadv.aaz5373

There are several important topics in the study of infectious diseases that have not been well explored due to technical difficulties. One such topic is pursued by Alizon et al. in “Modelling coinfections to detect within-host interactions from genotype combination prevalences” [1]. Both theory and several important examples have demonstrated that interactions among co-infecting strains can have outsized impacts on disease outcomes, transmission dynamics, and epidemiology. Unfortunately, empirical data on pathogen interactions and their outcomes is often correlational making results difficult to decipher.
The analytical framework developed by Alizon et al. [1] infers the presence and strength of pathogen interactions through their impact on transmission dynamics using a novel application of Approximate Bayesian Computation (ABC)-regression to epidemiological data. Traditional analytic approaches identify pathogen interactions when the observed distribution of pathogens among hosts differ from ‘neutral’ expectations. However, deviations from this expectation are not only a result of inter-strain interactions but can be caused by many ecological interactions, such as heterogeneity in host contact networks. To overcome this difficulty, Alizon et al [1] develop an analytical framework that incorporates explicit epidemiological models to allow inference of interactions among strains of Human Papillomaviruses (HPV) even with other ecological interactions that impact the distribution of strains among hosts. Alizon et al also demonstrate that using more of the available data, including the specific combination of strains present in hosts and knowledge of the connectivity of the hosts (i.e., super-spreaders), leads to more accurate inferences of the strength and direction of within-host interactions among coinfecting strains. This method successfully identified data generated from models with high and moderate inter-strain interaction intensity when the host population was homogeneous and was only slightly less successful when the host population was heterogeneous (super-spreaders present). By comparison, some previously published analytical methods could identify only some inter-strain interactions in datasets generated from models with homogeneous host populations, but host heterogeneity obscured these interactions.
This manuscript makes seamless connections between basic viral biology and its epidemiological consequences by tying them together with realistic models, illustrating the fundamental utility of biological modeling. This analytical framework provides crucial tools for experimentalists, facilitating collaborations with theoreticians to better understand the epidemiological consequences of co-infections. In addition, the method is simple enough to be applied by a broad base of experimentalists to the many pathogens where co-infections are common. Thus, this paper has the potential to impact several research fields and public health practice. Those attempting to apply this method should note the potential limitations noted by the authors. For example, it is not designed to detect the mechanisms of inter-strain interactions (there is no within host component of the models) but to identify the existence of interactions through patterns indicative of these interactions while ruling out other sources that could cause the pattern. This approach is likely to be most accurate when strain identification within hosts is precise and unbiased - which is unlikely in many systems where samples are taken only from symptomatic cases and strain detection is not sufficiently sensitive – and when host contact networks can be reasonably estimated. Importantly, a priori knowledge of the set of possible epidemiological models is needed for accurate parameter estimates, which may be true for several prominent pathogens, but not be so for many other pathogens and symbionts. We look forward to future extensions of this framework where this restriction is relaxed. Alizon et al. [1] have provided a framework that will facilitate theoretical and empirical work on the impact of coinfections on infectious disease and should shape future public health data collection standards.

References

[1] Alizon, S., Murall, C.L., Saulnier, E., & Sofonea, M.T. (2018). Detecting within-host interactions using genotype combination prevalence data. bioRxiv, 256586, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/256586

Understanding how livestock management is a source of social stress and disturbances for cattle is an important question with potential applications for animal welfare programs and sustainable development. In their article, Sosa and colleagues [1] first propose to evaluate the effects of individual characteristics on dyadic social relationships and on the social dynamics of four groups of cattle. Using network analyses, the authors provide an interesting and complete picture of dyadic interactions among groupmates. Although shown elsewhere, the authors demonstrate that individuals that are close in age and close in rank form stronger dyadic associations than other pairs. Second, the authors take advantage of some transfers of animals between groups -for management purposes- to assess how these transfers affect the social dynamics of groupmates. Their central finding is that the identity of transferred animals is a key-point. In particular, removing offspring strongly destabilizes the social relationships of mothers while adding a bull into a group also profoundly impacts female-female social relationships, as social networks before and after transfer of these key-animals are completely different. In addition, individuals, especially the young ones, that are transferred without familiar conspecifics take more time to socialize with their new group members than individuals transferred with familiar groupmates, generating a potential source of stress. Interestingly, the authors end up their article with some thoughts on the implications of their findings for animal welfare and ethics. This study provides additional evidence that empirical science has a major role to play in providing recommendations regarding societal questions such as livestock management and animal wellbeing.

References

[1] Sosa, S., Pelé, M., Debergue, E., Kuntz, C., Keller, B., Robic, F., Siegwalt-Baudin, F., Richer, C., Ramos, A., & Sueur C. (2018). Impact of group management and transfer on individual sociality in Highland cattle (Bos Taurus). arXiv:1805.11553v4 [q-bio.PE] peer-reviewed and recommended by PCI Ecol. https://arxiv.org/abs/1805.11553v4

Functional traits are “morpho-physio-phenological traits which impact fitness indirectly via their effects on growth, reproduction and survival” [1]. Most functional traits are defined at organ level, e.g. for leaves, roots and stems, and reflect key aspects of resource acquisition and resource use by organisms for their development and reproduction [2]. More rarely, some functional traits can be related to spatial development, such as vegetative height and lateral spread in plants.
Organ-level traits are especially popular because they can be measured in a standard way and easily compared over many plants. But these traits can broadly vary during the life of an organism. For instance, Roggy et al. [3] found that Leaf Mass Area can vary from 30 to 140 g.m^(-2) between seedling and adult stages for the canopy tree Dicorynia guianensis in French Guiana. Fortunel et al. [4] have also showed that developmental stages much contribute to functional trait variation within several Micropholis tree species in lowland Amazonia.
The way plants grow and invest resources into organs is variable during life and allows defining specific developmental sequences and architectural models [5,6]. There is clear ontogenic variation in leaf number, leaf properties and ramification patterns. Ontogenic variations reflect changing adaptation of an individual over its life, depending on the changing environmental conditions.
In this regard, measuring a single functional trait at organ level in adult trees should miss the variation of resource acquisition and use strategies over time. Thus we should built a more integrative approach of ecological development, also called “eco-devo” approach [7].
Although the ecological significance of ontogeny and developmental strategies is now well known, the extent to which it contributes to explain species survival and coexistence in communities is still broadly ignored in functional ecology. Levionnois et al. [8] investigated intraspecific variation of functional traits and growth trajectories in a typical, early-successional tree species in French Guiana, Amazonia. This species, Cecropia obtusa, is generalist regarding soil type and can be found on both white sand and ferralitic soil. The study examines whether there in intraspecific variation in functional traits and growth trajectories of C. obtusa in response to the contrasted soil types.
The tree communities observed on the two types of soils include species with distinctive functional trait values, that is, there are changes in species composition related to different species strategies along the classical wood and leaf economic spectra. The populations of C. obtusa found on the two soils showed some difference in functional traits, but it did not concern traits related to the main economic spectra. Conversely, the populations showed different growth strategies, in terms of spatial and temporal development.
The major lessons we can learn from the study are:
(i) Functional traits measured at organ level cannot reflect well how long-lived plants collect and invest resources during their life. The results show the potential of considering architectural and developmental traits together with organ-level functional traits, to better acknowledge the variation in ecological strategies over plant life, and thus to better understand community assembly processes.
(ii) What makes functional changes between communities differs when considering interspecific and intraspecific variation. Species turnover should encompass different corteges of soil specialists. These specialists are sorted along economic spectra, as shown in tropical rainforests and globally [2]. Conversely, a generalist species such as C. obtusa does occur on contrasted soil, which entails that it can accommodate the contrasted ecological conditions. However, the phenotypic adjustment is not related to how leaves and wood ensure photosynthesis, water and nutrient acquisition, but regards the way the resources are allocated to growth and reproduction over time.
The results of the study stress the need to better integrate growth strategies and ontogeny in the research agenda of functional ecology. We can anticipate that organ-level functional traits and growth trajectories will be more often considered together in ecological studies. The integration should help better understand the temporal niche of organisms, and how organisms can coexist in space and time with other organisms during their life. Recently, Klimešová et al. [9] have proposed standardized protocols for collecting plant modularity traits. Such effort to propose easy-to-measure traits representing plant development and ontogeny, with clear functional roles, should foster the awaited development of an “eco-devo” approach.

References

[1] Violle, C., Navas, M. L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., & Garnier, E. (2007). Let the concept of trait be functional!. Oikos, 116(5), 882-892. doi: 10.1111/j.0030-1299.2007.15559.x
[2] Díaz, S. et al. (2016). The global spectrum of plant form and function. Nature, 529(7585), 167-171. doi: 10.1038/nature16489
[3] Roggy, J. C., Nicolini, E., Imbert, P., Caraglio, Y., Bosc, A., & Heuret, P. (2005). Links between tree structure and functional leaf traits in the tropical forest tree Dicorynia guianensis Amshoff (Caesalpiniaceae). Annals of forest science, 62(6), 553-564. doi: 10.1051/forest:2005048
[4] Fortunel, C., Stahl, C., Heuret, P., Nicolini, E. & Baraloto, C. (2020). Disentangling the effects of environment and ontogeny on tree functional dimensions for congeneric species in tropical forests. New Phytologist. doi: 10.1111/nph.16393
[5] Barthélémy, D., & Caraglio, Y. (2007). Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Annals of botany, 99(3), 375-407. doi: 10.1093/aob/mcl260
[6] Hallé, F., & Oldeman, R. A. (1975). An essay on the architecture and dynamics of growth of tropical trees. Kuala Lumpur: Penerbit Universiti Malaya.
[7] Sultan, S. E. (2007). Development in context: the timely emergence of eco-devo. Trends in Ecology & Evolution, 22(11), 575-582. doi: 10.1016/j.tree.2007.06.014
[8] Levionnois, S., Tysklind, N., Nicolini, E., Ferry, B., Troispoux, V., Le Moguedec, G., Morel, H., Stahl, C., Coste, S., Caron, H. & Heuret, P. (2020). Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree. bioRxiv, 351197, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/351197
[9] Klimešová, J. et al. (2019). Handbook of standardized protocols for collecting plant modularity traits. Perspectives in Plant Ecology, Evolution and Systematics, 40, 125485. doi: 10.1016/j.ppees.2019.125485