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17 Mar 2021
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Intra and inter-annual climatic conditions have stronger effect than grazing intensity on root growth of permanent grasslands

Resolving herbivore influences under climate variability

Recommended by based on reviews by 3 anonymous reviewers

We know that herbivory can have profound influences on plant communities with respect to their distribution and productivity (recently reviewed by Jia et al. 2018). However, the degree to which these effects are realized belowground in the rhizosphere is far less understood. Indeed, many independent studies and synthesis find that the environmental context can be more important than the direct effects of herbivore activity and its removal of plant biomass (Andriuzzi and Wall 2017, Schrama et al. 2013). In spite of dedicated attention, generalizable conclusions remain a bit elusive (Sitters and Venterink 2015). Picon-Cochard and colleagues (2021) help address this research conundrum in an elegant analysis that demonstrates the interaction between long-term cattle grazing and climatic variability on primary production aboveground and belowground. 

Over the course of two years, Picon-Cochard et al. (2021) measured above and belowground net primary productivity in French grasslands that had been subject to ten years of managed cattle grazing. When they compared these data with climatic trends, they find an interesting interaction among grazing intensity and climatic factors influencing plant growth.  In short, and as expected, plants allocate more resources to root growth in dry years and more to above ground biomass in wet and cooler years. However, this study reveals the degree to which this is affected by cattle grazing. Grazed grasslands support warmer and dryer soils creating feedback that further and significantly promotes root growth over green biomass production.  

The implications of this work to understanding the capacity of grassland soils to store carbon is profound. This study addresses one brief moment in time of the long trajectory of this grazed ecosystem. The legacy of grazing does not appear to influence soil ecosystem functioning with respect to root growth except within the environmental context, in this case, climate. This supports the notion that long-term research in animal husbandry and grazing effects on landscapes is deeded. It is my hope that this study is one of many that can be used to synthesize many different data sets and build a deeper understanding of the long-term effects of grazing and herd management within the context of a changing climate.  Herbivory has a profound influence upon ecosystem health and the distribution of plant communities (Speed and Austrheim 2017), global carbon storage (Chen and Frank 2020) and nutrient cycling (Sitters et al. 2020). The analysis and results presented by Picon-Cochard (2021) help to resolve the mechanisms that underly these complex effects and ultimately make projections for the future.

References

Andriuzzi WS, Wall DH. 2017. Responses of belowground communities to large aboveground herbivores: Meta‐analysis reveals biome‐dependent patterns and critical research gaps. Global Change Biology 23:3857-3868. doi: https://doi.org/10.1111/gcb.13675

Chen J, Frank DA. 2020. Herbivores stimulate respiration from labile and recalcitrant soil carbon pools in grasslands of Yellowstone National Park. Land Degradation & Development 31:2620-2634. doi: https://doi.org/10.1002/ldr.3656

Jia S, Wang X, Yuan Z, Lin F, Ye J, Hao Z, Luskin MS. 2018. Global signal of top-down control of terrestrial plant communities by herbivores. Proceedings of the National Academy of Sciences 115:6237-6242. doi: https://doi.org/10.1073/pnas.1707984115

Picon-Cochard C, Vassal N, Martin R, Herfurth D, Note P, Louault F. 2021. Intra and inter-annual climatic conditions have stronger effect than grazing intensity on root growth of permanent grasslands. bioRxiv, 2020.08.23.263137, version 6 peer-reviewed and recommended by PCI Ecology. doi: https://doi.org/10.1101/2020.08.23.263137

Schrama M, Veen GC, Bakker EL, Ruifrok JL, Bakker JP, Olff H. 2013. An integrated perspective to explain nitrogen mineralization in grazed ecosystems. Perspectives in Plant Ecology, Evolution and Systematics 15:32-44. doi: https://doi.org/10.1016/j.ppees.2012.12.001

Sitters J, Venterink HO. 2015. The need for a novel integrative theory on feedbacks between herbivores, plants and soil nutrient cycling. Plant and Soil 396:421-426. doi: https://doi.org/10.1007/s11104-015-2679-y

Sitters J, Wubs EJ, Bakker ES, Crowther TW, Adler PB, Bagchi S, Bakker JD, Biederman L, Borer ET, Cleland EE. 2020. Nutrient availability controls the impact of mammalian herbivores on soil carbon and nitrogen pools in grasslands. Global Change Biology 26:2060-2071. doi: https://doi.org/10.1111/gcb.15023

Speed JD, Austrheim G. 2017. The importance of herbivore density and management as determinants of the distribution of rare plant species. Biological Conservation 205:77-84. doi: https://doi.org/10.1016/j.biocon.2016.11.030

Intra and inter-annual climatic conditions have stronger effect than grazing intensity on root growth of permanent grasslandsCatherine Picon-Cochard, Nathalie Vassal, Raphaël Martin, Damien Herfurth, Priscilla Note, Frédérique Louault<p>Background and Aims: Understanding how direct and indirect changes in climatic conditions, management, and species composition affect root production and root traits is of prime importance for the delivery of carbon sequestration services of gr...Agroecology, Biodiversity, Botany, Community ecology, Ecosystem functioningJennifer Krumins2020-08-30 19:27:30 View
06 Jan 2021
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Comparing statistical and mechanistic models to identify the drivers of mortality within a rear-edge beech population

The complexity of predicting mortality in trees

Recommended by based on reviews by Lisa Hülsmann and 2 anonymous reviewers

One of the main issues of forest ecosystems is rising tree mortality as a result of extreme weather events (Franklin et al., 1987). Eventually, tree mortality reduces forest biomass (Allen et al., 2010), although its effect on forest ecosystem fluxes seems not lasting too long (Anderegg et al., 2016). This controversy about the negative consequences of tree mortality is joined to the debate about the drivers triggering and the mechanisms accelerating tree decline. For instance, there is still room for discussion about carbon starvation or hydraulic failure determining the decay processes (Sevanto et al., 2014) or about the importance of mortality sources (Reichstein et al., 2013). Therefore, understanding and predicting tree mortality has become one of the challenges for forest ecologists in the last decade, doubling the rate of articles published on the topic (*). Although predicting the responses of ecosystems to environmental change based on the traits of species may seem a simplistic conception of ecosystem functioning (Sutherland et al., 2013), identifying those traits that are involved in the proneness of a tree to die would help to predict how forests will respond to climate threatens.
Modelling tree mortality is complex, involving multiple factors acting simultaneously at different scales, from tree genetics to ecosystem dynamics and from microsite conditions to global climatic events. Therefore, taking into account different approaches to reduce uncertainty of the predictions is needed (Bugmann et al., 2019). Petit-Cailleux et al. (2020) uses statistical and process-based models to detect the main mortality drivers of a drought- and frost-prone beech population. Particularly, they assessed the intra-individual characteristics of the population, that may play a decisive role explaining the differences in tree vulnerability to extreme weather events. Comparing the results of both analytical approaches, they find out several key factors, such as defoliation, leaf phenology and tree size, that were consistent between them. Even more, the process-based model showed the physiological mechanisms that may explain the individual vulnerability, for instance higher loss of hydraulic conductance may increase the mortality risk of trees with early budburst phenology and large stem diameter. The authors also successfully model annual mortality rate with a linear relationship including only three parameters: loss of conductance, biomass of reserves and late frost days.
This valuable study is a good example of the complexity in understanding and predicting tree mortality. The authors carried out the ambitious commitment of studying the inter-annual variation in mortality with 14-year dataset. However, it might be not enough time to control for the dependence of temporal data to soundly model mortality rate. The authors also acknowledge that the use of two approaches increases the knowledge from different perspectives, but at the same time comparing their results is difficult because the parameters used are not identical. Particularly, process-based models tend to consider the same microclimatic conditions for every tree in the population, and may produce inconsistences with statistical models. Alternatively, individual-based modelling might overcome some of the incompatibilities between the approaches (Zhu et al., 2019).

(*) Number (and percentage) of articles found in Web of Sciences after searching (December the 10th, 2020) “tree mortality”: from 163 (0.006%) in 2010 to 412 (0.013%) in 2020.

References

Allen et al. (2010). A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest ecology and management, 259(4), 660-684. doi: https://doi.org/10.1016/j.foreco.2009.09.001
Anderegg et al. (2016). When a tree dies in the forest: scaling climate-driven tree mortality to ecosystem water and carbon fluxes. Ecosystems, 19(6), 1133-1147. doi: https://doi.org/10.1007/s10021-016-9982-1
Bugmann et al. (2019). Tree mortality submodels drive simulated long‐term forest dynamics: assessing 15 models from the stand to global scale. Ecosphere, 10(2), e02616. doi: https://doi.org/10.1002/ecs2.2616
Franklin, J. F., Shugart, H. H. and Harmon, M. E. (1987) Death as an ecological process: the causes, consequences, and variability of tree mortality. BioScience, 37, 550–556. doi: https://doi.org/10.2307/1310665
Petit-Cailleux, C., Davi, H., Lefèvre, F., Garrigue, J., Magdalou, J.-A., Hurson, C., Magnanou, E. and Oddou-Muratorio, S. (2020) Comparing statistical and mechanistic models to identify the drivers of mortality within a rear-edge beech population. bioRxiv, 645747, ver 7 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/645747
Reichstein et al. (2013). Climate extremes and the carbon cycle. Nature, 500(7462), 287-295. doi: https://doi.org/10.1038/nature12350
Sevanto, S., Mcdowell, N. G., Dickman, L. T., Pangle, R., and Pockman, W. T. (2014). How do trees die? A test of the hydraulic failure and carbon starvation hypotheses. Plant, cell & environment, 37(1), 153-161. doi: https://doi.org/10.1111/pce.12141
Sutherland et al. (2013). Identification of 100 fundamental ecological questions. Journal of ecology, 101(1), 58-67. doi: https://doi.org/10.1111/1365-2745.12025
Zhu, Y., Liu, Z., and Jin, G. (2019). Evaluating individual-based tree mortality modeling with temporal observation data collected from a large forest plot. Forest Ecology and Management, 450, 117496. doi: https://doi.org/10.1016/j.foreco.2019.117496

Comparing statistical and mechanistic models to identify the drivers of mortality within a rear-edge beech populationCathleen Petit-Cailleux, Hendrik Davi, François Lefevre, Christophe Hurson, Joseph Garrigue, Jean-André Magdalou, Elodie Magnanou and Sylvie Oddou-Muratorio<p>Since several studies have been reporting an increase in the decline of forests, a major issue in ecology is to better understand and predict tree mortality. The interactions between the different factors and the physiological processes giving ...Climate change, Physiology, Population ecologyLucía DeSoto2019-05-24 11:37:38 View
14 Nov 2022
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Estimating abundance of a recovering transboundary brown bear population with capture-recapture models

A new and efficient approach to estimate, from protocol and opportunistic data, the size and trends of populations: the case of the Pyrenean brown bear

Recommended by based on reviews by Tim Coulson, Romain Pigeault and ?

In this study, the authors report a new method for estimating the abundance of the Pyrenean brown bear population. Precisely, the methodology involved aims to apply Pollock's closed robust design (PCRD) capture-recapture models to estimate population abundance and trends over time. Overall, the results encourage the use of PCRD to study populations' demographic rates, while minimizing biases due to inter-individual heterogeneity in detection probabilities.

Estimating the size and trends of animal population over time is essential for informing conservation status and management decision-making (Nichols & Williams 2006). This is particularly the case when the population is small, geographically scattered, and threatened. Although several methods can be used to estimate population abundance, they may be difficult to implement when individuals are rare, elusive, solitary, largely nocturnal, highly mobile, and/or occupy large home ranges in remote and/or rugged habitats. Moreover, in such standard methods,

  • the population is assumed to be closed both geographically (no immigration nor emigration) and demographically (no births nor deaths) and
  • all individuals are assumed to have identical detection probabilities regardless of their individual attributes (e.g., age, body mass, social status) and habitat features (home-range location and composition) (Otis et al. 1978).

However, these conditions are rarely met in real populations, such as wild mammals (e.g., Bellemain et al. 2005; Solbert et al. 2006), and therefore the risk of underestimating population size can rapidly increase because the assumption of perfect detection of all individuals in the population is violated.

Focusing on the critically endangered Pyrenean brown bear that was close to extinction in the mid-1990s, the study by Vanpe et al. (2022), uses protocol and opportunistic data to describe a statistical modeling exercise to construct mark-recapture histories from 2008 to 2020. Among the data, the authors collected non-invasive samples such as a mixture of hair and scat samples used for genetic identification, as well as photographic trap data of recognized individuals. These data are then analyzed in RMark to provide detection and survival estimates. The final model (i.e. PCRD capture-recapture) is then used to provide Bayesian population estimates. Results show a five-fold increase in population size between 2008 and 2020, from 13 to 66 individuals. Thus, this study represents the first published annual abundance and temporal trend estimates of the Pyrenean brown bear population since 2008.

Then, although the results emphasize that the PCRD estimates were broadly close to the MRS counts and had reasonably narrow associated 95% Credibility Intervals, they also highlight that the sampling effort is different according to individuals. Indeed, as expected, the detection of an individual depends on

  • the intraspecific home range size variation that results in individuals that move the most being most likely to be detected and
  • the mortality rate which is higher on cubs than on adults and subadults (due to infanticide by males, predation, death of the mother, or abandonment).

Overall, the PCRD capture-recapture modelling approach, involved in this study, provides robust estimates of abundance and demographic rates of the Pyrenean brown bear population (with associated uncertainty) while minimizing and considering bias due to inter-individual heterogeneity in detection probabilities.

The authors conclude that mark-recapture provides useful population estimates and urge wildlife ecologists and managers to use robust approaches, such as the RDPC capture-recapture model, when studying large mammal populations. This information is essential to inform management decisions and assess the conservation status of populations.

 

References

Bellemain, E.V.A., Swenson, J.E., Tallmon, D., Brunberg, S. and Taberlet, P. (2005). Estimating population size of elusive animals with DNA from hunter-collected feces: four methods for brown bears. Cons. Biol. 19(1), 150-161. https://doi.org/10.1111/j.1523-1739.2005.00549.x

Nichols, J.D. and Williams, B.K. (2006). Monitoring for conservation. Trends Ecol. Evol. 21(12), 668-673. https://doi.org/10.1016/j.tree.2006.08.007

Otis, D.L., Burnham, K.P., White, G.C. and Anderson, D.R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs (62), 3-135.

Solberg, K.H., Bellemain, E., Drageset, O.M., Taberlet, P. and Swenson, J.E. (2006). An evaluation of field and non-invasive genetic methods to estimate brown bear (Ursus arctos) population size. Biol. Conserv. 128(2), 158-168. https://doi.org/10.1016/j.biocon.2005.09.025

Vanpé C, Piédallu B, Quenette P-Y, Sentilles J, Queney G, Palazón S, Jordana IA, Jato R, Elósegui Irurtia MM, de la Torre JS, and Gimenez O (2022) Estimating abundance of a recovering transboundary brown bear population with capture-recapture models. bioRxiv, 2021.12.08.471719, ver. 4 recommended and peer-reviewed by PCI Ecology. https://doi.org/10.1101/2021.12.08.471719

Estimating abundance of a recovering transboundary brown bear population with capture-recapture modelsCécile Vanpé, Blaise Piédallu, Pierre-Yves Quenette, Jérôme Sentilles, Guillaume Queney, Santiago Palazón, Ivan Afonso Jordana, Ramón Jato, Miguel Mari Elósegui Irurtia, Jordi Solà de la Torre, Olivier Gimenez<p>Estimating the size of small populations of large mammals can be achieved via censuses, or complete counts, of recognizable individuals detected over a time period: minimum detected (population) size (MDS). However, as a population grows larger...Conservation biology, Demography, Population ecologyNicolas BECH2022-01-20 10:49:59 View
12 Jun 2019
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Environmental heterogeneity drives tsetse fly population dynamics and control

Modeling jointly landscape complexity and environmental heterogeneity to envision new strategies for tsetse flies control

Recommended by based on reviews by Timothée Vergne and 1 anonymous reviewer

Today, understanding spatio-temporal dynamics of pathogens is pivotal to understand their transmission and controlling them. First, understanding this dynamics can reveal the ecology of their transmission [1]. Indeed, such knowledge, based on data that are quite easy to access, can shed light on transmission modes, which could rely on different animal species that can be spatially distributed in a non-uniform way [2]. This is especially true for pathogens with complex life-cycles, despite that investigating such dynamics is very challenging and rely mostly on mathematical models.
Moreover, this knowledge can also highlight some weak points in a complex web of transmission and therefore allowing us to envision new innovative control strategies. This has been first proposed on human pathogens, where connectivity among populations can be analyzed to identify which connections need to be targeted to stop or slow down an epidemics [3]. However, this idea is increasingly recognized as a promising new approach for pathogens involving vector populations, especially regarding the complexity to decrease on a long-term the abundance of these vector populations [4].
In "Environmental heterogeneity drives tsetse fly population dynamics and control" [5], Cecilia and co-authors have developed a sophisticated spatio-temporal mechanistic model to figure out how local environment, involved within landscape of different complexities, can impact the population dynamics of tsetse flies, an invertebrate species that can serve as a vector for many pathogens of animal and human importance. They found that spatial patches with the lowest temperature mean and the lowest environmental fluctuations can act as refuge for this species, representing therefore preferential targets for disease control.
The reviewers and I agree that the mathematical framework developed address very well an important topic for both ecological and public health literature. More importantly, it shows how fundamental ecological knowledge can drive pathogen control strategies, opening an interesting avenue for cross-disciplinary research on vector-borne diseases.

References

[1] Grenfell, B. T., Bjørnstad, O. N., & Kappey, J. (2001). Travelling waves and spatial hierarchies in measles epidemics. Nature, 414(6865), 716-723. doi: 10.1038/414716a
[2] Perkins, S. E., Cattadori, I. M., Tagliapietra, V., Rizzoli, A. P., & Hudson, P. J. (2003). Empirical evidence for key hosts in persistence of a tick-borne disease. International journal for parasitology, 33(9), 909-917. doi: 10.1016/S0020-7519(03)00128-0
[3] Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A. (2006). The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences, 103(7), 2015-2020. doi: 10.1073/pnas.0510525103
[4] Pepin, K. M., Leach, C. B., Marques-Toledo, C., Laass, K. H., Paixao, K. S., et al. (2015) Utility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities. Parasites & Vectors 8, 1–15. doi: 10.1186/s13071-015-0659-y
[5] Cecilia, H., Arnoux, S., Picault, S., Dicko, A., Seck, M. T., Sall, B., Bassène, M., Vreysen, M., Pagabeleguem, S., Bancé, A., Bouyer, J. and Ezanno, P.(2019). Environmental heterogeneity drives tsetse fly population dynamics and control. bioRxiv 493650, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/493650

Environmental heterogeneity drives tsetse fly population dynamics and controlCecilia H, Arnoux S, Picault S, Dicko A, Seck MT, Sall B, Bassene M, Vreysen M, Pagabeleguem S, Bance A, Bouyer J, Ezanno P<p>A spatially and temporally heterogeneous environment may lead to unexpected population dynamics. Knowledge still is needed on which of the local environment properties favour population maintenance at larger scale. For pathogen vectors, such as...Biological control, Population ecology, Spatial ecology, Metacommunities & MetapopulationsBenjamin Roche2018-12-14 12:13:39 View
03 Jan 2024
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Diagnosis of planktonic trophic network dynamics with sharp qualitative changes

A new approach to describe qualitative changes of complex trophic networks

Recommended by based on reviews by Tim Coulson and 1 anonymous reviewer

Modelling the temporal dynamics of trophic networks has been a key challenge for community ecologists for decades, especially when anthropogenic and natural forces drive changes in species composition, abundance, and interactions over time. So far, most modelling methods fail to incorporate the inherent complexity of such systems, and its variability, to adequately describe and predict temporal changes in the topology of trophic networks. 

Taking benefit from theoretical computer science advances, Gaucherel and colleagues (2024) propose a new methodological framework to tackle this challenge based on discrete-event Petri net methodology. To introduce the concept to naïve readers the authors provide a useful example using a simplistic predator-prey model.

The core biological system of the article is a freshwater trophic network of western France in the Charente-Maritime marshes of the French Atlantic coast. A directed graph describing this system was constructed to incorporate different functional groups (phytoplankton, zooplankton, resources, microbes, and abiotic components of the environment) and their interactions. Rules and constraints were then defined to, respectively, represent physiochemical, biological, or ecological processes linking network components, and prevent the model from simulating unrealistic trajectories. Then the full range of possible trajectories of this mechanistic and qualitative model was computed.

The model performed well enough to successfully predict a theoretical trajectory plus two trajectories of the trophic network observed in the field at two different stations, therefore validating the new methodology introduced here. The authors conclude their paper by presenting the power and drawbacks of such a new approach to qualitatively model trophic networks dynamics.

Reference

Cedric Gaucherel, Stolian Fayolle, Raphael Savelli, Olivier Philippine, Franck Pommereau, Christine Dupuy (2024) Diagnosis of planktonic trophic network dynamics with sharp qualitative changes. bioRxiv 2023.06.29.547055, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.06.29.547055

Diagnosis of planktonic trophic network dynamics with sharp qualitative changesCedric Gaucherel, Stolian Fayolle, Raphael Savelli, Olivier Philippine, Franck Pommereau, Christine Dupuy<p>Trophic interaction networks are notoriously difficult to understand and to diagnose (i.e., to identify contrasted network functioning regimes). Such ecological networks have many direct and indirect connections between species, and these conne...Community ecology, Ecosystem functioning, Food webs, Freshwater ecology, Interaction networks, Microbial ecology & microbiologyFrancis Raoul Tim Coulson2023-07-03 10:42:34 View
11 Oct 2023
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Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis

Disentangling microbial exopolymer dynamics in intertidal sediments

Recommended by and ORCID_LOGO based on reviews by 2 anonymous reviewers

The secretion of extracellular polymeric substances (EPS) enables microorganisms to shape and interact with their environment [1]. EPS support cell adhesion and motility, offer protection from unfavorable conditions, and facilitate nutrient acquisition and transfer between microorganisms [2]. EPS production and consumption thus control the formation and structural organization of biofilms [3]. However, in marine environments, our understanding of the sources and composition of EPS is limited.
 
In this study, Hubas et al. [4] compare the carbon and nitrogen isotope ratios in EPS with the carbon isotope ratios of fatty acid biomarkers to identify the main EPS producers in intertidal sediments. The authors find pronounced differences in the diversity, composition, isotope signatures, and production/consumption dynamics of EPS between muddy and sandy environments. While the contribution of diatoms was highest in the bound fraction of EPS in muddy environments, diatom contribution was highest in the colloidal fraction of EPS in sandy environments. These differences between sites likely reflect the functional differences in EPS dynamics of epipelic and episammic sediment communities.
 
Taken together, the innovative approach of the authors provides insights into the diversity and origin of EPS in microphytobenthic communities and highlights the importance of different microbial groups in EPS production. These findings are vital for understanding EPS dynamics in microbial interactions and their role in the functioning of coastal ecosystems.

References

  1. Flemming, H.-C. (2016) EPS-then and now. Microorganisms 4, 41 https://doi.org/10.3390/microorganisms4040041
  2. Wolfaardt, G.M. et al. (1999) Function of EPS. In Microbial Extracellular Polymeric Substances, pp. 171–200, Springer Berlin Heidelberg https://doi.org/10.1007/978-3-642-60147-7
  3. Flemming, H.-C. et al. (2007) The EPS matrix: the “house of biofilm cells.” J. Bacteriol. 189, 7945–7947 https://doi.org/10.1128/jb.00858-07
  4. Hubas, C. et al. (2022) Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.12.02.516908
Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysisCédric Hubas, Julie Gaubert-Boussarie, An-Sofie D’Hondt, Bruno Jesus, Dominique Lamy, Vona Meleder, Antoine Prins, Philippe Rosa, Willem Stock, Koen Sabbe<p style="text-align: justify;">Extracellular polymeric substances (EPS) refer to a wide variety of high molecular weight molecules secreted outside the cell membrane by biofilm microorganisms. In the present study, EPS from marine microphytobenth...Biodiversity, Ecological stoichiometry, Ecosystem functioning, Food webs, Marine ecology, Microbial ecology & microbiology, Soil ecologyUte Risse-Buhl2022-12-06 14:13:11 View
28 Sep 2020
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The dynamics of spawning acts by a semelparous fish and its associated energetic costs

Extreme weight loss: when accelerometer could reveal reproductive investment in a semelparous fish species

Recommended by ORCID_LOGO based on reviews by Aidan Jonathan Mark Hewison, Loïc Teulier and 1 anonymous reviewer

Continuous observation of animal behaviour could be quite a challenge in the field, and the situation becomes even more complicated with aquatic species mostly active at night. In such cases, biologging techniques are real game changers in ecology, behavioural ecology or eco-physiology. An accelerating number of methodological applications of these tools in natural condition are thus published each year [1]. Biologging is not limited to movement ecology. For instance, fine grain information about energy expenditure can be inferred from body acceleration [2], and accelerometers has already proven useful in monitoring reproductive costs in some fish species [3,4]. The first part of the study by Tentelier et al. [5] is in line with this growing literature. It describes measurements of energy expenditure during reproduction in a fish species, Allis shad (Alosa Alosa), based on tail beat frequency and occurrence of spawning acts. The study has been convincingly conducted, and the results are important for fish biologists. But this is not the whole story: the authors added to this otherwise classical study a very original and insightful analysis which deserves closer interest.
Tentelier et al. propose to use static accelerometer to monitor change in body roundness through the reproductive season. These semelparous fish first mature and built up reserves in the Atlantic Ocean and migrate into fresh water to reproduce. Contrary to iteroparous species, female shads do not have to strategically preserve energy for future reproduction. The females die few days after spawning having exhausted their energetic reserves: they typically lose almost half of their body mass during the spawning season. The beautiful idea in this study was to track down information about this dramatic slimming in the accelerometer data. Indeed, the accelerometer was attached on the side of the fish (close to the dorsal fin). A change in its angle with the vertical plane could be correlated with the change in roundness, the angle declining with the female thinning. Accelerometers have already been used to record body posture [6] but, in the present study, the novelty was to monitor the change in body shape.
Unfortunately, the data by Tentelier et al. are inconclusive so far. Broadly speaking, the accelerometer angle recorded declined through the spawning season, indicating an average slimming of the females, but there was no correlation between the change in angle and the mass loss at the individual level. This was partly due to the fact that the dorsal position of the accelerometer was not optimized to measures egg laying whose effects are mostly observable on ventral side.
Yet, this nice idea deserves more scrutiny. The method seems to be sensitive enough to detect inflation of swim bladder, the gas-filled organ helping the fish to control their position in the water column, as the accelerometer angle increased when the fish stayed close to the water surface. Additional works and proper calibration are certainly needed to validate the use of accelerometer angle as a proxy for body roundness. The actual data were not strong enough to justify a standalone publication on the subject, but it would have been shame to lose traces of such analysis and keep it in the file drawer. This is why I strongly support its report as a side question in a broader study. Science progresses not only with neat conclusive studies but also when unexpected (apparently anecdotal) observations stimulate new researches.

References

[1] Börger L, Bijleveld AI, Fayet AL, Machovsky‐Capuska GE, Patrick SC, Street GM and Vander Wal E. (2020) Biologging special feature. J. Anim. Ecol. 89, 6–15. 10.1111/1365-2656.13163
[2] Wilson RP et al. (2020) Estimates for energy expenditure in free‐living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. 89, 161–172. 10.1111/1365-2656.13040
[3] Tsuda Y, Kawabe R, Tanaka H, Mitsunaga Y, Hiraishi T, Yamamoto K and Nashimoto K. (2006) Monitoring the spawning behaviour of chum salmon with an acceleration data logger. Ecol. Freshw. Fish 15, 264–274. 10.1111/j.1600-0633.2006.00147.x
[4] Sakaji H, Hamada K, Naito Y. 2018 Identifying spawning events of greater amberjack using accelerometers. Mar. Biol. Res. 14, 637–641. 10.1080/17451000.2018.1492140
[5] Tentelier C, Bouchard C, Bernardin A, Tauzin A, Aymes J-C, Lange F, Récapet C, Rives J (2020) The dynamics of spawning acts by a semelparous fish and its associated energetic costs. bioRxiv, 436295. doi: 10.1101/436295 ver. 7 peer-reviewed and recommended by PCI Ecology. 10.1101/436295
[6] Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP. 2013 Observing the unwatchable through acceleration logging of animal behavior. Anim. Biotelemetry 1, 20. 10.1186/2050-3385-1-20

The dynamics of spawning acts by a semelparous fish and its associated energetic costsCédric Tentelier, Colin Bouchard, Anaïs Bernardin, Amandine Tauzin, Jean-Christophe Aymes, Frédéric Lange, Charlotte Recapet, Jacques Rives<p>1. During the reproductive season, animals have to manage both their energetic budget and gamete stock. In particular, for semelparous capital breeders with determinate fecundity and no parental care other than gametic investment, the depletion...Behaviour & Ethology, Freshwater ecology, Life historyFrancois-Xavier Dechaume-Moncharmont2020-06-04 15:18:56 View
19 Dec 2020
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Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.)

A new member of the morphometrics jungle to better monitor vulnerable lagoons

Recommended by based on reviews by Julien Claude and 1 anonymous reviewer

In the recent years, morphometrics, the quantitative description of shape and its covariation [1] gained considerable momentum in evolutionary ecology. Using the form of organisms to describe, classify and try to understand their diversity can be traced back at least to Aristotle. More recently, two successive revolutions rejuvenated this idea [1–3]: first, a proper mathematical refoundation of the theory of shape, then a technical revolution in the apparatus able to acquire raw data. By using a feature extraction method and planning its massive use on data acquired by aerial drones, the study by Lacaux and colleagues [4] retraces this curse of events.
The radial symmetry of Aurelia spp. jelly fish, a common species complex, is affected by stress and more largely by environmental variations, such as pollution exposition. Aurelia spp. normally present four gonads so that the proportion of non-tetramerous individuals in a population has been proposed as a biomarker [5,6].
In this study, the authors implemented the Hough transform to largely automate the detection of the gonads in Aurelia spp. Such use of the Hough transform, a long-used approach to identify shapes through edge detection, is new to morphometrics. Here, the Aurelia spp. gonads are identified as ellipses from which aspect descriptors can be derived, and primarily counted and thus can be used to quantify the proportion of individuals presenting body plans disorders.

The sample sizes studied here were too low to allow finer-grained ecophysiological investigations. That being said, the proof-of-concept is convincing and this paper paths the way for an operational and innovative approach to the ecological monitoring of sensible aquatic ecosystems.

References

[1] Kendall, D. G. (1989). A survey of the statistical theory of shape. Statistical Science, 87-99. doi: https://doi.org/10.1214/ss/1177012589
[2] Rohlf, F. J., and Marcus, L. F. (1993). A revolution morphometrics. Trends in ecology & evolution, 8(4), 129-132. doi: https://doi.org/10.1016/0169-5347(93)90024-J
[3] Adams, D. C., Rohlf, F. J., and Slice, D. E. (2004). Geometric morphometrics: ten years of progress following the ‘revolution’. Italian Journal of Zoology, 71(1), 5-16. doi: https://doi.org/10.1080/11250000409356545
[4] Lacaux, C., Desolneux, A., Gadreaud, J., Martin-Garin, B. and Thiéry, A. (2020) Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.). bioRxiv, 2020.03.11.986984, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. doi: https://doi.org/10.1101/2020.03.11.986984
[5] Gershwin, L. A. (1999). Clonal and population variation in jellyfish symmetry. Journal of the Marine Biological Association of the United Kingdom, 79(6), 993-1000. doi: https://doi.org/10.1017/S0025315499001228
[6] Gadreaud, J., Martin-Garin, B., Artells, E., Levard, C., Auffan, M., Barkate, A.-L. and Thiéry, A. (2017) The moon jellyfish as a new bioindicator: impact of silver nanoparticles on the morphogenesis. In: Mariottini GL, editor. Jellyfish: ecology, distribution patterns and human interactions. Nova Science Publishers; 2017. pp. 277–292.

Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.)Céline Lacaux, Agnès Desolneux, Justine Gadreaud, Bertrand Martin-Garin and Alain Thiéry<p>Variations of the animal body plan morphology and morphometry can be used as prognostic tools of their habitat quality. The potential of the moon jellyfish (Aurelia spp.) as a new model organism has been poorly tested. However, as a tetramerous...MorphometricsVincent Bonhomme2020-03-18 17:40:51 View
28 Dec 2022
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Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse

An experimental approach for understanding how terrestrial isopods respond to environmental stressors

Recommended by based on reviews by Aaron Yilmaz and Michael Morris

​​In this article, the authors discuss the results of their study investigating the effects of heat stress and moisture stress on a terrestrial isopod Armadilldium vulgare, the common woodlouse [1]. Specifically, the authors have assessed how increased temperature or decreased moisture affects life history traits (such as growth, survival, and reproduction) as well as physiological traits (immune cell parameters and \( beta \)-galactosidase activity). This article quantitatively evaluates the effects of the two stressors on woodlouse. Terrestrial isopods like woodlouse are sensitive to thermal and moisture stress [2; 3] and are therefore good models to test hypotheses in global change biology and for monitoring ecosystem health.

​An important feature of this study is the combination of experimental, laboratory, and analytical techniques. Experiments were conducted under controlled conditions in the laboratory by modulating temperature and moisture, life history and physiological traits were measured/analyzed and then tested using models. Both stressors had negative impacts on survival and reproduction of woodlouse, and result in premature ageing. Although thermal stress did not affect survival, it slowed woodlouse growth. Moisture stress did not have a detectable effect on woodlouse growth but decreased survival and reproductive success. An important insight from this study is that effects of heat and moisture stressors on woodlouse are not necessarily linear, and experimental approaches can be used to better elucidate the mechanisms and understand how these organisms respond to environmental stress.

​This article is timely given the increasing attention on biological monitoring and ecosystem health.​

References:

[1] Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. bioRxiv, 2022.09.26.509512., ver. 3 peer-reviewd and recommended by PCI Ecology. https://doi.org/10.1101/2022.09.26.509512

[2] ​Warburg MR, Linsenmair KE, Bercovitz K (1984) The effect of climate on the distribution and abundance of isopods. In: Sutton SL, Holdich DM, editors. The Biology of Terrestrial Isopods. Oxford: Clarendon Press. pp. 339–367.​

[3] Hassall M, Helden A, Goldson A, Grant A (2005) Ecotypic differentiation and phenotypic plasticity in reproductive traits of Armadillidium vulgare (Isopoda: Oniscidea). Oecologia 143: 51–60.​ https://doi.org/10.1007/s00442-004-1772-3

Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouseCharlotte Depeux, Angele Branger, Theo Moulignier, Jérôme Moreau, Jean-Francois Lemaitre, Francois-Xavier Dechaume-Moncharmont, Tiffany Laverre, Hélène Paulhac, Jean-Michel Gaillard, Sophie Beltran-Bech<p>We tested independently the influences of increasing temperature and decreasing moisture on life history and physiological traits in the arthropod <em>Armadillidium vulgare</em>. Both increasing temperature and decreasing moisture led individua...Biodiversity, Evolutionary ecology, Experimental ecology, Life history, Physiology, Terrestrial ecology, ZoologyAniruddha Belsare2022-09-28 13:13:47 View
04 Apr 2023
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Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study

Species Distribution Models: the delicate balance between signal and noise

Recommended by ORCID_LOGO based on reviews by Alejandra Zarzo Arias and 1 anonymous reviewer

Species Distribution Models (SDMs) are one of the most commonly used tools to predict where species are, where they may be in the future, and, at times, what are the variables driving this prediction. As such, applying an SDM to a dataset is akin to making a bet: that the known occurrence data are informative, that the resolution of predictors is adequate vis-à-vis the scale at which their impact is expressed, and that the model will adequately capture the shape of the relationships between predictors and predicted occurrence.

In this contribution, Lambert & Virgili (2023) perform a comprehensive assessment of different sources of complications to this process, using replicated simulations of two synthetic species. Their experimental process is interesting, in that both the data generation and the data analysis stick very close to what would happen in "real life". The use of synthetic species is particularly relevant to the assessment of SDM robustness, as they enable the design of species for which the shape of the relationship is given: in short, we know what the model should capture, and can evaluate the model performance against a ground truth that lacks uncertainty.

Any simulation study is limited by the assumptions established by the investigators; when it comes to spatial data, the "shape" of the landscape, both in terms of auto-correlation and in where the predictors are available. Lambert & Virgili (2023) nicely circumvent these issues by simulating synthetic species against the empirical distribution of predictors; in other words, the species are synthetic, but the environment for which the prediction is made is real. This is an important step forward when compared to the use of e.g. neutral landscapes (With 1997), which can have statistical properties that are not representative of natural landscapes (see e.g. Halley et al., 2004).

A striking point in the study by Lambert & Virgili (2023) is that they reveal a deep, indeed deeper than expected, stochasticity in SDMs; whether this is true in all models remains an open question, but does not invalidate their recommendation to the community: the interpretation of outcomes is a delicate exercise, especially because measures that inform on the goodness of the model fit do not capture the predictive quality of the model outputs. This preprint is both a call to more caution, and a call to more curiosity about the complex behavior of SDMs, while also providing a sensible template to perform future analyses of the potential issues with predictive models.


References

Halley, J. M., et al. (2004) “Uses and Abuses of Fractal Methodology in Ecology: Fractal Methodology in Ecology.” Ecology Letters, vol. 7, no. 3, pp. 254–71. https://doi.org/10.1111/j.1461-0248.2004.00568.x.

Lambert, Charlotte, and Auriane Virgili (2023). Data Stochasticity and Model Parametrisation Impact the Performance of Species Distribution Models: Insights from a Simulation Study. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.01.17.524386

With, Kimberly A. (1997) “The Application of Neutral Landscape Models in Conservation Biology. Aplicacion de Modelos de Paisaje Neutros En La Biologia de La Conservacion.” Conservation Biology, vol. 11, no. 5, pp. 1069–80. https://doi.org/10.1046/j.1523-1739.1997.96210.x.

Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation studyCharlotte Lambert, Auriane Virgili<p>Species distribution models (SDM) are widely used to describe and explain how species relate to their environment, and predict their spatial distributions. As such, they are the cornerstone of most of spatial planning efforts worldwide. SDM can...Biogeography, Habitat selection, Macroecology, Marine ecology, Spatial ecology, Metacommunities & Metapopulations, Species distributions, Statistical ecologyTimothée Poisot2023-01-20 09:43:51 View