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28 Apr 2023
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Most diverse, most neglected: weevils (Coleoptera: Curculionoidea) are ubiquitous specialized brood-site pollinators of tropical flora

Pollination-herbivory by weevils claiming for recognition: the Cinderella among pollinators

Recommended by based on reviews by Susan Kirmse, Carlos Eduardo Nunes and 2 anonymous reviewers

Since Charles Darwin times, and probably earlier, naturalists have been eager to report the rarest pollinators being discovered, and this still happens even in recent times; e.g., increased evidence of lizards, cockroaches, crickets or earwigs as pollinators (Suetsugu 2018, Komamura et al. 2021, de Oliveira-Nogueira et al. 2023), shifts to invasive animals as pollinators, including passerine birds and rats (Pattemore & Wilcove 2012), new amazing cases of mimicry in pollination, such as “bleeding” flowers that mimic wounded insects (Heiduk et al., 2023) or even the possibility that a tree frog is reported for the first time as a pollinator (de Oliveira-Nogueira et al. 2023). This is in part due to a natural curiosity of humans about rarity, which pervades into scientific insight (Gaston 1994). Among pollinators, the apparent rarity of some interaction types is sometimes a symptom of a lack of enough inquiry. This seems to be the case of weevil pollination, given that these insects are widely recognized as herbivores, particularly those that use plant parts to nurse their breed and never were thought they could act also as mutualists, pollinating the species they infest. This is known as a case of brood site pollination mutualism (BSPM), which also involves an antagonistic counterpart (herbivory) to which plants should face. This is the focus of the manuscript (Haran et al. 2023) we are recommending here. There is wide treatment of this kind of pollination in textbooks, albeit focused on yucca-yucca moth and fig-fig wasp interactions due to their extreme specialization (Pellmyr 2003, Kjellberg et al. 2005), and more recently accompanied by Caryophyllaceae-moth relationship (Kephart et al. 2006). 

Here we find a detailed review that shows that the most diverse BSPM, in terms of number of plant and pollinator species involved, is that of weevils in the tropics. The mechanism of BSPM does not involve a unique morphological syndrome, as it is mostly functional and thus highly dependent on insect biology (Fenster & al. 2004), whereas the flower phenotypes are highly divergent among species. Probably, the inconspicuous nature of the interaction, and the overwhelming role of weevils as seed predators, even as pests, are among the causes of the neglection of weevils as pollinators, as it could be in part the case of ants as pollinators (de Vega et al. 2014). The paper by Haran et al (2023) comes to break this point.

Thus, the rarity of weevil pollination in former reports is not a consequence of an anecdotical nature of this interaction, even for the BSPM, according to the number of cases the authors are reporting, both in terms of plant and pollinator species involved. This review has a classical narrative format which involves a long text describing the natural history behind the cases. It is timely and fills the gap for this important pollination interaction for biodiversity and also for economic implications for fruit production of some crops. Former reviews have addressed related topics on BSPM but focused on other pollinators, such as those mentioned above. Besides, the review put much effort into the animal side of the interaction, which is not common in the pollination literature. Admittedly, the authors focus on the detailed description of some paradigmatic cases, and thereafter suggest that these can be more frequently reported in the future, based on varied evidence from morphology, natural history, ecology, and distribution of alleged partners. This procedure was common during the development of anthecology, an almost missing term for floral ecology (Baker 1983), relying on accumulative evidence based on detailed observations and experiments on flowers and pollinators. Currently, a quantitative approach based on the tools of macroecological/macroevolutionary analyses is more frequent in reviews. However, this approach requires a high amount of information on the natural history of the partnership, which allows for sound hypothesis testing. By accumulating this information, this approach allows the authors to pose specific questions and hypotheses which can be tested, particularly on the efficiency of the systems and their specialization degree for both the plants and the weevils, apparently higher for the latter. This will guarantee that this paper will be frequently cited by floral ecologists and evolutionary biologists and be included among the plethora of floral syndromes already described, currently based on more explicit functional grounds (Fenster et al. 2004). In part, this is one of the reasons why the sections focused on future prospects is so large in the review. 

I foresee that this mutualistic/antagonistic relationship will provide excellent study cases for the relative weight of these contrary interactions among the same partners and its relationship with pollination specialization-generalization and patterns of diversification in the plants and/or the weevils. As new studies are coming, it is possible that BSPM by weevils appears more common in non-tropical biogeographical regions. In fact, other BSPM are not so uncommon in other regions (Prieto-Benítez et al. 2017). In the future, it would be desirable an appropriate testing of the actual effect of phylogenetic niche conservatism, using well known and appropriately selected BSPM cases and robust phylogenies of both partners in the mutualism. Phylogenetic niche conservatism is a central assumption by the authors to report as many cases as possible in their review, and for that they used taxonomic relatedness. As sequence data and derived phylogenies for large numbers of vascular plant species are becoming more frequent (Jin & Quian 2022), I would recommend the authors to perform a comparative analysis using this phylogenetic information. At least, they have included information on phylogenetic relatedness of weevils involved in BSPM which allow some inferences on the multiple origins of this interaction. This is a good start to explore the drivers of these multiple origins through the lens of comparative biology.

References

Baker HG (1983) An Outline of the History of Anthecology, or Pollination Biology. In: L Real (ed). Pollination Biology. Academic Press.

de-Oliveira-Nogueira CH, Souza UF, Machado TM, Figueiredo-de-Andrade CA, Mónico AT, Sazima I, Sazima M, Toledo LF (2023). Between fruits, flowers and nectar: The extraordinary diet of the frog Xenohyla truncate. Food Webs 35: e00281. https://doi.org/10.1016/j.fooweb.2023.e00281

Fenster CB W, Armbruster S, Wilson P, Dudash MR, Thomson JD (2004). Pollination syndromes and floral specialization. Annu. Rev. Ecol. Evol. Syst. 35: 375–403. https://doi.org/10.1146/annurev.ecolsys.34.011802.132347

Gaston KJ (1994). What is rarity? In KJ Gaston (ed): Rarity. Population and Community Biology Series, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0701-3_1

Haran J, Kergoat GJ, Bruno, de Medeiros AS (2023) Most diverse, most neglected: weevils (Coleoptera: Curculionoidea) are ubiquitous specialized brood-site pollinators of tropical flora. hal. 03780127, version 2 peer-reviewed and recommended by Peer Community in Ecology. https://hal.inrae.fr/hal-03780127

Heiduk A, Brake I, Shuttleworth A, Johnson SD (2023) ‘Bleeding’ flowers of Ceropegia gerrardii (Apocynaceae-Asclepiadoideae) mimic wounded insects to attract kleptoparasitic fly pollinators. New Phytologist. https://doi.org/10.1111/nph.18888

Jin, Y., & Qian, H. (2022). V. PhyloMaker2: An updated and enlarged R package that can generate very large phylogenies for vascular plants. Plant Diversity, 44(4), 335-339. https://doi.org/10.1016/j.pld.2022.05.005

Kjellberg F, Jousselin E, Hossaert-Mckey M, Rasplus JY (2005). Biology, ecology, and evolution of fig-pollinating wasps (Chalcidoidea, Agaonidae). In: A. Raman et al (eds) Biology, ecology and evolution of gall-inducing arthropods 2, 539-572. Science Publishers, Enfield.

Komamura R, Koyama K, Yamauchi T, Konno Y, Gu L (2021). Pollination contribution differs among insects visiting Cardiocrinum cordatum flowers. Forests 12: 452. https://doi.org/10.3390/f12040452

Pattemore DE, Wilcove DS (2012) Invasive rats and recent colonist birds partially compensate for the loss of endemic New Zealand pollinators. Proc. R. Soc. B 279: 1597–1605. https://doi.org/10.1098/rspb.2011.2036

Pellmyr O (2003) Yuccas, yucca moths, and coevolution: a review. Ann. Missouri Bot. Gard. 90: 35-55. https://doi.org/10.2307/3298524

Prieto-Benítez S, Yela JL, Giménez-Benavides L (2017) Ten years of progress in the study of Hadena-Caryophyllaceae nursery pollination. A review in light of new Mediterranean data. Flora, 232, 63-72. https://doi.org/10.1016/j.flora.2017.02.004

Suetsugu K (2019) Social wasps, crickets and cockroaches contribute to pollination of the holoparasitic plant Mitrastemon yamamotoi (Mitrastemonaceae) in southern Japan. Plant Biology 21 176–182. https://doi.org/10.1111/plb.12889

Most diverse, most neglected: weevils (Coleoptera: Curculionoidea) are ubiquitous specialized brood-site pollinators of tropical floraJulien Haran, Gael J. Kergoat, Bruno A. S. de Medeiros<p style="text-align: justify;">In tropical environments, and especially tropical rainforests, a major part of pollination services is provided by diverse insect lineages. Unbeknownst to most, beetles, and more specifically hyperdiverse weevils (C...Biodiversity, Evolutionary ecology, Pollination, Tropical ecologyJuan Arroyo2022-09-28 11:54:37 View
12 Apr 2023
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Feeding and growth variations affect δ13C and δ15N budgets during ontogeny in a lepidopteran larva

Refining our understanding how nutritional conditions affect 13C and 15N isotopic fractionation during ontogeny in a herbivorous insect

Recommended by based on reviews by Anton Potapov and 1 anonymous reviewer

Using stable isotope fractionation to disentangle and understand the trophic positions of animals within the food webs they are embedded within has a long tradition in ecology (Post, 2002; Scheu, 2002). Recent years have seen increasing application of the method with several recent reviews summarizing past advancements in this field (e.g. Potapov et al., 2019; Quinby et al., 2020).

In their new manuscript, Charberet and colleagues (2023) set out to refine our understanding of the processes that lead to nitrogen and carbon stable isotope fractionation by investigating how herbivorous insect larvae (specifically, the noctuid moth Spodoptera littoralis) respond to varying nutritional conditions (from starving to ad libitum feeding) in terms of stable isotopes enrichment. Though the underlying mechanisms have been experimentally investigated before in terrestrial invertebrates (e.g. in wolf spiders; Oelbermann & Scheu, 2002), the elegantly designed and adequately replicated experiments by Charberet and colleagues add new insights into this topic. Particularly, the authors provide support for the hypotheses that (A) 15N is disproportionately accumulated under fast growth rates (i.e. when fed ad libitum) and that (B) 13C is accumulated under low growth rates and starvation due to depletion of 13C-poor fat tissues. Applying this knowledge to field samples where feeding conditions are usually not known in detail is not straightforward, but the new findings could still help better interpretation of field data under specific conditions that make starvation for herbivores much more likely (e.g. droughts).

Overall this study provides important methodological advancements for a better understanding of plant-herbivore interactions in a changing world.

REFERENCES 

Charberet, S., Maria, A., Siaussat, D., Gounand, I., & Mathieu, J. (2023). Feeding and growth variations affect δ13C and δ15N budgets during ontogeny in a lepidopteran larva. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.11.09.515573

Oelbermann, K., & Scheu, S. (2002). Stable Isotope Enrichment (δ 15N and δ 13C) in a Generalist Predator (Pardosa lugubris, Araneae: Lycosidae): Effects of Prey Quality. Oecologia, 130(3), 337–344. https://doi.org/10.1007/s004420100813

Post, D. M. (2002). Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology, 83(3), 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2

Potapov, A. M., Tiunov, A. V., & Scheu, S. (2019). Uncovering trophic positions and food resources of soil animals using bulk natural stable isotope composition. Biological Reviews, 94(1), 37–59. https://doi.org/10.1111/brv.12434

Quinby, B. M., Creighton, J. C., & Flaherty, E. A. (2020). Stable isotope ecology in insects: A review. Ecological Entomology, 45(6), 1231–1246. https://doi.org/10.1111/een.12934

Scheu, S. (2002). The soil food web: Structure and perspectives. European Journal of Soil Biology, 38(1), 11–20. https://doi.org/10.1016/S1164-5563(01)01117-7

Feeding and growth variations affect δ13C and δ15N budgets during ontogeny in a lepidopteran larvaSamuel M. Charberet, Annick Maria, David Siaussat, Isabelle Gounand, Jérôme Mathieu<p style="text-align: justify;">Isotopes are widely used in ecology to study food webs and physiology. The fractionation observed between trophic levels in nitrogen and carbon isotopes, explained by isotopic biochemical selectivity, is subject to ...Experimental ecology, Food webs, PhysiologyGregor Kalinkat2022-11-16 15:23:31 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 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
24 Mar 2023
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Rapid literature mapping on the recent use of machine learning for wildlife imagery

Review of machine learning uses for the analysis of images on wildlife

Recommended by based on reviews by Falk Huettmann and 1 anonymous reviewer

In the field of ecology, there is a growing interest in machine (including deep) learning for processing and automatizing repetitive analyses on large amounts of images collected from camera traps, drones and smartphones, among others. These analyses include species or individual recognition and classification, counting or tracking individuals, detecting and classifying behavior. By saving countless times of manual work and tapping into massive amounts of data that keep accumulating with technological advances, machine learning is becoming an essential tool for ecologists. We refer to recent papers for more details on machine learning for ecology and evolution (Besson et al. 2022, Borowiec et al. 2022, Christin et al. 2019, Goodwin et al. 2022, Lamba et al. 2019, Nazir & Kaleem 2021, Perry et al. 2022, Picher & Hartig 2023, Tuia et al. 2022, Wäldchen & Mäder 2018).

In their paper, Nakagawa et al. (2023) conducted a systematic review of the literature on machine learning for wildlife imagery. Interestingly, the authors used a method unfamiliar to ecologists but well-established in medicine called rapid review, which has the advantage of being quickly completed compared to a fully comprehensive systematic review while being representative (Lagisz et al., 2022). Through a rigorous examination of more than 200 articles, the authors identified trends and gaps, and provided suggestions for future work. Listing all their findings would be counterproductive (you’d better read the paper), and I will focus on a few results that I have found striking, fully assuming a biased reading of the paper. First, Nakagawa et al. (2023) found that most articles used neural networks to analyze images, in general through collaboration with computer scientists. A challenge here is probably to think of teaching computer vision to the generations of ecologists to come (Cole et al. 2023). Second, the images were dominantly collected from camera traps, with an increase in the use of aerial images from drones/aircrafts that raise specific challenges. Third, the species concerned were mostly mammals and birds, suggesting that future applications should aim to mitigate this taxonomic bias, by including, e.g., invertebrate species. Fourth, most papers were written by authors affiliated with three countries (Australia, China, and the USA) while India and African countries provided lots of images, likely an example of scientific colonialism which should be tackled by e.g., capacity building and the involvement of local collaborators. Last, few studies shared their code and data, which obviously impedes reproducibility. Hopefully, with the journals’ policy of mandatory sharing of codes and data, this trend will be reversed. 

REFERENCES

Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF (2022) Towards the fully automated monitoring of ecological communities. Ecology Letters, 25, 2753–2775. https://doi.org/10.1111/ele.14123

Borowiec ML, Dikow RB, Frandsen PB, McKeeken A, Valentini G, White AE (2022) Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13, 1640–1660. https://doi.org/10.1111/2041-210X.13901

Christin S, Hervet É, Lecomte N (2019) Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632–1644. https://doi.org/10.1111/2041-210X.13256

Cole E, Stathatos S, Lütjens B, Sharma T, Kay J, Parham J, Kellenberger B, Beery S (2023) Teaching Computer Vision for Ecology. https://doi.org/10.48550/arXiv.2301.02211

Goodwin M, Halvorsen KT, Jiao L, Knausgård KM, Martin AH, Moyano M, Oomen RA, Rasmussen JH, Sørdalen TK, Thorbjørnsen SH (2022) Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook†. ICES Journal of Marine Science, 79, 319–336. https://doi.org/10.1093/icesjms/fsab255

Lagisz M, Vasilakopoulou K, Bridge C, Santamouris M, Nakagawa S (2022) Rapid systematic reviews for synthesizing research on built environment. Environmental Development, 43, 100730. https://doi.org/10.1016/j.envdev.2022.100730

Lamba A, Cassey P, Segaran RR, Koh LP (2019) Deep learning for environmental conservation. Current Biology, 29, R977–R982. https://doi.org/10.1016/j.cub.2019.08.016

Nakagawa S, Lagisz M, Francis R, Tam J, Li X, Elphinstone A, Jordan N, O’Brien J, Pitcher B, Sluys MV, Sowmya A, Kingsford R (2023) Rapid literature mapping on the recent use of machine learning for wildlife imagery. EcoEvoRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.32942/X2H59D

Nazir S, Kaleem M (2021) Advances in image acquisition and processing technologies transforming animal ecological studies. Ecological Informatics, 61, 101212. https://doi.org/10.1016/j.ecoinf.2021.101212

Perry GLW, Seidl R, Bellvé AM, Rammer W (2022) An Outlook for Deep Learning in Ecosystem Science. Ecosystems, 25, 1700–1718. https://doi.org/10.1007/s10021-022-00789-y

Pichler M, Hartig F Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, n/a. https://doi.org/10.1111/2041-210X.14061

Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T (2022) Perspectives in machine learning for wildlife conservation. Nature Communications, 13, 792. https://doi.org/10.1038/s41467-022-27980-y

Wäldchen J, Mäder P (2018) Machine learning for image-based species identification. Methods in Ecology and Evolution, 9, 2216–2225. https://doi.org/10.1111/2041-210X.13075

Rapid literature mapping on the recent use of machine learning for wildlife imageryShinichi Nakagawa, Malgorzata Lagisz, Roxane Francis, Jessica Tam, Xun Li, Andrew Elphinstone, Neil R. Jordan, Justine K. O’Brien, Benjamin J. Pitcher, Monique Van Sluys, Arcot Sowmya, Richard T. Kingsford<p>1. Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, classify animals and their behaviours. Yet, we currently have a very few systematic liter...Behaviour & Ethology, Conservation biologyOlivier GimenezAnonymous2022-10-31 22:05:46 View
12 Mar 2023
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Different approaches to processing environmental DNA samples in turbid waters have distinct effects for fish, bacterial and archaea communities.

Processing environmental DNA samples in turbid waters from coastal lagoons

Recommended by based on reviews by David Murray-Stoker and Rutger De Wit

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

Different approaches to processing environmental DNA samples in turbid waters have distinct effects for fish, bacterial and archaea communities.Rachel Turba, Glory H. Thai, and David K Jacobs<p style="text-align: justify;">Coastal lagoons are an important habitat for endemic and threatened species in California that have suffered impacts from urbanization and increased drought. Environmental DNA has been promoted as a way to aid in th...Biodiversity, Community genetics, Conservation biology, Freshwater ecology, Marine ecology, Molecular ecologyClaudia Piccini David Murray-Stoker2022-06-20 20:31:51 View
01 Mar 2023
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Effects of adaptive harvesting on fishing down processes and resilience changes in predator-prey and tritrophic systems

Adaptive harvesting, “fishing down the food web”, and regime shifts

Recommended by based on reviews by Pierre-Yves HERNVANN and 1 anonymous reviewer

The mean trophic level of catches in world fisheries has generally declined over the 20th century, a phenomenon called "fishing down the food web" (Pauly et al. 1998). Several mechanisms have been proposed to explain this decline including the collapse of, or decline in, higher trophic level stocks leading to the inclusion of lower trophic level stocks in the fishery. Fishing down the food web may lead to a reduction in the resilience, i.e., the capacity to rebound from change, of the fished community, which is concerning given the necessity of resilience in the face of climate change. 

The practice of adaptive harvesting, which involves fishing stocks based on their availability, can also result in a reduction in the average trophic level of a fishery (Branch et al. 2010). Adaptive harvesting, similar to adaptive foraging, can affect the resilience of fisheries. Generally, adaptive foraging acts as a stabilizing force in communities (Valdovinos et al. 2010), however it is not clear how including harvesters as the adaptive foragers will affect the resilience of the system.

Tromeur and Loeuille (2023) analyze the effects of adaptively harvesting a trophic community. Using a system of ordinary differential equations representing a predator-prey model where both species are harvested, the researchers mathematically analyze the impact of increasing fishing effort and adaptive harvesting on the mean trophic level and resilience of the fished community. This is achieved by computing the equilibrium densities and equilibrium allocation of harvest effort.  In addition, the researchers numerically evaluate adaptive harvesting in a tri-trophic system (predator, prey, and resource). The study focuses on the effect of adaptively distributing harvest across trophic levels on the mean trophic level of catches, the propensity for regime shifts to occur, the ability to return to equilibrium after a disturbance, and the speed of this return. 

The results indicate that adaptive harvesting leads to a decline in the mean trophic level of catches, resulting in “fishing down the food web”. Furthermore, the study shows that adaptive harvesting may harm the overall resilience of the system. Similar results were observed numerically in a tri-trophic community.

While adaptive foraging is generally a stabilizing force on communities, the researchers found that adaptive harvesting can destabilize the harvested community. One of the key differences between adaptive foraging models and the model presented here, is that the harvesters do not exhibit population dynamics. This lack of a numerical response by the harvesters to decreasing population sizes of their stocks leads to regime shifts. The realism of a fishery that does not respond numerically to declining stock is debatable, however it is very likely that there will a least be significant delays due to social and economic barriers to leaving the fishery, that will lead to similar results.

This study is not unique in demonstrating the ability of adaptive harvesting to result in “fishing down the food web”. As pointed out by the researchers, the same results have been shown with several different model formulations (e.g., age and size structured models). Similarly, this study is not unique to showing that increasing adaptation speeds decreases the resilience of non-linear predator-prey systems by inducing oscillatory behaviours. Much of this can be explained by the destabilising effect of increasing interaction strengths on food webs (McCann et al. 1998). 

By employing a straightforward model, the researchers were able to demonstrate that adaptive harvesting, a common strategy employed by fishermen, can result in a decline in the average trophic level of catches, regime shifts, and reduced resilience in the fished community. While previous studies have observed some of these effects, the fact that the current study was able to capture them all with a simple model is notable. This modeling approach can offer insight into the role of human behavior on the complex dynamics observed in fisheries worldwide.

References

Branch, T. A., R. Watson, E. A. Fulton, S. Jennings, C. R. McGilliard, G. T. Pablico, D. Ricard, et al. 2010. The trophic fingerprint of marine fisheries. Nature 468:431–435. https://doi.org/10.1038/nature09528

Tromeur, E., and N. Loeuille. 2023. Effects of adaptive harvesting on fishing down processes and resilience changes in predator-prey and tritrophic systems. bioRxiv 290460, ver 5 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/290460

McCann, K., A. Hastings, and G.R. Huxel. 1998. Weak trophic interactions and the balance of nature. Nature 395: 794-798. https://doi.org/10.1038/27427

Pauly, D., V. Christensen, J. Dalsgaard, R. Froese, and F. Torres Jr. 1998. Fishing down marine food webs. Science 279:860–86. https://doi.org/10.1126/science.279.5352.860

Valdovinos, F.S., R. Ramos-Jiliberto, L. Garay-Naravez, P. Urbani, and J.A. Dunne. 2010. Consequences of adaptive behaviour for the structure and dynamics of food webs. Ecology Letters 13: 1546-1559. https://doi.org/10.1111/j.1461-0248.2010.01535.x

Effects of adaptive harvesting on fishing down processes and resilience changes in predator-prey and tritrophic systemsEric Tromeur, Nicolas Loeuille<p>Many world fisheries display a declining mean trophic level of catches. This "fishing down the food web" is often attributed to reduced densities of high-trophic-level species. We show here that the fishing down pattern can actually emerge from...Biodiversity, Community ecology, Food webs, Foraging, Population ecology, Theoretical ecologyAmanda Lynn Caskenette2022-05-03 21:09:35 View
28 Feb 2023
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Acoustic cues and season affect mobbing responses in a bird community

Two common European songbirds elicit different community responses with their mobbing calls

Recommended by based on reviews by 2 anonymous reviewers

Many bird species participate in mobbing in which individuals approach a predator while producing conspicuous vocalizations (Magrath et al. 2014). Mobbing is interesting to behavioral ecologists because of the complex array of costs of benefits. Costs range from the obvious risk of approaching a predator while drawing that predator’s attention to the more mundane opportunity costs of taking time away from other activities, such as foraging. Benefits may involve driving the predator to leave, teaching relatives to recognize predators, signaling quality to conspecifics, or others. An added layer of complexity in this system comes from the inter-specific interactions that often occur among different mobbing species (Magrath et al. 2014).

This study by Salis et al. (2023) explored the responses of a local bird community to mobbing calls produced by individuals of two common mobbing species in European forests, coal tits, and crested tits. Not only did they compare responses to these two different species, they assessed the impact of the number of mobbing individuals on the stimulus recordings, and they did so at two very different times of the year with different social contexts for the birds involved, winter (non-breeding) and spring (breeding). The experiment was well-designed and highly powered, and the authors tested and confirmed an important assumption of their design, and thus the results are convincing. It is clear that members of the local bird community responded differently to the two different species, and this result raises interesting questions about why these species differed in their tendency to attract additional mobbers. For instance, are species that recruit more co-mobbers more effective at recruiting because they are more reliable in their mobbing behavior (Magrath et al. 2014), more likely to reciprocate (Krams and Krama, 2002), or for some other reason? Hopefully this system, now of proven utility thanks to the current study, will be useful for following up on hypotheses such as these. Other convincing results, such as the higher rate of mobbing response in winter than in spring, also merit following up with further work.

Finally, their observation that playback of vocalizations of multiple individuals often elicited a more mobbing response that the playback of vocalizations of a single individual are interesting and consistent with other recent work indicating that groups of mobbers recruit more additional mobbers than do single mobbers (Dutour et al. 2021). However, as acknowledged in the manuscript, the design of the current study did not allow a distinction between the effect of multiple individuals signaling versus an effect of a stronger stimulus. Thus, this last result leaves the question of the effect of mobbing group size in these species open to further study.

REFERENCES

Dutour M, Kalb N, Salis A, Randler C (2021) Number of callers may affect the response to conspecific mobbing calls in great tits (Parus major). Behavioral Ecology and Sociobiology, 75, 29. https://doi.org/10.1007/s00265-021-02969-7

Krams I, Krama T (2002) Interspecific reciprocity explains mobbing behaviour of the breeding chaffinches, Fringilla coelebs. Proceedings of the Royal Society of London. Series B: Biological Sciences, 269, 2345–2350. https://doi.org/10.1098/rspb.2002.2155

Magrath RD, Haff TM, Fallow PM, Radford AN (2015) Eavesdropping on heterospecific alarm calls: from mechanisms to consequences. Biological Reviews, 90, 560–586. https://doi.org/10.1111/brv.12122

Salis A, Lena JP, Lengagne T (2023) Acoustic cues and season affect mobbing responses in a bird community. bioRxiv, 2022.05.05.490715, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.05.05.490715

Acoustic cues and season affect mobbing responses in a bird communityAmbre Salis, Jean Paul Lena, Thierry Lengagne<p>Heterospecific communication is common for birds when mobbing a predator. However, joining the mob should depend on the number of callers already enrolled, as larger mobs imply lower individual risks for the newcomer. In addition, some ‘communi...Behaviour & Ethology, Community ecology, Social structureTim Parker2022-05-06 09:29:30 View
20 Feb 2023
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Best organic farming deployment scenarios for pest control: a modeling approach

Towards model-guided organic farming expansion for crop pest management

Recommended by based on reviews by Julia Astegiano, Lionel Hertzog and Sylvain Bart

Reduce the impact the intensification of human activities has on the environmental is the challenge the humanity faces today, a major challenge that could be compared to climbing Everest without an oxygen supply. Indeed, over-population, pollution, burning fossil fuels, and deforestation are all evils which have had hugely detrimental effects on the environment such as climate change, soil erosion, poor air quality, and scarcity of drinking water to name but a few. In response to the ever-growing consumer demand, agriculture has intensified massively along with a drastic increase in the use of chemicals to ensure an adequate food supply while controlling crop pests. In this context, to address the disastrous effects of the intensive usage of pesticides on both human health and biodiversity, organic farming (OF) revealed as a miracle remedy with multiple benefits. Delattre et al. (2023) present a powerful modelling approach to decipher the crossed effects of the landscape structure and the OF expansion scenario on the pest abundance, both in organic and conventional (CF) crop fields. To this end, the authors ingeniously combined a grid-based landscape model with a spatially explicit predator-pest model. Based on an extensive in silico simulation process, they explore a diversity of landscape structures differing in their amount of semi-natural habitats (SHN) and in their fragmentation, to finally propose a ranking of various expansion scenarios according to the pest control methods in organic farming as well as to the pest and predators’ dissemination capacities. In total, 9 landscape structures (3 proportions of SHN x 3 fragmentation levels) were crossed with 3 expansion scenarios (RD = a random distribution of OF and CF in the grid; IP = isolated CF are converted; GP = CF within aggregates are converted), 4 pest management practices, 3 initial densities and 36 biological parameter combinations driving the predator’ and pest’s population dynamics. This exhaustive exploration of possible combinations of landscape and farming practices highlighted the main drivers of the various OF expansion scenarios, such as increased spillover of predators in isolated OF/CF fields, increased pest management efficiency in large patches of CF and the importance of the distance between OF and CF. In the end, this study brings to light the crucial role that landscape planning plays when OF practices have limited efficiency on pests. It also provides convincing arguments to the fact that converting to organic isolated CF as a priority seems to be the most promising scenario to limit pest densities in CF crops while improving predator to pest ratios (considered as a proxy of conservation biological control) in OF ones without increasing pest densities. Once further completed with model calibration validation based on observed life history traits data for both predators and pests, this work should be very helpful in sustaining policy makers to convince farmers of engaging in organic farming.

REFERENCES

Delattre T, Memah M-M, Franck P, Valsesia P, Lavigne C (2023) Best organic farming deployment scenarios for pest control: a modeling approach. bioRxiv, 2022.05.31.494006, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.05.31.494006

Best organic farming deployment scenarios for pest control: a modeling approachThomas Delattre, Mohamed-Mahmoud Memah, Pierre Franck, Pierre Valsesia, Claire Lavigne<p style="text-align: justify;">Organic Farming (OF) has been expanding recently around the world in response to growing consumer demand and as a response to environmental concerns. Its share of agricultural landscapes is expected to increase in t...Agroecology, Biological control, Landscape ecologySandrine Charles2022-06-03 11:41:14 View
03 Feb 2023
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The role of climate change and niche shifts in divergent range dynamics of a sister-species pair

Drivers of range expansion in a pair of sister grackle species

Recommended by based on reviews by 2 anonymous reviewers

The spatial distribution of a species is driven by both biotic and abiotic factors that may change over time (Soberón & Nakamura, 2009; Paquette & Hargreaves, 2021).  Therefore, species ranges are dynamic, especially in humanized landscapes where changes occur at high speeds (Sirén & Morelli, 2020). The distribution of many species is being reduced because of human impacts; however, some species are expanding their distributions, even over their niche (Lustenhouwer & Parker, 2022). One of the factors that may lead to a geographic niche expansion is behavioral flexibility (Mikhalevich et al., 2017), but the mechanisms determining range expansion through behavioral changes are not fully understood. 

The PCI Ecology study by Summers et al. (2023) uses a very large database on the current and historic distribution of two species of grackles that have shown different trends in their distribution. The great-tailed grackle has largely expanded its range over the 20th century, while the range of the boat-tailed grackle has remained very similar. They take advantage of this differential response in the distribution of the two species and run several analyses to test whether it was a change in habitat availability, in the realized niche, in habitat connectivity or in in the other traits or conditions that previously limited the species range, what is driving the observed distribution of the species. The study finds a change in the niche of great-tailed grackle, consistent with the high behavioral flexibility of the species.

The two reviewers and I have seen a lot of value in this study because 1) it addresses a very timely question, especially in the current changing world; 2) it is a first step to better understanding if behavioral attributes may affect species’ ability to change their niche; 3) it contrasts the results using several complementary statistical analyses, reinforcing their conclusions; 4) it is based on the preregistration Logan et al (2021), and any deviations from it are carefully explained and justified in the text and 5) the limitations of the study have been carefully discussed. It remains to know if the boat-tailed grackle has more limited behavioral flexibility than the great-tailed grackle, further confirming the results of this study.
 
References

Logan CJ, McCune KB, Chen N, Lukas D (2021) Implementing a rapid geographic range expansion - the role of behavior and habitat changes. http://corinalogan.com/Preregistrations/gxpopbehaviorhabitat.html

Lustenhouwer N, Parker IM (2022) Beyond tracking climate: Niche shifts during native range expansion and their implications for novel invasions. Journal of Biogeography, 49, 1481–1493. https://doi.org/10.1111/jbi.14395

Mikhalevich I, Powell R, Logan C (2017) Is behavioural flexibility evidence of cognitive complexity? How evolution can inform comparative cognition. Interface Focus, 7, 20160121. https://doi.org/10.1098/rsfs.2016.0121

Paquette A, Hargreaves AL (2021) Biotic interactions are more often important at species’ warm versus cool range edges. Ecology Letters, 24, 2427–2438. https://doi.org/10.1111/ele.13864

Sirén APK, Morelli TL (2020) Interactive range-limit theory (iRLT): An extension for predicting range shifts. Journal of Animal Ecology, 89, 940–954. https://doi.org/10.1111/1365-2656.13150

Soberón J, Nakamura M (2009) Niches and distributional areas: Concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106, 19644–19650. https://doi.org/10.1073/pnas.0901637106

Summers JT, Lukas D, Logan CJ, Chen N (2022) The role of climate change and niche shifts in divergent range dynamics of a sister-species pair. EcoEvoRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/879pe

The role of climate change and niche shifts in divergent range dynamics of a sister-species pairJeremy Summers, Dieter Lukas, Corina J. Logan, Nancy Chen<p>---This is a POST-STUDY manuscript for the PREREGISTRATION, which received in principle acceptance in 2020 from Dr. Sebastián González (reviewed by Caroline Nieberding, Tim Parker, and Pizza Ka Yee Chow; <a href="https://doi.org/10.24072/pci.ec...Behaviour & Ethology, Biogeography, Dispersal & Migration, Human impact, Landscape ecology, Preregistrations, Species distributionsEsther Sebastián González2022-05-26 20:07:33 View
27 Jan 2023
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Spatial heterogeneity of interaction strength has contrasting effects on synchrony and stability in trophic metacommunities

How does spatial heterogeneity affect stability of trophic metacommunities?

Recommended by based on reviews by Phillip P.A. Staniczenko, Ludek Berec and Diogo Provete

The temporal or spatial variability in species population sizes and interaction strength of animal and plant communities has a strong impact on aggregate community properties (for instance biomass), community composition, and species richness (Kokkoris et al. 2002). Early work on spatial and temporal variability strongly indicated that asynchronous population and environmental fluctuations tend to stabilise community structures and diversity (e.g. Holt 1984, Tilman and Pacala 1993, McCann et al. 1998, Amarasekare and Nisbet 2001). Similarly, trophic networks might be stabilised by spatial heterogeneity (Hastings 1977) and an asymmetry of energy flows along food chains (Rooney et al. 2006). The interplay between temporal, spatial, and trophic heterogeneity within the meta-community concept has got much less interest. In the recent preprint in PCI Ecology, Quévreux et al. (2023) report that Spatial heterogeneity of interaction strength has contrasting effects on synchrony and stability in trophic metacommunities. These authors rightly notice that the interplay between trophic and spatial heterogeneity might induce contrasting effects depending on the internal dynamics of the system. Their contribution builds on prior work (Quévreux et al. 2021a, b) on perturbed trophic cascades.

I found this paper particularly interesting because it is in the, now century-old, tradition to show that ecological things are not so easy. Since the 1930th, when Nicholson and Baily and others demonstrated that simple deterministic population models might generate stability and (pseudo-)chaos ecologists have realised that systems triggered by two or more independent processes might be intrinsically unpredictable and generate different outputs depending on the initial parameter settings. This resembles the three-body problem in physics. The present contribution of Quévreux et al. (2023) extends this knowledge to an example of a spatially explicit trophic model. Their main take-home message is that asymmetric energy flows in predator–prey relationships might have contrasting effects on the stability of metacommunities receiving localised perturbations. Stability is context dependent.

Of course, the work is merely a theoretical exercise using a simplistic trophic model. It demands verification with field data. Nevertheless, we might expect even stronger unpredictability in more realistic multitrophic situations. Therefore, it should be seen as a proof of concept. Remember that increasing trophic connectance tends to destabilise food webs (May 1972). In this respect, I found the final outlook to bioconservation ambitious but substantiated. Biodiversity management needs a holistic approach focusing on all aspects of ecological functioning. I would add the need to see stability and biodiversity within an evolutionary perspective.        

References

Amarasekare P, Nisbet RM (2001) Spatial Heterogeneity, Source‐Sink Dynamics, and the Local Coexistence of Competing Species. The American Naturalist, 158, 572–584. https://doi.org/10.1086/323586

Hastings A (1977) Spatial heterogeneity and the stability of predator-prey systems. Theoretical Population Biology, 12, 37–48. https://doi.org/10.1016/0040-5809(77)90034-X

Holt RD (1984) Spatial Heterogeneity, Indirect Interactions, and the Coexistence of Prey Species. The American Naturalist, 124, 377–406. https://doi.org/10.1086/284280

Kokkoris GD, Jansen VAA, Loreau M, Troumbis AY (2002) Variability in interaction strength and implications for biodiversity. Journal of Animal Ecology, 71, 362–371. https://doi.org/10.1046/j.1365-2656.2002.00604.x

May RM (1972) Will a Large Complex System be Stable? Nature, 238, 413–414. https://doi.org/10.1038/238413a0

McCann K, Hastings A, Huxel GR (1998) Weak trophic interactions and the balance of nature. Nature, 395, 794–798. https://doi.org/10.1038/27427

Quévreux P, Barbier M, Loreau M (2021) Synchrony and Perturbation Transmission in Trophic Metacommunities. The American Naturalist, 197, E188–E203. https://doi.org/10.1086/714131

Quévreux P, Pigeault R, Loreau M (2021) Predator avoidance and foraging for food shape synchrony and response to perturbations in trophic metacommunities. Journal of Theoretical Biology, 528, 110836. https://doi.org/10.1016/j.jtbi.2021.110836

Quévreux P, Haegeman B, Loreau M (2023) Spatial heterogeneity of interaction strength has contrasting effects on synchrony and stability in trophic metacommunities. hal-03829838, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://hal.science/hal-03829838

Rooney N, McCann K, Gellner G, Moore JC (2006) Structural asymmetry and the stability of diverse food webs. Nature, 442, 265–269. https://doi.org/10.1038/nature04887

Tilman D, Pacala S (1993) The maintenance of species richness in plant communities. In: Ricklefs, R.E., Schluter, D. (eds) Species Diversity in Ecological Communities: Historical and Geographical Perspectives. University of Chicago Press, pp. 13–25.

Spatial heterogeneity of interaction strength has contrasting effects on synchrony and stability in trophic metacommunitiesPierre Quévreux, Bart Haegeman and Michel Loreau<p>&nbsp;Spatial heterogeneity is a fundamental feature of ecosystems, and ecologists have identified it as a factor promoting the stability of population dynamics. In particular, differences in interaction strengths and resource supply between pa...Dispersal & Migration, Food webs, Interaction networks, Spatial ecology, Metacommunities & Metapopulations, Theoretical ecologyWerner Ulrich2022-10-26 13:38:34 View