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28 Mar 2019
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Direct and transgenerational effects of an experimental heat wave on early life stages in a freshwater snail

Escargots cooked just right: telling apart the direct and indirect effects of heat waves in freashwater snails

Recommended by based on reviews by Amanda Lynn Caskenette, Kévin Tougeron and arnaud sentis

Amongst the many challenges and forms of environmental change that organisms face in our era of global change, climate change is perhaps one of the most straightforward and amenable to investigation. First, measurements of day-to-day temperatures are relatively feasible and accessible, and predictions regarding the expected trends in Earth surface temperature are probably some of the most reliable we have. It appears quite clear, in particular, that beyond the overall increase in average temperature, the heat waves locally experienced by organisms in their natural habitats are bound to become more frequent, more intense, and more long-lasting [1]. Second, it is well appreciated that temperature is a major environmental factor with strong impacts on different facets of organismal development and life-history [2-4]. These impacts have reasonably clear mechanistic underpinnings, with definite connections to biochemistry, physiology, and considerations on energetics. Third, since variation in temperature is a challenge already experienced by natural populations across their current and historical ranges, it is not a completely alien form of environmental change. Therefore, we already learnt quite a lot about it in several species, and so did the species, as they may be expected to have evolved dedicated adaptive mechanisms to respond to elevated temperatures. Last, but not least, temperature is quite amenable to being manipulated as an experimental factor.
For all these reasons, experimental studies of the consequences of increased temperature hit some of a sweetspot and are a source of very nice research, in many different organisms. The work by Leicht and Seppala [5] complements a sequence of earlier studies by this group, using the freshwater snail Lymnaea stagnalis as their model system [6-7].
In the present study, the authors investigate how a heat wave (a period of abnormally elevated temperature, here 25°C versus a normal 15°C) may have indirect effects on the next generation, through maternal effects. They question whether such indirect effects exist, and if they exist, how they compare, in terms of effect size, with the (more straightforward) direct effects observed in individuals that directly experience a heat wave. Transgenerational effects are well-known to occur following periods of physiological stress, and might thus have non negligible contributions to the overall effect of warming.
In this freshwater snail, heat has very strong direct effects: mortality increases at high temperature, but survivors grow much bigger, with a greater propensity to lay eggs and a (spectacular) three-fold increase in the number of eggs laid [6]. Considering that, it is easy to consider that transgenerational effects should be small game. And indeed, the present study also observes the big and obvious direct effects of elevated temperature: higher mortality, but greater propensity to oviposit. However, it was also found that the eggs were smaller if from mothers exposed to high temperature, with a correspondingly smaller size of hatchlings. This suggests that a heat wave causes the snails to lay more eggs, but smaller ones, reminiscent of a size-number trade-off. Unfortunately, clutch size could not be measured in this experiment, so this cannot be investigated any further. For this trait, the indirect effect may indeed be regarded as small game : eggs and hatchlings were about 15 % smaller, an effect size pretty small compared to the mammoth direct positive effect of temperature on shell length (see Figure 4 ; and also [6]). The same is true for developmental time (Figure 3).
However, for some traits the story was different. In particular, it was found that the (smaller) eggs produced from heated mothers were more likely to hatch by almost 10% (Figure 2). Here the indirect effect not only goes against the direct effect (hatching rate is lower at high temperature), but it also has similar effect size. As a consequence, taking into account both the indirect and direct effects, hatching success is essentially the same at 15°C and 25°C (Figure 2). Survival also had comparable effect sizes for direct and indirect effects. Indeed, survival was reduced by about 20% regardless of whom endured the heat stress (the focal individual or her mother; Figure 4). Interestingly, the direct and indirect effects were not quite cumulative: if a mother experienced a heat wave, heating up the offspring did not do much more damage, as though the offspring were ‘adapted’ to the warmer conditions (but keep in mind that, surprisingly, the authors’ stats did not find a significant interaction; Table 2).
At the end of the day, even though at first heat seems a relatively simple and understandable component of environmental change, this study shows how varied its effects can be effects on different components of individual fitness. The overall impact most likely is a mix of direct and indirect effects, of shifts along allocation trade-offs, and of maladaptive and adaptive responses, whose overall ecological significance is not so easy to grasp. That said, this study shows that direct and indirect (maternal) effects can sometimes go against one another and have similar intensities. Indirect effects should therefore not be overlooked in this kind of studies. It also gives a hint of what an interesting challenge it is to understand the adaptive or maladaptive nature of organism responses to elevated temperatures, and to evaluate their ultimate fitness consequences.

References

[1] Meehl, G. A., & Tebaldi, C. (2004). More intense, more frequent, and longer lasting heat waves in the 21st century. Science (New York, N.Y.), 305(5686), 994–997. doi: 10.1126/science.1098704
[2] Adamo, S. A., & Lovett, M. M. E. (2011). Some like it hot: the effects of climate change on reproduction, immune function and disease resistance in the cricket Gryllus texensis. The Journal of Experimental Biology, 214(Pt 12), 1997–2004. doi: 10.1242/jeb.056531
[3] Deutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B., & Naylor, R. L. (2018). Increase in crop losses to insect pests in a warming climate. Science (New York, N.Y.), 361(6405), 916–919. doi: 10.1126/science.aat3466
[4] Sentis, A., Hemptinne, J.-L., & Brodeur, J. (2013). Effects of simulated heat waves on an experimental plant–herbivore–predator food chain. Global Change Biology, 19(3), 833–842. doi: 10.1111/gcb.12094
[5] Leicht, K., & Seppälä, O. (2019). Direct and transgenerational effects of an experimental heat wave on early life stages in a freshwater snail. BioRxiv, 449777, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/449777
[6] Leicht, K., Seppälä, K., & Seppälä, O. (2017). Potential for adaptation to climate change: family-level variation in fitness-related traits and their responses to heat waves in a snail population. BMC Evolutionary Biology, 17(1), 140. doi: 10.1186/s12862-017-0988-x
[7] Leicht, K., Jokela, J., & Seppälä, O. (2013). An experimental heat wave changes immune defense and life history traits in a freshwater snail. Ecology and Evolution, 3(15), 4861–4871. doi: 10.1002/ece3.874

Direct and transgenerational effects of an experimental heat wave on early life stages in a freshwater snailKatja Leicht, Otto Seppälä<p>Global climate change imposes a serious threat to natural populations of many species. Estimates of the effects of climate change‐mediated environmental stresses are, however, often based only on their direct effects on organisms, and neglect t...Climate changevincent calcagno2018-10-22 22:19:22 View
01 Mar 2022
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Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring

How to evaluate and interpret the contribution of species turnover and interaction rewiring when comparing ecological networks?

Recommended by ORCID_LOGO based on reviews by Ignasi Bartomeus and 1 anonymous reviewer

A network includes a set of vertices or nodes (e.g., species in an interaction network), and a set of edges or links (e.g., interactions between species). Whether and how networks vary in space and/or time are questions often addressed in ecological research. 

Two ecological networks can differ in several extents: in that species are different in the two networks and establish new interactions (species turnover), or in that species that are present in both networks establish different interactions in the two networks (rewiring). The ecological meaning of changes in network structure is quite different according to whether species turnover or interaction rewiring plays a greater role. Therefore, much attention has been devoted in recent years on quantifying and interpreting the relative changes in network structure due to species turnover and/or rewiring.

Poisot et al. (2012) proposed to partition the global variation in structure between networks, \( \beta_{WN} \) (WN = Whole Network) into two terms: \( \beta_{OS} \) (OS = Only Shared species) and \( \beta_{ST} \) (ST = Species Turnover), such as \( \beta_{WN} = \beta_{OS} + \beta_{ST} \).

The calculation lays on enumerating the interactions between species that are common or not to two networks, as illustrated on Figure 1 for a simple case. Specifically, Poisot et al. (2012) proposed to use a Sorensen type measure of network dissimilarity, i.e., \( \beta_{WN} = \frac{a+b+c}{(2a+b+c)/2} -1=\frac{b+c}{2a+b+c} \) , where \( a \) is the number of interactions shared between the networks, while \( b \) and \( c \) are interaction numbers unique to one and the other network, respectively. \( \beta_{OS} \) is calculated based on the same formula, but only for the subnetworks including the species common to the two networks, in the form \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a_{OS}+b_{OS}+c_{OS}} \) (e.g., Fig. 1). \( \beta_{ST} \) is deduced by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and represents in essence a "dissimilarity in interaction structure introduced by dissimilarity in species composition" (Poisot et al. 2012).

Figure 1. Ecological networks exemplified in Fründ (2021) and discussed in Poisot (2022). a is the number of shared links (continuous lines in right figures), while b+c is the number of edges unique to one or the other network (dashed lines in right figures).

Alternatively, Fründ (2021) proposed to define \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a+b+c} \) and \( \beta_{ST} = \frac{b_{ST}+c_{ST}}{2a+b+c} \), where \( b_{ST}=b-b_{OS} \)  and \( c_{ST}=c-c_{OS} \) , so that the components \( \beta_{OS} \) and \( \beta_{ST} \) have the same denominator. In this way, Fründ (2021) partitioned the count of unique \( b+c=b_{OS}+b_{ST}+c_{ST} \) interactions, so that \( \beta_{OS} \) and \( \beta_{ST} \) sums to \( \frac{b_{OS}+c_{OS}+b_{ST}+c_{ST}}{2a+b+c} = \frac{b+c}{2a+b+c} = \beta_{WN} \). Fründ (2021) advocated that this partition allows a more sensible comparison of \( \beta_{OS} \) and \( \beta_{ST} \), in terms of the number of links that contribute to each component.

For instance, let us consider the networks 1 and 2 in Figure 1 (left panel) such as \( a_{OS}=2 \) (continuous lines in right panel), \( b_{ST} + c_{ST} = 1 \) and \( b_{OS} + c_{OS} = 1 \) (dashed lines in right panel), and thereby \( a = 2 \), \( b+c=2 \), \( \beta_{WN} = 1/3 \). Fründ (2021) measured \( \beta_{OS}=\beta_{ST}=1/6 \) and argued that it is appropriate insofar as it reflects that the number of unique links in the OS and ST components contributing to network dissimilarity (dashed lines) are actually equal. Conversely, the formula of Poisot et al. (2012) yields \( \beta_{OS}=1/5 \), hence \( \beta_{ST} = \frac{1}{3}-\frac{1}{5}=\frac{2}{15}<\beta_{OS} \). Fründ (2021) thus argued that the method of Poisot tends to underestimate the contribution of species turnover.

To clarify and avoid misinterpretation of the calculation of \( \beta_{OS} \) and \( \beta_{ST} \) in Poisot et al. (2012), Poisot (2022) provides a new, in-depth mathematical analysis of the decomposition of \( \beta_{WN} \). Poisot et al. (2012) quantify in \( \beta_{OS} \) the actual contribution of rewiring in network structure for the subweb of common species. Poisot (2022) thus argues that \( \beta_{OS} \) relates only to the probability of rewiring in the subweb, while the definition of \( \beta_{OS} \) by Fründ (2021) is relative to the count of interactions in the global network (considered in denominator), and is thereby dependent on both rewiring probability and species turnover. Poisot (2022) further clarifies the interpretation of \( \beta_{ST} \). \( \beta_{ST} \) is obtained by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and thus represents the influence of species turnover in terms of the relative architectures of the global networks and of the subwebs of shared species. Coming back to the example of Fig.1., the Poisot et al. (2012) formula posits that \( \frac{\beta_{ST}}{\beta_{WN}}=\frac{2/15}{1/3}=2/5 \), meaning that species turnover contributes two-fifths of change in network structure, while rewiring in the subweb of common species contributed three fifths.  Conversely, the approach of Fründ (2021) does not compare the architectures of global networks and of the subwebs of shared species, but considers the relative contribution of unique links to network dissimilarity in terms of species turnover and rewiring. 

Poisot (2022) concludes that the partition proposed in Fründ (2021) does not allow unambiguous ecological interpretation of rewiring. He provides guidelines for proper interpretation of the decomposition proposed in Poisot et al. (2012).

References

Fründ J (2021) Dissimilarity of species interaction networks: how to partition rewiring and species turnover components. Ecosphere, 12, e03653. https://doi.org/10.1002/ecs2.3653

Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361. https://doi.org/10.1111/ele.12002

Poisot T (2022) Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. EcoEvoRxiv Preprints, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/gxhu2

Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiringTimothée Poisot<p style="text-align: justify;">Despite having established its usefulness in the last ten years, the decomposition of ecological networks in components allowing to measure their β-diversity retains some methodological ambiguities. Notably, how to ...Biodiversity, Interaction networks, Theoretical ecologyFrançois Munoz2021-07-31 00:18:41 View
17 May 2023
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Distinct impacts of food restriction and warming on life history traits affect population fitness in vertebrate ectotherms

Effect of food conditions on the Temperature-Size Rule

Recommended by based on reviews by Wolf Blanckenhorn and Wilco Verberk

Temperature-size rule (TSR) is a phenomenon of plastic changes in body size in response to temperature, originally observed in more than 80% of ectothermic organisms representing various groups (Atkinson 1994). In particular, ectotherms were observed to grow faster and reach smaller size at higher temperature and grow slower and achieve larger size at lower temperature. This response has fired the imagination of researchers since its invention, due to its counterintuitive pattern from an evolutionary perspective (Berrigan and Charnov 1994). The main question to be resolved is: why do organisms grow fast and achieve smaller sizes under more favourable conditions (= relatively higher temperature), while they grow longer and achieve larger sizes under less favourable conditions (relatively lower temperature), if larger size means higher fitness, while longer development may be risky? 

This evolutionary conundrum still awaits an ultimate explanation (Angilletta Jr et al. 2004; Angilletta and Dunham 2003; Verberk et al. 2021). Although theoretical modelling has shown that such a growth pattern can be achieved as a response to temperature alone, with a specific combination of energetic parameters and external mortality (Kozłowski et al. 2004), it has been suggested that other temperature-dependent environmental variables may be the actual drivers of this pattern. One of the most frequently invoked variable is the relative oxygen availability in the environment (e.g., Atkinson et al. 2006; Audzijonyte et al. 2019; Verberk et al. 2021; Woods 1999), which decreases with temperature increase. Importantly, this effect is more pronounced in aquatic systems (Forster et al. 2012). However, other temperature-dependent parameters are also being examined in the context of their possible effect on TSR induction and strength.

Food availability is among the interfering factors in this regard. In aquatic systems, nutritional conditions are generally better at higher temperature, while a range of relatively mild thermal conditions is considered. However, there are no conclusive results so far on how nutritional conditions affect the plastic body size response to acute temperature changes. A study by Bazin et al. (2023) examined this particular issue, the effects of food and temperature on TSR, in medaka fish. An important value of the study was to relate the patterns found to fitness. This is a rare and highly desirable approach since evolutionary significance of any results cannot be reliably interpreted unless shown as expressed in light of fitness. 

The authors compared the body size of fish kept at 20°C and 30°C under conditions of food abundance or food restriction. The results showed that the TSR (smaller body size at 30°C compared to 20°C) was observed in both food treatments, but the effect was delayed during fish development under food restriction. Regarding the relevance to fitness, increased temperature resulted in more eggs laid but higher mortality, while food restriction increased survival but decreased the number of eggs laid in both thermal treatments. Overall, food restriction seemed to have a more severe effect on development at 20°C than at 30°C, contrary to the authors’ expectations. 

I found this result particularly interesting. One possible interpretation, also suggested by the authors, is that the relative oxygen availability, which was not controlled for in this study, could have affected this pattern. According to theoretical predictions confirmed in quite many empirical studies so far, oxygen restriction is more severe at higher temperatures. Perhaps for these particular two thermal treatments and in the case of the particular species studied, this restriction was more severe for organismal performance than the food restriction. This result is an example that all three variables, temperature, food and oxygen, should be taken into account in future studies if the interrelationship between them is to be understood in the context of TSR. It also shows that the reasons for growing smaller in warm may be different from those for growing larger in cold, as suggested, directly or indirectly, in some previous studies (Hessen et al. 2010; Leiva et al. 2019). 

Since medaka fish represent predatory vertebrates, the results of the study contribute to the issue of global warming effect on food webs, as the authors rightly point out. This is an important issue because the general decrease in the size or organisms in the aquatic environment with global warming is a fact (e.g., Daufresne et al. 2009), while the question of how this might affect entire communities is not trivial to resolve (Ohlberger 2013). 

REFERENCES

Angilletta Jr, M. J., T. D. Steury & M. W. Sears, 2004. Temperature, growth rate, and body size in ectotherms: fitting pieces of a life–history puzzle. Integrative and Comparative Biology 44:498-509. https://doi.org/10.1093/icb/44.6.498

Angilletta, M. J. & A. E. Dunham, 2003. The temperature-size rule in ectotherms: Simple evolutionary explanations may not be general. American Naturalist 162(3):332-342. https://doi.org/10.1086/377187

Atkinson, D., 1994. Temperature and organism size – a biological law for ectotherms. Advances in Ecological Research 25:1-58. https://doi.org/10.1016/S0065-2504(08)60212-3

Atkinson, D., S. A. Morley & R. N. Hughes, 2006. From cells to colonies: at what levels of body organization does the 'temperature-size rule' apply? Evolution & Development 8(2):202-214 https://doi.org/10.1111/j.1525-142X.2006.00090.x

Audzijonyte, A., D. R. Barneche, A. R. Baudron, J. Belmaker, T. D. Clark, C. T. Marshall, J. R. Morrongiello & I. van Rijn, 2019. Is oxygen limitation in warming waters a valid mechanism to explain decreased body sizes in aquatic ectotherms? Global Ecology and Biogeography 28(2):64-77 https://doi.org/10.1111/geb.12847

Bazin, S., Hemmer-Brepson, C., Logez, M., Sentis, A. & Daufresne, M. 2023. Distinct impacts of food restriction and warming on life history traits affect population fitness in vertebrate ectotherms. HAL, ver.3  peer-reviewed and recommended by PCI Ecology. https://hal.inrae.fr/hal-03738584v3

Berrigan, D. & E. L. Charnov, 1994. Reaction norms for age and size at maturity in response to temperature – a puzzle for life historians. Oikos 70:474-478. https://doi.org/10.2307/3545787

Daufresne, M., K. Lengfellner & U. Sommer, 2009. Global warming benefits the small in aquatic ecosystems. Proceedings of the National Academy of Sciences USA 106(31):12788-93 https://doi.org/10.1073/pnas.0902080106

Forster, J., A. G. Hirst & D. Atkinson, 2012. Warming-induced reductions in body size are greater in aquatic than terrestrial species. Proceedings of the National Academy of Sciences of the United States of America 109(47):19310-19314. https://doi.org/10.1073/pnas.1210460109

Hessen, D. O., P. D. Jeyasingh, M. Neiman & L. J. Weider, 2010. Genome streamlining and the elemental costs of growth. Trends in Ecology & Evolution 25(2):75-80. https://doi.org/10.1016/j.tree.2009.08.004

Kozłowski, J., M. Czarnoleski & M. Dańko, 2004. Can optimal resource allocation models explain why ectotherms grow larger in cold? Integrative and Comparative Biology 44(6):480-493. https://doi.org/10.1093/icb/44.6.480

Leiva, F. P., P. Calosi & W. C. E. P. Verberk, 2019. Scaling of thermal tolerance with body mass and genome size in ectotherms: a comparison between water- and air-breathers. Philosophical Transactions of the Royal Society B 374:20190035. https://doi.org/10.1098/rstb.2019.0035

Ohlberger, J., 2013. Climate warming and ectotherm body szie - from individual physiology to community ecology. Functional Ecology 27:991-1001. https://doi.org/10.1111/1365-2435.12098

Verberk, W. C. E. P., D. Atkinson, K. N. Hoefnagel, A. G. Hirst, C. R. Horne & H. Siepel, 2021. Shrinking body sizes in response to warming: explanations for the temperature-size rule with special emphasis on the role of oxygen. Biological Reviews 96:247-268. https://doi.org/10.1111/brv.12653

Woods, H. A., 1999. Egg-mass size and cell size: effects of temperature on oxygen distribution. American Zoologist 39:244-252. https://doi.org/10.1093/icb/39.2.244

Distinct impacts of food restriction and warming on life history traits affect population fitness in vertebrate ectothermsSimon Bazin, Claire Hemmer-Brepson, Maxime Logez, Arnaud Sentis, Martin Daufresne<p>The reduction of body size with warming has been proposed as the third universal response to global warming, besides geographical and phenological shifts. Observed body size shifts in ectotherms are mostly attributed to the temperature size rul...Climate change, Experimental ecology, Freshwater ecology, Phenotypic plasticity, Population ecologyAleksandra Walczyńska2022-07-27 09:28:29 View
06 Dec 2019
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Does phenology explain plant-pollinator interactions at different latitudes? An assessment of its explanatory power in plant-hoverfly networks in French calcareous grasslands

The role of phenology for determining plant-pollinator interactions along a latitudinal gradient

Recommended by based on reviews by Ignasi Bartomeus, Phillip P.A. Staniczenko and 1 anonymous reviewer

Increased knowledge of what factors are determining species interactions are of major importance for our understanding of dynamics and functionality of ecological communities [1]. Currently, when ongoing temperature modifications lead to changes in species temporal and spatial limits the subject gets increasingly topical. A species phenology determines whether it thrive or survive in its environment. However, as the phenologies of different species are not necessarily equally affected by environmental changes, temporal or spatial mismatches can occur and affect the species-species interactions in the network [2] and as such the full network structure.
In this preprint by Manincor et al. [3] the authors explore the effect of phenology overlap on a large network of species interactions in calcareous grasslands in France. They analyze if and how this effect varies along a latitudinal gradient using empirical data on six plant-hoverfly networks. When comparing ecological network along gradients a well-known problem is that the network metrics is dependent on network size [4]. Therefore, instead of focusing on complete network structure the authors here focus on the factors that determine the probability of interactions and interaction frequency (number of visits). The authors use Bayesian Structural Equation Models (SEM) to link the interaction probability and number of visits to phenology overlap and species abundance. SEM is a multivariate technique that can be used to test several hypotheses and evaluate multiple causal relationships using both observed and latent variables to explain some other observed variables. The authors provide a nice description of the approach for this type of study system. In addition, the study also tests whether phenology affects network compartmentalization, by analyzing species subgroups using a latent block model (LBM) which is a clustering method particularly well-suited for weighted networks.
The authors identify phenology overlap as an important determinant of plant-pollinator interactions, but also conclude this factor alone is not sufficient to explain the species interactions. Species abundances was important for number of visits. Plant phenology drives the duration of the phenology overlap between plant and hoverflies in the studied system. This in turn influences either the probability of interaction or the expected number of visits, as well as network compartmentalization. Longer phenologies correspond to lower modularity inferring less constrained interactions, and shorter phenologies correspond to higher modularity inferring more constrained interactions.
What make this study particularly interesting is the presentation of SEMs as an innovative approach to compare networks of different sizes along environmental gradients. The authors show that these methods can be a useful tool when the aim is to understand the structure of plant-pollinator networks and data is varying in complexities. During the review process the authors carefully addressed to the comments from the two reviewers and the manuscript improved during the process. Both reviewers have expertise highly relevant for the research performed and the development of the manuscript. In my opinion this is a highly interesting and valuable piece of work both when it comes to the scientific question and the methodology. I look forward to further follow this research.

References

[1] Pascual, M., and Dunne, J. A. (Eds.). (2006). Ecological networks: linking structure to dynamics in food webs. Oxford University Press.
[2] Parmesan, C. (2007). Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Global Change Biology, 13(9), 1860-1872. doi: 10.1111/j.1365-2486.2007.01404.x
[3] de Manincor, N., Hautekeete, N., Piquot, Y., Schatz, B., Vanappelghem, C. and Massol, F. (2019). Does phenology explain plant-pollinator interactions at different latitudes? An assessment of its explanatory power in plant-hoverfly networks in French calcareous grasslands. Zenodo, 2543768, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.5281/zenodo.2543768
[4] Staniczenko, P. P., Kopp, J. C., and Allesina, S. (2013). The ghost of nestedness in ecological networks. Nature communications, 4, 1391. doi: 10.1038/ncomms2422

Does phenology explain plant-pollinator interactions at different latitudes? An assessment of its explanatory power in plant-hoverfly networks in French calcareous grasslandsNatasha de Manincor, Nina Hautekeete, Yves Piquot, Bertrand Schatz, Cédric Vanappelghem, François Massol<p>For plant-pollinator interactions to occur, the flowering of plants and the flying period of pollinators (i.e. their phenologies) have to overlap. Yet, few models make use of this principle to predict interactions and fewer still are able to co...Interaction networks, Pollination, Statistical ecologyAnna Eklöf2019-01-18 19:02:13 View
06 Oct 2020
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Does space use behavior relate to exploration in a species that is rapidly expanding its geographic range?

Explore and move: a key to success in a changing world?

Recommended by based on reviews by Joe Nocera, Marion Nicolaus and Laure Cauchard

Changes in the spatial range of many species are one of the major consequences of the profound alteration of environmental conditions due to human activities. Some species expand, sometimes spectacularly during invasions; others decline; some shift. Because these changes result in local biodiversity loss (whether local species go extinct or are replaced by colonizing ones), understanding the factors driving spatial range dynamics appears crucial to predict biodiversity dynamics. Identifying the factors that shape individual movement is a main step towards such understanding. The study described in this preregistration (McCune et al. 2020) falls within this context by testing possible links between individual exploration behaviour and movements related to daily space use in an avian study model currently rapidly expanding, the great-tailed grackle (Quiscalus mexicanus).

Movement and exploration: which direction(s) for the link between exploration and dispersal?
Individuals are known to differ in their tendency to explore the environment (Réale et al. 2007; Wolf and Weissing 2012) and therefore in their motivation to move. Accordingly, exploration has been shown to relate to dispersal behaviour, i.e. movements between breeding sites (Dingemanse et al. 2003, Le Galliard et al. 2011, Rasmussen and Belk 2012; reviews in Cote et al. 2010, Ronce et al. 2012). Yet, the mechanisms underlying this link often remain unclear, due to the correlative nature of the data. A classical assumption is that dispersers may benefit from a high capacity to explore, allowing them to familiarize quicker with their new environment once reached, thus alleviating dispersal costs (Bonte et al. 2012). The association between dispersal and exploration would in this case result from selection for this combination of traits (Ronce et al. 2012), even though dispersal event itself may be independent from (and precede the effect of) exploration behaviour. Alternatively (but not exclusively), dispersal may simply be the final outcome of longer movements by individuals exploring larger ranges (Badyaev et al. 1996, Schliehe-Diecks et al. 2012). In the absence of easy ways to manipulate dispersal behaviour, on the one hand, and exploration tendency, on the other hand, investigating detailed, small-scale individual movements in relation to exploration should thus shed light on which processes may yield the observed relations between exploration as an individual personality trait and large-scale, long-term movements, such as dispersal, underlying species range dynamics.
In this project, the exploration behaviour of grackles will be measured in controlled conditions using standardized tests in captivity (McCune et al. 2019) before individuals are released and their daily space use behaviour will then be measured using remote tracking over long time periods (McCune et al. 2020). Importantly, these coupled measures will be obtained for individuals captured in three different populations: within the historical range of the species, in the middle of its expanding range and at the edge of the range (McCune et al. 2020). Therefore, the project will test (i) whether daily space use of individuals is linked to their intrinsic exploration tendency and (ii) whether space use differs between individuals from different populations along the expanding range. The preregistration echoes a complementary project by the same team that will focus on exploration and test (iii) whether exploration tendency differs between individuals from these different populations. Taken together, these three analyses will therefore provide solid background information to assess the role of exploration in the individuals’ decisions leading to movement and range dynamics in this species.
As underlined in the preregistration, previous studies addressing the links between individual exploration behaviour and movements have mostly focused on dispersal. A first type of studies have (as will be done here) measured exploration behaviour of individuals, often in captivity (Dingemanse et al. 2003, Korsten et al. 2013) but also in the wild (Rasmussen and Belk 2012, Debeffe et al. 2013), and related these measures to subsequent dispersal behaviour. The (often implicit) underlying assumption is that more exploratory individuals will be more likely to move further, explore different habitats and thus end up breeding farther than less explorative ones. In other words, exploration tendency precedes and drives dispersal. Sometimes, exploratory behaviour is measured on individuals of known dispersal status, i.e. after the dispersal event (Hoset et al. 2011), in which case selection for certain exploration phenotypes among dispersers may already have occurred. Besides this first approach, another type of studies have measured ‘exploration’ behaviour under the form of prospecting movements of individuals and linked these movements to subsequent dispersal (often in the context of habitat selection). While these studies were in the past based on direct thus potentially biased observations (Reed et al. 1999), they now rely more and more on technological advances using (miniaturized) remote tracking devices (Ponchon et al. 2013) that provide far more complete and unbiased movement data, and sometimes also complementary measures of individuals’ internal state. In this case, the implicit assumption is that individuals prospecting farther and/or in more habitat patches will be more likely to settle in a site located farther away from their departure site, because of a more exhaustive sampling of possible sites allowing individuals to identify higher-quality sites (Badyaev et al. 1996). In other words, exploration tendency would not directly lead to higher movements or longer distances, but would allow individuals to optimize their habitat choice among more numerous options, thus leading to an increased dispersal probability or distance; the relation between exploration and dispersal would thus be indirect. Prospecting studies address more closely the underlying mechanisms of movement; however, they cannot easily separate intrinsic individual exploratory tendency from the prospecting movements themselves, with potential feedback effects of the information already gathered on future exploration of other sites or patches, thus on subsequent movements.
By focusing on individual daily space use movements as a mechanistic approach to understand large-scale movements potentially involved in colonization and range expansion, the grackle study described in this preregistration (McCune et al. 2020) will thus contribute to bridge the knowledge gaps between exploration and dispersal. By linking exploration measures obtained from a battery of standardized tests conducted in controlled conditions to individual daily space use and movements recorded in the wild, the grackle project is set in between previous studies addressing the links between exploration and dispersal: it will document exploration in a separate and independent context with respect to the movements themselves, and it will use a mechanistic view of detailed movements by the same individuals in the wild to explore potential implications for dispersal and range expansion. Testing differences between the three study populations over the species range will indeed inform about potential large-scale, population implications of among-individual variation in the link between exploration and movements. Because this study will only measure already settled adult individuals whose previous history is unknown, there will nevertheless be no direct possible exploration of the link with either previous or subsequent dispersal behaviour. Thus, the potential links studied here relate more directly to post-dispersal benefits of exploration for an optimal exploitation of the new environment. Yet, if exploration is a life-long personality trait linked to daily movement patterns, it may also relate to natal dispersal movements in young individuals.

Evolutionary and conservation perspectives
If the results of the project reveal that exploration tendency and daily space use movements are indeed linked, and that individuals from populations across the species range differ in these traits, new questions will emerge. A first question would be whether such among-individual differences are at the origin of range expansion or rather one of its consequences since, again, we deal with correlative data here. In other words, individuals may differ in exploration tendency, and this may confer them different ability to move around, find and colonize new habitats; or individuals may show differences in exploration following arrival in a new habitat, either because more explorative individuals gain fitness benefits and are thus selected, or because of behavioural plasticity and post-colonization adjustment of exploration behaviour when facing new ecological and social conditions in the new environment. Another open question relates to the link between daily space use and dispersal: is dispersal a by-product of higher daily movements that allow individuals to discover new favorable places where to settle? Exploring this link could involve measuring just fledged individuals before natal dispersal occurs and/or individuals chosen according to their own dispersal history, and this would then imply long-term population monitoring as an efficient (but constraining) tool to address such questions. Finally, assessing the fitness consequences of the link between exploration and space use behaviour, and whether these consequences differ between populations along the range expansion, would also be needed to understand the contribution of this link to the invasion success of this species.
The study model chosen for this project is a rapidly expanding species. Importantly, however, and as emphasized in the preregistration, documenting links between exploration and daily space use patterns as well as differences between populations with different trajectories can provide crucial information in general to understand population persistence in response to global climate and landscape changes, both regarding invasion ability or extinction risk. The information should be key to assess the probability that a species may decline, persist or expand in studies addressing biodiversity and community dynamics in a changing world.

References

Badayev, A. V., Martin, T. E and Etges, W. J. 1996. Habitat sampling and habitat selection by female wild turkeys: ecological correlates and reproductive consequences. Auk 113: 636-646. doi: https://doi.org/10.2307/4088984
Bonte, D. et al. 2012. Costs of dispersal. Biological Reviews 87: 290-312. doi: https://doi.org/10.1111/j.1469-185X.2011.00201.x
Cote, J., Clobert, J., Brodin, T., Fogarty, S. and Sih, A. 2010. Personality-dependent dispersal: characterization, ontogeny and consequences for spatially structured populations. Philosophical Transactions of the Royal Society B 365: 4065-4576. doi: https://doi.org/10.1098/rstb.2010.0176
Debeffe, L., Morellet, N., Cargnelutti, B., Lourtet, B., Coulon, A., Gaillard, J.-M., Bon, R. and Hewison A. J. M. 2013. Exploration as a key component of natal dispersal: dispersers explore more than philopatric individuals in roe deer. Animal Behaviour 86: 143-151. doi: https://doi.org/10.1016/j.anbehav.2013.05.005
Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. and Drent, P. J. 2003. Natal dispersal and personalities in great tits (Parus major). Proceedings of the Royal Society B 270: 741-747. doi: https://doi.org/10.1098/rspb.2002.2300
Hoset, K. S., Ferchaud, A.-L., Dufour, F., Mersch, D., Cote, J. and Le Galliard, J.-F. 2011. Natal dispersal correlates with behavioral traits that are not consistent across early life stages. Behavioral Ecology 22: 176–183. doi: https://doi.org/10.1093/beheco/arq188
Korsten, P., van Overveld, T., Adriaensen, F. and Matthysen, E. 2013. Genetic integration of local dispersal and exploratory behaviour in a wild bird. Nature Communications 4: 2362. doi: https://doi.org/10.1038/ncomms3362
Le Galliard, J.-F., Rémy, A., Ims, R. A. and Lambin, X. 2011. Patterns and processes of dispersal behaviour in arvicoline rodents. Molecular Ecology 21: 505-523. doi: https://doi.org/10.1111/j.1365-294X.2011.05410.x
McCune K, Ross C, Folsom M, Bergeron L, Logan CJ. 2020. Does space use behavior relate to exploration in a species that is rapidly expanding its geographic range? http://corinalogan.com/Preregistrations/gspaceuse.html In principle acceptance by PCI Ecology of the version on 23 Sep 2020 https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/gspaceuse.Rmd.
McCune K, MacPherson M, Rowney C, Bergeron L, Folsom M, Logan CJ. 2019. Is behavioral flexibility linked with exploration, but not boldness, persistence, or motor diversity? (http://corinalogan.com/Preregistrations/gexploration.html) In principle acceptance by PCI Ecology of the version on 27 Mar 2019 https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/gexploration.Rmd
Ponchon, A., Grémillet, D., Doligez, B., Chambert, T., Tveraa, T., González-Solís, J. and Boulinier, T. 2013. Tracking prospecting movements involved in breeding habitat selection: insights, pitfalls and perspectives. Methods in Ecology and Evolution 4: 143-150. doi: https://doi.org/10.1111/j.2041-210x.2012.00259.x
Rasmussen, J. E. and Belk, M. C. 2012. Dispersal behavior correlates with personality of a North American fish. Current Zoology 58: 260–270. doi: https://doi.org/10.1093/CZOOLO%2F58.2.260
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. and Dingemanse, N. J. 2007. Integrating animal temperament within ecology and evolution. Biological Reviews 82: 291-318. doi: https://doi.org/10.1111/j.1469-185x.2007.00010.x
Reed, J. M., Boulinier, T., Danchin, E. and Oring, L. W. 1999. Informed dispersal: prospecting by birds for breeding sites. Current Ornithology 15: 189-259. doi: https://doi.org/10.1007/978-1-4757-4901-4_5
Ronce, O. and Clobert, J. 2012. Dispersal syndromes. pp. 119-138 In Dispersal Ecology and Evolution (eds. Clobert, J., Baguette, M., Benton, T. G. and Bullock, J. M.), pp. 119-138. Oxford University Press.
Schliehe-Diecks, S., Eberle, M. and Kappeler, P. M. 2012. Walk the line - dispersal movements of gray mouse lemurs (Microcebus murinus). Behavioral Ecology and Sociobiology 66: 1175-1185. doi: https://dx.doi.org/10.1007%2Fs00265-012-1371-y
Wolf, M. and Weissing, F. J. 2012. Animal personalities: consequences for ecology and evolution. Trends in Ecology and Evolution 27: 452-461. doi: https://doi.org/10.1016/j.tree.2012.05.001

Does space use behavior relate to exploration in a species that is rapidly expanding its geographic range?Kelsey B. McCune, Cody Ross, Melissa Folsom, Luisa Bergeron, Corina LoganGreat-tailed grackles (Quiscalus mexicanus) are rapidly expanding their geographic range (Wehtje 2003). Range expansion could be facilitated by consistent behavioural differences between individuals on the range edge and those in other parts of th...Behaviour & Ethology, Biological invasions, Conservation biology, Habitat selection, Phenotypic plasticity, Preregistrations, Spatial ecology, Metacommunities & MetapopulationsBlandine Doligez2019-09-30 19:27:40 View
31 Jan 2019
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Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed grackles

From cognition to range dynamics: advancing our understanding of macroecological patterns

Recommended by based on reviews by 2 anonymous reviewers

Understanding the distribution of species on earth is one of the fundamental challenges in ecology and evolution. For a long time, this challenge has mainly been addressed from a correlative point of view with a focus on abiotic factors determining a species abiotic niche (classical bioenvelope models; [1]). It is only recently that researchers have realized that behaviour and especially plasticity in behaviour may play a central role in determining species ranges and their dynamics [e.g., 2-5]. Blaisdell et al. propose to take this even one step further and to analyse how behavioural flexibility and possibly associated causal cognition impacts range dynamics.
The current preregistration is integrated in an ambitious long-term research plan that aims at addressing the above outlined question and focuses specifically on investigating whether more behaviourally flexible individuals are better at deriving causal inferences. The model system the authors plan on using are Great-tailed Grackles which have expanded their range into North America during the last century. The preregistration by Blaisdell et al. is a great example of the future of scientific research: it includes conceptual models, alternative hypotheses and testable predictions along with a sound sampling and analysis plan and embraces the principles of Open Science. Overall, the research the authors propose is fascinating and of highest relevance, as it aims at bridging scales from the microscopic mechanisms that underlie animal behaviour to macroscopic, macroecological consequences (see also [3]). I am very much looking forward to the results the authors will report.

References
[1] Elith, J. & Leathwick, J. R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40: 677-697. doi: 10.1146/annurev.ecolsys.110308.120159
[2] Kubisch, A.; Degen, T.; Hovestadt, T. & Poethke, H. J. (2013) Predicting range shifts under global change: the balance between local adaptation and dispersal. Ecography 36: 873-882. doi: 10.1111/j.1600-0587.2012.00062.x
[3] Keith, S. A. & Bull, J. W. (2017) Animal culture impacts species' capacity to realise climate-driven range shifts. Ecography, 40: 296-304. doi: 10.1111/ecog.02481
[4] Sullivan, L. L.; Li, B.; Miller, T. E.; Neubert, M. G. & Shaw, A. K. (2017) Density dependence in demography and dispersal generates fluctuating invasion speeds. Proc. Natl. Acad. Sci. USA, 114: 5053-5058. doi: 10.1073/pnas.1618744114
[5] Fronhofer, E. A.; Nitsche, N. & Altermatt, F. (2017) Information use shapes the dynamics of range expansions into environmental gradients. Glob. Ecol. Biogeogr. 26: 400-411. doi: 10.1111/geb.12547

Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed gracklesAaron Blaisdell, Zoe Johnson-Ulrich, Luisa Bergeron, Carolyn Rowney, Benjamin Seitz, Kelsey McCune, Corina LoganThis PREREGISTRATION has undergone one round of peer reviews. We have now revised the preregistration and addressed reviewer comments. The DOI was issued by OSF and refers to the whole GitHub repository, which contains multiple files. The specific...Behaviour & Ethology, Preregistrations, ZoologyEmanuel A. Fronhofer2018-08-20 11:09:48 View
30 Mar 2021
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Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed grackles

From cognition to range dynamics – and from preregistration to peer-reviewed preprint

Recommended by based on reviews by Laure Cauchard and 1 anonymous reviewer

In 2018 Blaisdell and colleagues set out to study how causal cognition may impact large scale macroecological patterns, more specifically range dynamics, in the great-tailed grackle (Fronhofer 2019). This line of research is at the forefront of current thought in macroecology, a field that has started to recognize the importance of animal behaviour more generally (see e.g. Keith and Bull (2017)). Importantly, the authors were pioneering the use of preregistrations in ecology and evolution with the aim of improving the quality of academic research.

Now, nearly 3 years later, it is thanks to their endeavour of making research better that we learn that the authors are “[...] unable to speculate about the potential role of causal cognition in a species that is rapidly expanding its geographic range.” (Blaisdell et al. 2021; page 2). Is this a success or a failure? Every reader will have to find an answer to this question individually and there will certainly be variation in these answers as becomes clear from the referees’ comments. In my opinion, this is a success story of a more stringent and transparent approach to doing research which will help us move forward, both methodologically and conceptually.

References

Fronhofer (2019) From cognition to range dynamics: advancing our understanding of macroe-
cological patterns. Peer Community in Ecology, 100014. doi: https://doi.org/10.24072/pci.ecology.100014

Keith, S. A. and Bull, J. W. (2017) Animal culture impacts species' capacity to realise climate-driven range shifts. Ecography, 40: 296-304. doi: https://doi.org/10.1111/ecog.02481

Blaisdell, A., Seitz, B., Rowney, C., Folsom, M., MacPherson, M., Deffner, D., and Logan, C. J. (2021) Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed grackles. PsyArXiv, ver. 5 peer-reviewed and recommended by Peer community in Ecology. doi: https://doi.org/10.31234/osf.io/z4p6s

Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed gracklesBlaisdell A, Seitz B, Rowney C, Folsom M, MacPherson M, Deffner D, Logan CJ<p>Behavioral flexibility, the ability to change behavior when circumstances change based on learning from previous experience, is thought to play an important role in a species’ ability to successfully adapt to new environments and expand its geo...PreregistrationsEmanuel A. Fronhofer2020-11-27 09:49:55 View
02 Aug 2021
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Dynamics of Fucus serratus thallus photosynthesis and community primary production during emersion across seasons: canopy dampening and biochemical acclimation

Towards a better understanding of the effects of self-shading on Fucus serratus populations

Recommended by ORCID_LOGO based on reviews by Gwenael Abril, Francesca Rossi and 1 anonymous reviewer

The importance of the vertical structure of vegetation cover for the functioning, management and conservation of ecosystems has received particular attention from ecologists in the last decades. Canopy architecture has many implications for light extinction coefficient, temperature variation reduction, self-shading which are all key parameters for the structuring and functioning of different ecosystems such as grasslands [1,2], forests [3,4], phytoplankton communities [5, 6], macroalgal populations [7] and even underwater animal forests such as octocoral communities [8].

This research topic, therefore, benefits from a large body of literature and the facilitative role of self-shadowing is no longer in question. However, it is always puzzling to note that some of the most common ecosystems turn out to be amongst the least known. This is precisely the case of the Fucus serratus communities which are widespread in Northeast Atlantic along the Atlantic coast of Europe from Svalbard to Portugal, as well as Northwest Atlantic & Gulf of St. Lawrence, easily accessible at low tide, but which have comparatively received less attention than more emblematic macro-algal communities such as Laminariales.

The lack of attention paid to these most common Fucales is particularly critical as some species such as F. serratus are proving to be particularly vulnerable to environmental change, leading to a predicted northward retreat from its current southern boundary [9].

In the present study [10], the authors showed the importance of the vegetation cover in resisting tide-induced environmental stresses. The canopy of F. serratus mitigates stress levels experienced in the lower layers during emersion, while various acclimation strategies take over to maintain the photosynthetic apparatus in optimal conditions.

They hereby highlight adaptation mechanisms to the extreme environment represented by the intertidal zone. These adaptation strategies were expected and similar mechanisms had been shown at the cellular level previously [11]. The earliest studies on the subject have shown that the structure of the bottom, the movement of water, and light availability all "influence the distribution of Fucaceae and disturb the regularity of their fine zonation, which itself is caused by the most important factor, desiccation", as Zaneveld states in his review [12]. He observed that the causes of the zonal distribution of marine algae are numerous, and identified several points of interest such as the relative period of emersion, the rapidity of desiccation, the loss of water, and the thickness of the cell walls.

The present study thus highlights the existence of facilitative mechanisms associated with F. serratus canopy and nicely confirms previous work with in situ observations. It also highlights the importance of the vegetative cover in combating desiccation and introduces the dampening effect as a facilitating mechanism.

The effect of the vegetation cover can sometimes even be felt beyond its immediate area of influence. A recent study shows that ground-level ozone is significantly reduced by the combined effects of canopy shading and turbulence [4]. Below the canopy, the light intensity becomes sufficiently low which inhibits ozone formation due to the decrease in the rates of hydroxyl radical formation and the rates of conversion of nitrogen dioxide to nitrogen oxide by photolysis. In addition, reductions in light levels associated with foliage promote ozone-destroying reactions between plant-emitted species, such as nitric oxide and/or alkenes, and ozone itself. The reduction in diffusivity slows the upward transport of surface emitted species, partially decoupling the area under the canopy from the rest of the atmosphere.

By analogy with the work of Makar et al [4], and in the light of the results provided by the authors of this study, one may wonder whether the canopy dampening of F. serratus communities (and other common fucoids widely distributed on our coasts) might not also influence atmospheric chemistry, both at the Earth's surface and in the atmospheric boundary layer. The lack of accumulation of reactive oxygen species under the canopy found by the authors is consistent with this hypothesis and suggests that the damping effect of F. serratus may well have much wider consequences than expected.

References

[1] Jurik TW, Kliebenstein H (2000) Canopy Architecture, Light Extinction and Self-Shading of a Prairie Grass, Andropogon Gerardii. The American Midland Naturalist, 144, 51–65. http://www.jstor.org/stable/3083010

[2] Mitchley J, Willems JH (1995) Vertical canopy structure of Dutch chalk grasslands in relation to their management. Vegetatio, 117, 17–27. https://doi.org/10.1007/BF00033256

[3] Kane VR, Gillespie AR, McGaughey R, Lutz JA, Ceder K, Franklin JF (2008) Interpretation and topographic compensation of conifer canopy self-shadowing. Remote Sensing of Environment, 112, 3820–3832. https://doi.org/10.1016/j.rse.2008.06.001

[4] Makar PA, Staebler RM, Akingunola A, Zhang J, McLinden C, Kharol SK, Pabla B, Cheung P, Zheng Q (2017) The effects of forest canopy shading and turbulence on boundary layer ozone. Nature Communications, 8, 15243. https://doi.org/10.1038/ncomms15243

[5] Shigesada N, Okubo A (1981) Analysis of the self-shading effect on algal vertical distribution in natural waters. Journal of Mathematical Biology, 12, 311–326. https://doi.org/10.1007/BF00276919

[6] Barros MP, Pedersén M, Colepicolo P, Snoeijs P (2003) Self-shading protects phytoplankton communities against H2O2-induced oxidative damage. Aquatic Microbial Ecology, 30, 275–282. https://doi.org/10.3354/ame030275

[7] Ørberg SB, Krause-Jensen D, Mouritsen KN, Olesen B, Marbà N, Larsen MH, Blicher ME, Sejr MK (2018) Canopy-Forming Macroalgae Facilitate Recolonization of Sub-Arctic Intertidal Fauna and Reduce Temperature Extremes. Frontiers in Marine Science, 5. https://doi.org/10.3389/fmars.2018.00332

[8] Nelson H, Bramanti L (2020) From Trees to Octocorals: The Role of Self-Thinning and Shading in Underwater Animal Forests. In: Perspectives on the Marine Animal Forests of the World (eds Rossi S, Bramanti L), pp. 401–417. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-57054-5_12

[9] Jueterbock A, Kollias S, Smolina I, Fernandes JMO, Coyer JA, Olsen JL, Hoarau G (2014) Thermal stress resistance of the brown alga Fucus serratus along the North-Atlantic coast: Acclimatization potential to climate change. Marine Genomics, 13, 27–36. https://doi.org/10.1016/j.margen.2013.12.008

[10] Migné A, Duong G, Menu D, Davoult D, Gévaert F (2021) Dynamics of Fucus serratus thallus photosynthesis and community primary production during emersion across seasons: canopy dampening and biochemical acclimation. HAL, hal-03079617, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://hal.archives-ouvertes.fr/hal-03079617

[11] Lichtenberg M, Kühl M (2015) Pronounced gradients of light, photosynthesis and O2 consumption in the tissue of the brown alga Fucus serratus. New Phytologist, 207, 559–569. https://doi.org/10.1111/nph.13396

[12] Zaneveld JS (1937) The Littoral Zonation of Some Fucaceae in Relation to Desiccation. Journal of Ecology, 25, 431–468. https://doi.org/10.2307/2256204

Dynamics of Fucus serratus thallus photosynthesis and community primary production during emersion across seasons: canopy dampening and biochemical acclimationAline Migné, Gwendoline Duong, Dominique Menu, Dominique Davoult & François Gévaert<p style="text-align: justify;">The brown alga <em>Fucus serratus</em> forms dense stands on the sheltered low intertidal rocky shores of the Northeast Atlantic coast. In the southern English Channel, these stands have proved to be highly producti...Marine ecologyCédric Hubas2021-01-05 16:24:02 View
20 Oct 2021
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Eco-evolutionary dynamics further weakens mutualistic interaction and coexistence under population decline

Doomed by your partner: when mutualistic interactions are like an evolutionary millstone around a species’ neck

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Mutualistic interactions are the weird uncles of population and community ecology. They are everywhere, from the microbes aiding digestion in animals’ guts to animal-pollination services in ecosystems; They increase productivity through facilitation; They fascinate us when small birds pick the teeth of a big-mouthed crocodile. Yet, mutualistic interactions are far less studied and understood than competition or predation. Possibly because we are naively convinced that there is no mystery here: isn’t it obvious that mutualistic interactions necessarily facilitate species coexistence? Since mutualistic species benefit from one another, if one species evolves, the other should just follow, isn’t that so?

It is not as simple as that, for several reasons. First, because simple mutualistic Lotka-Volterra models showed that most of the time mutualistic systems should drift to infinity and be unstable (e.g. Goh 1979). This is not what happens in natural populations, so something is missing in simple models. At a larger scale, that of communities, this is even worse, since we are still far from understanding the link between the topology of mutualistic networks and the stability of a community. Second, interactions are context-dependent: mutualistic species exchange resources, and thus from the point of view of one species the interaction is either beneficial or not, depending on the net gain of energy (e.g. Holland and DeAngelis 2010). In other words, considering interactions as mutualistic per se is too caricatural. Third, since evolution is blind, the evolutionary response of a species to an environmental change can have any effect on its mutualistic partner, and not necessarily a neutral or positive effect. This latter reason is particularly highlighted by the paper by A. Weinbach et al. (2021).

Weinbach et al. considered a simple two-species mutualistic Lotka-Volterra model and analyzed the evolutionary dynamics of a trait controlling for the rate of interaction between the two species by using the classical Adaptive Dynamics framework. They showed that, depending on the form of the trade-off between this interaction trait and its effect on the intrinsic growth rate, several situations can occur at evolutionary equilibrium: species can stably coexist and maintain their interaction, or the interaction traits can evolve to zero where species can coexist without any interactions.

Weinbach et al. then investigated the fate of the two-species system if a partner species is strongly affected by environmental change, for instance, a large decrease of its growth rate. Because of the supposed trade-off between the interaction trait and the growth rate, the interaction trait in the focal species tends to decrease as an evolutionary response to the decline of the partner species. If environmental change is too large, the interaction trait can evolve to zero and can lead the partner species to extinction. An “evolutionary murder”.

Even though Weinbach et al. interpreted the results of their model through the lens of plant-pollinators systems, their model is not specific to this case. On the contrary, it is very general, which has advantages and caveats. By its generality, the model is informative because it is a proof of concept that the evolution of mutualistic interactions can have unexpected effects on any category of mutualistic systems. Yet, since the model lacks many specificities of plant-pollinator interactions, it is hard to evaluate how their result would apply to plant-pollinators communities.

I wanted to recommend this paper as a reminder that it is certainly worth studying the evolution of mutualistic interactions, because i) some unexpected phenomenons can occur, ii) we are certainly too naive about the evolution and ecology of mutualistic interactions, and iii) one can wonder to what extent we will be able to explain the stability of mutualistic communities without accounting for the co-evolutionary dynamics of mutualistic species.

References

Goh BS (1979) Stability in Models of Mutualism. The American Naturalist, 113, 261–275. http://www.jstor.org/stable/2460204.

Holland JN, DeAngelis DL (2010) A consumer–resource approach to the density-dependent population dynamics of mutualism. Ecology, 91, 1286–1295. https://doi.org/10.1890/09-1163.1

Weinbach A, Loeuille N, Rohr RP (2021) Eco-evolutionary dynamics further weakens mutualistic interaction and coexistence under population decline. bioRxiv, 570580, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/570580

Eco-evolutionary dynamics further weakens mutualistic interaction and coexistence under population declineAvril Weinbach, Nicolas Loeuille, Rudolf P. Rohr<p style="text-align: justify;">With current environmental changes, evolution can rescue declining populations, but what happens to their interacting species? Mutualistic interactions can help species sustain each other when their environment wors...Coexistence, Eco-evolutionary dynamics, Evolutionary ecology, Interaction networks, Pollination, Theoretical ecologySylvain Billiard2019-09-05 11:29:45 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