Humans are dramatically modifying many aspects of our planet via increasing concentrations of carbon dioxide in the atmosphere, patterns of land-use change, and unsustainable exploitation of the planet’s resources. These changes impact the abundance of species of wild organisms, with winners and losers. Identifying how different species and groups of species are influenced by anthropogenic activity in different biomes, continents, and habitats, has become a pressing scientific question with many publications reporting analyses of disparate data on species population sizes. Many conclusions are based on the linear analysis of rather short time series of organismal abundances.
 
There has been particular interest in how arthropods are impacted by environmental change, with several recent papers reporting contradictory results. To investigate why these contradictions might arise, Duchenne et al. (2022) conducted an analysis of four published data sets along with a series of experimental analyses of simulated time series to examine the power of widely used statistical analyses to gain inference on temporal trends. Their important paper reveals that accurate inference on dynamics, particularly of species that exhibit large temporal fluctuations in abundance, requires time series that are substantially longer than are typically collected, as well as careful thought as to whether linear models are appropriate. Linear analyses of short time series are susceptible to providing unreliable inference as trends can be strongly influenced by points at either end of the time series. 
 
Duchenne et al.’s paper provides important insight on the conditions when strong inference on temporal trends of arthropod (and other species) abundances can be made, and when they should be treated with caution. They do not doubt that many insect and arachnid species are changing their abundances, and that patterns in these changes may vary spatially. What their results do say is that we should treat grand claims of population recovery or rapid declines apparently to extinction with caution when they are based on short time series, particularly of species that show significant boom and bust dynamics. In many ways, these results are not unexpected, but it is nice to see such careful and thoughtful analyses and interpretation. More data are required for most arthropod species before clear assessments of abundance trends can be made. Given our reliance on many arthropods for food, pollination, and numerous ecosystem services, and the ability of other species to spread devastating human diseases such as dengue and malaria, it is advisable that we slow our modification of their habitats while additional data are collected to allow us to better characterise the trajectory of arthropod populations to understand what the consequences of our actions on the natural world are likely to be.  
 
References

Duchenne F, Porcher E, Mihoub J-B, Loïs G, Fontaine C (2022) Controversy over the decline of arthropods: a matter of temporal baseline? bioRxiv, 2022.02.09.479422, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.02.09.479422

Climate change is accelerating (IPCC 2022), and so applies ever stronger selective pressures on biodiversity (Segan et al. 2016). Possible responses include range shifts or adaptations to new climatic conditions (Bellard et al. 2012), but there is still much uncertainty about the extent of most species' adaptive capacities and the impact of extreme climatic events.
 
The pine processionary is a major pest of pine trees in the Mediterranean area. It is notably one of the few species for which a clear link between recent climate change and its northward expansion has been established (Battisti et al. 2005), and as such is often considered as globally benefitting from climate change. However, recent results show a retraction of its range at the southern limit (Bourougaaoui et al. 2021), exposed to high warming (+1.4°C in Tunisia since 1901 as opposed to +1.12°C on average in the Northern hemisphere) and extreme summer temperature events (Verner et al. 2013). Thus, it is possible that the species' adaptive abilities are being challenged at the southern limit of its native range by the magnitude of observed climate change.
 
In this work, Bourougaaoui et al. (2022) investigate how climate change over the last 30 years has impacted the reproductive success of the pine processionary moth in Tunisia. A major methodological interest of this study is that they used data both from historical collections and from recent samplings, which raised a challenge for running a longitudinal analysis as sampling locations differed between the two periods. By applying a grouping method to local climatic data, the authors were able to define several large climatic clusters within the country, and analyze long-term data from different sites within the same clusters. They find that both fecundity and hatching rate decreased over the period, while at the same time both the average temperature increased and climate variability increased. One of the main conclusions is that recurrent episodes of extreme heat during summer might have a larger impact than the long-term increase of average temperature, which strongly echoes how the intensification of weather extremes is currently proving one of the most important dimensions of climate change.
 
However, a most interesting hypothesis also arises from the analysis of the differences between climatic clusters: preexisting adaptations to heat, for instance, phenological shifts that allow the most sensitive stages to develop earlier in the season before the extreme heat events are most likely to occur, might actually reduce impacts in the historically warmest areas. Thus the greatest climate vulnerability might not always stand where one expects it.
 
References

Battisti A, Stastny M, Netherer S, Robinet C, Schopf A, Roques A, Larsson S (2005) Expansion of Geographic Range in the Pine Processionary Moth Caused by Increased Winter Temperatures. Ecological Applications, 15, 2084–2096. https://doi.org/10.1890/04-1903

Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecology Letters, 15, 365–377. https://doi.org/10.1111/j.1461-0248.2011.01736.x

Bourougaaoui A, Ben Jamâa ML, Robinet C (2021) Has North Africa turned too warm for a Mediterranean forest pest because of climate change? Climatic Change, 165, 46. https://doi.org/10.1007/s10584-021-03077-1

Bourougaaoui A, Robinet C, Jamaa MLB, Laparie M (2022) Effects of climate warming on the pine processionary moth at the southern edge of its range: a retrospective analysis on egg survival in Tunisia. bioRxiv, 2021.08.17.456665, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.08.17.456665

IPCC. 2022. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. In Press.

Segan DB, Murray KA, Watson JEM (2016) A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Global Ecology and Conservation, 5, 12–21. https://doi.org/10.1016/j.gecco.2015.11.002

Verner D (2013) Tunisia in a Changing Climate : Assessment and Actions for Increased Resilience and Development. World Bank, Washington, DC. https://doi.org/10.1596/978-0-8213-9857-9  

One of the long-standing questions in evolutionary ecology is on the mechanisms involved in arms race coevolution. One way to address this question is to understand the conditions under which one species evolves traits in response to the presence of a second species and so on. However, specialized pairwise interactions are by far less common in nature than interactions involving a higher number of interacting species (Bascompte, Jordano 2013). While interactions between large sets of species are the norm rather than the exception in mutualistic (pollination, seed dispersal), and antagonist (herbivory, parasitism) relationships, few is known on the way species identify, process, and respond to information provided by other interacting species under field conditions (Schaefer, Ruxton 2011). 

Zu et al. (2020) addressed this general question by developing an interesting information theory-based approach that hypothesized conditional entropy in chemical communication plays a role as proxy of fitness in plant-herbivore communities. More specifically, plant fitness was assumed to be related to the efficiency to code signals by plant species, and herbivore fitness to the capacity to decode plant signals. In this way, from the plant perspective, the elaboration of plant signals that elude decoding by herbivores is expected to be favored, as herbivores are expected to attack plants with simple chemical signals. The empirical observation upon which the model was tested was the redundancy in volatile organic compounds (VOC) found across plant species in a plant-herbivore community. Interestingly, Zu et al.’s model predicted successfully that VOC redundancy in the plant community associates with increased conditional entropy, which conveys herbivore confusion and plant protection against herbivory. In this way, plant species that evolve VOCs already present in the community might be benefitted, ultimately leading to the patterns of VOC redundancy commonly observed in nature.

Bass & Kessler performed a series of interesting observations on Zu et al. (2020), that can be organized along three lines of reasoning. First, from an evolutionary perspective, Bass & Kessler note the important point that accepting that conditional information entropy, estimated from the contribution of every plant species to volatile redundancy implies that average plant fitness seems to depend on community-level properties (i.e., what the other species in the community are doing) rather than on population-level characteristics (I.e., what the individuals belonging a population are doing). While the level at which selection acts upon is a longstanding debate (e.g., Goodnight, 1990; Williams, 1992), the model seems to contradict one of the basic tenets of Darwinian evolution. The extent to which this important observation invalidates the contribution of Zu et al. (2020) is open to scrutiny. However, one can indulge the evolutionary criticism by arguing that every theoretical model performs a number of assumptions to preserve the simplicity of analyses. Furthermore, even accepting the criticism, the overall information-based framework is valuable as it provides a fresh perspective to the way coding and decoding chemical information in plant-herbivore interactions may result in arm race coevolution. The question to be assessed by members of the scientific community is how strong the evolutionary assumptions are to be acceptable. A second line of reasoning involves consideration of additional routes of chemical information transfer. If chemical volatiles are involved in another ecological function unrelated to arm race (as they are) such as toxicity, crypsis, aposematism, etc., the conditional information indices considered as proxy to plant and herbivore fitness may be only secondarily related to arms race. This is an interesting observation, which suggests that VOC production may have more than one ecological function, as it often happens in “pleiotropic” traits (Strauss, Irwin 2004). This is an exciting avenue for future research. Finally, a third category of comments involves the relationship between conditional information entropy and plant and herbivore fitness. Bass & Kessler developed a Bayesian treatment of the community-level information developed by Zu et al. (2020) that permitted to estimate fitness on a species rather than community level. Their results revealed that community conditional entropies fail to align with species-level indices, suggesting that conclusions of Strauss & Irwin (2004) are not commensurate with fitness at the species level, where the analysis seems to be pertinent. In general, I strongly recommend Bass & Kessler’s contribution as it provides a series of observations and new perspectives to Zu et al. (2020). Rather than restricting their manuscript to blind criticisms, Bass & Kessler provides new interesting perspectives, which is always welcome as it improves the value and scope of the original work.

References

Bascompte J, Jordano P (2013) Mutualistic Networks. Princeton University Press. https://doi.org/10.23943/princeton/9780691131269.001.0001

Bass E, Kessler A (2022) Comment on “Information arms race explains plant-herbivore chemical communication in ecological communities.” EcoEvoRxiv, ver. 8 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.32942/osf.io/xsbtm

Goodnight CJ (1990) Experimental Studies of Community Evolution I: The Response to Selection at the Community Level. Evolution, 44, 1614–1624. https://doi.org/10.1111/j.1558-5646.1990.tb03850.x

Schaefer HM, Ruxton GD (2011) Plant-Animal Communication. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:osobl/9780199563609.001.0001

Strauss SY, Irwin RE (2004) Ecological and Evolutionary Consequences of Multispecies Plant-Animal Interactions. Annual Review of Ecology, Evolution, and Systematics, 35, 435–466. https://doi.org/10.1146/annurev.ecolsys.35.112202.130215

Williams GC (1992) Natural Selection: Domains, Levels, and Challenges. Oxford University Press, Oxford, New York.

Zu P, Boege K, del-Val E, Schuman MC, Stevenson PC, Zaldivar-Riverón A, Saavedra S (2020) Information arms race explains plant-herbivore chemical communication in ecological communities. Science, 368, 1377–1381. https://doi.org/10.1126/science.aba2965

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

There is growing evidence of worldwide decline of populations of top predators, including marine ones (Heithaus et al, 2008, Mc Cauley et al., 2015), with cascading effects expected at the ecosystem level, due to global change and human activities, including habitat loss or fragmentation, the collapse or the range shifts of their preys. On a global scale, seabirds are among the most threatened group of birds, about one-third of them being considered as threatened or endangered (Votier& Sherley, 2017). The large consequences of the decrease of the populations of preys they feed on (Cury et al, 2011) points diet flexibility as one important element to understand for effective management (McInnes et al, 2017).  Nevertheless, morphological inventory of preys requires intrusive protocols, and the differential digestion rate of distinct taxa may lead to a large bias in morphological-based diet assessments. The use of DNA metabarcoding on feces (or diet DNA, dDNA) now allows non-invasive approaches facilitating the recollection of samples and the detection of multiple preys independently of their digestion rates (Deagle et al., 2019). Although no gold standard exists yet to avoid bias associated with metabarcoding (primer bias, gaps in reference databases, inability to differentiate primary from secondary predation…), the use of these recent techniques has already improved the knowledge of the foraging behaviour and diet of many animals (Ando et al., 2020).

Both promise and shortcomings of this approach are illustrated in the article “Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica)” by Quereteja et al. (2021). In this work, the authors assessed the nature and spatio-temporal flexibility of the foraging behaviour and consequent diet of the endangered petrel Procellaria westlandica from New-Zealand through metabarcoding of faeces samples.

The results of this dDNA, non-invasive approach, identify some expected and also unexpected prey items, some of which require further investigation likely due to large gaps in the reference databases. They also reveal the temporal (before and after hatching) and spatial (across colonies only 1.5km apart) flexibility of the foraging behaviour, additionally suggesting a possible influence of fisheries activities in the surroundings of the colonies. This study thus both underlines the power of the non-invasive metabarcoding approach on faeces, and the important results such analysis can deliver for conservation, pointing a potential for diet flexibility that may be essential for the resilience of this iconic yet endangered species.

References

Ando H, Mukai H, Komura T, Dewi T, Ando M, Isagi Y (2020) Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA, 2, 391–406. https://doi.org/10.1002/edn3.117

Cury PM, Boyd IL, Bonhommeau S, Anker-Nilssen T, Crawford RJM, Furness RW, Mills JA, Murphy EJ, Österblom H, Paleczny M, Piatt JF, Roux J-P, Shannon L, Sydeman WJ (2011) Global Seabird Response to Forage Fish Depletion—One-Third for the Birds. Science, 334, 1703–1706. https://doi.org/10.1126/science.1212928

Deagle BE, Thomas AC, McInnes JC, Clarke LJ, Vesterinen EJ, Clare EL, Kartzinel TR, Eveson JP (2019) Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Molecular Ecology, 28, 391–406. https://doi.org/10.1111/mec.14734

Heithaus MR, Frid A, Wirsing AJ, Worm B (2008) Predicting ecological consequences of marine top predator declines. Trends in Ecology & Evolution, 23, 202–210. https://doi.org/10.1016/j.tree.2008.01.003

McCauley DJ, Pinsky ML, Palumbi SR, Estes JA, Joyce FH, Warner RR (2015) Marine defaunation: Animal loss in the global ocean. Science, 347, 1255641. https://doi.org/10.1126/science.1255641

McInnes JC, Jarman SN, Lea M-A, Raymond B, Deagle BE, Phillips RA, Catry P, Stanworth A, Weimerskirch H, Kusch A, Gras M, Cherel Y, Maschette D, Alderman R (2017) DNA Metabarcoding as a Marine Conservation and Management Tool: A Circumpolar Examination of Fishery Discards in the Diet of Threatened Albatrosses. Frontiers in Marine Science, 4, 277. https://doi.org/10.3389/fmars.2017.00277

Querejeta M, Lefort M-C, Bretagnolle V, Boyer S (2021) Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica). bioRxiv, 2020.10.30.360289, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.10.30.360289

Votier SC, Sherley RB (2017) Seabirds. Current Biology, 27, R448–R450. https://doi.org/10.1016/j.cub.2017.01.042