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

Morand et al. (2024) designed convolutional neural networks to predict the occurrences of 38 marine animals worldwide. The environmental predictors were sea surface temperature, chlorophyll concentration, salinity and fifteen others. The time of some of the predictors was chosen to be as close as possible to the time of the observed occurrence.

This approach has previously only been applied to the analysis of the distribution of terrestrial plant species (Botella et al. 2018, Deneu et al. 2021), so the application here to very different marine ecosystems and organisms is a novelty worth highlighting and discussing.

A very interesting feature of PCI Ecology is that reviews are provided with the final manuscript and the present recommendation text.

In the case of the Morand et al. article, the reviewers provided very detailed and insightful comments that deserve to be published and read alongside the article.

The reviewers' comments question the ecological significance and implications of choosing fine temporal and spatial scales in CNN distribution modelling in order to obtain species distribution modelling (SDM).

The main question debated during the review process was whether the CNN modeling approach used here can be defined as a kind of niche modeling.

The fact is that most of the organisms studied here are mobile, and the authors have taken into account precise environmental information at dates close to those of species appearance (for example, "Temperature and chlorophyll values were also included 15 and 5 days before the occurrences"). In doing so, they took into account the fine spatial and temporal scales of species occurrences and environmental conditions, which can be influenced by both environmental preferences and the movement behaviors of individuals. The question then arises: does this approach really represent the ecological niches of the marine organisms selected? Given that most selected organisms may have specific seasonal movement dynamics, the CNN model also learns the individual movement behaviors of organisms over seasons and years. The ecological niche is a broader concept that takes into account all the environmental conditions that enable species to persist over the course of their lives and over generations. This differs from the case of sessile land plants, which must respond to the environmental context only at the points of appearance.

This is not a shortcoming of the methodology proposed here but rather an interesting conceptual issue to be considered and discussed. Modelling the occurrence of individuals at a given time and position can characterize not only the species' niche but also the dynamics of organisms' temporal movements. As a result, the model predicts the position of individuals at a given time, while the niche should also represent the role of environmental conditions faced by individuals at other times in their lives.
A relevant perspective would then be to analyze whether and how the neural network can help disentangle the ranges of environmental conditions defining the niche from those influencing the movement dynamics of individuals.

Another interesting point is that the CNN model is used here as a multi-species classifier, meaning that it provides the ranked probability that a given observation corresponds to one of the 38 species considered in the study, depending on the environmental conditions at the location and time of the observation. In other words, the model provides the relative chance of choosing each of the 38 species at a given time and place. Imagine that you are only studying two species that have exactly the same niche, a standard SDM approach should provide a high probability of occurrence close to 1 in localities where environmental conditions are very and equally suited to both species, while the CNN classifier would provide a value close to 0.5 for both species, meaning that we have an equal chance of choosing one or the other. Consequently, the fact that the probability given by the classifier is higher for a species at a given point than at another point does not (necessarily) mean that the first point presents better environmental conditions for that species but rather that we are more likely to choose it over one of the other species at this point than at another. In fact, the classification task also reflects whether the other 37 species are more or less likely to be found at each point. The classifier, therefore, does not provide the relative probability of occurrence of a species in space but rather a relative chance of finding it instead of one of the other 37 species at each point of space and time.

It is important that an ecologist designing a multi-species classifier for species distribution modelling is well aware of this point and does not interpret the variation of probabilities for a species in space as an indication of more or less suitable habitat for that specific species. On the other hand, predicting the relative probabilities of finding species to a given point at a given time gives an indication of the dynamics of their local co-occurrence. In this respect, the CNN approach is closer to a joint species distribution model (jSDM). As Ovaskainen et al. (2017) mention, "By simultaneously drawing on the information from multiple species, these (jSDM) models allow one to seek community-level patterns in how species respond to their environment". Let's return to the two species example we used above. The fact that the probabilities are 0.5 for both species actually suggests that both species can coexist at the same abundance at this location. In this respect, the CNN multi-species classifier offers promising prospects for the prediction of assemblages and habitats thanks to the relative importance of the most characteristic/dominant species from a species pool. The species pool comprises all classified species and must be sufficiently representative of the ecological diversity of species niches in the area.

Finally, CNN-based species distribution modelling is a powerful and promising tool for studying the distributions of multi-species assemblages as a function of local environmental features but also of the spatial heterogeneity of each feature around the observation point in space and time (Deneu et al. 2021). It allows acknowledging the complex effects of environmental predictors and the roles of their spatial and temporal heterogeneity through the convolution operations performed in the neural network. As more and more computationally intensive tools become available, and as more and more environmental data becomes available at finer and finer temporal and spatial scales, the CNN approach is likely to be increasingly used to study biodiversity patterns across spatial and temporal scales.

References

Botella, C., Joly, A., Bonnet, P., Monestiez, P., and Munoz, F. (2018). Species distribution modeling based on the automated identification of citizen observations. Applications in Plant Sciences, 6(2), e1029. https://doi.org/10.1002/aps3.1029

Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F., and Joly, A. (2021). Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Computational Biology, 17(4), e1008856. https://doi.org/10.1371/journal.pcbi.1008856

Morand, G., Joly, A., Rouyer, T., Lorieul, T., and Barde, J. (2024) Predicting species distributions in the open ocean with convolutional neural networks. bioRxiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2023.08.11.551418

Ovaskainen, O., Tikhonov, G., Norberg, A., Guillaume Blanchet, F., Duan, L., Dunson, D., ... and Abrego, N. (2017). How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology letters, 20(5), 561-576. https://doi.org/10.1111/ele.12757

In this study, Ottaviani et al. (2025) examined the variation in the use of terms related to "novelty" in 52,236 abstracts published between 1997 and 2017 across 17 ecological journals. They also analyzed the change in the frequency of terms related to "confirmatory" results. Their findings revealed a clear and consistent increase in the use of "novelty" terms, while the frequency of "confirmatory" terms remained relatively stable. This trend was observed across all the ecological journals, with the exception of Austral Ecology. Furthermore, the greater use of "novelty" terms was correlated with higher citation counts and publication in journals with higher impact factors. These findings should prompt further reflection on our research practices and may be connected to ongoing discussions in the philosophy of science.

Thomas S. Kuhn's seminal work, The Structure of Scientific Revolutions (1962), challenged traditional views of scientific progress. Central to Kuhn's argument is the idea that science progresses through periods of adherence to a dominant "paradigm"—a framework that provides scientists with puzzles to solve and the tools to solve them. A scientific crisis arises when the paradigm fails to address emerging anomalies, leading to the replacement of the old paradigm with a new one, a process Kuhn calls a "scientific revolution." Kuhn's perspective stands in stark contrast to previous views, which held that science progresses through the steady accumulation of truths or the gradual refinement of theories, often guided by the scientific method. One might wonder if the growing emphasis on "novelty" in ecological research mirrors the idea that theories are gradually refined until an exceptional discovery sparks a paradigm shift. In ecology, such a shift could be seen in the transition from niche-based theories of biodiversity dynamics (1960s-2000) to the radical neutral theory (Hubbell, 2001), which posits that diverse ecosystems can exist without niche differences. This paradigm was initially met with fierce opposition but eventually led to more integrative theories, recognizing the combined influence of both niche-based and neutral processes (Gravel et al., 2006, among others).

What, then, is the current paradigm in ecology? Kuhn's theory of scientific progress suggests alternating periods of "normal" and "revolutionary" science. Normal science is characterized by cumulative puzzle-solving within established frameworks, while revolutionary science involves major shifts that can invalidate previous knowledge, a phenomenon Kuhn terms "Kuhn-loss." Kuhn rejected both the traditional and Popperian views on scientific revolutions. He argued that normal science depends on a shared commitment to certain beliefs, values, methods, and even metaphysical assumptions, which he referred to as a "disciplinary matrix" or "paradigm." This collective commitment is essential for scientific progress and must be instilled during the training of scientists. Kuhn's emphasis on the conservative nature of normal science contrasts with the heroic idea of continuous innovation and Popper's view of scientists constantly seeking to falsify theories. However, contemporary ecological research often follows the hypothetico-deductive approach championed by Popper. In light of these contrasting views, one might ask: What is the status of "novelty" in modern ecology? Is it contributing to the gradual solving of scientific puzzles, or is it focused on refuting hypotheses? Should "novelty" and "confirmatory" research be seen as opposites, or should both contribute to the advancement of science? Finally, is the increasing use of "novelty" terms a precursor to a scientific revolution, as Kuhn defined it, or merely a semantic trend driven by editorial policies aimed at attracting readers rather than contributing to real scientific progress?

In conclusion, Ottaviani's study provides compelling evidence of the growing use of "novelty" terms in ecological journals, but it remains unclear whether this trend signals the onset of a Kuhnian "scientific revolution." This work should spark further discussion on the nature of current research practices, which may either facilitate or hinder the emergence of new paradigms.

References

Gravel, D., Canham, C. D., Beaudet, M., & Messier, C. (2006). Reconciling niche and neutrality: the continuum hypothesis. Ecology letters, 9(4), 399-409. https://doi.org/10.1111/j.1461-0248.2006.00884.x

Hubbell, S. P. (2001). The Unified Neutral Theory of Biodiversity and Biogeography, vol.1, Princeton and Oxford: Princeton University Press.

Kuhn, T. S. (1962). The structure of scientific revolutions. International Encyclopedia of Unified Science, vol.2, 1962.

Ottaviani, G., Martinez, A., Petit Bon, M., Mammola, S. (2025). On the quest for novelty in ecology. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2023.02.27.530333

As biodiversity has become a major global concern for a variety of stakeholders, and society in general, assessments of biodiversity trends at all spatial scales have flourished in the past decades. To assess trends, one needs data, and the more precise the data, the more precise the trend. Or, if precision is not perfect, uncertainty in the data must be acknowledged and accounted for. Such considerations have already been raised in ecology, most notably regarding the value of species distribution data to model the current and future distribution of species (Rocchini et al., 2011, Duputié et al., 2014, Tessarolo et al., 2021), leading to serious doubts regarding the value of public databases (Maldonado et al., 2015). And more recently similar issues have been raised regarding databases of species traits (Augustine et al., 2024), emphasizing the importance of good data practice and traceability.

Science is by nature a self-correcting human process, with many steps of the scientific activity prone to errors and misinterpretations. Collation of ecological data, sadly, is proof of this. Spurred by the astonishing results of Hallmann et al. (2017) regarding the decline of insect biomass, and to more precisely answer the question of biodiversity trends in insects and settle an ongoing debate (Cardinale et al., 2018), van Klink et al. (2020, 2021) established the InsectChange database. Several perceptive comments have already been made regarding the possible issues in the methods and interpretations of this study (Desquilbet et al., 2020, Jähnig et al., 2021, Duchenne et al., 2022). However, the biggest issue might have been finally unearthed by Gaume & Desquilbet (2024): with poorly curated data, the InsectChange database is unlikely to support most of the initial claims regarding insect biodiversity trends.

The compilation of errors and inconsistencies present in InsectChange and evinced by Gaume & Desquilbet (2024) is stunning to say the least, with a mix of field and experimental data combined without regard for experimental manipulation of environmental factors, non-standardised transformations of abundances, the use of non-insect taxa to compute insect trends, and inadequate geographical localizations of samplings. I strongly advise all colleagues interested in the study of biodiversity from global databases to consider the points raised by the authors, as it is quite likely that other databases might suffer from the same ailments as well. Reading this paper is also educating and humbling in its own way, since the publication of the original papers based on InsectChange seems to have proceeded without red flags from reviewers or editors. The need for publishing fast results that will make the next buzz, thus obeying the natural selection of bad science (Smaldino and McElreath, 2016), might be the systemic culprit. However, this might also be the opportunity ecology needs to consider the reviewing and curation of data as a crucial step of science quality assessment. To make final assessments, let us proceed with less haste.

References

Augustine, S. P., Bailey-Marren, I., Charton, K. T., Kiel, N. G. & Peyton, M. S. (2024) Improper data practices erode the quality of global ecological databases and impede the progress of ecological research. Global Change Biology, 30, e17116. https://doi.org/10.1111/gcb.17116

Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. (2018) Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biological Conservation, 219, 175-183. https://doi.org/10.1016/j.biocon.2017.12.021

Desquilbet, M., Gaume, L., Grippa, M., Céréghino, R., Humbert, J.-F., Bonmatin, J.-M., Cornillon, P.-A., Maes, D., Van Dyck, H. & Goulson, D. (2020) Comment on “Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances”. Science, 370, eabd8947. https://doi.org/10.1126/science.abd8947

Duchenne, F., Porcher, E., Mihoub, J.-B., Loïs, G. & Fontaine, C. (2022) Controversy over the decline of arthropods: a matter of temporal baseline? Peer Community Journal, 2. https://doi.org/10.24072/pcjournal.131

Duputié, A., Zimmermann, N. E. & Chuine, I. (2014) Where are the wild things? Why we need better data on species distribution. Global Ecology and Biogeography, 23, 457-467. https://doi.org/10.1111/geb.12118

Gaume, L. & Desquilbet, M. (2024) InsectChange: Comment. biorXiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2023.06.17.545310

Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., Stenmans, W., Müller, A., Sumser, H., Hörren, T., Goulson, D. & de Kroon, H. (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLOS ONE, 12, e0185809. https://doi.org/10.1371/journal.pone.0185809

Jähnig, S. C., Baranov, V., Altermatt, F., Cranston, P., Friedrichs-Manthey, M., Geist, J., He, F., Heino, J., Hering, D., Hölker, F., Jourdan, J., Kalinkat, G., Kiesel, J., Leese, F., Maasri, A., Monaghan, M. T., Schäfer, R. B., Tockner, K., Tonkin, J. D. & Domisch, S. (2021) Revisiting global trends in freshwater insect biodiversity. WIREs Water, 8, e1506. https://doi.org/10.1002/wat2.1506

Maldonado, C., Molina, C. I., Zizka, A., Persson, C., Taylor, C. M., Albán, J., Chilquillo, E., Rønsted, N. & Antonelli, A. (2015) Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? Global Ecology and Biogeography, 24, 973-984. https://doi.org/10.1111/geb.12326

Rocchini, D., Hortal, J., Lengyel, S., Lobo, J. M., Jiménez-Valverde, A., Ricotta, C., Bacaro, G. & Chiarucci, A. (2011) Accounting for uncertainty when mapping species distributions: The need for maps of ignorance. Progress in Physical Geography, 35, 211-226. https://doi.org/10.1177/0309133311399491

Smaldino, P. E. & McElreath, R. (2016) The natural selection of bad science. Royal Society Open Science, 3. https://doi.org/10.1098/rsos.160384

Tessarolo, G., Ladle, R. J., Lobo, J. M., Rangel, T. F. & Hortal, J. (2021) Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models. Ecography, 44, 1743-1755. https://doi.org/10.1111/ecog.05793

van Klink, R., Bowler, D. E., Comay, O., Driessen, M. M., Ernest, S. K. M., Gentile, A., Gilbert, F., Gongalsky, K. B., Owen, J., Pe'er, G., Pe'er, I., Resh, V. H., Rochlin, I., Schuch, S., Swengel, A. B., Swengel, S. R., Valone, T. J., Vermeulen, R., Wepprich, T., Wiedmann, J. L. & Chase, J. M. (2021) InsectChange: a global database of temporal changes in insect and arachnid assemblages. Ecology, 102, e03354. https://doi.org/10.1002/ecy.3354

van Klink, R., Bowler, D. E., Gongalsky, K. B., Swengel, A. B., Gentile, A. & Chase, J. M. (2020) Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science, 368, 417-420. https://doi.org/10.1126/science.aax9931

The late eminent ecologist Gary Polis once stated that “most catalogued food-webs are oversimplified caricatures of actual communities” and are “grossly incomplete representations of communities in terms of both diversity and trophic connections.” Not content with that damning indictment, he went further by railing that “theorists are trying to explain phenomena that do not exist” [1]. The latter critique might have been push back for Robert May´s ground-breaking but ultimately flawed research on the relationship between food-web complexity and stability [2]. Polis was a brilliant ecologist, and his thinking was clearly influenced by his experiences researching desert food webs. Those food webs possess an uncommon combination of properties, such as frequent omnivory, cannibalism, and looping; high linkage density (L/S); and a nearly complete absence of apex consumers, since few species completely lack predators or parasites [3]. During my PhD studies, I was lucky enough to visit Joshua Tree National Park on the way to a conference in New England, and I could immediately see the problems posed by desert ecosystems. At the time, I was ruminating on the “harsh-benign” hypothesis [4], which predicts that the relative importance of abiotic and biotic forces should vary with changes in local environmental conditions (from harsh to benign). Specifically, in more “harsh” environments, abiotic factors should determine community composition whilst weakening the influence of biotic interactions. However, in the harsh desert environment I saw first-hand evidence that species interactions were not diminished; if anything, they were strengthened. Teddy-bear chollas possessed murderously sharp defenses to protect precious water, creosote bushes engaged in belowground “chemical warfare” (allelopathy) to deter potential competitors, and rampant cannibalism amongst scorpions drove temporal and spatial ontogenetic niche partitioning. Life in the desert was hard, but you couldn´t expect your competition to go easy on you.
If that experience colored my thinking about nature’s reaction to a capricious environment, then the seminal work by Robert Paine on the marine rocky shore helped further cement the importance of biotic interactions. The insights provided by Paine [5] brings us closer to the research reported in the preprint “The return of the trophic chain: fundamental vs realized interactions in a simple arthropod food web” [6], given that the authors in that study hold the environment constant and test the interactions between different permutations of a simple community. Paine [5] was able to elegantly demonstrate using the chief protagonist Pisaster ochraceus (a predatory echinoderm also known as the purple sea star) that a keystone consumer could exert strong top-down control that radically reshaped the interactions amongst other community members. What was special about this study was that the presence of Pisaster promoted species diversity by altering competition for space by sedentary species, providing a rare example of an ecological network experiment combining trophic and non-trophic interactions. Whilst there are increasing efforts to describe these interactions (e.g., competition and facilitation) in multiplex networks [7], the authors of “The return of the trophic chain: fundamental vs realized interactions in a simple arthropod food web” [6] have avoided strictly competitive interactions for the sake of simplicity. They do focus on two trophic forms of competition, namely intraguild predation and apparent competition. These two interaction motifs, along with prey switching are relevant to my own research on the influence of cross-ecosystem prey subsidies to receiving food webs [8]. In particular, the apparent competition motif may be particularly important in the context of emergent adult aquatic insects as prey subsidies to terrestrial consumers. This was demonstrated by Henschel et al. [9] where the abundances of emergent adult aquatic midges in riparian fields adjacent to a large river helped stimulate higher abundances of spiders and lower abundances of herbivorous leafhoppers, leading to a trophic cascade. The aquatic insects had a bottom-up effect on spiders and this subsidy facilitated a top-down effect that cascaded from spiders to leafhoppers to plants. The apparent competition motif becomes relevant because the aquatic midges exerted a negative indirect effect on leafhoppers mediated through their common arachnid predators.
In the preprint “The return of the trophic chain: fundamental vs realized interactions in a simple arthropod food web” [6], the authors have described different permutations of a simple mite community present in avocado orchards (Persea americana). This community comprises of two predators (Euseius stipulatus and Neoseiulus californicus), one herbivore as shared prey (Oligonychus perseae), and pollen of Carpobrotus edulis as alternative food resource, with the potential for the intraguild predation and apparent competition interaction motifs to be expressed. The authors determined that these motifs should be realized based off pairwise feeding trials. It is common for food-web researchers to depict potential food webs, which contain all species sampled and all potential trophic links based on laboratory feeding trials (as demonstrated here) or from observational data and literature reviews [10]. In reality, not all these potential feeding links are realized because species may partition space and time, thus driving alternative food-web architectures. In “The return of the trophic chain: fundamental vs realized interactions in a simple arthropod food web” [6], the authors are able to show that placing species in combinations that should yield more complex interaction motifs based off pairwise feeding trials fails to deliver – the predators revert to their preferred prey resulting in modular and simple trophic chains to be expressed. Whilst these realized interaction motifs may be stable, there might also be a tradeoff with function by yielding less top-down control than desirable when considering the potential for ecosystem services such as pest management. These are valuable insights, although it should be noted that here the fundamental niche is described in a strictly Eltonian sense as a trophic role [11]. Adding additional niche dimensions (sensu [12]), such as a thermal gradient could alter the observed interactions, although it might be possible to explain these contingencies through metabolic and optimal foraging theory combined with species traits. Nonetheless, the results of these experiments further demonstrate the need for ecologists to cross-validate theory with empirical approaches to develop more realistic and predictive food-web models, lest they invoke the wrath of Gary Polis´ ghost by “trying to explain phenomena that do not exist”.

References

[1] Polis, G. A. (1991). Complex trophic interactions in deserts: an empirical critique of food-web theory. The American Naturalist, 138(1), 123-155. doi: 10.1086/285208
[2] May, R. M. (1973). Stability and complexity in model ecosystems. Princeton University Press, Princeton, NJ, USA
[3] Dunne, J. A. (2006). The network structure of food webs. In Pascual, M., & Dunne, J. A. (eds) Ecological Networks: Linking Structure to Dynamics in Food Webs. Oxford University Press, New York, USA, 27-86
[4] Menge, B. A., & Sutherland, J. P. (1976). Species diversity gradients: synthesis of the roles of predation, competition, and temporal heterogeneity. The American Naturalist, 110(973), 351-369. doi: 10.1086/283073
[5] Paine, R. T. (1966). Food web complexity and species diversity. The American Naturalist, 100(910), 65-75. doi: 10.1086/282400
[6] Torres-Campos, I., Magalhães, S., Moya-Laraño, J., & Montserrat, M. (2018). The return of the trophic chain: fundamental vs realized interactions in a simple arthropod food web. bioRxiv, 324178, ver. 5 peer-reviewed and recommended by PCI Ecol. doi: 10.1101/324178
[7] Kéfi, S., Berlow, E. L., Wieters, E. A., Joppa, L. N., Wood, S. A., Brose, U., & Navarrete, S. A. (2015). Network structure beyond food webs: mapping non‐trophic and trophic interactions on Chilean rocky shores. Ecology, 96(1), 291-303. doi: 10.1890/13-1424.1
[8] Burdon, F. J., & Harding, J. S. (2008). The linkage between riparian predators and aquatic insects across a stream‐resource spectrum. Freshwater Biology, 53(2), 330-346. doi: 10.1111/j.1365-2427.2007.01897.x
[9] Henschel, J. R., Mahsberg, D., & Stumpf, H. (2001). Allochthonous aquatic insects increase predation and decrease herbivory in river shore food webs. Oikos, 93(3), 429-438. doi: 10.1034/j.1600-0706.2001.930308.x
[10] Brose, U., Pavao-Zuckerman, M., Eklöf, A., Bengtsson, J., Berg, M. P., Cousins, S. H., Mulder, C., Verhoef, H. A., & Wolters, V. (2005). Spatial aspects of food webs. In de Ruiter, P., Wolters, V., Moore, J. C., & Melville-Smith, K. (eds) Dynamic Food Webs. vol 3. Academic Press, Burlington, 463-469
[11] Elton, C. (1927). Animal Ecology. Sidgwick and Jackson, London, UK
[12] Hutchinson, G. E. (1957). Concluding Remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415-427. doi: 10.1101/sqb.1957.022.01.039