The dynamic nature of cereal food webs challenges the suitability of snapshot sampling for assessing ecosystem services

The dynamic nature of cereal food webs challenges the suitability of snapshot sampling for assessing ecosystem services | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The dynamic nature of cereal food webs challenges the suitability of snapshot sampling for assessing ecosystem services Pedro Nuno Branco Leote, Michael Traugott, Oskar Ragnar Rennstam Rubbmark This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6774692/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Current theory of food web dynamics is based on mid- to long-term data. However, the short-term dynamics are still poorly understood, as the technology to study them is not readily available. We addressed this gap, by sampling invertebrate food webs bi-weekly, with generalist and specialist predators, herbivores and detritivores as prey, in barley field over two years. We measured weighted connectance, community composition and link rewiring dissimilarity, as well as species-level specialisation of predators. We expected the connectance to be lowest, and both dissimilarities to be highest during the mid-season, whereas specialisation was predicted to follow prey abundance. We found differing trends across years, with connectance declining over time, and community and rewiring dissimilarity fluctuating more, in the second year, but not the first, when an aphid infestation occurred. Specialisation varied across the season, with aphids and cereal leaf beetles being consumed by more predator species as their abundances peaked, and by fewer before and after that, while springtails were universally consumed. Our results show that agro-ecosystems are highly dynamic, and that food webs undergo continuous restructuring even over very short timeframes; potentially leading to a considerable systematic under- or overestimation of ecosystem functioning assessment. Biological sciences/Ecology/Agri ecology Biological sciences/Ecology/Ecological networks Biological sciences/Ecology/Ecosystem services time series food webs network complexity community turnover invertebrate food webs cereal crops biological control Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction As seasons progress and habitats change, it is well known that there will be phenological shifts in the communities and interaction networks that the species that inhabit most temperate ecosystem form (Gadelha et al. 2017 , Morente-López et al. 2018 ). Furthermore, we also know that such changes, regardless if natural ( e.g. seasonal variation, phenology, weather) or human-induced (e.g. agricultural management such as the application of chemicals, tilling, harvesting), can influence the strength and reliability of ecosystem services that human food production relies on (Foley et al. 2011 ). However, to understand how to promote ecosystem services, we first need to understand the changes that the species communities that inhabit agro-ecosystems go through. Not least, because we know from theory that timing, functional roles and redundancy will be decided by a community-wide process, that depend on seasonal changes that occur within a habitat (Mouillot et al. 2013 , Frainer et al. 2014 ). In arable systems, habitat changes over the course of a year are quite apparent. In just a few months, non-perennial crops, such as cereals, undergo a cycle of sowing, growth, ripening and harvest, each of which is associated with a starkly contrasting habitat setting (Zadoks et al. 1974 ). However, we so far have a relatively poor understanding of what these changes mean for the food web interactions that live within arable fields. It is, for example, likely that food webs, in response to habitat changes occurring during the crop growth, will go through cycles of greater complexity when resources are abundant and lesser complexity when resources are scarce (Thompson & Townsend 1999 ). Food web changes also occur because consumers will modify their behaviour, by adapting their realized niches to either avoid competition or explore new resources as different food sources become more or less available (Terry et al. 2017 ), or in response to phenological changes in the predator community (Kaartinen & Roslin 2012 ). However, hardly any empirical data is available on how food webs respond to changes in the ecosystem induced by phenology and resource availability. There are several reasons for why data with sufficient temporal resolution is missing and has been difficult to attain, such as the logistics required for the repeated sampling, the methodological challenges of developing assays and/or primers with the necessary balance of specificity and sensitivity, the costs of infrastructure and equipment needed, or the knowledge to curate and analyse such data (Cuff et al. 2022 , Cuff et al. 2023 , Deagle et al. 2023 ). Here, we address this important knowledge gap by using a molecular analysis of trophic interactions between generalist (ground and rove beetles, and spiders) and specialist predators (lacewings, ladybugs and hoverfly larvae), detritivore (earthworms and springtails) and herbivore prey (aphids and cereal leaf beetle). This work is a follow-up to a previous article, using the same community and diet data, where we focused on the effects of fertilization on prey abundance, intraguild predation and biological control (Leote et al. 2024 ). Both are a part of a larger study we conducted in Central European barley fields, where we investigated food web interactions, as well as changes in communities, every two weeks throughout the cereal growth period, in replicated fields across two years. The fields were also fertilised on half of their respective areas, to induce different baseline primary productivity and prey abundances (Rowen et al. 2019 , Leote et al. 2024 ). Our system is characterized by herbivorous insects, including the cereal leaf beetle Oulema melanopus , and three species of aphids that commonly occur in cereals, Rhopalosiphum padi , Metopolophium dirhodum , and Sitobion avenae (Van Emden & Harrington 2017 , Van de Vijver et al. 2019 ). Each of these species is attacked by a suite of natural enemies, among them generalist predators (Thies et al. 2011 , Kheirodin et al. 2020 ). These are thought to reduce the chance of pests to become established in fields, whereas once they have reached higher densities, they are more effectively controlled by specialized enemies that are drawn to highly infested fields (Snyder & Ives 2003 ). Outside of these periods of greater prey abundance, detritivore prey such as earthworms and springtails are valuable food sources, that can help sustain predator populations outside of the cereal growth season (Symondson et al. 2000 ); affecting adult populations, and their potential for pest control, in the following crop season (Eitzinger & Traugott 2011 ). We devised three hypotheses, which conceptualize how food webs change according to our current understanding of food web and functional agro-ecology. First, the increase in detritivore and herbivore prey abundance on the fertilised side of the field and the lower competition (observed in Leote et al. 2024 ), should allow predators to explore a wider dietary niche early in the season, which should be reflected in a greater number of realized trophic links. As such, we predict food web complexity, measured with weighted connectance (Bersier et al. 2002 ), to be higher in the fertilised treatment. Moreover, because connectance decreases with species richness (Banašek-Richter et al. 2009 , Calizza et al. 2019 ), we expect weighted connectance to be lowest during the middle of the cereal growth season, when primary productivity and diversity in the crop fields is expected to be highest (Thompson & Townsend 1999 , Worm & Duffy 2003 , Nie et al. 2023 , Leote et al. 2024 ). In our second hypothesis, we expect that specialisation on individual prey taxa , measured with Blüthgen’s d’ (Blüthgen et al. 2006 ), should change over time in response to the availability of each prey, and be lower in the fertilised treatment; where we expect that an increased prey availability should allow predators to have more overlapping diets. However, over time we also expect specialisation should increase, as lowering competition and interference also will enable predators to focus on preferred prey. Lastly, in our third hypothesis, we expect trophic link rewiring or community composition dissimilarity (Poisot et al. 2012 ) to reach a peak towards the middle of the season, in response to the drastic habitat changes that occur as the cereal ripens and many herbivorous prey species migrate away from fields. Results Food webs The bipartite food webs for each sampling session of each year are described below (Fig. 1 , but please see the supplementary Figs. 1 through 13 for each food web with higher resolution), to show the relative importance of each group (colour coded), through the eigenvector centrality (node diameter), and diet detection proportion (line width). First and foremost, the number of trophic links established changed over time (supplementary tables 9 and 10, and supplementary Fig. 14 for the link densities, and supplementary tables 11 and 12 for eigenvector centralities). In 2020 and 2021, mean link density was 2.30 and 1.50, respectively, at the start of the season; then in the first year it reached a peak of 4.00 during the 5th session, while in the second year it was 3.44 during the 4th session. Finally, by the end of sampling, link density was 3.30 for 2020, and 2.87 for 2021. Springtails (bright green) remained central throughout the season, with an eigenvector centrality ranging between 0.931 and 1 in 2020, and 0.950 and 1 in 2021. Earthworms (dark green), although less central than springtails, had a centrality between 0.613–0.826 early on and 0.628–0.800 late in the season in 2020, decreasing to 0.208 when aphids were at their peak in the 4th sampling session. In 2021, however, earthworms were more consistently central to the food webs, with a centrality between 0.646–0.870 with the exception of the 5th sampling session, when their centrality decreased to 0.422. On the other hand, aphids (bright red) gained relevance in the middle sampling sessions in both years, rising in centrality from a mean of 0.121 and 0.131, to 0.571 and 0.598 in 2020 and 2021, respectively. The cereal leaf beetle (CLB, dark red), showed a peak in centrality of 0.573 and 0.858, during the 3rd session in both years. The intraguild prey (IGP, bright blue), consisting of beetles and spiders, were more central at the end of the season in 2020, with a centrality of 0.395, when compared to the rest of sampling that ranged from 0.153 to 0.238. In the following year, centrality had two peaks, one in the 3rd session with 0.351 and later in the 5th session with 0.416. The specialist aphid predators (dark orange), encompassing ladybugs, lacewings and hoverflies (in larval stage), were relatively peripheral to the food webs, with centrality values as low as 0.034 in 2020 and 0 in 2021. Nonetheless, they displayed a peak of 0.289 and 0.200 centrality, during the 5th sampling session in the corresponding years. Food web complexity The food web weighted connectance in both years was correlated to sampling session (2020 – LMM, χ 2 = 25.617, df = 6, N = 168, p < 0.001; 2021 – LMM, χ 2 = 41.091, df = 5, N = 144, p < 0.001), but was not affected by fertilisation (2020 – LMM, χ 2 = 0.589, df = 1, N = 168, p = 0.292; 2021 – LMM, χ 2 = 0.027, df = 1, N = 144, p = 0.869). In the first year, the weighted connectance rose until the 4th session, during late stem elongation, then decreased towards the end of the season, while in the second year it decreased from the beginning until the fourth session, then slowly increased (Fig. 2 ). Specialisation The species level specialisation ( d’ , Blüthgen et al. 2006 ) had different responses to fertilisation and time depending on the prey target. Starting with prey in 2020, the specialisation on aphids was affected by both fertilisation and session, as a joint or interaction effect (GLMM – χ 2 = 14.479, df = 6, N = 109, p = 0.025), with the fertilised treatment having higher values towards the end of the season (Fig. 3 ). For the cereal leaf beetle, fertilisation had no effect on specialisation (GLM – F = 1.383, df = 1, N = 37, p = 0.249), but session did (GLM – F = 5.315, df = 6, N = 37, p < 0.001), with a slight increase towards the end of sampling (Fig. 3 ). Earthworm specialisation, on the other hand, was neither affected by fertilisation (GLM – F = 0.914, df = 1, N = 41, p = 0.346) nor session (GLM – F = 0.319, df = 6, N = 41, p = 0.922, Fig. 3 ). For springtails, 95% (40/42) of all d’ values calculated were 0 ( d’ = 0 indicates a complete overlap on a given target), as a result, a model could not be estimated (Fig. 3 ). Regarding intraguild prey, the specialisation on beetles was not affected by either fertilisation treatment (GLMM – χ 2 = 0.937, df = 1, N = 158, p = 0.333), nor session (GLMM – χ 2 = 7.559, df = 6, N = 158, p = 0.272, Fig. 3 ). As with beetles, specialisation on spiders was also not affected by fertilisation (GLM – F = 0.2995, df = 1, N = 30, p = 0.5897) nor session (GLM - F = 2.050, df = 6, N = 30, p = 0.099, Fig. 3 ). Lastly, for specialist predators, such as ladybugs and hoverflies, consumed by the generalist predator taxa sampled, the specialisation was once again not affected by fertilisation (GLM – F = 1.271, df = 1, N = 47, p = 0.266) nor session (GLM – F = 2.130, df = 6, N = 47, p = 0.071, Fig. 3 ). Moving onto prey in 2021, specialisation on aphids was affected by fertilisation treatment (GLMM – χ 2 = 6.054, df = 1, N = 92, p = 0.014) but not session (GLMM – χ 2 = 5.258, df = 5, N = 92, p = 0.385), with fertilisation increasing specialization (Fig. 4 ). On the cereal leaf beetle, both fertilisation (GLMM – Chi = 14.415, df = 5, N = 28, p = 0.013) and session (GLMM – Chi = 3.905, df = 1, N = 28, p = 0.048) had an effect on specialisation, with an inverse bell shape over time, with a pronounced increase in the fertilised treatment (Fig. 4 ). In contrast, earthworms were neither affected by fertilisation (GLM – F = 0.867, df = 1, N = 33, p = 0.359), nor session (GLM – F = 0.858, df = 5, N = 33, p = 0.522, Fig. 4 ). As in the previous year, 97% (35/36) of the springtails d’ values were 0, hence a model could not be estimated, but it once again points to this group being a staple food source for generalist predators (Fig. 4 ). Specialisation on beetles as intraguild prey was not significantly affected by fertilisation (GLMM – χ 2 = 0.109, df = 1, N = 130, p = 0.740), as opposed to session (GLMM – χ 2 = 14.326, df = 5, N = 130, p = 0.012), with a clear increase after the 3rd session (Fig. 5 ). For spiders, neither the treatment (GLM – F = 0.233, df = 1, N = 31, p = 0.634) nor the sampling session had an effect on specialisation (GLM – F = 0.323, df = 5, N = 31, p = 0.895, Fig. 4 ). For specialist predators, neither fertilisation (GLM – F = 0.004, df = 1, N = 23, p = 0.951) nor session (GLM – F = 0.707, df = 4, N = 23, p = 0.597) had an effect on specialisation, though on the fertilised treatment there were no detections until the 3rd sampling session (Fig. 4 ). Trophic link rewiring and community composition dissimilarity As with complexity, the fertilisation treatment did not affect the trophic link rewiring (2020 – LMM, χ 2 = 0.199, df = 1, N = 143, p = 0.655; 2021 – LMM, χ 2 = 0.007, df = 1, N = 116, p = 0.934). However, it changed over the season in both years (2020 – LMM, χ 2 = 27.455, df = 5, N = 143, p < 0.001; 2021 – LMM, χ 2 = 35.558, df = 4, N = 116, p < 0.001), by increasing in the first few sessions, then in 2020 alone it dipped during the mid-season, rising again in the end (Fig. 5 ). Network community composition was similarly affected, once again changing across sampling sessions (2020 – LMM, χ 2 = 26.919, df = 5, N = 143, p < 0.001; 2021 – LMM, χ 2 = 45.448, df = 4, N = 116, p < 0.001), but not with fertilisation (2020 – LMM, χ 2 = 0.662, df = 1, N = 143, p = 0.416; 2021 – LMM, χ 2 = 0.021, df = 1, N = 116, p = 0.884) each year (Fig. 5 ). In both years, community composition dissimilarity dropped until the 3rd session, then for 2020 is stabilized, while it rose again in 2021, albeit slightly (Fig. 5 ). In 2020, during the middle of the season, when aphids reached their peak (sessions 4 and 5, Leote et al. 2024 ), the trophic links changed less as seen from the drop in rewiring to approximately 0.5 (Fig. 5 ). In 2021 the early rewiring (sessions 1 and 2) was less common, at approximately 0.2–0.3, although this appears to be the result of a few outliers (Fig. 5 ). Discussion Our findings show how dynamic food web interactions within invertebrate communities in agro-ecosystems can be, even over very short time scales. Furthermore, we also show that over these short time scales, weighted connectance did not increase towards the mid-season peak of productivity, as expected in our first hypothesis, and instead decreased, in accordance with previous studies (Worm & Duffy 2003 , Parker & Huryn 2013 , Nie et al. 2023 ). A low complexity may also indicate that there is not a strong selective pressure for predators to differentiate niches, which may be one of the reasons why we did not observe any strong fertilisation effects in our study. The above could have a positive influence on the biological control services provided by generalist predators, considering that this may have allowed them greater freedom from competition, to exploit aphid and cereal leaf beetle prey (Michalko et al. 2019 , Leote et al. 2024 ). As such, the regulation of these pest prey was generalized across the predator community and therefore overlapping, only partially confirming our second hypothesis. However, this pattern was not evident for other prey. Of these, springtails seemingly have the role of a staple food, as they were very generally consumed by the predator community throughout the season. Furthermore, there is seemingly a conflict between specialist and generalist predators (Rosenheim et al. 1999 , Snyder & Ives 2001 , Diehl et al. 2013 ), which consume both the specialists and their prey, in this case, the aphids. However, this may rather have a legacy effect on pest regulation, as specialists mostly appeared to be a generally consumed prey only after the peak aphid infestation. We can furthermore show that rewiring within food webs, especially at the onset of the season, was increasing drastically until mid-season, as opposed to community composition, partially supporting our third hypothesis. As the fields grew taller and greener, more species came in and established interactions amongst themselves, then left at the end of the season; a pattern that has been observed before (Collins et al. 2002 , Thomas et al. 2002 , Holland et al. 2004 , Öberg & Ekbom 2006 ). The centrality of different prey also shifted, with aphids becoming more central and accounting for larger proportions of the predators’ diets, precisely when their abundance reached its peak in the study fields. Meanwhile ubiquitous prey, like springtails, remained central food sources throughout the entire sampling period. Similarly, the food webs’ complexity changed over time, each year displaying a different trend, which may be linked to an aphid infestation that took place in 2020 (Leote et al. 2024 ); something which we discuss further down below. In terms of specialisation across prey, springtails were a widely used food source in both years, and the prevalence of maximum overlap is a strong indicator that springtails were a staple food source, for all predator taxa sampled. However, note that without an available measurement of consumed amounts, we cannot know whether this “stapleness” means they are a main source of energy, or just consumed ubiquitously in low quantities; posing an interesting question on how occurrence frequency correlates to consumption rates in field settings. Without that information, we cannot know whether the absence of changes in overlap in this study, or activity-density found by others (Birkhofer et al. 2008 ), correspond to prey switching, away from pests. Spiders too appeared to be consumed by nearly all beetle species in 2020, pointing to a widespread occurrence of IGP. This did not take place in the following year, with fewer detections as well, probably as a result of a decline in spider abundance in the late season (Supplementary Fig. 15). As for predation on aphid specialist predators, the number of detections early in the season in both years is low, being altogether absent until the 3rd session in the fertilised treatment in 2021. These specialist predators follow aphid densities, lagging behind in their arrival to the fields and population growth (Raymond et al. 2015 ), hence this low number of detections was expected in the early season. Regarding earthworms, their consumption appears to be sporadic; possibly due to them being low quality (Fawki et al. 2005 ) and, therefore, nonpreferred food sources when compared to other prey (Symondson et al. 2000 ), even for species known to consume them. In contrast to the earthworms, the specialisation on cereal leaf beetles was in line with their seasonal abundance (McPherson 1983 ) for both years. Lastly, the specialisation on aphids poses an interesting case study. As mentioned above, there was a difference in aphid abundance across years (Leote et al. 2024 ). The first year, with the infestation, specialisation on aphids showed a shallow inverted bell shape, which would be consistent with the peak in abundance. In contrast, there was no such effect in 2021, where specialisation was lower in the fertilised treatment. Following that line of thought, we can gain further insight on the trends in community composition and rewiring dissimilarity seen in 2020 and 2021, once again leaning on the difference in prey abundance between years, caused by aphids. While phenology would mostly account for “when” species turnover, competition should account, at least partially, for their behaviour and by “how many” species are replaced. As such, the reduction of competition among predators, induced by the aphid infestation, possibly contributed to the shallower fluctuations in community composition seen in 2020. Likewise, for the trophic link rewiring, there was a brief period around the 4th and 5th sessions, of the same year, when predators diet changed less; this coincided with peak aphid abundance in the fields, and is reflected by their centrality in the food webs. When looking at all the parameters measured in this study, most of them changed across time, in accordance with Thompson & Townsend ( 1999 ). At a short, intra-annual scale, there were changes in rewiring, much like in long-term studies (Olesen et al. 2011 ), but we also observed a temporal variability for the weighted connectance that other studies did not (Trøjelsgaard & Olesen 2016 ). Given the link between species richness and connectance (Banašek-Richter et al. 2009 , Calizza et al. 2019 ), this change can be explained by the species’ phenologies (Suzuki et al. 2023 ). Such changes over a short period imply that biological control, as an ecosystem service, may fluctuate over time in effectiveness. Adding to that, intraguild predation and general interference between generalist and specialist predators introduce another layer of complexity (Hindayana et al. 2001 , Janssen et al. 2006 , Lucas & Rosenheim 2011 , Liang et al. 2022 ). Moreover, we know that the services provided by certain species of hoverflies can change over time, depending on their life-cycle stage, for example going from predators to pollinators (Dunn et al. 2020 , Rodríguez-Gasol et al. 2020 ). Given the time frame of these changes, sampling the system without sufficient temporal replication within the same year, or once per year at the same period over multiple years, or locations, increases the likelihood of spatial or temporal uncoupling of species interactions (Jordano 2016 ), leading to their underrepresentation or absence altogether. Furthermore, beyond the design and replication of sampling, there is also the matter of the sampling technique used, as it can have distinct biases and implications, not just for food webs, but ecological networks in general (Jordano 2016 , Dormann et al. 2017 ). For the former in particular, the advantages and disadvantages of molecular analysis of food webs have been given considerable attention over the years (e.g: Symondson 2002 , King et al. 2008 , Clare 2014 , Symondson & Harwood 2014 ). However, among them, aspects such as the detection time of prey in the gut (Greenstone et al. 2007 löp et al. 2019 ), accounting for a wider time interval than direct observations, bear direct relevance for the interpretation of the data collected. Likewise, the inability to quantify consumption through molecular means, which may nonetheless be extremely difficult or near-impossible to do through observation for certain taxa in field studies (Symondson 2002 , Clare 2014 ), are also particularly relevant for biological control. Conclusion Quantitative assessments of consumption, how plastic food webs are, and how niches change over time, are likely to be of key importance for building a more in depth understanding of theoretical and empirical food webs. This will allow us to explore the finer details of food webs, without which it will be difficult to identify nodes that are either central within food webs or account for a considerable portion of the energy requirements of predators. These nodes are keystones of the food webs, and what we can show here is that how central each prey is within food webs is something that can, and does, change very quickly. Some prey are, for example, central in food webs only for a limited period of time, such as outbreak or pest species, whereas others are central throughout the season, such as staple food sources (e.g. springtails). The capacity of different prey (either as staple foods or as temporarily available resources) to sustain the predator community and affect predator species’ behaviour and competition, is also likely to play a large role in shaping food webs. That food web rewiring, in general, was greater than species turnover supports this, and indicates that food web changes, to a great extent, are due to shifts in behaviour, rather than species turnover. Additionally, the considerable short-term dynamics of our food webs, coupled with the tendency of most studies on ecosystem services to sample when services are needed (i.e. during the peak of the crop season), implies that the dynamic nature of food webs could be one of the reasons for why studies on biological control have produced inconsistent results (Raymond et al. 2015 , Karp et al. 2018 ). Parameters, such as specialisation, have been overestimated, due to the inevitability of incomplete sampling of empirical networks (Fründ et al. 2015, Jordano 2016 ). For the same reasons, it may also be that we systematically have either underestimated, or overestimated ecosystem functioning, depending on when sampling has occurred. This ought to be considered when sampling or modelling such systems, or when attempting to manage them to strengthen ecosystem services, such as biological control. Methods Study site The study site was in Kematen in Tirol, Austria, where spring barley ( Hordeum vulgare L.) was grown in six organically managed fields, three in 2020 and three in 2021. The fields were tilled, pressed and fertilised prior to sowing, between March and April, and the barley ripened around late July to early August, after which point it was harvested. Before sowing, each field was split and one half was fertilised with cattle manure and the other remaining unfertilised, as a control. The manure was applied independently by each field ’ s respective owner, at a rate of 1.5 metric tons (1 500kg) per hectare, using manure spreaders. For each treatment, four 5x5m sampling plots were drawn (Fig. 6 ), with no barriers (e.g.: cages or snail fences, Staudacher et al. 2018 ) within the fields. Sampling plots were at least 5m from the field edge to avoid edge effects, 10m way from one another along the field’s width, and between 15 to 20m along the length of the field. To avoid contamination of the fertilisation effect, plots were at least 25m away from the border between the treatments within a field. Sampling methods Sampling was conducted every two weeks, between the 21st of April and the 14th of July in 2020, and the 3rd of May and the 12th of July in 2021, and all fields in each year were sampled on the same week. Dry pitfall traps were active for a single day, with wood chips within, to catch live predators to obtain gut content for molecular dietary analysis (Staudacher et al. 2016 ). These predators were ground (Carabidae) and rove (Staphylinidae) beetles, and spiders (Araneae). Additionally, transects were carried out along a single side of each plot, on the outside border, to avoid trampling on the inside. Thirty 30 individual barley plant tillers were collected to count the number of aphids per tiller for community analysis (Leote et al. 2024 ). Molecular analysis We captured a total of 2404 ground beetles, 913 rove beetles and 567 spiders in 2020, and 1977 ground beetles, 891 rove beetles and 250 spiders in 2021. The beetles’ gut content and the spiders’ full bodies were extracted using a BioSprint 96 DNA Blood Kit (Qiagen, Hilden, Germany) on a QIAGEN Biosprint96® workstation, following the manufacturer’s recommendations. Each sample was analysed three times, with different multiplex-PCR assays. The first focused on several prey taxa (assay in Rennstam Rubbmark et al. 2019 ), earthworms, aphids, springtails and the cereal leaf beetle. The second targeted generalist and specialist predators, such as spiders, lacewings and ladybeetles (primers from Sint et al. 2014 and Staudacher et al. 2016 ). The third assay identified the genus of beetles consumed, from a selected set of common taxa consisting of Bembidion spp., Harpalus spp., Poecilus spp., Pterostichus spp., Philonthus carbonarius and Philonthus cognatus . The molecular gut content analysis is described in greater detail in Leote et al. ( 2024 ), but the multiplex PCR assays can also be found here, in the supplementary materials (Supplementary Tables 4 to 6), for convenience. Data analysis All data analysis was carried out using R 4.1.2 (R Core Team, 2024 ) and RStudio 2023.03.1 + 446 (Posit Team, 2024 ), with the packages lme4 (Bates et al. 2015 ) for linear mixed effects modelling (LMM), glmmTMB (Brooks et al. 2017 ) for generalised linear mixed effects modelling (GLMM) and the glm function from base R for generalised linear modelling (GLM). The package bipartite (Dormann et al. 2009 and Dormann 2011 ) was used for food web generation, and the calculation of several metrics: species level specialisation (Blüthgen et al. 2006 ), trophic link rewiring and community composition dissimilarity (Poisot et al. 2012 ), and weighted connectance (Bersier et al. 2002 ; using the mean proportion of diet detections as weights; Table 1 ). The package tidyverse (Wickham et al. 2019 ) was used for data processing, as well as to provide greater reproducibility of the data matrices obtained from the archived raw data, and ggplot2 (Wickham 2016) was used for graphic creation, for the same reasons. Plots with loess smoothing were created using the standard loess method from the geom_smooth function from ggplot2 , and standard error as the shaded ribbons. For dietary data, detections were averaged for all individuals of a given species in each unique combination of plot, treatment, field and sampling session; resulting in a mean proportion of the detections. As an example, the diet proportions for the species Poecilus cupreus in plot 1, fertilised treatment, field 1 on sampling session 1 was the mean of all P.cupreus individual diet detections; which were recorded either as 1 – detected, or 0 – not detected, for each diet target in the assays (Tables 2 and 3 ). All self-detections (e.g. P. cupreus testing positive for Poecilus in PCR assay 3) were manually removed (setting the detection to 0, instead of 1). Table 1 – Variables measured, with their respective calculations and what they represent in the context of this study. Variable Calculation Representation Weighted connectance Link density divided by species, using mean proportion of diet detections as weights Food web complexity Rewiring dissimilarity Food web dissimilarity explained by rewiring (for the shared species subweb) Trophic link dissimilarity Community dissimilarity Food web dissimilarity explained by species turnover Community turnover Species-level specialisation Standardized “partner diversity” for each species (Kullback-Leibler distance) Predator dietary overlap Table 2 – Example diet data matrix, with three individuals Poecilus cupreus , two captured in one sample, and one in another, each with a single prey target detection (A and B). Session Field Treatment Plot Species Prey A Prey B 1 1 Fertilised 1 P. cupreus 0 1 1 1 Fertilised 1 P. cupreus 1 0 1 1 Fertilised 2 P. cupreus 1 0 Table 3 – Example of diet matrix above, after calculating mean averages. By calculating the average mean diet of the species P. cupreus for a sample (unique combination of sampling session, field, treatment and plot), we obtained the mean dietary proportion for that predator species in its respective sample, which was then used as weights for the calculation of weighted connectance. Session Field Treatment Plot Species Prey A Prey B 1 1 Fertilised 1 P. cupreus 0.5 0.5 1 1 Fertilised 2 P. cupreus 1 0 Prior to testing, the heteroscedasticity of models was checked through visual inspection of quantile-quantile and residual vs predicted plots (Zuur et al. 2010 ). The error structures of GLMMs and GLMs with proportion data were adapted to ordered beta ( ordbeta family in glmmTMB ) and quasibinomial ( quasibinomial family in glm ), respectively. Due to the experimental design of our study, the experimental units were the fields, thus the sampling plots within them represent pseudoreplicates. In order to address the correlation among the plots in the same field, and minimize the likelihood of falsely detecting significant differences we followed the method in Zimmerman et al. (2021). By using mixed effects models with field (replicate-level grouping variable) as a random effect, it allowed us to correct for type I error and address the pseudoreplication bias. In order to explicitly test the hypothesis of our study within our models, we set up appropriate contrasts for each variable. The session variable, being categorical, had a sliding contrast so that each session would be directly compared to the previous one; fertilisation had a treatment contrast with the unfertilised, or control, treatment as the baseline; while field, used as a random factor, had a sum contrast, so that the grand mean of all fields would be the reference value, as opposed any individual one. The significance of the variables and interaction terms was assessed using the anova function of base R, through Chi-squared (χ 2 ) tests for the mixed effects models, or with F tests for the GLMs, with the threshold defined at 0.05. Three sets of linear mixed effects models (LMM) were created for each year, to test the effects of time and fertilisation on different variables. Our first model analysed how sampling session and fertilisation affected the weighted connectance of the food webs, with field as a random factor. The second and third models looked at how session and fertilisation affected the rewiring dissimilarity and community composition dissimilarity, respectively, with field as a random factor. The analysis of the species level specialisation ( d ’) for each prey target was carried out with generalised linear mixed effects models (GLMM), or with generalised linear models (GLM) when the number of diet detections was too low to allow fitting mixed effect models. In both cases, the models tested the effects of sampling session and fertilisation on the standardized d’ , with the GLMMs using the sampling field as a random factor. In total there were seven models for each year, GLMMs for aphid and IGP beetle d’ , and GLMs for cereal leaf beetle, earthworms, specialists, IGP spiders. For the springtails in particular, neither GLMMs nor GLMs worked, as over 95% of samples returned a d’ of zero, thus it was not possible to fit any models. Declarations Conflicts of interest: The authors declare they have no conflicts of interest. Author Contribution Author contributions: PNBL and ORRR carried out the field work, PNBL carried out the lab work, PNBL and ORRR conducted the data analysis, PNBL wrote the first draft, ORRR and MT designed the study and procured the funding, all authors contributed to the revisions. Acknowledgement The authors would like to thank Britta Frei, Claire Rotondo and all the field assistants, Karin Wastian for designing the Philonthus spp. primers and Christiane Zeisler for the support with the lab work and molecular data analyses. This research was funded in whole/in part by the Austrian Science Fund (FWF) grant DOI https://doi.org/10.55776/P32964. For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. The first author was also funded by the Vice-Rectorate for Research of the University of Innsbruck with the Excellence Scholarship for Doctoral Programs. Data Availability Data availability statement: All data has been archived in the repository Figshare (https://figshare.com/), under the DOI: https://doi.org/10.6084/m9.figshare.26893354https://doi.org/10.6084/m9.figshare.26893405 https://doi.org/10.6084/m9.figshare.26893624 https://doi.org/10.6084/m9.figshare.26893630 https://doi.org/10.6084/m9.figshare.26893690 https://doi.org/10.6084/m9.figshare.26893759 References Banašek-Richter, C. et al. Complexity in quantitative food webs. Ecology 90 (6), 1470–1477. https://doi.org/10.1890/08-2207.1 (2009). Bates, D. et al. Package ‘lme4’. convergence, 12(1), 2. (2015). https://doi.org/10.18637/jss.v067.i01 Bauer, T. Prey-capture in a ground-beetle larva. Anim. Behav. 30 (1), 203–208. https://doi.org/10.1016/S0003-3472(82)80255-8 (1982). Bersier, L. F., Banašek-Richter, C. & Cattin, M. F. Quantitative descriptors of food-web matrices. Ecology 83 (9), 2394–2407. https://doi.org/10.1890/0012-9658(2002 (2002). )083[2394:QDOFWM]2.0.CO;2. Birkhofer, K., Wise, D. H. & Scheu, S. Subsidy from the detrital food web, but not microhabitat complexity, affects the role of generalist predators in an aboveground herbivore food web. Oikos 117 (4), 494–500. https://doi.org/10.1111/j.0030-1299.2008.16361.x (2008). Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialisation in species interaction networks. BMC Ecol. 6 (1), 1–12. https://doi.org/10.1186/1472-6785-6-9 (2006). Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9 (2), 378–400. https://doi.org/10.3929/ethz-b-000240890 (2017). Calizza, E., Rossi, L., Careddu, G., Sporta Caputi, S. & Costantini, M. L. Species richness and vulnerability to disturbance propagation in real food webs. Sci. Rep. 9 (1), 19331. https://doi.org/10.1038/s41598-019-55960-8 (2019). Clare, E. L. Molecular detection of trophic interactions: emerging trends, distinct advantages, significant considerations and conservation applications. Evol. Appl. 7 (9), 1144–1157. https://doi.org/10.1111/eva.12225 (2014). Collins, K. L., Boatman, N. D., Wilcox, A., Holland, J. M. & Chaney, K. Influence of beetle banks on cereal aphid predation in winter wheat. Agric. Ecosyst. Environ. 93 (1–3), 337–350. https://doi.org/10.1016/S0167-8809(01)00340-1 (2002). Cuff, J. P. et al. The predator problem and PCR primers in molecular dietary analysis: swamped or silenced; depth or breadth? Mol. Ecol. Resour. 23 (1), 41–51. https://doi.org/10.1111/1755-0998.13705 (2023). Cuff, J. P., Windsor, F. M., Tercel, M. P., Kitson, J. J. & Evans, D. M. Overcoming the pitfalls of merging dietary metabarcoding into ecological networks. Methods Ecol. Evol. 13 (3), 545–559. https://doi.org/10.1111/2041-210X.13796 (2022). Deagle, B. E., Pansu, J., McInnes, J. & Traugott, M. 4 Revealing animal diet and food webs through DNA metabarcoding. Appl. Environ. Genomics , 30. (2023). Diehl, E., Sereda, E., Wolters, V. & Birkhofer, K. Effects of predator specialization, host plant and climate on biological control of aphids by natural enemies: a meta-analysis. J. Appl. Ecol. 50 (1), 262–270. https://doi.org/10.1111/1365-2664.12032 (2013). Dormann, C. F., Fründ, J., Blüthgen, N. & Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. (2009). Dormann, C. F. How to be a specialist? Quantifying specialisation in pollination networks. Netw. Biology . 1 (1), 1–20 (2011). Dormann, C. F., Fründ, J. & Schaefer, H. M. Identifying causes of patterns in ecological networks: opportunities and limitations. Annu. Rev. Ecol. Evol. Syst. 48 (1), 559–584. https://doi.org/10.1146/annurev-ecolsys-110316-022928 (2017). Dunn, L., Lequerica, M., Reid, C. R. & Latty, T. Dual ecosystem services of syrphid flies (Diptera: Syrphidae): pollinators and biological control agents. Pest Manag. Sci. 76 (6), 1973–1979. https://doi.org/10.1002/ps.5807 (2020). Eitzinger, B. & Traugott, M. Which prey sustains cold-adapted invertebrate generalist predators in arable land? Examining prey choices by molecular gut‐content analysis. J. Appl. Ecol. 48 (3), 591–599. https://doi.org/10.1111/j.1365-2664.2010.01947.x (2011). Fawki, S., Smerup, S. & Toft, S. Food preferences and food value for the carabid beetles Pterostichus melanarius , P. versicolor and Carabus nemoralis . In Proceedings of the 11th European Carabidologist Meeting (pp. 99–109). (2005), January. Foley, J. A. et al. Solutions for a cultivated planet. Nature 478 (7369), 337–342. https://doi.org/10.1038/nature10452 (2011). Frainer, A., McKie, B. G. & Malmqvist, B. When does diversity matter? Species functional diversity and ecosystem functioning across habitats and seasons in a field experiment. J. Anim. Ecol. 83 (2), 460–469. https://doi.org/10.1111/1365-2656.12142 (2014). Fründ, J., McCann, K. S. & Williams, N. M. Sampling bias is a challenge for quantifying specialization and network structure: lessons from a quantitative niche model. Oikos 125 (4), 502–513. https://doi.org/10.1111/oik.02256 (2016). Fülöp, D., Szita, É., Gerstenbrand, R., Tholt, G. & Samu, F. Consuming alternative prey does not influence the DNA detectability half-life of pest prey in spider gut contents. PeerJ 7 , e7680. https://doi.org/10.7717/peerj.7680 (2019). Gadelha, Y. E., Lange, D., Dattilo, W. & Lopes, B. C. Phenological phases of the host plant shape plant–treehopper interaction networks. Ecol. Entomol. 42 (6), 827–837. https://doi.org/10.1111/een.12457 (2017). Greenstone, M. H., Rowley, D. L., Weber, D. C., Payton, M. E. & Hawthorne, D. J. Feeding mode and prey detectability half-lives in molecular gut-content analysis: an example with two predators of the Colorado potato beetle. Bull. Entomol. Res. 97 (2), 201–209. https://doi.org/10.1017/S000748530700497X (2007). Hindayana, D., Meyhöfer, R., Scholz, D. & Poehling, H. M. Intraguild predation among the hoverfly Episyrphus balteatus de Geer (Diptera: Syrphidae) and other aphidophagous predators. Biol. Control . 20 (3), 236–246. https://doi.org/10.1006/bcon.2000.0895 (2001). Holland, J. M. et al. The spatial dynamics and movement of Pterostichus melanarius and P. madidus (Carabidae) between and within arable fields in the UK. Int. J. Ecol. Environ. Sci. 30 , 35–53 (2004). Holopainen, J. K. & Helenius, J. Gut contents of ground beetles (Col., Carabidae), and activity of these and other epigeal predators during an outbreak of Rhopalosiphum padi (Hom., Aphididae). Acta Agriculturae Scand. B-Plant Soil. Sci. 42 (1), 57–61. https://doi.org/10.1080/09064719209410199 (1992). Janssen, A. et al. Intraguild predation usually does not disrupt biological control. Trophic guild Biol. Interact. control , 21–44. (2006). Jordano, P. Sampling networks of ecological interactions. Funct. Ecol. 30 (12), 1883–1893. https://doi.org/10.1111/1365-2435.12763 (2016). Kaartinen, R. & Roslin, T. High temporal consistency in quantitative food web structure in the face of extreme species turnover. Oikos 121 (11), 1771–1782. https://doi.org/10.1111/j.1600-0706.2012.20108.x (2012). Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proceedings of the National Academy of Sciences, 115(33), E7863-E7870. (2018). https://doi.org/10.1073/pnas.1800042115 Kheirodin, A., Sharanowski, B. J., Cárcamo, H. A. & Costamagna, A. C. Consumption of cereal leaf beetle, Oulema melanopus , by generalist predators in wheat fields detected by molecular analysis. Entomol. Exp. Appl. 168 (1), 59–69. https://doi.org/10.1111/eea.12835 (2020). Kielty, J. P., Allen-Williams, L. J., Underwood, N. & Eastwood, E. A. Behavioral responses of three species of ground beetle (Coleoptera: Carabidae) to olfactory cues associated with prey and habitat. J. Insect Behav. 9 , 237–250. https://doi.org/10.1007/BF02213868 (1996). King, R. A., Read, D. S., Traugott, M. & Symondson, W. O. C. INVITED REVIEW: Molecular analysis of predation: a review of best practice for DNA-based approaches. Mol. Ecol. 17 (4), 947–963. https://doi.org/10.1111/j.1365-294X.2007.03613.x (2008). Leote, P. N. B., Rubbmark, O. R. R. & Traugott, M. High resolution temporal data shows how increasing prey availability reduces early season intraguild predation and pest spread in cereal crops. Biol. Control . 105549. https://doi.org/10.1016/j.biocontrol.2024.105549 (2024). Lucas, É. & Rosenheim, J. A. Influence of extraguild prey density on intraguild predation by heteropteran predators: A review of the evidence and a case study. Biol. Control . 59 (1), 61–67. https://doi.org/10.1016/j.biocontrol.2011.05.010 (2011). Liang, Y. et al. Flower provision reduces intraguild predation between predators and increases aphid biocontrol in tomato. J. Pest Sci. 95 (1), 461–472. https://doi.org/10.1007/s10340-021-01396-x (2022). McPherson, R. M. Seasonal abundance of cereal leaf beetles (Coleoptera: Chrysomelidae) in Virginia small grains and corn. J. Econ. Entomol. 76 (6), 1269–1272. https://doi.org/10.1093/jee/76.6.1269 (1983). Michalko, R., Pekár, S. & Entling, M. H. An updated perspective on spiders as generalist predators in biological control. Oecologia 189 , 21–36. https://doi.org/10.1007/s00442-018-4313-1 (2019). Morente-López, J., Lara-Romero, C., Ornosa, C. & Iriondo, J. M. Phenology drives species interactions and modularity in a plant-flower visitor network. Sci. Rep. 8 (1), 9386. https://doi.org/10.1038/s41598-018-27725-2 (2018). Mouillot, D., Graham, N. A., Villéger, S., Mason, N. W. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28 (3), 167–177. https://doi.org/10.1016/j.tree.2012.10.004 (2013). Neutel, A. M. et al. Reconciling complexity with stability in naturally assembling food webs. Nature 449 (7162), 599–602. https://doi.org/10.1038/nature06154 (2007). Nie, S. et al. Will a large complex system be productive? Ecol. Lett. 26 (8), 1325–1335. https://doi.org/10.1111/ele.14242 (2023). Öberg, S. & Ekbom, B. Recolonisation and distribution of spiders and carabids in cereal fields after spring sowing. Ann. Appl. Biol. 149 (2), 203–211. https://doi.org/10.1111/j.1744-7348.2006.00088.x (2006). Olesen, J. M., Stefanescu, C. & Traveset, A. Strong, long-term temporal dynamics of an ecological network. PloS one . 6 (11), e26455. https://doi.org/10.1371/journal.pone.0026455 (2011). Parker, S. M. & Huryn, A. D. Disturbance and productivity as codeterminants of stream food web complexity in the Arctic. Limnol. Oceanogr. 58 (6), 2158–2170. https://doi.org/10.4319/lo.2013.58.6.2158 (2013). Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15 (12), 1353–1361. https://doi.org/10.1111/ele.12002 (2012). Polis, G. A. & Strong, D. R. Food web complexity and community dynamics. Am. Nat. 147 (5), 813–846. https://doi.org/10.1086/285880 (1996). Posit team. RStudio: Integrated Development Environment for R. Posit Software, (2024). PBC & Boston MA. http://www.posit.co/ R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. (2024). https://www.R-project.org/ Raymond, L., Ortiz-Martínez, S. A. & Lavandero, B. Temporal variability of aphid biological control in contrasting landscape contexts. Biol. Control . 90 , 148–156. https://doi.org/10.1016/j.biocontrol.2015.06.011 (2015). Rennstam Rubbmark, O., Sint, D., Cupic, S. & Traugott, M. When to use next generation sequencing or diagnostic PCR in diet analyses. Mol. Ecol. Resour. 19 (2), 388–399. https://doi.org/10.1111/1755-0998.12974 (2019). Rodríguez-Gasol, N., Alins, G., Veronesi, E. R. & Wratten, S. The ecology of predatory hoverflies as ecosystem-service providers in agricultural systems. Biol. Control . 151 , 104405. https://doi.org/10.1016/j.biocontrol.2020.104405 (2020). Rosenheim, J. A., Limburg, D. D. & Colfer, R. G. Impact of generalist predators on a biological control agent, Chrysoperla carnea: direct observations. Ecol. Appl. 9 (2), 409–417. https://doi.org/10.1890/1051-0761(1999)009[0409:IOGPOA]2.0.CO;2 (1999). Rowen, E., Tooker, J. F. & Blubaugh, C. K. Managing fertility with animal waste to promote arthropod pest suppression. Biol. Control . 134 , 130–140. https://doi.org/10.1016/j.biocontrol.2019.04.012 (2019). Seric Jelaska, L., Symondson, O. C. & W Predation on epigeic, endogeic and anecic earthworms by carabids active in spring and autumn. Periodicum biologorum . 118 (3), 281–289. https://doi.org/10.18054/pb.2016.118.3.4709 (2016). Sint, D., Niederklapfer, B., Kaufmann, R. & Traugott, M. Group-specific multiplex PCR detection systems for the identification of flying insect prey. PloS one . 9 (12), e115501. https://doi.org/10.1371/journal.pone.0115501 (2014). Snyder, W. E. & Ives, A. R. Generalist predators disrupt biological control by a specialist parasitoid. Ecology 82 (3), 705–716. https://doi.org/10.1890/0012-9658(2001)082 (2001). [0705:GPDBCB]2.0.CO;2. Snyder, W. E. & Ives, A. R. Interactions between specialist and generalist natural enemies: parasitoids, predators, and pea aphid biocontrol. Ecology 84 (1), 91–107. https://doi.org/10.1890/0012-9658(2003 (2003). )084[0091:IBSAGN]2.0.CO;2. Staudacher, K., Jonsson, M. & Traugott, M. Diagnostic PCR assays to unravel food web interactions in cereal crops with focus on biological control of aphids. J. Pest Sci. 89 , 281–293. https://doi.org/10.1007/s10340-015-0685-8 (2016). Staudacher, K. et al. Habitat heterogeneity induces rapid changes in the feeding behaviour of generalist arthropod predators. Funct. Ecol. 32 (3), 809–819. https://doi.org/10.1111/1365-2435.13028 (2018). Sunderland, K. D. & Vickerman, G. P. Aphid feeding by some polyphagous predators in relation to aphid density in cereal fields. J. Appl. Ecol. 389–396. https://doi.org/10.2307/2402334 (1980). Suzuki, S. S., Baba, Y. G. & Toju, H. Dynamics of species-rich predator–prey networks and seasonal alternations of core species. Nat. Ecol. Evol. 7 (9), 1432–1443. https://doi.org/10.1038/s41559-023-02130-9 (2023). Symondson, W. O. C. Molecular identification of prey in predator diets. Mol. Ecol. 11 (4), 627–641. https://doi.org/10.1046/j.1365-294X.2002.01471.x (2002). Symondson, W. O. C., Glen, D. M., Erickson, M. L., Liddell, J. E. & Langdon, C. J. Do earthworms help to sustain the slug predator Pterostichus melanarius (Coleoptera: Carabidae) within crops? Investigations using monoclonal antibodies. Mol. Ecol. 9 (9), 1279–1292. https://doi.org/10.1046/j.1365-294x.2000.01006.x (2000). Symondson, W. O. C. & Harwood, J. D. Special issue on molecular detection of trophic interactions: Unpicking the tangled bank. Mol. Ecol. 23 (15), 3601–3604. http://dx.doi.org/10.1111/mec.12831 (2014). Terry, J. C. D., Morris, R. J. & Bonsall, M. B. Trophic interaction modifications: an empirical and theoretical framework. Ecol. Lett. 20 (10), 1219–1230. https://doi.org/10.1111/ele.12824 (2017). Thies, C. et al. The relationship between agricultural intensification and biological control: experimental tests across Europe. Ecol. Appl. 21 (6), 2187–2196. https://doi.org/10.1890/10-0929.1 (2011). Thomas, C. G., Holland, J. M. & Brown, N. J. The spatial distribution of carabid beetles in agricultural landscapes 305–344 (The agroecology of carabid beetles, 2002). Thompson, R. M. & Townsend, C. R. The effect of seasonal variation on the community structure and food-web attributes of two streams: implications for food-web science 75–88 (Oikos, 1999). https://doi.org/10.2307/3546998 Trøjelsgaard, K. & Olesen, J. M. Ecological networks in motion: micro-and macroscopic variability across scales. Funct. Ecol. 30 (12), 1926–1935. https://doi.org/10.1111/1365-2435.12710 (2016). Van de Vijver, E. et al. Inter-and intrafield distribution of cereal leaf beetle species (Coleoptera: Chrysomelidae) in Belgian winter wheat. Environ. Entomol. 48 (2), 276–283. https://doi.org/10.1111/eea.12835 (2019). Van Emden, H. F. & Harrington, R. (eds) Aphids as crop pests (Cabi, 2017). H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer- New York, (2016). Wickham, H. et al. Welcome to the Tidyverse. J. open. source Softw. 4 (43), 1686. https://doi.org/10.21105/joss.01686 (2019). Worm, B. & Duffy, J. E. Biodiversity, productivity and stability in real food webs. Trends Ecol. Evol. 18 (12), 628–632. https://doi.org/10.1016/j.tree.2003.09.003 (2003). Zadoks, J. C., Chang, T. T. & Konzak, C. F. A decimal code for the growth stages of cereals. Weed Res. 14 (6), 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x (1974). Zimmermann, P., Tasser, E., Leitinger, G. & Tappeiner, U. Effects of land-use and land-cover pattern on landscape-scale biodiversity in the European Alps. Agric. Ecosyst. Environ. 139 (1–2), 13–22. https://doi.org/10.1016/j.agee.2010.06.010 (2010). Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1 (1), 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x (2010). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials28052025.docx Cite Share Download PDF Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 28 Jun, 2025 Reviewers agreed at journal 28 Jun, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 26 Jun, 2025 Editor invited by journal 13 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 29 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6774692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":478272241,"identity":"bca2fc55-6485-4b35-a2c5-f5bc01599a5f","order_by":0,"name":"Pedro Nuno Branco Leote","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACHsYDIIofRCQUEKeFAajFgEGyAaTFgBQtBmC7iNFiLt174MCHij9yxudXJ354YMAgzy92AL8WyznnEg7OOGNgbHbj7WYJoMMMZ85OwK/F4EaOwWHeNoPEbTfObgBpSTC4TYyWv20G9ZtnnN38g3gtjG0GCQb8vduIs8VyRl7CwZ4zxoYzbvBus0gwkCDsF3OJ3IMPflTIyfP3n91880eFjTy/NCGHwVkSYJUS+JWjauE/QFj1KBgFo2AUjEwAAHHWSMPUseJRAAAAAElFTkSuQmCC","orcid":"","institution":"University of Innsbruck","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"Nuno Branco","lastName":"Leote","suffix":""},{"id":478272243,"identity":"f2fe314e-e4ad-4898-b817-6c8a24d7574f","order_by":1,"name":"Michael Traugott","email":"","orcid":"","institution":"University of Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Traugott","suffix":""},{"id":478272244,"identity":"96fd07e2-fe5e-4fa4-b0bd-65e264519db5","order_by":2,"name":"Oskar Ragnar Rennstam Rubbmark","email":"","orcid":"","institution":"University of Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Oskar","middleName":"Ragnar Rennstam","lastName":"Rubbmark","suffix":""}],"badges":[],"createdAt":"2025-05-29 08:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6774692/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6774692/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-25603-2","type":"published","date":"2025-11-24T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85769586,"identity":"5e99eb21-819e-414a-ad54-08fd8f110308","added_by":"auto","created_at":"2025-07-01 13:18:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7962032,"visible":true,"origin":"","legend":"\u003cp\u003eFood web diagrams for each sampling session (numbers) in 2020 and 2021, respectively, each group has been colour-coded, with node diameter corresponding to the eigenvector centrality and line width representing the diet detection proportion of the trophic link. From top to bottom in the figure; Predators: yellow – Carabid beetles, orange – Staphylinid beetles, brown – spiders; Prey: bright red – aphids, dark red – cereal leaf beetle, dark green – earthworms, bright green – springtails, bright blue – intraguild predation prey (beetles and spiders), dark orange – aphid specialists (hoverflies, ladybugs and lacewings).\u003c/p\u003e","description":"","filename":"Fig1Allfoodwebs202021.png","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/3b264d03d498466d78078403.png"},{"id":85768032,"identity":"ad06af5d-dc19-445c-9c1c-652bcbf2636e","added_by":"auto","created_at":"2025-07-01 12:54:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":424558,"visible":true,"origin":"","legend":"\u003cp\u003eFood web complexity across sampling sessions and fertilisation treatments for 2020 and 2021, respectively. Complexity was measured by calculating weighted connectance (Bersier et al. 2002), using the networklevel function of the bipartite R package and the mean proportion of diet detections as weights. The lines correspond to loess smoothing, using the geom_smooth function of the ggplot2 R package, with the standard error as the shaded areas.\u003c/p\u003e","description":"","filename":"Fig2Weightedconnectance202021.png","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/98e75a1d46f5b16144970a9b.png"},{"id":85768034,"identity":"23607305-0ab0-4d67-817e-238f2e8ca364","added_by":"auto","created_at":"2025-07-01 12:54:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":593730,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies level specialisation (Blüthgen’s d’) across sampling sessions and fertilisation treatments, in 2020, by prey group. The lines correspond to loess smoothing, using the geom_smooth function of the ggplot2 R package, with the standard error as the shaded areas. CLB – cereal leaf beetle (Oulema melanopus); IGP beetles – intraguild predation, corresponds to generalist predators consumed by sampled generalist predators; Specialists – aphid specialist predators consumed by sampled generalist predators.\u003c/p\u003e","description":"","filename":"Fig3Specieslevelspec2020.png","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/7816f3c21b2aed170d3a4bf9.png"},{"id":85768266,"identity":"1015708b-7851-484f-af48-acee25480492","added_by":"auto","created_at":"2025-07-01 13:02:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":616660,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies level specialisation (Blüthgen’s d’) across sampling sessions and fertilisation treatments, in 2021, by prey group. The lines correspond to loess smoothing, using the geom_smooth function of the ggplot2 R package, with the standard error as the shaded areas. CLB – cereal leaf beetle (Oulema melanopus); IGP beetles – intraguild predation, corresponds to generalist predators consumed by sampled generalist predators; Specialists – aphid specialist predators consumed by sampled generalist predators.\u003c/p\u003e","description":"","filename":"Fig4Specieslevelspec2021.png","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/6086c14f18403e1da10c2d06.png"},{"id":85769241,"identity":"699419b5-1351-42a4-9c08-a08de119867a","added_by":"auto","created_at":"2025-07-01 13:10:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":719832,"visible":true,"origin":"","legend":"\u003cp\u003eFood web dissimilarity across sampling sessions, for 2020 and 2021, respectively. Two indices were calculated, the trophic link rewiring and community dissimilarity using the betalinkr function from the bipartite R package. The lines correspond to loess smoothing, using the geom_smooth function of the ggplot2 R package, with the standard error as the shaded areas.\u003c/p\u003e","description":"","filename":"Fig5Dissimilarities202021.png","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/a5a00f95d46227a0124a20fd.png"},{"id":85768264,"identity":"e46aaecd-371c-4027-bf63-5b3b0b8f5ee1","added_by":"auto","created_at":"2025-07-01 13:02:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66517,"visible":true,"origin":"","legend":"\u003cp\u003eField and sampling plot schematic (not to scale). Each field had one half of its area fertilised with manure at rate of 1.5t/ha, and the other half was left untreated as a control, with no physical barriers between treatments (e.g no snail fences). Community sampling was done through wet pitfall trap sampling, while live predators were collected wity dry pitfall traps to sample their diets. Plant collecting transects were done alongside the margins of the sampling plots, to count the number of aphids per cereal tiller in the fields. Sampling plots were at least 5m from the field edge to avoid edge effects, 10m way from one another along the field’s width, and between 15 to 20m along the length of the field. To avoid contamination of the fertilisation effect, plots were at least 25m away from the border between the treatments within a field.\u003c/p\u003e","description":"","filename":"Fig6MethodsFieldschematic.png","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/ef159e96f13996be265926fe.png"},{"id":97178762,"identity":"de8f2802-9555-4a70-9268-ed99576aee69","added_by":"auto","created_at":"2025-12-01 16:13:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10955957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/1f54b445-1f27-4e4a-98cd-f5f7f9c182c4.pdf"},{"id":85768068,"identity":"f867d5c8-4817-4665-b581-b4a226c4dfcd","added_by":"auto","created_at":"2025-07-01 12:54:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22175680,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials28052025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6774692/v1/cbe90285419d7e8c37d3390b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The dynamic nature of cereal food webs challenges the suitability of snapshot sampling for assessing ecosystem services","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs seasons progress and habitats change, it is well known that there will be phenological shifts in the communities and interaction networks that the species that inhabit most temperate ecosystem form (Gadelha et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Morente-L\u0026oacute;pez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, we also know that such changes, regardless if natural (\u003cem\u003ee.g.\u003c/em\u003e seasonal variation, phenology, weather) or human-induced \u003cem\u003e(e.g.\u003c/em\u003e agricultural management such as the application of chemicals, tilling, harvesting), can influence the strength and reliability of ecosystem services that human food production relies on (Foley et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, to understand how to promote ecosystem services, we first need to understand the changes that the species communities that inhabit agro-ecosystems go through. Not least, because we know from theory that timing, functional roles and redundancy will be decided by a community-wide process, that depend on seasonal changes that occur within a habitat (Mouillot et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Frainer et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn arable systems, habitat changes over the course of a year are quite apparent. In just a few months, non-perennial crops, such as cereals, undergo a cycle of sowing, growth, ripening and harvest, each of which is associated with a starkly contrasting habitat setting (Zadoks et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). However, we so far have a relatively poor understanding of what these changes mean for the food web interactions that live within arable fields. It is, for example, likely that food webs, in response to habitat changes occurring during the crop growth, will go through cycles of greater complexity when resources are abundant and lesser complexity when resources are scarce (Thompson \u0026amp; Townsend \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Food web changes also occur because consumers will modify their behaviour, by adapting their realized niches to either avoid competition or explore new resources as different food sources become more or less available (Terry et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), or in response to phenological changes in the predator community (Kaartinen \u0026amp; Roslin \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, hardly any empirical data is available on how food webs respond to changes in the ecosystem induced by phenology and resource availability.\u003c/p\u003e \u003cp\u003eThere are several reasons for why data with sufficient temporal resolution is missing and has been difficult to attain, such as the logistics required for the repeated sampling, the methodological challenges of developing assays and/or primers with the necessary balance of specificity and sensitivity, the costs of infrastructure and equipment needed, or the knowledge to curate and analyse such data (Cuff et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Cuff et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Deagle et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we address this important knowledge gap by using a molecular analysis of trophic interactions between generalist (ground and rove beetles, and spiders) and specialist predators (lacewings, ladybugs and hoverfly larvae), detritivore (earthworms and springtails) and herbivore prey (aphids and cereal leaf beetle). This work is a follow-up to a previous article, using the same community and diet data, where we focused on the effects of fertilization on prey abundance, intraguild predation and biological control (Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both are a part of a larger study we conducted in Central European barley fields, where we investigated food web interactions, as well as changes in communities, every two weeks throughout the cereal growth period, in replicated fields across two years. The fields were also fertilised on half of their respective areas, to induce different baseline primary productivity and prey abundances (Rowen et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our system is characterized by herbivorous insects, including the cereal leaf beetle \u003cem\u003eOulema melanopus\u003c/em\u003e, and three species of aphids that commonly occur in cereals, \u003cem\u003eRhopalosiphum padi\u003c/em\u003e, \u003cem\u003eMetopolophium dirhodum\u003c/em\u003e, and \u003cem\u003eSitobion avenae\u003c/em\u003e (Van Emden \u0026amp; Harrington \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Van de Vijver et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Each of these species is attacked by a suite of natural enemies, among them generalist predators (Thies et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Kheirodin et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These are thought to reduce the chance of pests to become established in fields, whereas once they have reached higher densities, they are more effectively controlled by specialized enemies that are drawn to highly infested fields (Snyder \u0026amp; Ives \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Outside of these periods of greater prey abundance, detritivore prey such as earthworms and springtails are valuable food sources, that can help sustain predator populations outside of the cereal growth season (Symondson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); affecting adult populations, and their potential for pest control, in the following crop season (Eitzinger \u0026amp; Traugott \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e We devised three hypotheses, which conceptualize how food webs change according to our current understanding of food web and functional agro-ecology. First, the increase in detritivore and herbivore prey abundance on the fertilised side of the field and the lower competition (observed in Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), should allow predators to explore a wider dietary niche early in the season, which should be reflected in a greater number of realized trophic links. As such, we predict food web complexity, measured with weighted connectance (Bersier et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), to be higher in the fertilised treatment. Moreover, because connectance decreases with species richness (Banašek-Richter et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Calizza et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we expect weighted connectance to be lowest during the middle of the cereal growth season, when primary productivity and diversity in the crop fields is expected to be highest (Thompson \u0026amp; Townsend \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Worm \u0026amp; Duffy \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Nie et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our second hypothesis, we expect that specialisation on individual prey \u003cem\u003etaxa\u003c/em\u003e, measured with Bl\u0026uuml;thgen\u0026rsquo;s \u003cem\u003ed\u0026rsquo;\u003c/em\u003e (Bl\u0026uuml;thgen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), should change over time in response to the availability of each prey, and be lower in the fertilised treatment; where we expect that an increased prey availability should allow predators to have more overlapping diets. However, over time we also expect specialisation should increase, as lowering competition and interference also will enable predators to focus on preferred prey. Lastly, in our third hypothesis, we expect trophic link rewiring or community composition dissimilarity (Poisot et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to reach a peak towards the middle of the season, in response to the drastic habitat changes that occur as the cereal ripens and many herbivorous prey species migrate away from fields.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFood webs\u003c/h2\u003e \u003cp\u003eThe bipartite food webs for each sampling session of each year are described below (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, but please see the supplementary Figs.\u0026nbsp;1 through 13 for each food web with higher resolution), to show the relative importance of each group (colour coded), through the eigenvector centrality (node diameter), and diet detection proportion (line width). First and foremost, the number of trophic links established changed over time (supplementary tables 9 and 10, and supplementary Fig.\u0026nbsp;14 for the link densities, and supplementary tables 11 and 12 for eigenvector centralities). In 2020 and 2021, mean link density was 2.30 and 1.50, respectively, at the start of the season; then in the first year it reached a peak of 4.00 during the 5th session, while in the second year it was 3.44 during the 4th session. Finally, by the end of sampling, link density was 3.30 for 2020, and 2.87 for 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpringtails (bright green) remained central throughout the season, with an eigenvector centrality ranging between 0.931 and 1 in 2020, and 0.950 and 1 in 2021. Earthworms (dark green), although less central than springtails, had a centrality between 0.613\u0026ndash;0.826 early on and 0.628\u0026ndash;0.800 late in the season in 2020, decreasing to 0.208 when aphids were at their peak in the 4th sampling session. In 2021, however, earthworms were more consistently central to the food webs, with a centrality between 0.646\u0026ndash;0.870 with the exception of the 5th sampling session, when their centrality decreased to 0.422.\u003c/p\u003e \u003cp\u003eOn the other hand, aphids (bright red) gained relevance in the middle sampling sessions in both years, rising in centrality from a mean of 0.121 and 0.131, to 0.571 and 0.598 in 2020 and 2021, respectively. The cereal leaf beetle (CLB, dark red), showed a peak in centrality of 0.573 and 0.858, during the 3rd session in both years.\u003c/p\u003e \u003cp\u003eThe intraguild prey (IGP, bright blue), consisting of beetles and spiders, were more central at the end of the season in 2020, with a centrality of 0.395, when compared to the rest of sampling that ranged from 0.153 to 0.238. In the following year, centrality had two peaks, one in the 3rd session with 0.351 and later in the 5th session with 0.416. The specialist aphid predators (dark orange), encompassing ladybugs, lacewings and hoverflies (in larval stage), were relatively peripheral to the food webs, with centrality values as low as 0.034 in 2020 and 0 in 2021. Nonetheless, they displayed a peak of 0.289 and 0.200 centrality, during the 5th sampling session in the corresponding years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFood web complexity\u003c/h3\u003e\n\u003cp\u003eThe food web weighted connectance in both years was correlated to sampling session (2020 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;25.617, df\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;168, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 2021 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;41.091, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;144, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but was not affected by fertilisation (2020 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.589, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;168, p\u0026thinsp;=\u0026thinsp;0.292; 2021 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.027, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;144, p\u0026thinsp;=\u0026thinsp;0.869). In the first year, the weighted connectance rose until the 4th session, during late stem elongation, then decreased towards the end of the season, while in the second year it decreased from the beginning until the fourth session, then slowly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSpecialisation\u003c/h3\u003e\n\u003cp\u003eThe species level specialisation (\u003cem\u003ed\u0026rsquo;\u003c/em\u003e, Bl\u0026uuml;thgen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) had different responses to fertilisation and time depending on the prey target. Starting with prey in 2020, the specialisation on aphids was affected by both fertilisation and session, as a joint or interaction effect (GLMM \u0026ndash; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;14.479, df\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;109, p\u0026thinsp;=\u0026thinsp;0.025), with the fertilised treatment having higher values towards the end of the season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the cereal leaf beetle, fertilisation had no effect on specialisation (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;1.383, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;37, p\u0026thinsp;=\u0026thinsp;0.249), but session did (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;5.315, df\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a slight increase towards the end of sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Earthworm specialisation, on the other hand, was neither affected by fertilisation (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.914, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;41, p\u0026thinsp;=\u0026thinsp;0.346) nor session (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.319, df\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;41, p\u0026thinsp;=\u0026thinsp;0.922, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For springtails, 95% (40/42) of all \u003cem\u003ed\u0026rsquo;\u003c/em\u003e values calculated were 0 (\u003cem\u003ed\u0026rsquo;\u003c/em\u003e = 0 indicates a complete overlap on a given target), as a result, a model could not be estimated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding intraguild prey, the specialisation on beetles was not affected by either fertilisation treatment (GLMM \u0026ndash; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.937, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;158, p\u0026thinsp;=\u0026thinsp;0.333), nor session (GLMM \u0026ndash; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.559, df\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;158, p\u0026thinsp;=\u0026thinsp;0.272, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As with beetles, specialisation on spiders was also not affected by fertilisation (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.2995, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;30, p\u0026thinsp;=\u0026thinsp;0.5897) nor session (GLM - F\u0026thinsp;=\u0026thinsp;2.050, df\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;30, p\u0026thinsp;=\u0026thinsp;0.099, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLastly, for specialist predators, such as ladybugs and hoverflies, consumed by the generalist predator \u003cem\u003etaxa\u003c/em\u003e sampled, the specialisation was once again not affected by fertilisation (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;1.271, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;47, p\u0026thinsp;=\u0026thinsp;0.266) nor session (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;2.130, df\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;47, p\u0026thinsp;=\u0026thinsp;0.071, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoving onto prey in 2021, specialisation on aphids was affected by fertilisation treatment (GLMM \u0026ndash; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.054, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;92, p\u0026thinsp;=\u0026thinsp;0.014) but not session (GLMM \u0026ndash; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.258, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;92, p\u0026thinsp;=\u0026thinsp;0.385), with fertilisation increasing specialization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the cereal leaf beetle, both fertilisation (GLMM \u0026ndash; Chi\u0026thinsp;=\u0026thinsp;14.415, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;28, p\u0026thinsp;=\u0026thinsp;0.013) and session (GLMM \u0026ndash; Chi\u0026thinsp;=\u0026thinsp;3.905, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;28, p\u0026thinsp;=\u0026thinsp;0.048) had an effect on specialisation, with an inverse bell shape over time, with a pronounced increase in the fertilised treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, earthworms were neither affected by fertilisation (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.867, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;33, p\u0026thinsp;=\u0026thinsp;0.359), nor session (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.858, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;33, p\u0026thinsp;=\u0026thinsp;0.522, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As in the previous year, 97% (35/36) of the springtails d\u0026rsquo; values were 0, hence a model could not be estimated, but it once again points to this group being a staple food source for generalist predators (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecialisation on beetles as intraguild prey was not significantly affected by fertilisation (GLMM \u0026ndash; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.109, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;130, p\u0026thinsp;=\u0026thinsp;0.740), as opposed to session (GLMM \u0026ndash; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;14.326, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;130, p\u0026thinsp;=\u0026thinsp;0.012), with a clear increase after the 3rd session (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For spiders, neither the treatment (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.233, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;31, p\u0026thinsp;=\u0026thinsp;0.634) nor the sampling session had an effect on specialisation (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.323, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;31, p\u0026thinsp;=\u0026thinsp;0.895, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor specialist predators, neither fertilisation (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.004, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;=\u0026thinsp;0.951) nor session (GLM \u0026ndash; F\u0026thinsp;=\u0026thinsp;0.707, df\u0026thinsp;=\u0026thinsp;4, N\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;=\u0026thinsp;0.597) had an effect on specialisation, though on the fertilised treatment there were no detections until the 3rd sampling session (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTrophic link rewiring and community composition dissimilarity\u003c/h3\u003e\n\u003cp\u003eAs with complexity, the fertilisation treatment did not affect the trophic link rewiring (2020 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.199, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;143, p\u0026thinsp;=\u0026thinsp;0.655; 2021 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.007, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;116, p\u0026thinsp;=\u0026thinsp;0.934). However, it changed over the season in both years (2020 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;27.455, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;143, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 2021 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;35.558, df\u0026thinsp;=\u0026thinsp;4, N\u0026thinsp;=\u0026thinsp;116, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), by increasing in the first few sessions, then in 2020 alone it dipped during the mid-season, rising again in the end (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNetwork community composition was similarly affected, once again changing across sampling sessions (2020 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;26.919, df\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;143, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 2021 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;45.448, df\u0026thinsp;=\u0026thinsp;4, N\u0026thinsp;=\u0026thinsp;116, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not with fertilisation (2020 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.662, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;143, p\u0026thinsp;=\u0026thinsp;0.416; 2021 \u0026ndash; LMM, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.021, df\u0026thinsp;=\u0026thinsp;1, N\u0026thinsp;=\u0026thinsp;116, p\u0026thinsp;=\u0026thinsp;0.884) each year (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In both years, community composition dissimilarity dropped until the 3rd session, then for 2020 is stabilized, while it rose again in 2021, albeit slightly (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2020, during the middle of the season, when aphids reached their peak (sessions 4 and 5, Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the trophic links changed less as seen from the drop in rewiring to approximately 0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In 2021 the early rewiring (sessions 1 and 2) was less common, at approximately 0.2\u0026ndash;0.3, although this appears to be the result of a few outliers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings show how dynamic food web interactions within invertebrate communities in agro-ecosystems can be, even over very short time scales. Furthermore, we also show that over these short time scales, weighted connectance did not increase towards the mid-season peak of productivity, as expected in our first hypothesis, and instead decreased, in accordance with previous studies (Worm \u0026amp; Duffy \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Parker \u0026amp; Huryn \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Nie et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A low complexity may also indicate that there is not a strong selective pressure for predators to differentiate niches, which may be one of the reasons why we did not observe any strong fertilisation effects in our study.\u003c/p\u003e \u003cp\u003eThe above could have a positive influence on the biological control services provided by generalist predators, considering that this may have allowed them greater freedom from competition, to exploit aphid and cereal leaf beetle prey (Michalko et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As such, the regulation of these pest prey was generalized across the predator community and therefore overlapping, only partially confirming our second hypothesis. However, this pattern was not evident for other prey. Of these, springtails seemingly have the role of a staple food, as they were very generally consumed by the predator community throughout the season. Furthermore, there is seemingly a conflict between specialist and generalist predators (Rosenheim et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Snyder \u0026amp; Ives \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Diehl et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which consume both the specialists and their prey, in this case, the aphids. However, this may rather have a legacy effect on pest regulation, as specialists mostly appeared to be a generally consumed prey only after the peak aphid infestation.\u003c/p\u003e \u003cp\u003eWe can furthermore show that rewiring within food webs, especially at the onset of the season, was increasing drastically until mid-season, as opposed to community composition, partially supporting our third hypothesis. As the fields grew taller and greener, more species came in and established interactions amongst themselves, then left at the end of the season; a pattern that has been observed before (Collins et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Thomas et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Holland et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u0026Ouml;berg \u0026amp; Ekbom \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The centrality of different prey also shifted, with aphids becoming more central and accounting for larger proportions of the predators\u0026rsquo; diets, precisely when their abundance reached its peak in the study fields. Meanwhile ubiquitous prey, like springtails, remained central food sources throughout the entire sampling period.\u003c/p\u003e \u003cp\u003eSimilarly, the food webs\u0026rsquo; complexity changed over time, each year displaying a different trend, which may be linked to an aphid infestation that took place in 2020 (Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); something which we discuss further down below. In terms of specialisation across prey, springtails were a widely used food source in both years, and the prevalence of maximum overlap is a strong indicator that springtails were a staple food source, for all predator \u003cem\u003etaxa\u003c/em\u003e sampled. However, note that without an available measurement of consumed amounts, we cannot know whether this \u0026ldquo;stapleness\u0026rdquo; means they are a main source of energy, or just consumed ubiquitously in low quantities; posing an interesting question on how occurrence frequency correlates to consumption rates in field settings. Without that information, we cannot know whether the absence of changes in overlap in this study, or activity-density found by others (Birkhofer et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), correspond to prey switching, away from pests.\u003c/p\u003e \u003cp\u003eSpiders too appeared to be consumed by nearly all beetle species in 2020, pointing to a widespread occurrence of IGP. This did not take place in the following year, with fewer detections as well, probably as a result of a decline in spider abundance in the late season (Supplementary Fig.\u0026nbsp;15). As for predation on aphid specialist predators, the number of detections early in the season in both years is low, being altogether absent until the 3rd session in the fertilised treatment in 2021. These specialist predators follow aphid densities, lagging behind in their arrival to the fields and population growth (Raymond et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), hence this low number of detections was expected in the early season.\u003c/p\u003e \u003cp\u003eRegarding earthworms, their consumption appears to be sporadic; possibly due to them being low quality (Fawki et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and, therefore, nonpreferred food sources when compared to other prey (Symondson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), even for species known to consume them. In contrast to the earthworms, the specialisation on cereal leaf beetles was in line with their seasonal abundance (McPherson \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) for both years. Lastly, the specialisation on aphids poses an interesting case study. As mentioned above, there was a difference in aphid abundance across years (Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The first year, with the infestation, specialisation on aphids showed a shallow inverted bell shape, which would be consistent with the peak in abundance. In contrast, there was no such effect in 2021, where specialisation was lower in the fertilised treatment.\u003c/p\u003e \u003cp\u003eFollowing that line of thought, we can gain further insight on the trends in community composition and rewiring dissimilarity seen in 2020 and 2021, once again leaning on the difference in prey abundance between years, caused by aphids. While phenology would mostly account for \u0026ldquo;when\u0026rdquo; species turnover, competition should account, at least partially, for their behaviour and by \u0026ldquo;how many\u0026rdquo; species are replaced. As such, the reduction of competition among predators, induced by the aphid infestation, possibly contributed to the shallower fluctuations in community composition seen in 2020. Likewise, for the trophic link rewiring, there was a brief period around the 4th and 5th sessions, of the same year, when predators diet changed less; this coincided with peak aphid abundance in the fields, and is reflected by their centrality in the food webs.\u003c/p\u003e \u003cp\u003eWhen looking at all the parameters measured in this study, most of them changed across time, in accordance with Thompson \u0026amp; Townsend (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). At a short, intra-annual scale, there were changes in rewiring, much like in long-term studies (Olesen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), but we also observed a temporal variability for the weighted connectance that other studies did not (Tr\u0026oslash;jelsgaard \u0026amp; Olesen \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Given the link between species richness and connectance (Banašek-Richter et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Calizza et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), this change can be explained by the species\u0026rsquo; phenologies (Suzuki et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such changes over a short period imply that biological control, as an ecosystem service, may fluctuate over time in effectiveness. Adding to that, intraguild predation and general interference between generalist and specialist predators introduce another layer of complexity (Hindayana et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Janssen et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Lucas \u0026amp; Rosenheim \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Liang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, we know that the services provided by certain species of hoverflies can change over time, depending on their life-cycle stage, for example going from predators to pollinators (Dunn et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Rodr\u0026iacute;guez-Gasol et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the time frame of these changes, sampling the system without sufficient temporal replication within the same year, or once per year at the same period over multiple years, or locations, increases the likelihood of spatial or temporal uncoupling of species interactions (Jordano \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), leading to their underrepresentation or absence altogether. Furthermore, beyond the design and replication of sampling, there is also the matter of the sampling technique used, as it can have distinct biases and implications, not just for food webs, but ecological networks in general (Jordano \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Dormann et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For the former in particular, the advantages and disadvantages of molecular analysis of food webs have been given considerable attention over the years (e.g: Symondson \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, King et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Clare \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Symondson \u0026amp; Harwood \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, among them, aspects such as the detection time of prey in the gut (Greenstone et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003el\u0026ouml;p et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), accounting for a wider time interval than direct observations, bear direct relevance for the interpretation of the data collected. Likewise, the inability to quantify consumption through molecular means, which may nonetheless be extremely difficult or near-impossible to do through observation for certain \u003cem\u003etaxa\u003c/em\u003e in field studies (Symondson \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Clare \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), are also particularly relevant for biological control.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eQuantitative assessments of consumption, how plastic food webs are, and how niches change over time, are likely to be of key importance for building a more in depth understanding of theoretical and empirical food webs. This will allow us to explore the finer details of food webs, without which it will be difficult to identify nodes that are either central within food webs or account for a considerable portion of the energy requirements of predators. These nodes are keystones of the food webs, and what we can show here is that how central each prey is within food webs is something that can, and does, change very quickly. Some prey are, for example, central in food webs only for a limited period of time, such as outbreak or pest species, whereas others are central throughout the season, such as staple food sources (e.g. springtails).\u003c/p\u003e \u003cp\u003eThe capacity of different prey (either as staple foods or as temporarily available resources) to sustain the predator community and affect predator species\u0026rsquo; behaviour and competition, is also likely to play a large role in shaping food webs. That food web rewiring, in general, was greater than species turnover supports this, and indicates that food web changes, to a great extent, are due to shifts in behaviour, rather than species turnover. Additionally, the considerable short-term dynamics of our food webs, coupled with the tendency of most studies on ecosystem services to sample when services are needed (i.e. during the peak of the crop season), implies that the dynamic nature of food webs could be one of the reasons for why studies on biological control have produced inconsistent results (Raymond et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Karp et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Parameters, such as specialisation, have been overestimated, due to the inevitability of incomplete sampling of empirical networks (Fr\u0026uuml;nd \u003cem\u003eet al.\u003c/em\u003e 2015, Jordano \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For the same reasons, it may also be that we systematically have either underestimated, or overestimated ecosystem functioning, depending on when sampling has occurred. This ought to be considered when sampling or modelling such systems, or when attempting to manage them to strengthen ecosystem services, such as biological control.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThe study site was in Kematen in Tirol, Austria, where spring barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) was grown in six organically managed fields, three in 2020 and three in 2021. The fields were tilled, pressed and fertilised prior to sowing, between March and April, and the barley ripened around late July to early August, after which point it was harvested. Before sowing, each field was split and one half was fertilised with cattle manure and the other remaining unfertilised, as a control. The manure was applied independently by each field\u003cem\u003e\u0026rsquo;\u003c/em\u003es respective owner, at a rate of 1.5 metric tons (1 500kg) per hectare, using manure spreaders. For each treatment, four 5x5m sampling plots were drawn (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), with no barriers (e.g.: cages or snail fences, Staudacher et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) within the fields. Sampling plots were at least 5m from the field edge to avoid edge effects, 10m way from one another along the field\u0026rsquo;s width, and between 15 to 20m along the length of the field. To avoid contamination of the fertilisation effect, plots were at least 25m away from the border between the treatments within a field.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSampling methods\u003c/h2\u003e \u003cp\u003eSampling was conducted every two weeks, between the 21st of April and the 14th of July in 2020, and the 3rd of May and the 12th of July in 2021, and all fields in each year were sampled on the same week. Dry pitfall traps were active for a single day, with wood chips within, to catch live predators to obtain gut content for molecular dietary analysis (Staudacher et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These predators were ground (Carabidae) and rove (Staphylinidae) beetles, and spiders (Araneae). Additionally, transects were carried out along a single side of each plot, on the outside border, to avoid trampling on the inside. Thirty 30 individual barley plant tillers were collected to count the number of aphids per tiller for community analysis (Leote et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMolecular analysis\u003c/h2\u003e \u003cp\u003eWe captured a total of 2404 ground beetles, 913 rove beetles and 567 spiders in 2020, and 1977 ground beetles, 891 rove beetles and 250 spiders in 2021. The beetles\u0026rsquo; gut content and the spiders\u0026rsquo; full bodies were extracted using a BioSprint 96 DNA Blood Kit (Qiagen, Hilden, Germany) on a QIAGEN Biosprint96\u0026reg; workstation, following the manufacturer\u0026rsquo;s recommendations. Each sample was analysed three times, with different multiplex-PCR assays. The first focused on several prey \u003cem\u003etaxa\u003c/em\u003e (assay in Rennstam Rubbmark et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), earthworms, aphids, springtails and the cereal leaf beetle. The second targeted generalist and specialist predators, such as spiders, lacewings and ladybeetles (primers from Sint et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e and Staudacher et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The third assay identified the genus of beetles consumed, from a selected set of common \u003cem\u003etaxa\u003c/em\u003e consisting of \u003cem\u003eBembidion\u003c/em\u003e spp., \u003cem\u003eHarpalus\u003c/em\u003e spp., \u003cem\u003ePoecilus\u003c/em\u003e spp., \u003cem\u003ePterostichus\u003c/em\u003e spp., \u003cem\u003ePhilonthus carbonarius\u003c/em\u003e and \u003cem\u003ePhilonthus cognatus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe molecular gut content analysis is described in greater detail in Leote et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but the multiplex PCR assays can also be found here, in the supplementary materials (Supplementary Tables\u0026nbsp;4 to 6), for convenience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAll data analysis was carried out using R 4.1.2 (R Core Team, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and RStudio 2023.03.1\u0026thinsp;+\u0026thinsp;446 (Posit Team, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with the packages \u003cem\u003elme4\u003c/em\u003e (Bates et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for linear mixed effects modelling (LMM), \u003cem\u003eglmmTMB\u003c/em\u003e (Brooks et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for generalised linear mixed effects modelling (GLMM) and the \u003cem\u003eglm\u003c/em\u003e function from base R for generalised linear modelling (GLM). The package \u003cem\u003ebipartite\u003c/em\u003e (Dormann et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e and Dormann \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used for food web generation, and the calculation of several metrics: species level specialisation (Bl\u0026uuml;thgen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), trophic link rewiring and community composition dissimilarity (Poisot et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and weighted connectance (Bersier et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; using the mean proportion of diet detections as weights; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The package \u003cem\u003etidyverse\u003c/em\u003e (Wickham et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used for data processing, as well as to provide greater reproducibility of the data matrices obtained from the archived raw data, and \u003cem\u003eggplot2\u003c/em\u003e (Wickham 2016) was used for graphic creation, for the same reasons. Plots with loess smoothing were created using the standard loess method from the \u003cem\u003egeom_smooth\u003c/em\u003e function from \u003cem\u003eggplot2\u003c/em\u003e, and standard error as the shaded ribbons. For dietary data, detections were averaged for all individuals of a given species in each unique combination of plot, treatment, field and sampling session; resulting in a mean proportion of the detections. As an example, the diet proportions for the species \u003cem\u003ePoecilus cupreus\u003c/em\u003e in plot 1, fertilised treatment, field 1 on sampling session 1 was the mean of all \u003cem\u003eP.cupreus\u003c/em\u003e individual diet detections; which were recorded either as 1 \u0026ndash; detected, or 0 \u0026ndash; not detected, for each diet target in the assays (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All self-detections (e.g. \u003cem\u003eP. cupreus\u003c/em\u003e testing positive for \u003cem\u003ePoecilus\u003c/em\u003e in PCR assay 3) were manually removed (setting the detection to 0, instead of 1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Variables measured, with their respective calculations and what they represent in the context of this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalculation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepresentation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted connectance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLink density divided by species, using mean proportion of diet detections as weights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFood web complexity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRewiring dissimilarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood web dissimilarity explained by rewiring (for the shared species subweb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrophic link dissimilarity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity dissimilarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood web dissimilarity explained by species turnover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunity turnover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies-level specialisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized \u0026ldquo;partner diversity\u0026rdquo; for each species (Kullback-Leibler distance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredator dietary overlap\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Example diet data matrix, with three individuals \u003cem\u003ePoecilus cupreus\u003c/em\u003e, two captured in one sample, and one in another, each with a single prey target detection (A and B).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrey A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrey B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP. cupreus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP. cupreus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP. cupreus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Example of diet matrix above, after calculating mean averages. By calculating the average mean diet of the species \u003cem\u003eP. cupreus\u003c/em\u003e for a sample (unique combination of sampling session, field, treatment and plot), we obtained the mean dietary proportion for that predator species in its respective sample, which was then used as weights for the calculation of weighted connectance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrey A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrey B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP. cupreus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP. cupreus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrior to testing, the heteroscedasticity of models was checked through visual inspection of quantile-quantile and residual vs predicted plots (Zuur et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The error structures of GLMMs and GLMs with proportion data were adapted to ordered beta (\u003cem\u003eordbeta\u003c/em\u003e family in \u003cem\u003eglmmTMB\u003c/em\u003e) and quasibinomial (\u003cem\u003equasibinomial\u003c/em\u003e family in \u003cem\u003eglm\u003c/em\u003e), respectively.\u003c/p\u003e \u003cp\u003eDue to the experimental design of our study, the experimental units were the fields, thus the sampling plots within them represent pseudoreplicates. In order to address the correlation among the plots in the same field, and minimize the likelihood of falsely detecting significant differences we followed the method in Zimmerman \u003cem\u003eet al.\u003c/em\u003e (2021). By using mixed effects models with field (replicate-level grouping variable) as a random effect, it allowed us to correct for type I error and address the pseudoreplication bias.\u003c/p\u003e \u003cp\u003eIn order to explicitly test the hypothesis of our study within our models, we set up appropriate contrasts for each variable. The session variable, being categorical, had a sliding contrast so that each session would be directly compared to the previous one; fertilisation had a treatment contrast with the unfertilised, or control, treatment as the baseline; while field, used as a random factor, had a sum contrast, so that the grand mean of all fields would be the reference value, as opposed any individual one.\u003c/p\u003e \u003cp\u003eThe significance of the variables and interaction terms was assessed using the \u003cem\u003eanova\u003c/em\u003e function of base R, through Chi-squared (χ\u003csup\u003e2\u003c/sup\u003e) tests for the mixed effects models, or with F tests for the GLMs, with the threshold defined at 0.05.\u003c/p\u003e \u003cp\u003eThree sets of linear mixed effects models (LMM) were created for each year, to test the effects of time and fertilisation on different variables. Our first model analysed how sampling session and fertilisation affected the weighted connectance of the food webs, with field as a random factor. The second and third models looked at how session and fertilisation affected the rewiring dissimilarity and community composition dissimilarity, respectively, with field as a random factor.\u003c/p\u003e \u003cp\u003eThe analysis of the species level specialisation (\u003cem\u003ed\u003c/em\u003e\u0026rsquo;) for each prey target was carried out with generalised linear mixed effects models (GLMM), or with generalised linear models (GLM) when the number of diet detections was too low to allow fitting mixed effect models. In both cases, the models tested the effects of sampling session and fertilisation on the standardized \u003cem\u003ed\u0026rsquo;\u003c/em\u003e, with the GLMMs using the sampling field as a random factor. In total there were seven models for each year, GLMMs for aphid and IGP beetle \u003cem\u003ed\u0026rsquo;\u003c/em\u003e, and GLMs for cereal leaf beetle, earthworms, specialists, IGP spiders. For the springtails in particular, neither GLMMs nor GLMs worked, as over 95% of samples returned a \u003cem\u003ed\u0026rsquo;\u003c/em\u003e of zero, thus it was not possible to fit any models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest:\u003c/h2\u003e \u003cp\u003eThe authors declare they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributions: PNBL and ORRR carried out the field work, PNBL carried out the lab work, PNBL and ORRR conducted the data analysis, PNBL wrote the first draft, ORRR and MT designed the study and procured the funding, all authors contributed to the revisions.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Britta Frei, Claire Rotondo and all the field assistants, Karin Wastian for designing the Philonthus spp. primers and Christiane Zeisler for the support with the lab work and molecular data analyses. This research was funded in whole/in part by the Austrian Science Fund (FWF) grant DOI https://doi.org/10.55776/P32964. For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. The first author was also funded by the Vice-Rectorate for Research of the University of Innsbruck with the Excellence Scholarship for Doctoral Programs.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availability statement: All data has been archived in the repository Figshare (https://figshare.com/), under the DOI: https://doi.org/10.6084/m9.figshare.26893354https://doi.org/10.6084/m9.figshare.26893405 https://doi.org/10.6084/m9.figshare.26893624 https://doi.org/10.6084/m9.figshare.26893630 https://doi.org/10.6084/m9.figshare.26893690 https://doi.org/10.6084/m9.figshare.26893759\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBanašek-Richter, C. et al. Complexity in quantitative food webs. \u003cem\u003eEcology\u003c/em\u003e \u003cb\u003e90\u003c/b\u003e (6), 1470\u0026ndash;1477. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/08-2207.1\u003c/span\u003e\u003cspan address=\"10.1890/08-2207.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D. et al. Package \u0026lsquo;lme4\u0026rsquo;. convergence, 12(1), 2. (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauer, T. Prey-capture in a ground-beetle larva. \u003cem\u003eAnim. Behav.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (1), 203\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0003-3472(82)80255-8\u003c/span\u003e\u003cspan address=\"10.1016/S0003-3472(82)80255-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1982).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBersier, L. F., Banašek-Richter, C. \u0026amp; Cattin, M. F. Quantitative descriptors of food-web matrices. \u003cem\u003eEcology\u003c/em\u003e \u003cb\u003e83\u003c/b\u003e (9), 2394\u0026ndash;2407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658(2002\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658(2002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002). )083[2394:QDOFWM]2.0.CO;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirkhofer, K., Wise, D. H. \u0026amp; Scheu, S. Subsidy from the detrital food web, but not microhabitat complexity, affects the role of generalist predators in an aboveground herbivore food web. \u003cem\u003eOikos\u003c/em\u003e \u003cb\u003e117\u003c/b\u003e (4), 494\u0026ndash;500. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.0030-1299.2008.16361.x\u003c/span\u003e\u003cspan address=\"10.1111/j.0030-1299.2008.16361.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBl\u0026uuml;thgen, N., Menzel, F. \u0026amp; Bl\u0026uuml;thgen, N. Measuring specialisation in species interaction networks. \u003cem\u003eBMC Ecol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (1), 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1472-6785-6-9\u003c/span\u003e\u003cspan address=\"10.1186/1472-6785-6-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. \u003cem\u003eR J.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (2), 378\u0026ndash;400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3929/ethz-b-000240890\u003c/span\u003e\u003cspan address=\"10.3929/ethz-b-000240890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalizza, E., Rossi, L., Careddu, G., Sporta Caputi, S. \u0026amp; Costantini, M. L. Species richness and vulnerability to disturbance propagation in real food webs. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (1), 19331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-55960-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-55960-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClare, E. L. Molecular detection of trophic interactions: emerging trends, distinct advantages, significant considerations and conservation applications. \u003cem\u003eEvol. Appl.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (9), 1144\u0026ndash;1157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/eva.12225\u003c/span\u003e\u003cspan address=\"10.1111/eva.12225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins, K. L., Boatman, N. D., Wilcox, A., Holland, J. M. \u0026amp; Chaney, K. Influence of beetle banks on cereal aphid predation in winter wheat. \u003cem\u003eAgric. Ecosyst. Environ.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e (1\u0026ndash;3), 337\u0026ndash;350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0167-8809(01)00340-1\u003c/span\u003e\u003cspan address=\"10.1016/S0167-8809(01)00340-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuff, J. P. et al. The predator problem and PCR primers in molecular dietary analysis: swamped or silenced; depth or breadth? \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (1), 41\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13705\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuff, J. P., Windsor, F. M., Tercel, M. P., Kitson, J. J. \u0026amp; Evans, D. M. Overcoming the pitfalls of merging dietary metabarcoding into ecological networks. \u003cem\u003eMethods Ecol. Evol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (3), 545\u0026ndash;559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.13796\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.13796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeagle, B. E., Pansu, J., McInnes, J. \u0026amp; Traugott, M. 4 Revealing animal diet and food webs through DNA metabarcoding. \u003cem\u003eAppl. Environ. Genomics\u003c/em\u003e, 30. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiehl, E., Sereda, E., Wolters, V. \u0026amp; Birkhofer, K. Effects of predator specialization, host plant and climate on biological control of aphids by natural enemies: a meta-analysis. \u003cem\u003eJ. Appl. Ecol.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (1), 262\u0026ndash;270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.12032\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.12032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDormann, C. F., Fr\u0026uuml;nd, J., Bl\u0026uuml;thgen, N. \u0026amp; Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDormann, C. F. How to be a specialist? Quantifying specialisation in pollination networks. \u003cem\u003eNetw. Biology\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e (1), 1\u0026ndash;20 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDormann, C. F., Fr\u0026uuml;nd, J. \u0026amp; Schaefer, H. M. Identifying causes of patterns in ecological networks: opportunities and limitations. \u003cem\u003eAnnu. Rev. Ecol. Evol. Syst.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e (1), 559\u0026ndash;584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-ecolsys-110316-022928\u003c/span\u003e\u003cspan address=\"10.1146/annurev-ecolsys-110316-022928\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunn, L., Lequerica, M., Reid, C. R. \u0026amp; Latty, T. Dual ecosystem services of syrphid flies (Diptera: Syrphidae): pollinators and biological control agents. \u003cem\u003ePest Manag. Sci.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e (6), 1973\u0026ndash;1979. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ps.5807\u003c/span\u003e\u003cspan address=\"10.1002/ps.5807\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEitzinger, B. \u0026amp; Traugott, M. Which prey sustains cold-adapted invertebrate generalist predators in arable land? Examining prey choices by molecular gut‐content analysis. \u003cem\u003eJ. Appl. Ecol.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e (3), 591\u0026ndash;599. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2664.2010.01947.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2664.2010.01947.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFawki, S., Smerup, S. \u0026amp; Toft, S. Food preferences and food value for the carabid beetles \u003cem\u003ePterostichus melanarius\u003c/em\u003e, \u003cem\u003eP. versicolor\u003c/em\u003e and \u003cem\u003eCarabus nemoralis\u003c/em\u003e. In Proceedings of the 11th European Carabidologist Meeting (pp. 99\u0026ndash;109). (2005), January.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoley, J. A. et al. Solutions for a cultivated planet. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e478\u003c/b\u003e (7369), 337\u0026ndash;342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature10452\u003c/span\u003e\u003cspan address=\"10.1038/nature10452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrainer, A., McKie, B. G. \u0026amp; Malmqvist, B. When does diversity matter? Species functional diversity and ecosystem functioning across habitats and seasons in a field experiment. \u003cem\u003eJ. Anim. Ecol.\u003c/em\u003e \u003cb\u003e83\u003c/b\u003e (2), 460\u0026ndash;469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2656.12142\u003c/span\u003e\u003cspan address=\"10.1111/1365-2656.12142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFr\u0026uuml;nd, J., McCann, K. S. \u0026amp; Williams, N. M. Sampling bias is a challenge for quantifying specialization and network structure: lessons from a quantitative niche model. \u003cem\u003eOikos\u003c/em\u003e \u003cb\u003e125\u003c/b\u003e (4), 502\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/oik.02256\u003c/span\u003e\u003cspan address=\"10.1111/oik.02256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF\u0026uuml;l\u0026ouml;p, D., Szita, \u0026Eacute;., Gerstenbrand, R., Tholt, G. \u0026amp; Samu, F. Consuming alternative prey does not influence the DNA detectability half-life of pest prey in spider gut contents. \u003cem\u003ePeerJ\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, e7680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7717/peerj.7680\u003c/span\u003e\u003cspan address=\"10.7717/peerj.7680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadelha, Y. E., Lange, D., Dattilo, W. \u0026amp; Lopes, B. C. Phenological phases of the host plant shape plant\u0026ndash;treehopper interaction networks. \u003cem\u003eEcol. Entomol.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (6), 827\u0026ndash;837. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/een.12457\u003c/span\u003e\u003cspan address=\"10.1111/een.12457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenstone, M. H., Rowley, D. L., Weber, D. C., Payton, M. E. \u0026amp; Hawthorne, D. J. Feeding mode and prey detectability half-lives in molecular gut-content analysis: an example with two predators of the Colorado potato beetle. \u003cem\u003eBull. Entomol. Res.\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e (2), 201\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S000748530700497X\u003c/span\u003e\u003cspan address=\"10.1017/S000748530700497X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHindayana, D., Meyh\u0026ouml;fer, R., Scholz, D. \u0026amp; Poehling, H. M. Intraguild predation among the hoverfly Episyrphus balteatus de Geer (Diptera: Syrphidae) and other aphidophagous predators. \u003cem\u003eBiol. Control\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e (3), 236\u0026ndash;246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/bcon.2000.0895\u003c/span\u003e\u003cspan address=\"10.1006/bcon.2000.0895\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolland, J. M. et al. The spatial dynamics and movement of Pterostichus melanarius and P. madidus (Carabidae) between and within arable fields in the UK. \u003cem\u003eInt. J. Ecol. Environ. Sci.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 35\u0026ndash;53 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolopainen, J. K. \u0026amp; Helenius, J. Gut contents of ground beetles (Col., Carabidae), and activity of these and other epigeal predators during an outbreak of \u003cem\u003eRhopalosiphum padi\u003c/em\u003e (Hom., Aphididae). \u003cem\u003eActa Agriculturae Scand. B-Plant Soil. Sci.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (1), 57\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09064719209410199\u003c/span\u003e\u003cspan address=\"10.1080/09064719209410199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1992).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssen, A. et al. Intraguild predation usually does not disrupt biological control. \u003cem\u003eTrophic guild Biol. Interact. control\u003c/em\u003e, 21\u0026ndash;44. (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJordano, P. Sampling networks of ecological interactions. \u003cem\u003eFunct. Ecol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (12), 1883\u0026ndash;1893. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.12763\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.12763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaartinen, R. \u0026amp; Roslin, T. High temporal consistency in quantitative food web structure in the face of extreme species turnover. \u003cem\u003eOikos\u003c/em\u003e \u003cb\u003e121\u003c/b\u003e (11), 1771\u0026ndash;1782. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0706.2012.20108.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0706.2012.20108.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proceedings of the National Academy of Sciences, 115(33), E7863-E7870. (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1800042115\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1800042115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKheirodin, A., Sharanowski, B. J., C\u0026aacute;rcamo, H. A. \u0026amp; Costamagna, A. C. Consumption of cereal leaf beetle, \u003cem\u003eOulema melanopus\u003c/em\u003e, by generalist predators in wheat fields detected by molecular analysis. \u003cem\u003eEntomol. Exp. Appl.\u003c/em\u003e \u003cb\u003e168\u003c/b\u003e (1), 59\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/eea.12835\u003c/span\u003e\u003cspan address=\"10.1111/eea.12835\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKielty, J. P., Allen-Williams, L. J., Underwood, N. \u0026amp; Eastwood, E. A. Behavioral responses of three species of ground beetle (Coleoptera: Carabidae) to olfactory cues associated with prey and habitat. \u003cem\u003eJ. Insect Behav.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 237\u0026ndash;250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02213868\u003c/span\u003e\u003cspan address=\"10.1007/BF02213868\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing, R. A., Read, D. S., Traugott, M. \u0026amp; Symondson, W. O. C. INVITED REVIEW: Molecular analysis of predation: a review of best practice for DNA-based approaches. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (4), 947\u0026ndash;963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-294X.2007.03613.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-294X.2007.03613.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeote, P. N. B., Rubbmark, O. R. R. \u0026amp; Traugott, M. High resolution temporal data shows how increasing prey availability reduces early season intraguild predation and pest spread in cereal crops. \u003cem\u003eBiol. Control\u003c/em\u003e. 105549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocontrol.2024.105549\u003c/span\u003e\u003cspan address=\"10.1016/j.biocontrol.2024.105549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucas, \u0026Eacute;. \u0026amp; Rosenheim, J. A. Influence of extraguild prey density on intraguild predation by heteropteran predators: A review of the evidence and a case study. \u003cem\u003eBiol. Control\u003c/em\u003e. \u003cb\u003e59\u003c/b\u003e (1), 61\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocontrol.2011.05.010\u003c/span\u003e\u003cspan address=\"10.1016/j.biocontrol.2011.05.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang, Y. et al. Flower provision reduces intraguild predation between predators and increases aphid biocontrol in tomato. \u003cem\u003eJ. Pest Sci.\u003c/em\u003e \u003cb\u003e95\u003c/b\u003e (1), 461\u0026ndash;472. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10340-021-01396-x\u003c/span\u003e\u003cspan address=\"10.1007/s10340-021-01396-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcPherson, R. M. Seasonal abundance of cereal leaf beetles (Coleoptera: Chrysomelidae) in Virginia small grains and corn. \u003cem\u003eJ. Econ. Entomol.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e (6), 1269\u0026ndash;1272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jee/76.6.1269\u003c/span\u003e\u003cspan address=\"10.1093/jee/76.6.1269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichalko, R., Pek\u0026aacute;r, S. \u0026amp; Entling, M. H. An updated perspective on spiders as generalist predators in biological control. \u003cem\u003eOecologia\u003c/em\u003e \u003cb\u003e189\u003c/b\u003e, 21\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00442-018-4313-1\u003c/span\u003e\u003cspan address=\"10.1007/s00442-018-4313-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorente-L\u0026oacute;pez, J., Lara-Romero, C., Ornosa, C. \u0026amp; Iriondo, J. M. Phenology drives species interactions and modularity in a plant-flower visitor network. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 9386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-27725-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-27725-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouillot, D., Graham, N. A., Vill\u0026eacute;ger, S., Mason, N. W. \u0026amp; Bellwood, D. R. A functional approach reveals community responses to disturbances. \u003cem\u003eTrends Ecol. Evol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (3), 167\u0026ndash;177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2012.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2012.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeutel, A. M. et al. Reconciling complexity with stability in naturally assembling food webs. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e449\u003c/b\u003e (7162), 599\u0026ndash;602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature06154\u003c/span\u003e\u003cspan address=\"10.1038/nature06154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie, S. et al. Will a large complex system be productive? \u003cem\u003eEcol. Lett.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (8), 1325\u0026ndash;1335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.14242\u003c/span\u003e\u003cspan address=\"10.1111/ele.14242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;berg, S. \u0026amp; Ekbom, B. Recolonisation and distribution of spiders and carabids in cereal fields after spring sowing. \u003cem\u003eAnn. Appl. Biol.\u003c/em\u003e \u003cb\u003e149\u003c/b\u003e (2), 203\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-7348.2006.00088.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-7348.2006.00088.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlesen, J. M., Stefanescu, C. \u0026amp; Traveset, A. Strong, long-term temporal dynamics of an ecological network. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e (11), e26455. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0026455\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0026455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker, S. M. \u0026amp; Huryn, A. D. Disturbance and productivity as codeterminants of stream food web complexity in the Arctic. \u003cem\u003eLimnol. Oceanogr.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e (6), 2158\u0026ndash;2170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4319/lo.2013.58.6.2158\u003c/span\u003e\u003cspan address=\"10.4319/lo.2013.58.6.2158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoisot, T., Canard, E., Mouillot, D., Mouquet, N. \u0026amp; Gravel, D. The dissimilarity of species interaction networks. \u003cem\u003eEcol. Lett.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (12), 1353\u0026ndash;1361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12002\u003c/span\u003e\u003cspan address=\"10.1111/ele.12002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolis, G. A. \u0026amp; Strong, D. R. Food web complexity and community dynamics. \u003cem\u003eAm. Nat.\u003c/em\u003e \u003cb\u003e147\u003c/b\u003e (5), 813\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/285880\u003c/span\u003e\u003cspan address=\"10.1086/285880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosit team. RStudio: Integrated Development Environment for R. Posit Software, (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePBC \u0026amp; Boston MA. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.posit.co/\u003c/span\u003e\u003cspan address=\"http://www.posit.co/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond, L., Ortiz-Mart\u0026iacute;nez, S. A. \u0026amp; Lavandero, B. Temporal variability of aphid biological control in contrasting landscape contexts. \u003cem\u003eBiol. Control\u003c/em\u003e. \u003cb\u003e90\u003c/b\u003e, 148\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocontrol.2015.06.011\u003c/span\u003e\u003cspan address=\"10.1016/j.biocontrol.2015.06.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRennstam Rubbmark, O., Sint, D., Cupic, S. \u0026amp; Traugott, M. When to use next generation sequencing or diagnostic PCR in diet analyses. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (2), 388\u0026ndash;399. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.12974\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.12974\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Gasol, N., Alins, G., Veronesi, E. R. \u0026amp; Wratten, S. The ecology of predatory hoverflies as ecosystem-service providers in agricultural systems. \u003cem\u003eBiol. Control\u003c/em\u003e. \u003cb\u003e151\u003c/b\u003e, 104405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocontrol.2020.104405\u003c/span\u003e\u003cspan address=\"10.1016/j.biocontrol.2020.104405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenheim, J. A., Limburg, D. D. \u0026amp; Colfer, R. G. Impact of generalist predators on a biological control agent, Chrysoperla carnea: direct observations. \u003cem\u003eEcol. Appl.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (2), 409\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/1051-0761(1999)009[0409:IOGPOA]2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1890/1051-0761(1999)009[0409:IOGPOA]2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowen, E., Tooker, J. F. \u0026amp; Blubaugh, C. K. Managing fertility with animal waste to promote arthropod pest suppression. \u003cem\u003eBiol. Control\u003c/em\u003e. \u003cb\u003e134\u003c/b\u003e, 130\u0026ndash;140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocontrol.2019.04.012\u003c/span\u003e\u003cspan address=\"10.1016/j.biocontrol.2019.04.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeric Jelaska, L., Symondson, O. C. \u0026amp; W Predation on epigeic, endogeic and anecic earthworms by carabids active in spring and autumn. \u003cem\u003ePeriodicum biologorum\u003c/em\u003e. \u003cb\u003e118\u003c/b\u003e (3), 281\u0026ndash;289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18054/pb.2016.118.3.4709\u003c/span\u003e\u003cspan address=\"10.18054/pb.2016.118.3.4709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSint, D., Niederklapfer, B., Kaufmann, R. \u0026amp; Traugott, M. Group-specific multiplex PCR detection systems for the identification of flying insect prey. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (12), e115501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0115501\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0115501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder, W. E. \u0026amp; Ives, A. R. Generalist predators disrupt biological control by a specialist parasitoid. \u003cem\u003eEcology\u003c/em\u003e \u003cb\u003e82\u003c/b\u003e (3), 705\u0026ndash;716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658(2001)082\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658(2001)082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001). [0705:GPDBCB]2.0.CO;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder, W. E. \u0026amp; Ives, A. R. Interactions between specialist and generalist natural enemies: parasitoids, predators, and pea aphid biocontrol. \u003cem\u003eEcology\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e (1), 91\u0026ndash;107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658(2003\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658(2003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003). )084[0091:IBSAGN]2.0.CO;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaudacher, K., Jonsson, M. \u0026amp; Traugott, M. Diagnostic PCR assays to unravel food web interactions in cereal crops with focus on biological control of aphids. \u003cem\u003eJ. Pest Sci.\u003c/em\u003e \u003cb\u003e89\u003c/b\u003e, 281\u0026ndash;293. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10340-015-0685-8\u003c/span\u003e\u003cspan address=\"10.1007/s10340-015-0685-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaudacher, K. et al. Habitat heterogeneity induces rapid changes in the feeding behaviour of generalist arthropod predators. \u003cem\u003eFunct. Ecol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (3), 809\u0026ndash;819. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.13028\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.13028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSunderland, K. D. \u0026amp; Vickerman, G. P. Aphid feeding by some polyphagous predators in relation to aphid density in cereal fields. \u003cem\u003eJ. Appl. Ecol.\u003c/em\u003e 389\u0026ndash;396. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2402334\u003c/span\u003e\u003cspan address=\"10.2307/2402334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1980).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki, S. S., Baba, Y. G. \u0026amp; Toju, H. Dynamics of species-rich predator\u0026ndash;prey networks and seasonal alternations of core species. \u003cem\u003eNat. Ecol. Evol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (9), 1432\u0026ndash;1443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41559-023-02130-9\u003c/span\u003e\u003cspan address=\"10.1038/s41559-023-02130-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSymondson, W. O. C. Molecular identification of prey in predator diets. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (4), 627\u0026ndash;641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-294X.2002.01471.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-294X.2002.01471.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSymondson, W. O. C., Glen, D. M., Erickson, M. L., Liddell, J. E. \u0026amp; Langdon, C. J. Do earthworms help to sustain the slug predator \u003cem\u003ePterostichus melanarius\u003c/em\u003e (Coleoptera: Carabidae) within crops? Investigations using monoclonal antibodies. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (9), 1279\u0026ndash;1292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-294x.2000.01006.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-294x.2000.01006.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSymondson, W. O. C. \u0026amp; Harwood, J. D. Special issue on molecular detection of trophic interactions: Unpicking the tangled bank. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (15), 3601\u0026ndash;3604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1111/mec.12831\u003c/span\u003e\u003cspan address=\"10.1111/mec.12831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerry, J. C. D., Morris, R. J. \u0026amp; Bonsall, M. B. Trophic interaction modifications: an empirical and theoretical framework. \u003cem\u003eEcol. Lett.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (10), 1219\u0026ndash;1230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12824\u003c/span\u003e\u003cspan address=\"10.1111/ele.12824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThies, C. et al. The relationship between agricultural intensification and biological control: experimental tests across Europe. \u003cem\u003eEcol. Appl.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (6), 2187\u0026ndash;2196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/10-0929.1\u003c/span\u003e\u003cspan address=\"10.1890/10-0929.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas, C. G., Holland, J. M. \u0026amp; Brown, N. J. \u003cem\u003eThe spatial distribution of carabid beetles in agricultural landscapes\u003c/em\u003e305\u0026ndash;344 (The agroecology of carabid beetles, 2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson, R. M. \u0026amp; Townsend, C. R. \u003cem\u003eThe effect of seasonal variation on the community structure and food-web attributes of two streams: implications for food-web science\u003c/em\u003e75\u0026ndash;88 (Oikos, 1999). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3546998\u003c/span\u003e\u003cspan address=\"10.2307/3546998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTr\u0026oslash;jelsgaard, K. \u0026amp; Olesen, J. M. Ecological networks in motion: micro-and macroscopic variability across scales. \u003cem\u003eFunct. Ecol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (12), 1926\u0026ndash;1935. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.12710\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.12710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan de Vijver, E. et al. Inter-and intrafield distribution of cereal leaf beetle species (Coleoptera: Chrysomelidae) in Belgian winter wheat. \u003cem\u003eEnviron. Entomol.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e (2), 276\u0026ndash;283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/eea.12835\u003c/span\u003e\u003cspan address=\"10.1111/eea.12835\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Emden, H. F. \u0026amp; Harrington, R. (eds) \u003cem\u003eAphids as crop pests\u003c/em\u003e (Cabi, 2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer- New York, (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham, H. et al. Welcome to the Tidyverse. \u003cem\u003eJ. open. source Softw.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (43), 1686. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21105/joss.01686\u003c/span\u003e\u003cspan address=\"10.21105/joss.01686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorm, B. \u0026amp; Duffy, J. E. Biodiversity, productivity and stability in real food webs. \u003cem\u003eTrends Ecol. Evol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (12), 628\u0026ndash;632. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2003.09.003\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2003.09.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZadoks, J. C., Chang, T. T. \u0026amp; Konzak, C. F. A decimal code for the growth stages of cereals. \u003cem\u003eWeed Res.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (6), 415\u0026ndash;421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-3180.1974.tb01084.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3180.1974.tb01084.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1974).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmermann, P., Tasser, E., Leitinger, G. \u0026amp; Tappeiner, U. Effects of land-use and land-cover pattern on landscape-scale biodiversity in the European Alps. \u003cem\u003eAgric. Ecosyst. Environ.\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e (1\u0026ndash;2), 13\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agee.2010.06.010\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2010.06.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuur, A. F., Ieno, E. N. \u0026amp; Elphick, C. S. A protocol for data exploration to avoid common statistical problems. \u003cem\u003eMethods Ecol. Evol.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e (1), 3\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2041-210X.2009.00001.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2041-210X.2009.00001.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"time series food webs, network complexity, community turnover, invertebrate food webs, cereal crops, biological control","lastPublishedDoi":"10.21203/rs.3.rs-6774692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6774692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent theory of food web dynamics is based on mid- to long-term data. However, the short-term dynamics are still poorly understood, as the technology to study them is not readily available. We addressed this gap, by sampling invertebrate food webs bi-weekly, with generalist and specialist predators, herbivores and detritivores as prey, in barley field over two years. We measured weighted connectance, community composition and link rewiring dissimilarity, as well as species-level specialisation of predators. We expected the connectance to be lowest, and both dissimilarities to be highest during the mid-season, whereas specialisation was predicted to follow prey abundance. We found differing trends across years, with connectance declining over time, and community and rewiring dissimilarity fluctuating more, in the second year, but not the first, when an aphid infestation occurred. Specialisation varied across the season, with aphids and cereal leaf beetles being consumed by more predator species as their abundances peaked, and by fewer before and after that, while springtails were universally consumed. Our results show that agro-ecosystems are highly dynamic, and that food webs undergo continuous restructuring even over very short timeframes; potentially leading to a considerable systematic under- or overestimation of ecosystem functioning assessment.\u003c/p\u003e","manuscriptTitle":"The dynamic nature of cereal food webs challenges the suitability of snapshot sampling for assessing ecosystem services","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 12:54:49","doi":"10.21203/rs.3.rs-6774692/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-18T04:04:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T05:05:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T20:16:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T13:59:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159120897233522461145022817459896743947","date":"2025-06-29T07:49:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281706852300033441369613421131454632636","date":"2025-06-29T01:19:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3304111691989378005528948767540238168","date":"2025-06-28T20:55:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-26T18:12:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-26T18:11:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-13T09:24:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T15:17:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-29T08:42:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb83a11b-5122-4148-bd84-be06639074a5","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50775120,"name":"Biological sciences/Ecology/Agri ecology"},{"id":50775121,"name":"Biological sciences/Ecology/Ecological networks"},{"id":50775122,"name":"Biological sciences/Ecology/Ecosystem services"}],"tags":[],"updatedAt":"2025-12-01T16:07:03+00:00","versionOfRecord":{"articleIdentity":"rs-6774692","link":"https://doi.org/10.1038/s41598-025-25603-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-24 15:58:03","publishedOnDateReadable":"November 24th, 2025"},"versionCreatedAt":"2025-07-01 12:54:49","video":"","vorDoi":"10.1038/s41598-025-25603-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-25603-2","workflowStages":[]},"version":"v1","identity":"rs-6774692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6774692","identity":"rs-6774692","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}