# Vulnerability of terrestrial vertebrate food webs to anthropogenic threats in Europe

Louise M J O'Connor, Francesca Cosentino, Michael B J Harfoot, Luigi Maiorano, Chiara Mancino, Laura J Pollock, Wilfried Thuiller

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## To cite this version:

Louise M J O'Connor, Francesca Cosentino, Michael B J Harfoot, Luigi Maiorano, Chiara Mancino, et al.. Vulnerability of terrestrial vertebrate food webs to anthropogenic threats in Europe. Global Change Biology, 2024, 30 (3), 10.1111/gcb.17253. hal-04467616

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Vulnerability of terrestrial vertebrate food webs to
anthropogenic threats in Europe

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Louise M. J. O'Connor¹,²*, Francesca Cosentino³, Michael B. J. Harfoot⁴,⁵, Luigi Maiorano³, Chiara Mancino³, Laura J. Pollock⁶, Wilfried Thuiller¹

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¹ Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d'Écologie Alpine F- 38000 Grenoble, France

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² Biodiversity and Natural Resources programme, International Institute of Applied Systems Analysis (IIASA), Laxenburg, Austria

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³ Department of Biology and Biotechnologies “Charles Darwin”, University of Rome “La Sapienza”, Rome, Italy

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⁴ UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, UK

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⁵ Vizzuality, 123 Calle de Fuencarral, 28010, Madrid, Spain

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⁶ Department of Biology, McGill University, Montreal H3A 1B1, Canada

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* corresponding author: louise.mj.oconnor@gmail.com

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**List of authors ORCID:** LOC: 0000-0002-6671-9144 ; FC: 0000-0001-9064-2696 ; CM: 0000-0002-8212-9195 ; LM: 0000-0002-2957-8979 ; WT: 0000-0002-5388-5274

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**Key words:** trophic interactions, anthropogenic pressures, European vertebrates, agricultural intensification, direct exploitation, pollution, climate change

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**Running title:** Threats to European vertebrate food webs

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Abstract

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Vertebrate species worldwide are currently facing significant declines in many populations. Although we have gained substantial knowledge about the direct threats that affect individual species, these threats only represent a fraction of the broader vertebrate threat profile, which is also shaped by species interactions. For example, threats faced by prey species can jeopardise the survival of their predators due to food resource scarcity. Yet, indirect threats arising from species interactions have received limited investigation thus far. In this study, we investigate the indirect consequences of anthropogenic threats on biodiversity in the context of European vertebrate food webs. We integrated data on trophic interactions among over 800 terrestrial vertebrates, along with their associated human-induced threats. We quantified and mapped the vulnerability of various components of the food web, including species, interactions, and trophic groups to six major threats: pollution, agricultural intensification, climate change, direct exploitation, urbanisation, and invasive alien species and diseases. Direct exploitation and agricultural intensification were two major threats for terrestrial vertebrate food webs: affecting 34% and 31% of species respectively, they threaten 85% and 69% of interactions in Europe. By integrating network ecology with threat impact assessments, our study contributes to a better understanding of the magnitude of anthropogenic impacts on biodiversity.
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# Introduction

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Biodiversity is currently declining, and there are concerns that we may be entering the 6th mass extinction (Ceballos et al., 2017). Ecologists have made significant progress in understanding the major threats that impact different components of biodiversity: land use change (e.g., agricultural expansion, deforestation), direct exploitation (e.g. hunting and trapping), pollution, climate change and IAS (invasive alien species) (Joppa et al., 2016; Maxwell et al., 2016; IPBES, 2019). These human activities result in species and population declines by affecting various aspects of life history (breeding, nesting and feeding) and overall fitness. To gain a comprehensive understanding of the scale of the impacts of multiple human-induced threats to biodiversity, a macroecological perspective has recently emerged, thanks to the increasing availability of datasets at large spatial and taxonomic scales (Rigal et al., 2023). Recent studies have mapped the impacts of these threats on a global scale and have revealed that different groups of species face distinct direct threats (Harfoot et al., 2021, Newbold et al., 2020). For example, pollution was shown to be a prevalent threat for amphibians worldwide, while direct exploitation is a greater threat to mammals and birds (Harfoot et al., 2021). Some functional groups (carnivores, large endotherms and small ectotherms) were also found to be disproportionately impacted in disturbed landscapes compared with other species (Newbold et al., 2020).

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However, previous studies primarily focused on direct threats to species and did not adequately consider the cascading impacts that can arise from biotic interactions, particularly trophic interactions. Species are indeed not independent of each other. Species are interconnected in food webs, meaning that any human activity that threatens one species or a group of similar species may indirectly affect others (Strona & Bradshaw, 2018). For example, the decline or loss of prey can significantly impact a predator's feeding success and survival, potentially leading to predator extinction (Dobson et al., 2009). These indirect threats can be particularly strong in the case of trophic specialists (a predator feeding on a specific prey), or when a threat extirpates a set of species that constitute the overall resource required by one or more predators. Examining trophic interactions can thus shed light on the cascading impact of threats on interacting communities and ecosystem functioning (Keyes et al., 2021; Morton et al., 2022). However, the overall impact of multiple threats on the intricate web of interactions within ecosystems remains poorly understood. This is mainly due to i) a lack of available data for interactions and ii) the fact that numerous studies focused on one guild, within which trophic interactions are limited. A recent study used simulations to quantify the loss of biotic interactions in seed dispersal networks following habitat loss and showed that small amounts of habitat loss can cause up to 10% of species to lose their interaction partners (Sandor et al., 2022). But these questions have yet to be addressed at a macroecological level and for large food webs.

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In this study, we aim to bridge this knowledge gap by addressing two key questions: i) Can considering species interactions improve our understanding of the impact of multiple threats on biodiversity? ii) Which species, interactions, and trophic groups
are most vulnerable to which threats, and where? To answer these questions, we analysed the vulnerability of all described European vertebrate species and their trophic interactions to six major threats: agricultural intensification, direct exploitation, urbanisation, climate change, pollution and invasive alien species (IAS) and diseases. These six major threats each affect over 200 terrestrial vertebrates species in Europe (Table S1), and are defined as follows: i) Agricultural intensification involves expansion of agricultural land into previously uncultivated areas, the simplification of agricultural landscapes through the removal of green linear elements (e.g. hedgerows, woodland), and the widespread use of pesticides and fertilisers to maximise yields. ii) Direct exploitation in the context of terrestrial vertebrates involves hunting, persecution (direct killing or trapping due to perceived threats to human interests), and collection for the pet trade. iii) Urbanisation encompasses the expansion of housing, construction of commercial and industrial infrastructure, leisure facilities, and increased human disturbance. iv) Climate change in the context of European ecosystems refers to warming temperatures, changes in precipitation patterns, and extreme weather events. v) Pollution refers to the introduction and accumulation of harmful substances in the environment, including chemicals, industrial pollutants, heavy metals, as well as the accumulation of solid waste (e.g. plastics and landfill). vi) IAS and diseases refer to the biotic threats posed by introduced species to vulnerable native species. We integrated data from the IUCN European regional red list data on species threats, trophic interactions (Maiorano et al., 2020), and geographic distributions (Maiorano et al., 2013). First, we examined whether species' vulnerability to threats is related to their position in the food web, including their trophic role and the number of prey and predators, to identify whether certain trophic roles are more vulnerable to specific threats. Next, we quantified the vulnerability of interactions, with a particular focus on bottom-up risks, as the loss or decline of prey resources directly compromises the survival of predator species. Finally, we mapped the vulnerability of local food webs to the six different threats across Europe. By integrating network ecology with assessments of threat impacts, our study contributes to a better understanding of the scale of human activity impacts on biodiversity.
118 Material and methods

119 Species threats data

120 We used the European Red List of species (European Red Lists of Species, 2018),
121 which describes the types of threats that each species is known to be vulnerable to,
122 anywhere within their range. We extracted the threat data for all 935 terrestrial
123 vertebrate species available in the dataset. Because the data on threats to species is
124 available in the form of sentences, we searched for character strings that correspond
125 to different threats (Table S1). We define threats as human activities that have led to,
126 are currently causing, or may potentially result in the decline or loss of biodiversity
127 (Joppa et al., 2016). Building on the IUCN red list threat classification scheme, we first
128 considered 9 primary threat categories (direct exploitation, agricultural
129 intensification, invasive alien species and diseases, pollution, climate change,
130 urbanisation, aquaculture and fishing, logging and forestry, and mining and energy
131 production), and 20 subcategories (Table S1). While there is some overlap with the
132 IUCN threat classification, our threat classification diverged from the IUCN global
133 threat classification in order to be more relevant to the context of European terrestrial
134 vertebrates. We then focused on the 6 major threat categories which affected the
135 highest number of terrestrial vertebrate species in Europe (at least 200 species),
136 following Harfoot et al., 2021. Results showing threat subcategories can be found in
137 the supporting information. The six major threats were:

138 1) Urbanisation is associated with habitat destruction and increased disturbance by humans due to visitation and higher human density (Alberti et al., 2020; Des Roches et al., 2021). It includes development of housing, of commercial infrastructure, and of infrastructure for tourism and leisure.
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142 2) Direct exploitation leads to population declines, and it refers to the intentional
143 harvesting or removal of individuals for various purposes, such as recreational
144 hunting, persecution (poisoning, trapping, or shooting a species due to
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146 3) Agricultural intensification refers to the increased productivity of agricultural
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148 management practices, expansion of agricultural land, increased agrochemical
149 and pesticide use, loss of hedges and green linear elements, simplification of
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151 4) Pollution refers to the introduction and accumulation of contaminants into the
152 natural environment (in particular for vertebrates, in the water and in the soil),
153 leading to habitat degradation and population declines due to e.g. the loss of
154 food sources or direct toxicity to organisms.
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155 5) Invasive alien species and Diseases refer to the biotic threats to vulnerable
156 native species. Invasive Alien species with documented impacts on European
157 vertebrates include the American mink, Louisiana crayfish, gray squirrel, and
158 the raccoon directly impact native vertebrates through predation and
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competition for resources and the spread of diseases. Pathogens include fungus, parasites, viruses with a documented impact on vertebrates in Europe, such as chytridiomycosis, myxomatosis, malaria, influenza.

6) Climate change refers to any alteration in the climatic conditions that influence species distributions, phenology and life history: this includes warming, droughts, severe winters, extreme weather events.

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## Metaweb of trophic interactions

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We used an updated version of the Tetra-EU metaweb of the trophic interactions between all vertebrate species in Europe (Maiorano et al., 2020), which includes species that occur in the entire European subcontinent plus Turkey. This version distinguishes obligate interactions (i.e., typical food resources for the predator species), from occasional feeding interactions (which do not sustain the predator). Here, we chose to consider only the obligate feeding interactions on the adult life stage of the prey species, since these are necessary for the survival of the predator, and represent significant pathways of energy flow. Although occasional interactions may act as a buffer for predators in case of typical prey declining due to anthropogenic pressures (or other drivers), from the perspective of assessing threat impacts, we believed obligate interactions are more informative, because the loss of obligate interactions directly compromises the survival of the predator. Therefore, we assumed that occasional interactions are unlikely to play a role in the propagation of threats in the food web. Furthermore, the metaweb includes potential interactions between pairs of species that do not necessarily co-occur: these potential interactions would take place due to trait matching between the potential prey-predator pair, for example if their ranges would overlap in changing conditions (Maiorano et al., 2020). For example, the wolverine only occurs in the north of Europe, but would potentially be able to feed on any species of rabbit or vole living in the south of Europe, if they co-occurred. Because we are only interested in interactions that currently exist, we used species distributions (see below) to remove the interactions between species that never co-occur across Europe given their spatial distributions from Maiorano et al. (2013). In practice, we built a co-occurrence matrix based on species distributions, and then multiplied the co-occurrence matrix with the metaweb adjacency matrix to remove the interactions between species that did not co-occur. We corrected taxonomical mismatches between the metaweb data and the threats data (Supporting Information – Annex 1), and also removed the species that are disconnected from the metaweb (i.e. that are neither a predator nor a prey in the metaweb). The resulting metaweb contained 1084 species and 12,226 interactions. The metaweb dataset and the threats dataset had a total of 884 species in common, for which we could analyse the vulnerability of species and interactions at the metaweb level (Figures 1 to 3).
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## Species distributions

198 In addition to analysing threats at the level of the metaweb, we also analysed the vulnerability of vertebrate food webs across space. The study area covered the spatial extent of the European Union (EU) with the United Kingdom (EU28 +), Norway, Switzerland, and the Western Balkans (Serbia, Kosovo, North Macedonia, Montenegro, Albania, and Bosnia and Herzegovina). We excluded Iceland, Turkey and Macaronesia to avoid border effects. We considered all species that are included in both the European red list dataset and the European Tetra-EU database (Maiorano et al., 2020), which includes only native species and resident and breeding birds. We extracted the distributions of the species occurring in the study area from (Maiorano et al., 2013). These distributions were obtained by combining the extent of occurrence for each species with their habitat requirements (also known as Area of Habitat maps (Lumbierres et al., 2022)). Species distributions were mapped in a regular grid of 300 m resolution, where cells had values of zero for unsuitable habitat, one for marginal habitat (habitat where the species can be present, but does not persist in the absence of primary habitat) and two for primary habitat. Here, we treated primary habitat only as 'suitable habitat', which provides a better prediction of the actual species distribution (Ficetola et al., 2015). Given that a number of vertebrate species have large home ranges (e.g. 100 km²), we upscaled distribution maps to a 10 × 10 km equal-size area grid (ETRS89; total of 49,818 grid cells). We considered the species potentially present in a 10 × 10 km cell if the grid cell contained at least one 300 x 300 m cell of suitable habitat. This led to a total of 804 species included in the spatial analyses for which we had spatial distributions in the study area, trophic interactions, and vulnerability to threats.
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## Quantifying the vulnerability of food webs to multiple threats

222 We quantified the main threats faced by species and their interactions, as well as particular trophic groups.
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224 First, we investigated whether a species' vulnerability to different threats was related to the trophic role of the species. To build trophic groups, we used the same methodology as in (O'Connor et al., 2020): we used the stochastic blockmodel (SBM) on the metaweb (R package *blockmodels*; Leger et al., 2016) to group together the species that eat the same food and are eaten by similar sets of predators. The output of the SBM is an aggregated graph with nodes representing trophic groups, containing species that have the same probability of interacting with all other nodes in the graph (O'Connor et al., 2019). We also included non-vertebrate diet categories in the metaweb in order to refine trophic groups of species that feed on other species than terrestrial vertebrates. Thus, two species belonging to the same trophic group have similar sets of predators and food resources (including terrestrial vertebrate prey species, and diet categories). Diet categories were: algae, aquatic vegetation, fishes, aquatic invertebrates, aerial invertebrates, invertebrates on ground, invertebrates on
vegetation, fruits, flowers, nectar, bulbs, berries, leaves, bark, seeds and grains, nuts, woody vegetation, other plant parts, mushrooms, mosses and lichens, cultivated plants, domestic animals, garbage, detritus, dung, carrion (Maiorano et al., 2020). The goodness of fit of the model is assessed with the Integrated Classification Likelihood (ICL) information criterion (Figure S2). Using the SBM, we partitioned the metaweb along 2 to 30 groups, and selected the optimal number of groups based on the partitioning of the metaweb that maximised the ICL criterion. This partitioned the metaweb into 28 trophic groups (Figure S1 and S2, Table S2). We then computed the proportion of species within each group that are vulnerable to each threat.

In addition, we computed the degree (number of prey, number of predators, and sum of prey and predators (i.e. number of neighbours in the graph)) of each species using the R package *igraph* (Csardi & Nepusz, 2006). This allowed us to investigate whether highly connected species are vulnerable to certain threats, which would have a potential higher impact on the rest of the food web (Figure 2 and S3).

Second, we quantified the indirect threats to predator species as the number of species' prey that are affected by each threat. To do so, we performed a matrix multiplication between:

- the threats matrix, of dimensions 6 x 804, where element (i, j) is equal to 1 if species j is threatened by threat i, and

- the adjacency matrix representing the food web, of dimensions 804 x 804, where element (i, j) is equal to 1 if species i is eaten by species j

This matrix multiplication results in a matrix of dimensions 6 x 804, where each element (i, j) is equal to the number of prey species of predator j that are affected by threat i. Then, we divided each value by the total number of prey species of the predator, to get a proportion of vulnerable prey for each predator for each threat (Figure 3).

In this matrix multiplication, we assumed that predators are indirectly vulnerable to threats that affect their prey. We only quantified the threats associated with species that are neighbours in the food web, rather than cascading threats across multiple trophic levels.

## Mapping the vulnerability of food webs to anthropogenic threats across Europe

We combined the species distributions with the metaweb to build local food webs in each grid cell. We assumed that if two species interact in the metaweb and they both occur in the grid cell, then the interaction exists in the local food web. We assumed that an interaction is vulnerable if it is associated with a prey and/or predator species that is impacted by a threat. In other words, vulnerable interactions can be both top-down (the species may stop functioning as a predator) or bottom-up (the species may
stop functioning as a resource). We quantified the proportion of vulnerable species, and vulnerable interactions in the food web that are associated to species that are vulnerable to each threat type. We first compared the proportion of vulnerable species and vulnerable interactions at the level of the metaweb. Then, we quantified these proportions across space: for each grid cell, we quantify the proportion of species, and interactions that are vulnerable to the major threat types (Figure 4 and 5).

We then identified food web vulnerability hotspots to each major threat (Figure 6). To make the metric comparable for all threat types, we first standardised (between 0 and 1) the number of vulnerable species to each threat type, and the number of vulnerable interactions, across all grid cells in Europe. Then, we multiplied these two values for each threat type, so that hotspots of food web vulnerability are areas with many vulnerable species and many vulnerable interactions. Consequently, areas with either few vulnerable interactions, or few species, cannot be food web vulnerability hotspots. This metric expresses a first estimate of the relative magnitude of the impact of each threat on vertebrate assemblages across Europe, taking into account not only species but also their interactions.

All analyses were performed using R version 4.2.2 (R Core Team, 2022). For Figures 3 and 5 we used the R package *ghibli* (Henderson et al., 2022). The code and processed data for the analysis are openly available in the GitHub repository maintained by the authors: https://github.com/LouiseOC/EU-foodweb-threats.git.
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# Results

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**Different trophic groups face different threats.** Direct exploitation was the most impactful threat for European vertebrates, affecting 34% (387) species, essentially top predators (Figure 1A) with a high number of prey (Figure 2). Species vulnerable to direct exploitation were highly connected in the metaweb (they had a total degree of 29 on average [95% CI: 25 to 34]), with 22 prey [95% CI: 17 to 27], and 7.5 predators [95% CI: 6.7 to 8.2] on average. As a result, 85% interactions in the metaweb were vulnerable to direct exploitation (Figure 1B). Agricultural intensification was the second most impactful threat, affecting 31% (285) species and 69% metaweb interactions. Importantly, species vulnerable to agricultural intensification were also highly connected, with a total degree of 28 species on average [95% CI: 23 to 33], including 18 prey [95% CI: 12 to 23] and 10 predators [95% CI: 8.9 to 11] on average. In particular, over 70% of birds of prey (groups 2, and 24) and 78% of generalist predator species (group 13) were vulnerable to agricultural intensification, primarily due to the use of pesticides (Figure S4); 42% of shrews and moles (group 19) were also affected (Figure S1). Urbanisation was the third most prevalent threat, affecting 255 species and 42% metaweb interactions, across multiple trophic levels: 50% of birds of prey (groups 2 and 24), 50% of macro vipers (group 23), 41% of amphibians (group 1) and 41% wading birds (group 15) were found to be vulnerable to urbanisation. Species vulnerable to urbanisation had a total degree of 18 [95% CI: 14 to 22] with 9.9 prey [95% CI: 5.9 to 14] and 7.9 predators [95% CI: 7.0 to 8.9] (Figure 2). Climate change was a threat for 26% (235) species and 41% interactions across different trophic levels: passerines (group 11), birds of prey (group 24), aquatic predators (group 9). In terms of degree, species vulnerable to climate change had a total degree of 19 [95% CI: 15 to 23] with 10 prey [95% CI: 6.1 to 15] and 8.6 predators [95% CI: 7.6 to 9.6] on average. Pollution, invasive alien species and wetlands loss were major threats to all water dependent trophic groups: amphibians (group 1), herbivorous water birds (group 15) and aquatic predators (group 9, including the otter and predatory wading birds). Aquatic predators were also highly vulnerable to direct exploitation (which affects 75% of aquatic predators) and agricultural intensification (56%). Species vulnerable to pollution had a total degree of 19 [95% CI: 14 to 24] with 10 prey [95% CI: 4.8 to 15] and 8.8 predators [95% CI: 7.5 to 10]; and IAS and diseases affected species that had fewer interactions in the metaweb on average compared with other threat types: species vulnerable to IAS and diseases had a total degree of 15 [95% CI: 11 to 18], including 7.0 prey [95% CI: 3.5 to 10] and 7.9 predators [95% CI: 6.7 to 9.0] on average.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Threats to the European vertebrate metaweb. (A) Each network represents the metaweb, where nodes are trophic groups, and links represent feeding interactions between trophic groups. The colour intensity of the node represents the proportion of species in each group affected by the threat. Link colour is the mixed colour of the two interacting nodes. Node size represents the number of species within the group, and link width represents the number of feeding interactions between trophic groups. (B) Barplots showing the percentage of species vulnerable to each threat and the percentage of interactions in the metaweb associated with these species. Right: percentage species vulnerable to each threat type. Left: percentage of interactions in the metaweb associated with vulnerable species (both predator and prey). These results only include interactions between species that co-occur at least once in Europe.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 2:** Relationship between the degree of species and their vulnerability to each threat. We consider 3 types of degree: number of prey in orange, number of predators in green and total number of neighbours (i.e., total degree) in purple. The y-axis is presented on a pseudo-logarithmic scale.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 3:** Indirect vulnerability of predators in the metaweb to each threat. These boxplots show, for each predator, the percentage of prey species that are vulnerable to different subcategories of threat. Each dot corresponds to one predator species (i.e. that have at least one terrestrial vertebrate prey). The y axis shows the percentage of prey of this predator species that is affected by the threat type on the x axis.</figcaption></figure>
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*Most predators are indirectly vulnerable to agricultural intensification.*

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When focusing on the vulnerability of prey for predators, we found that agriculture was the most impactful threat for predator resources overall: on average, 33% of predators' prey species are vulnerable to agricultural intensification (Figure 3) [95% CI: 31 to 35%]. For example, *Lynx pardinus*, a specialist predator of the European rabbit, was indirectly highly vulnerable to agricultural intensification, hunting, and diseases that affect its main prey species. Climate change was another major indirect threat for predators (affecting 23% prey per predator on average [95% CI: 21 to 25%]) (Figure 3), as well as urbanisation (affecting 27% of prey per predator [95% CI: 25 to 30%]), and pollution, which affected 30% of prey per predator on average [95% CI: 27 to 34%]. IAS and diseases affected 23% of prey per predator on average [95% CI: 20 to 26]. On average, 20% of prey species per predator were affected by direct exploitation [95% CI: 21 to 25].

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*Interactions are disproportionately more vulnerable than species.* In the metaweb and across space, a higher number of vulnerable species inevitably leads to a higher number of vulnerable interactions, but we found that some threats had a disproportionate impact on interactions relative to the number of vulnerable species (Figures 1B, 4 and 5). In the metaweb, we found that for instance, IAS and diseases and pollution both affected 23% species and 30% metaweb interactions, while pesticides affected 15% species and 53% interactions (Figure 1B and Figure S3). Direct exploitation affected 34% species but these species were responsible for 85% interactions in the metaweb, because these species were highly connected (Figure 2). Similarly, across Europe, we found that the proportion of vulnerable interactions was consistently higher than the proportion of vulnerable species in food webs (Figures 4 and 5). In some areas, 100% interactions in the food web were vulnerable to major threats, while only a fraction of the species were vulnerable (Figure 5). The difference between species vulnerability and interaction vulnerability varied with the threat type. In the case of direct exploitation and agricultural intensification, the proportion of threatened interactions was 85% [95% CI: 85.17 to 85.25%] and 79% [95% CI: 78.9 to 79.1%] on average, respectively. This was 2 to 3 times higher than the proportion of threatened species, with 38% affected on average in both cases [95% CI: 38.25 to 38.31% for direct exploitation; and 38.4 to 38.5% for agricultural intensification]. The difference between the proportion of vulnerable species and vulnerable interactions in local food webs was lower for other major threats: on average across Europe, climate change affected 28% species [95% CI: 27.78 to 27.84] and 39% interactions [95% CI: 38.8 to 38.9]; 22% species [95% CI: 22.3 to 22.4] and 35% interactions [95% CI: 35.1 to 35.3] were vulnerable to pollution; 21% species [95% CI: 21.3 to 21.4] and 33% interactions [95% CI: 32.5 to 32.6] were vulnerable to IAS and diseases. Combining the number of both vulnerable interactions and species, we highlighted hotspots of food web vulnerability to major threats across Europe, i.e. with a high number of vulnerable species and interactions (Figure 6). These were mostly located in species-rich areas, but spatial patterns of vulnerability hotspots differed between threat types. Hotspots of food web vulnerability to direct exploitation and agricultural intensification were more vulnerable than species.
423 intensification were located in Spain (Pyrenees, Cantabria, and the Central system),
424 the southeast of France, northern Greece and the Baltic states. Hotspots of food web
425 vulnerability to pollution were concentrated in the east of France, northeast Poland,
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<figure><img src="image_6.png" /><figcaption>**Figure 4: Vulnerability of species and interactions across Europe to the six major threats.**

Top row: percentage of species in the grid cell that are vulnerable to each threat. Bottom row: percentage of interactions in the local food web that the vulnerable species are associated with. The colour gradient used is the same for all maps.</figcaption></figure>
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<figure><img src="image_7.png" /><figcaption>**Figure 5:** Violin plots and boxplots showing the percentage of vulnerable species (left) and interactions (right) to different threats across all grid cells as mapped in Figure 4.</figcaption></figure>

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<figure><img src="image_8.png" /><figcaption>**Figure 6:** Hotspots of food web vulnerability to six major threats. The value in each cell is the multiplication of the number of vulnerable species (scaled between 0 and 1) and the number of vulnerable interactions (scaled between 0 and 1) in each grid cell, for each threat. Red colour indicates areas with a high vulnerability of both species and interactions to each threat. Blue indicates low vulnerability values of species and interactions.</figcaption></figure>
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# Discussion

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In 1962, Rachel Carson published *Silent Spring*, describing how the impacts of anthropogenic threats such as pesticides can spread in the entire food chain. Since then, conservation efforts may have increased globally (Maxwell et al., 2020), but remain insufficient compared to the scale and intensity of anthropogenic pressures on ecosystems. Sixty years later, we are only starting to understand how different types of threats are impacting interaction networks. Our study is one of the first to analyse the potential impacts of anthropogenic pressures on food webs at a macroecological scale (Fricke, Hsieh, et al., 2022; Botella et al., 2024). Our findings suggest that species interactions can improve our understanding of the far-reaching impact of threats on biodiversity. In particular, we showed that interactions tend to be disproportionately more vulnerable to certain threats relative to species, in particular in the cases of direct exploitation and agricultural intensification. Because both threats affect highly connected species, these pressures may cause ecosystem disruption and extinction cascades (Morton et al., 2022).

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The impact of direct exploitation on terrestrial vertebrates is unsurprising given the long list of species that have been hunted to extinction in the past (Dirzo et al., 2014; Fricke, Hsieh, et al., 2022). Our results highlight the need for a strict regulation of the direct exploitation of species (and particularly of top predators) in Europe in order to avoid large-scale disruptions of food webs (Estes et al., 2011). The scale of the impact of agricultural intensification on species and their interactions is also deeply worrying (Rigal et al., 2023). It is well established that agricultural intensification has a negative impact on many species through multiple processes: the use of pesticides and nitrates leads to a decline of biodiversity, particularly of invertebrates (Hallmann et al., 2017; Seibold et al., 2019). In Europe, over 300,000 tonnes of pesticides are used annually, and this trend is on the rise (European Commission, 2023), despite the documented negative impacts on biodiversity and on human health. Other impacts of agricultural intensification include: excessive nutrient input leading to the eutrophication of ecosystems; direct disturbance and mortality of species such as ground-nesting birds and small mammals; the loss of habitats as well as hedges and green linear elements that form essential habitats for many species and their prey; expansion of agricultural land and the simplification of agricultural landscapes and the loss of habitat heterogeneity (Stanton et al., 2018). Our study goes a step further by describing the staggering impact of direct exploitation and agricultural intensification on terrestrial vertebrate food webs across Europe (Figure 1 and 5). In particular, we found that agricultural intensification poses a significant threat to essential feeding resources for predator species. This is consistent with recent findings that suggest that food webs complexity and functional diversity decrease in highly intensive landscapes (Etard et al., 2022; Botella et al., 2024). The loss of interactions and food web complexity in intensive agricultural landscapes is concerning as terrestrial vertebrates and their interactions underpin essential ecosystem services, such as pest control (Civantos et al., 2012). The disruption of food webs in highly intensive agricultural landscapes may
result in the loss of natural processes that regulate pest populations, further amplifying pest outbreaks and the reliance on pesticide use. In particular, we found that agricultural intensification affects highly connected prey - and previous studies have found that highly connected prey species are critical for the robustness of food webs, and indirectly support many ecosystem services (Keyes et al., 2021). Therefore, there is a pressing need to preserve biodiversity in agricultural systems (Ortiz et al., 2021), by de-intensifying agricultural practices, decreasing pesticide use, and re-establishing habitat heterogeneity within agricultural landscapes. While IAS and diseases and pollution impact relatively less species and interactions overall among terrestrial vertebrates, they affect over half of water-dependent species and trophic groups, meaning that freshwater and wetland food webs are at serious risk of disruption (Figure S5) (Dudgeon et al., 2006; Haase et al., 2023). Incorporating biotic interactions has the potential to improve our understanding of the scale of impacts of anthropogenic threats on biodiversity and nature's contributions to people (Fricke, Ordonez, et al., 2022) and to better inform policy making on threat mitigation.

Interactions may disappear before species are lost (Valiente-Banuet et al., 2015), which triggers extinction debts. Further research is needed to investigate extinction debts and community dynamics following the loss or decline of interactions. In this study, we did not capture temporal or spatial variability since the threat dataset lacks temporal and spatial specificity (Harfoot et al., 2021). Yet, the vulnerability of species to anthropogenic threats may vary spatially across their ranges, and through time. Intraspecific traits variation as well as population abundance and dynamics may drive this spatial and temporal variation and modify the sensitivity of populations in different contexts to certain anthropogenic threats. Furthermore, interactions themselves can vary across space and time, due to interaction plasticity and behavioural changes, and species may adapt to the loss of interactions through interaction rewiring (Kamaru et al., 2024). It is challenging to include interaction plasticity with our metaweb approach, which is inherently static (Thuiller et al., 2024). However, the metaweb includes information on whether interactions are obligate or occasional. In the main analysis, we focused on the obligate interactions since they are critical for predator survival. Yet, interaction plasticity could influence the patterns of vulnerability, such as when predators switch to occasional interactions in the case of food limitations. In a preliminary analysis, we investigated whether considering occasional interactions in addition to obligate interactions influences the vulnerability of the metaweb: we found that considering both occasional and obligate interactions in the metaweb does not change the main results (Annex 2). Yet, there is evidence that interaction networks with many weak interactions (e.g. occasional interactions) and few strong links (e.g. obligate interactions) can contribute to buffering against disturbances (Tylianakis et al., 2010). There is a need for a more spatially and temporally explicit analysis to capture the variation both in species interactions and in their vulnerability to threats over space and time.

Here, we only investigated the vulnerability of food webs to threats, and not the actual impact of threats. To assess the actual impact of threats, we could mobilise spatial
data that reflect the risk of threat, related to land use intensification, agriculture
intensification, forest management, urbanisation, loss of forests, wetlands or
grasslands, or to pollution (Schürings et al., 2024). Combining then the spatial risk of
threat with the vulnerability of food webs to each threat would allow estimating threat
impact across Europe in a spatially explicit way. In the case of agricultural intensity,
this can be applied to current conditions (Dou et al., 2021), past changes (e.g. through
CORINE change), or future scenarios of change (Powers & Jetz, 2019). Such an
analysis would allow to i) identify orphaned species that lose all their interactions
(Sandor et al., 2022), ii) to locate areas where predators are losing vertebrate prey due
to the combined action of multiple threats locally, and iii) to quantify the cascading
impact of threats on food webs across space. There is an overwhelming scientific
consensus that different drivers of biodiversity loss interact synergistically (Isbell et
al., 2022) and one threat exacerbates the effect of another (e.g., climate and
agriculture) (Williams & Newbold, 2021). Food webs offer the opportunity to
investigate the synergistic effect between threats on a species or community: a
predator can lose part of its prey due to one threat, and the rest of its prey due to a
different threat. Quantifying the impact of multiple threats on a food web can give a
more realistic view of the risks posed by human activities to biodiversity (Botella et al.,
2024). Furthermore, our analysis focused on the vulnerability of pairwise interactions,
but we did not investigate the cascading impacts across trophic levels and secondary
extinction risks (Estes et al., 2011). To do so, we would need to include invertebrate
and plant species, as they form a big proportion of the diets of many terrestrial
vertebrates. In addition, our analysis focused on trophic interactions – but non-
trophic interactions (e.g. mutualism and competition) can also shape ecosystem
dynamics and resilience (Bascompte & Jordano, 2007; Domínguez-Garcia & Kéfi,
2024). Further research is needed for developing robust metrics to measure the
vulnerability of interaction networks across space. In particular, one limitation of the
metric we used here is that vulnerability hotspots tended to occur in species-rich areas.
These species-rich areas are also a result of geographic (and taxonomic) sampling
biases due to accessibility and socio-economic factors (Hortal et al., 2015; Garcia-
Rosellò et al., 2023). There is thus a need to develop metrics which can quantify food
web vulnerability in a way that is independent from network size. Exploring deviations
from null models (Gaüzère et al., 2022), or using insights from graph theory
(Bascompte 2007), are promising avenues which could help improve estimates of food
web vulnerability across space. We expect that the severity of cascading extinctions
will result from the combination of the environmental threats faced by individual
species, and the susceptibility of the community itself to propagate perturbations due
to food web topology (Tylianakis et al., 2010). Trophic group redundancy, for example,
is a key driver of food web resilience, as non-threatened species can fill the functional
role associated with threatened species and their interactions (Sanders et al., 2018).
Moving forward, identifying what components of biodiversity are most at risk, and
where, will be critical to inform threat mitigation strategies and help locate priority
areas for conservation (Tulloch et al., 2015).
573 Acknowledgements

574 The authors would like to acknowledge funding from the FutureWeb project funded
575 through the 2017–2018 Belmont Forum and BiodivERsA joint call for research
576 proposals, under the BiodivScen ERA-Net COFUND program, and with the funding
577 organisation Agence Nationale pour la Recherche (ANR-18-EBI4–0009). LOC
578 received funding from the French Ministry of Higher Education and Research
579 (MESRI). We also acknowledge support from the Horizon Europe NaturaConnect (No:
580 101060429) and Obsgession (No: 101134954) projects, the French Biodiversity Office
581 and the CESAB working group IMPACTS.

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<figure><img src="image_9.png" /></figure>

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**Figure S1:** Heatmap representing the proportion of vertebrate species within each trophic group (numbered from 1 to 28, in rows) that are sensitive to each main threat (A) and threat subcategory (B), according to the European red list database.
<figure><img src="image_10.png" /></figure>

**Figure S2:** Selecting the optimum number of trophic groups with the integrated Classification Likelihood Information Criterion. We partitioned the metaweb along a range of 2 to 30 classes (Q, on the x-axis) and selected the number of groups that maximised the ICL criterion (y-axis).

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<figure><img src="image_11.png" /><figcaption>**Figure S3:** Bar plots of the percentage of vulnerable species and vulnerable interactions in the metaweb (extended version of Figure 1B). Threat types are arranged by percentage of vulnerable species (top to bottom).</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure S4:** Extended version of Figure 2 with subcategories of threats.</figcaption></figure>
<figure><img src="image_13.png" /></figure>

**Figure S5:** Extended version of Figure 3 with subcategories of threats, showing for each predator, the percentage of prey species that are vulnerable to different subcategories of threat (on the x-axis, and in different colours). Each dot represents a predator species. Interestingly, this figure shows that, while wetland loss was not considered among the major threats in this study due to a relatively smaller number of species impacted (Table S1), wetland loss stands out here as it tends to impact a large percentage of prey resources for many predator species.

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<table><caption>773 Table S1: Threat classification and keywords used to create the threat classification scheme.</caption><thead><tr><th>Threat category</th><th>Threat subcategory</th><th>Number of vulnerable species</th><th>Key words and character strings</th><th>Correspondence with IUCN threat classification scheme (2022)</th></tr></thead><tbody><tr><td rowspan="4">Residential and commercial development (i.e. Urbanisation)</td><td>leisure, tourism</td><td>146</td><td>"ski|ski resort|skiing|alpinism|mountaineering|tourism|tourist|recreati|Recreation|leisure|golf|yacht|resort|hotel"</td><td>1. Residential & Commercial development / 1.3 Tourism and recreation areas<br>6. Human intrusions and disturbance / 6.1 Recreational activities</td></tr><tr><td>construction</td><td>71</td><td>"construction|infrastructure development|industrial development|commercial development"</td><td>1. Residential & Commercial development / 1.2 Commercial & Industrial areas</td></tr><tr><td>human disturbance</td><td>48</td><td>"disturbance by human|human disturb|disturbed by human|disturbance by walk|disturbance by people"</td><td>6. Human intrusions and disturbance</td></tr><tr><td>housing and urban expansion</td><td>69</td><td>"housing|urban expansion|urbanization|urbanisation|urban development"</td><td>1. Residential & Commercial development / 1.1 housing & urban areas</td></tr><tr><td rowspan="2">Agricultural intensification</td><td>agriculture expansion and intensification</td><td>202</td><td>"to agriculture|livestock|farming|overgrazing|increased cultivation|abandonment of traditional agriculture|intensive crop|agricultural intensification|intensive arable agriculture|intensive farming|agricultural development|fertilisation|agricultural activities|intensification of farm|hedgerow"</td><td>2.1 Annual & perennial non-timber crops; 2.3 Livestock farming & ranching; 9.3 Agricultural & forestry effluents</td></tr><tr><td>pesticides</td><td>140</td><td>"pesticide|herbicide|Pesticide|biocide|rodenticide"</td><td>9.3.3 Herbicides & pesticides</td></tr></tbody></table>
<table><tr><td>Fishing industry</td><td>fishing industry, aquaculture</td><td>57</td><td>"aquaculture|Bycatch|bycatch|entanglement|entangled|fishing net|fishing gear|gillnet|driftnet|fishnet|drown|longline fish|trawl fish|intensification of fish|fisheries|overfishing"</td><td>2.4 Marine &amp; freshwater aquaculture<br>5.4 Fishing &amp; harvesting aquatic resources</td></tr><tr><td>Pollution</td><td>including chemical pollution in water and soils</td><td>219</td><td>"pollut|chemical|contamin|antibiotic|sewage|waste water|water pollution|Water pollution|carbamate|run-off|organochloride|phosphate|nutrient|eutrophic|acidification|hypertrophication|toxic"</td><td>9 Pollution</td></tr><tr><td rowspan="3">Energy production and mining</td><td>mining, quarries</td><td>51</td><td>"coal min|acid rain|mining|gravel extract|sand extract|peat extract|peat-extract|quarries|quarry| mineral"</td><td>3.2 Mining &amp; quarrying<br>9.2.2 Seepage from mining</td></tr><tr><td>oil and gas exploration, extraction and transport</td><td>62</td><td>" oil spill|petrol| oil pollution| gas | Oil"</td><td>3.1 Oil &amp; gas drilling<br>9.2.1 Oil spills</td></tr><tr><td>renewables (wind farms, hydroelectricity, tidal energy plants)</td><td>66</td><td>"tidal energy|wind turbine|wind energy|wind farm|Wind farm|hydroelectr"</td><td>3.3 Renewable energy</td></tr><tr><td rowspan="3">Forestry</td><td>logging</td><td>76</td><td>"logging| sylvi| wood harvesting| forestry| plantation"</td><td rowspan="3">5.3 Logging &amp; wood harvesting<br>2.2 Wood &amp; pulp plantations</td></tr><tr><td>deforestation</td><td>28</td><td>"clear-cut|deforest|forest loss|loss of forest|removal of wood"</td></tr><tr><td>afforestation</td><td>60</td><td>"reforest|afforest"</td></tr><tr><td>Direct exploitation</td><td>hunting and exploitation</td><td>233</td><td>" trapping|hunting|hunted|Hunting|shoot|game|killing|p</td><td>5.1 Hunting &amp; collecting terrestrial animals</td></tr></table>
<table><tr><td></td><td></td><td></td><td>coaching|consumption|harvest| fur| exploitation for food"</td><td></td></tr><tr><td></td><td>persecution</td><td>154</td><td>"persecut|Persecut|killed when encounter|destroy egg|destroy nest|destruction of nest|destruction of roost|poison bait|poisoning"</td><td></td></tr><tr><td></td><td>collection and pet trade</td><td>118</td><td>"collect| pet|pet trade"</td><td></td></tr><tr><td rowspan="2">Invasive species and diseases</td><td>invasive alien species</td><td>130</td><td>"introduc|invasi|alien|exotic|American mink|Louisiana crayfish"</td><td rowspan="2">8 Invasive & other problematic species, genes & diseases</td></tr><tr><td>diseases and pathogens</td><td>123 212</td><td>"fung|diseas|virus|viral|hytridiomyco|yxomatos|pathogen|parasites|malaria|influenza"</td></tr><tr><td>Climate change and severe weather</td><td>including warming, droughts, severe winters, extreme weather events</td><td>235</td><td>"climate|Climate|climat|cold winter|harsh winter|Harsh winter|severe winter|desertification|drought|aridity|dessic|desicc|decreased spring rain|shorter rainy season|reduced winter precipit|reduced precipit|reduced rain|storms|extremes|temperature extreme|extreme temperature|extreme weather|extreme climat|Extreme weather|Extreme climat| Floods | floods | hot| Hot| warm| Warm| heat| Heat|temperature rise|increasing temperature|increased spring temperature|increased summer tempera"</td><td>11 Climate change & severe weather</td></tr><tr><td rowspan="2">Other threats</td><td>loss of wetlands</td><td>158</td><td>"drainage|wetland|freshwater|water extraction|water abstraction"</td><td>7.2 Dams & water management/use<br>7.3 Other ecosystem modifications</td></tr><tr><td>loss of coast and dune habitats</td><td>72</td><td>"coast|dune"</td><td>7.3 Other ecosystem modifications</td></tr></table>
<table><tr><td></td><td></td><td></td><td></td><td></td></tr><tr><td>fires</td><td>85</td><td>" fire | Fire | burn"</td><td>7.1.1 Increase in fire frequency/intensity</td></tr><tr><td>roads and rail lines</td><td>80</td><td>"traffic accident | road | highway | collision with cars | cars | vehicles | heavy traffic | train | trains | rail"</td><td>4.1 Roads & railroads</td></tr></table>

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<table><caption>776 Table S2: Description of the species that typically characterise each trophic group</caption><thead><tr><td>Trophic group ID</td><td>Composition</td><td>Size (number of species)</td></tr></thead><tbody><tr><td>1</td><td>All amphibians: frogs, toads, and salamanders</td><td>78</td></tr><tr><td>2</td><td>Birds of prey (Montagu's harrier, five Falcon species, Arctic Skua) that feed on rodents, small birds, bird eggs, invertebrates</td><td>7</td></tr><tr><td>3</td><td>Diet categories: Plant parts (Berries, Bulbs, Flowers, Fruits, Leaves, Nectar, Other plant parts, Seeds, Grains, Forbs), aerial and aquatic invertebrates, fish</td><td>13</td></tr><tr><td>4</td><td>Rodents: mice (29 species in the Muridae family), voles and lemmings (48 species in the Cricetidae family) feeding on plants and invertebrates</td><td>77</td></tr><tr><td>5</td><td>Omnivorous species of birds, reptiles, and mammals that eat insects, berries, seeds (e.g. wild boar)</td><td>14</td></tr><tr><td>6</td><td>Lizards (Lacertidae)</td><td>92</td></tr><tr><td>7</td><td>Medium-sized birds of prey and owls: 7 species of the Accipitridae family (e.g. Buteo buteo, Clanga pomarina), 4 species of the Strigidae family, 2 species of the Corvidae family, and 1 skua (Stercorariidae)</td><td>14</td></tr><tr><td>8</td><td>4 Dolichophis species (whip Snakes) and the desert monitor</td><td>5</td></tr><tr><td>9</td><td>Intermediate freshwater predators feeding on fish, amphibians, aquatic invertebrates: mostly birds (wading birds (7 species of Ardeidae, 2 species of Rallidae), Smew (Mergellus albellus), Glossy ibis, black stork (Ciconia nigra)), the otter (Lutra lutrea), and 2 aquatic dependent snakes (Natrix species)</td><td>16</td></tr><tr><td>10</td><td>Hedgehogs, rabbits and hares, large rodents (marmots), squirrels</td><td>35</td></tr></tbody></table>
<table><tr><td>11</td><td>Passeriformes: Sylviidae, Muscicapidae, Tichodromidae, Troglodytidae, Sittidae, Regulidae, Hirundinidae, Pycnonotidae, Prunellidae, Cisticolidae, Paridae, Calcariidae, Phylloscopidae, Acrocephalidae, Aegithalidae, Bombycillidae, Certhiidae, Scotocercidae, Cinclidae, Locustellidae, Oriolidae, Panuridae, Remizidae</td><td>111</td></tr><tr><td>12</td><td>Predatory reptiles: 5 vipers, large lizards (8 Agamidae, 2 Chamaeleonidae, Timon lepidus), snake-like lizards (2 Blanidae, 3 Anguidae), 15 geckos (Phyllodactylidae, Gekkonidae, Eublepharis), 16 colubers (Eirenis genus), 17 skinks.</td><td>73</td></tr><tr><td>13</td><td>Intermediate, generalist predators (birds and mammals): white stork, night heron, corsac fox, Egyptian mongoose, red kite</td><td>9</td></tr><tr><td>14</td><td>Vipers that eats insects, lizards, small mammals</td><td>7</td></tr><tr><td>15</td><td>Medium and large sized omnivorous bird species. Most species in this group are semi-aquatic, including wading birds (Scolopacidae, Rallidae, Podicipedidae, the Flamingo), ducks (36 species of Anatidae), and seabirds (including seagulls, sterns, auks (6 species of Alcidae), skua), 40 species in this group are not semi-aquatic, but rather medium large birds living on steppe habitat: 17 species of the Phasianidae family, 7 species of Corvidae, 3 Glareolidae, and 3 species of Otididae, 9 Columbidae, 2 sandgrouse (Pterocles), and 2 woodpeckers.</td><td>160</td></tr><tr><td>16</td><td>Top predators feeding on mammals and birds: 2 foxes (red and arctic), the jackal (Canis aureus), 2 raptors (eastern imperial eagle (Aquila heliacal) and Falco cherrug), 3 large owls (including the eagle owl).</td><td>8</td></tr><tr><td>17</td><td>Top predators: wolverine, other mustelids (Mustela and Martes genus, the marbled polecat), wild cat, brown bear, seagulls (Larus), some large snakes</td><td>21</td></tr><tr><td>18</td><td>Diet categories: invertebrates found on ground and vegetation</td><td>2</td></tr><tr><td>19</td><td>Insectivorous small mammals: Shrews (28 Soricidae spp.) and moles (8 Talpidae spp.)</td><td>36</td></tr><tr><td>20</td><td>Peripheral species that have few predators (e.g. wolf or lynx) and/or no vertebrate prey: swifts, deer, vultures, ungulates, bats, turtles</td><td>141</td></tr><tr><td>21</td><td>Other diet categories: Algae, Aquatic vegetation, Mosses and Lichens, Mushrooms, Nuts, Cultivated Plants, Bark, Woody Vegetation, Carrion, Coprofagous, Detritus, Garbage.</td><td>12</td></tr></table>
<table><tr><td>22</td><td>Vipers (Vipera) and colubers (Telescopus, Elaphe, Spalerosophis, Hemorrhois and Hierophis spp.)</td><td>12</td></tr><tr><td>23</td><td>2 Macrovipera spp.</td><td>2</td></tr><tr><td>24</td><td>Large birds of prey: Hawks (Accipiter spp.), Eagles (Aquila spp., Clanga clanga, Haliaeetus albicilla, Hieraaetus pennatus), Harriers (Circus spp.)</td><td>11</td></tr><tr><td>25</td><td>Mammals, reptiles and snakes, intermediate predators</td><td>14</td></tr><tr><td>26</td><td>Small mouse-like rodents: Gliridae, Dipodidae, Sminthidae,</td><td>21</td></tr><tr><td>27</td><td>Top predators: birds of prey (e.g., Aquila nipalensis, Circaetus gallicus), owls (e.g. Bubo scandiacus, Surnia ulula), wolf, lynx, large gulls (2 Larus spp.)</td><td>15</td></tr><tr><td>28</td><td>Passerines (finches, thrush, starlings) that eat invertebrates</td><td>94</td></tr></table>
778 Annex 1: Resolving mismatches in taxonomic classification
779 between datasets

<table><tr><td>780</td><td>167 Amphibian, Reptile, Bird and Mammal species names listed in the European red list dataset were not in the Tetra-EU dataset. We corrected taxonomical mismatches (due to the use of synonyms) for 71 species. The remaining 96 species were either i)</td></tr><tr><td>781</td><td>non-native (e.g. the American bullfrog; the Canada goose; the black rat); or marine</td></tr><tr><td>782</td><td>mammals (e.g. pinnipeds and cetaceans); or ii) occurred outside of the European continent, in Africa or Asia (e.g. Pelophylax saharicus; Trogonophis wiegmanni; Psammodromus blanci; Sclarcis perspicillata; Timon tangitanus; Accipiter badius; Picus sharpei); or iii) migratory birds that are vagrant in Europe (e.g. Calidris bairdii; Numenius tenuirostris) or that winter in Europe (e.g. Branta ruficollis); or iv) species that have only recently been recognized as a distinct species (e.g. Larus michahellis; Phylloscopus canariensis; Phylloscopus ibericus); or v) extinct species (the great auk; the Canary island oystercatcher; the aurochs; the Sardinian pika).</td></tr><tr><td>783</td><td></td></tr><tr><td>784</td><td></td></tr><tr><td>785</td><td></td></tr><tr><td>786</td><td></td></tr><tr><td>787</td><td></td></tr><tr><td>788</td><td></td></tr><tr><td>789</td><td></td></tr><tr><td>790</td><td></td></tr><tr><td>791</td><td></td></tr></table>
Annex 2: Including both typical and occasional interactions does not change main results

Predators may switch to occasional interactions, in the case of food limitations for example, which could influence the vulnerability of species and interactions. We performed a second set of analysis that considered both occasional and obligate interactions in the metaweb to test whether including both types of interactions would change the results. Figures below show that the main findings stay true when considering both obligatory and occasional interactions.

<figure><img src="image_14.png" /></figure>

**Figure A2.1.** Bar plots of the percentage of vulnerable species and vulnerable interactions in the metaweb, when considering both occasional and obligate interactions. Threat types are arranged by percentage of vulnerable species (top to bottom). In comparison with Figures 1B and S3, we see that the results in terms of vulnerable species and interactions are the same: 1) there is a symmetric relationship between the percentage of vulnerable species and the percentage of vulnerable interactions in the metaweb; and 2) for direct exploitation, and agricultural intensification (including agricultural expansion and pesticides), there is a greater difference between the percentage of vulnerable species and the percentage of vulnerable interactions than for any other threat type. Minor differences between the main analysis and this one include : i) reforestation has a greater impact on interactions in the metaweb when including occasional interactions; ii) agricultural expansion affects a greater proportion of interactions when occasional interactions are included; and iii) pesticides affect a greater percentage of interactions when occasional interactions are excluded.
<figure><img src="image_15.png" /><figcaption>**Figure A2.2:** Each network represents the metaweb (the version with both occasional and obligatory interactions), where nodes are trophic groups, and links represent feeding interactions between trophic groups. The colour intensity of the node represents the proportion of species in each group affected by the threat. Node size represents the number of species within the group, and link width represents the number of feeding interactions between trophic groups. This shows that, even when considering both occasional and obligatory interactions, trends are similar to the main analysis: 1) direct exploitation primarily affects top predators and intermediate predators; 2) agricultural intensification affects some groups of top predators as well as basal species.</figcaption></figure>

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