Ecological networks are more sensitive to plant than to animal extinction under climate change — TEI-tagged view

Matthias SchleuningpersName, Jochen FründpersName, Oliver SchweigerpersName, Erik WelkpersName, Jörg AlbrechtpersName, Matthias AlbrechtpersName, Marion BeilpersName, Gita BenadipersName, Nico BlüthgenpersName, Helge BruelheidepersName, Katrin Böhning-GaesepersName, D. Matthias DehlingpersName, Carsten F. DormannpersName, Nina ExelerpersName, Nina FarwigpersName, Alexander HarpkepersName, Thomas HicklerpersName, Anselm KratochwilpersName, Michael KuhlmannpersName, Ingolf KühnpersName, Denis MichezpersName, Sonja Mudri-StojnićpersName, Michaela PleinpersName, Pierre RasmontpersName, Angelika SchwabepersName, Josef SettelepersName, Ante VujićpersName, Christiane N. WeinerpersName, Martin WiemerspersName, Christian HofpersName (2016date). “Ecological networks are more sensitive to plant than to animal extinction under climate change.” Nature Communicationstitle·j.

Object of study — how climate impacts on species propagate through plant-animal mutualistic networksterm·dom

Research question — whether biotic specialists have narrow climatic niches and whether plant or animal extinctions cascade more

Contribution — linking species distribution modelsterm·mth to 13 networks and simulating climate-driven coextinctionsterm·dom

Full text — tagged

Ecological networksterm·dom are more sensitive to plant than to animal extinction under climate changeterm·dom Abstract Impacts of climate changeterm·dom on individual species are increasingly well documented, but we lack understanding of how these effects propagate through ecological communities. Here we combine species distribution modelsterm·mth with ecological networkterm·dom analyses to test potential impacts of climate changeterm·dom on >700 plant and animal species in pollination and seed-dispersal networksterm·dom from central Europe. We discover that animal species that interact with a low diversity of plant species have narrow climatic niches and are most vulnerable to climate changeterm·dom. In contrast, biotic specializationterm·dom of plants is not related to climatic niche breadthterm·dom and vulnerability. A simulationterm·mth model incorporating different scenarios of species coextinctionterm·dom and capacities for partner switchesterm·dom shows that projected plant extinctions under climate changeterm·dom are more likely to trigger animal coextinctionsterm·dom than vice versa. This result demonstrates that impacts of climate changeterm·dom on biodiversityterm·dom can be amplified via extinction cascadesterm·dom from plants to animals in ecological networksterm·dom.

Climate changeterm·dom forces species either to move or to adapt to changing conditions 1 , 2 . Although models predicting the responses of individual species to climate changeterm·dom are widely utilized 2 , it is not yet clear to what extent a changing climate will affect biotic interactions between species 3 , 4 . Ecological theory predicts that abundant generalist species tend to have large ranges 5 and, consequently, occupy wide climatic niches 6 , whereas species specialized on specific interaction partners have small ranges, occupy narrow climatic niches and may therefore be particularly vulnerable to climate changeterm·dom 7 .

In ecological communities, species are embedded in networks of interacting species, for instance, in mutualistic networksterm·dom between plant species and animal pollinators or seed dispersers 8 . Species in these networks vary in the number of interaction partners, for example, because of differences in species traits 9 , and thus differ in their degree of biotic specializationterm·dom 8 . So far, it has not been tested how biotic specializationterm·dom in ecological networksterm·dom relates to a species' climatic niche breadthterm·dom and its vulnerability to climate changeterm·dom. However, a quantitative understanding of this relationship is required to predict the likelihood of species extinctions and coextinctionsterm·dom from ecological communities under climate changeterm·dom 10 .

Here we test the two hypotheses that plants and animals with (1) narrow climatic niches and (2) a projected loss in climatic suitability are biotic specialists that interact with a low diversity of partners. We additionally simulate (3) how the relationship between biotic specializationterm·dom and vulnerability to climate changeterm·dom affects the risk of species coextinctionsterm·dom of plants and animals under future climatic conditions. We analysed data on climatic niche breadthterm·dom for 295 species of plants and their insect pollinators (196 bee, 70 butterfly and 97 hoverfly species) and seed dispersers (51 bird species) from central Europe. For each species, we quantified the change in climatic suitability across a species' current European range under projected climate changeterm·dom according to two circulation models and two representative concentration pathways (RCPs 6.0 and 8.5). We linked projected changes in climatic suitability to data on biotic specializationterm·dom derived from 8 quantitative pollination and 5 quantitative seed-dispersal networks recorded in 13 regions across central Europe. Networks describe interaction frequenciesterm·mth between plant and animal species, that is, the number of visits of an animal to a plant species, and yield empirical estimates of biotic specializationterm·dom for each species in each network.

We find that animal species with narrow climatic niches and a projected loss in climatic suitability interact with a low diversity of plant partners, whereas we do not find analogous relationships for plants. This important difference between plant and animal species affects the likelihood of species coextinctionsterm·dom under climate changeterm·dom. We simulate different scenarios of species coextinctionterm·dom and capacities for partner switchesterm·dom and show with these simulationsterm·mth that mutualistic networksterm·dom are more sensitive to projected plant than to animal extinctions under climate changeterm·dom. We conclude that a high potential for adaptive partner switchesterm·dom is required to stabilize mutualistic networksterm·dom against extinction cascadesterm·dom from plants to animals under climate changeterm·dom.

Results Climatic niche breadthterm·dom and biotic specializationterm·dom In line with the first hypothesis, we found that animals' climatic niche breadthterm·dom was positively associated with the effective number of plant partners in the regional pollination and seed-dispersal networksterm·dom ( Fig. 1a,b ). In contrast, climatic niche breadthterm·dom of plants was not related to the effective number of animal partners ( Fig. 1a,b ). For both plants and animals, we found no relationship between climatic niche breadthterm·dom and complementary specializationterm·mth d′ (a measure of the uniqueness of interaction partners relative to other species; Supplementary Table 1 ). These trends were qualitatively similar across the individual networks ( Supplementary Table 2 ).

Biotic specializationterm·dom in relation to climatic niche breadthterm·dom and vulnerability to climate changeterm·dom. Associations of ( a , b ) realized climatic niche breadthterm·dom (climatic hypervolume 60 , OMI climatic niche breadthterm·dom 61 ) and ( c , d ) projected climatic suitability change (RCP 6.0, RCP 8.5 scenarios 65 ; year 2070) with the effective number of partners ( e H ) of plant ( n =295) and animal ( n =414) species in 13 mutualistic interaction networks from central Europe. Specialization is the effective number of interaction partners 66 of plant (blue) and animal (red) species in each network (shown on a log-scale). Trend lines indicate the estimated slope ( β ) in a mixed-effects model accounting for effects of network identity and animal and plant taxonomy on model intercepts. Shown are species' mean partial residuals plus intercept from these models; symbol size is proportional to the weight of each species in the analysis, corresponding to its number of occurrences across networks and, in the case of climatic suitability change, the accuracy of the species distribution modelterm·mth (TSS max value 64 ); given are slope estimates±1 s.e. for plants and animals, P values were derived by Kenward–Roger approximation: ** P <0.01 and *** P <0.001 (for full statistics see Supplementary Table 1 ).

Vulnerability to climate changeterm·dom and biotic specializationterm·dom Consistent with the second hypothesis, we found that animals projected to lose climatic suitability across their current European range had a low diversity of plant partners in the regional networks ( Fig. 1c,d ). There was no analogous relationship for plants and their effective number of animal partners ( Fig. 1c,d ). Changes in climatic suitability were unrelated to complementary specializationterm·mth d' for both plants and animals ( Supplementary Table 1 ). Trends were again similar across the individual networks ( Supplementary Table 2 ).

Species coextinctionsterm·dom under climate changeterm·dom We simulated secondary extinctionsterm·dom of animal and plant species from mutualistic networksterm·dom as a consequence of sequential species loss from the other trophic level. Extending upon previous simulationsterm·mth of species coextinctionsterm·dom 11 , 12 , 13 , we informed our simulationterm·mth model with projected changes in climatic suitability for plant and animal species and removed species sequentially from the highest to the lowest decrease in climatic suitability ( Fig. 2 ). We modelled species coextinctionterm·dom under different scenarios of species' sensitivity to partner loss assuming that a 25, 50 or 75% decrease in total interaction frequency would trigger the secondary extinctionterm·dom of a species from the regional network. These thresholds are probably more realistic than the assumption that all interaction partners must be lost to trigger secondary extinctionterm·dom 11 , 12 , 13 , given the frequency of coextinctionsterm·dom reported in empirical and modelling studies 14 , 15 . In the simulationterm·mth, we further accounted for the potential flexibility of species in the choice of their interaction partners by reallocating a varying proportion of lost interaction events to persisting species. We account for a potential rewiring of interactions to new partners 13 , 16 by comparing a scenario of constrained rewiring to persisting partners with a scenario of unconstrained rewiring to all persisting species. We did not consider, however, that new species may enter the interaction networks and, thus, may overestimate extinction risks under climate changeterm·dom 17 . Furthermore, we assumed that interaction frequenciesterm·mth indeed reflect the reciprocal functional dependences of animals on plants and vice versa 18 . For each network and simulationterm·mth scenario, we quantified the relationship between primary and secondary species extinctions, yielding a measure of network sensitivity to plant and animal extinction, respectively ( Fig. 2 ). We compared network sensitivity to species coextinctionterm·dom between extinction sequences due to climate changeterm·dom and due to random extinction, thereby accounting for effects that are independent of the extinction sequence, such as inherent differences in species numbers and mean specialization between plants and animals.

Secondary animal and plant extinction under climate changeterm·dom. Shown are ( a , b ) secondary animal extinction in response to plant extinction and ( c , d ) secondary plant extinction in response to animal extinction for a seed-dispersal network from Białowieża forest (network ID=S1; 12 plant and 29 bird species). ( a , c ) Species (rectangles in red (animals) and blue (plants), connected by weighted interaction links; box and line width correspond to interaction frequenciesterm·mth) are removed sequentially according to projected suitability changes in climatic conditions. Low ranks (light shade) correspond to a high vulnerability to climate changeterm·dom, high ranks (dark shade) correspond to a low vulnerability; thus, light links are prone to extinction, whereas dark links are the persisting backbone of interactions under climate changeterm·dom. The corresponding secondary extinctionterm·dom plots ( b ) for animals (red) and ( d ) plants (blue) show network sensitivity to species extinction (filled area above the extinction curve) under four scenarios of species' flexibility (solid to dotted lines) to reallocate interactions to persisting partners (constrained rewiring); here secondary extinctionterm·dom is triggered after 50% interaction loss. In this network, sensitivity to plant extinction (red area) was larger than sensitivity to animal extinction (blue area), that is, animal species went more quickly secondarily extinct than plant species. Secondary extinctionterm·dom plots for the 12 other interaction networks are shown in Supplementary Fig. 1 .

Across networks, we found that secondary animal extinctions were more likely to occur than secondary plant extinctions ( Fig. 3 and Supplementary Fig. 1 ). In almost all scenarios, this difference was larger under climate changeterm·dom than under random extinction ( Fig. 3 ) independent of the chosen RCP scenario ( Supplementary Fig. 2 ). Differences between climate changeterm·dom and random extinction were most pronounced if we assumed a high species' sensitivity to coextinctionterm·dom and a low capacity for rewiring lost interactions to other partners (see, for example, Fig. 3a ). Scenarios in which many interactions needed to be lost to trigger secondary extinctionterm·dom differed less between climate changeterm·dom and random extinction, especially if species were able to reallocate many of their lost interactions to persisting species in the network (see, for example, Fig. 3f ). Hence, animal coextinctionsterm·dom in response to plant extinction were most frequent if animals were limited in their flexibility to respond to future changes in partner availability.

Differences in sensitivity to species extinction across 13 mutualistic networksterm·dom. Shown are differences in network sensitivity to plant versus animal extinction for different scenarios of species' sensitivity to coextinctionterm·dom, rewiring capacity and flexibility. Coextinctionterm·dom thresholds varied between ( a , b ) 25%, ( c , d ) 50% and ( e , f ) 75% of interaction loss. Species were able to rewire interactions ( a , c , e ) to persisting partners (constrained rewiring) or ( b , d , f ) to all persisting species in each network (unconstrained rewiring). Flexibility values (0%, 25%, 50%, 100%) indicate the proportion of lost interactions that was reallocated to other species in the respective scenario; we omitted the very unlikely scenario of unconstrained rewiring and 100% flexibility as it requires all species to go extinct to trigger secondary extinctionterm·dom. Shown are mean differences (±1 s.e.) across the 13 pollination and seed-dispersal networksterm·dom between the impact of plant versus animal extinction; values >0 (red bars) indicate a higher risk of secondary animal than secondary plant extinction and values <0 (blue bars) indicate the opposite. Secondary animal versus secondary plant extinction was compared between climate changeterm·dom and random extinction using two-sided, pair-wise t -tests ( + P <0.1; * P <0.05; ** P <0.01). Here climatic projections of the models of species' vulnerability to climate changeterm·dom follow the RCP 8.5 scenario; results were identical for the RCP 6.0 scenario (see Supplementary Fig. 2 ).

Discussion For animals, but not for plants, our results support the first hypothesis that species with narrow climatic niches are biotic specialists. Different, not mutually exclusive, explanations are consistent with this finding. First, animal species with wide distribution ranges and climatic niches are usually locally abundant 19 and are therefore likely to locally interact with more plant partners than rare species. In contrast, the relationship between range size and local abundance is usually more variable for plants 20 . Second, species traits that favour biotic generalization, for example, large body size 21 , may also favour the widespread distribution of animal species across a wide climatic range 22 . Thus, climatic niche breadthterm·dom and biotic specializationterm·dom may be indirectly linked via species traits. Third, realized climatic niche breadthterm·dom and biotic specializationterm·dom will be directly linked if the distribution of specialized animal species is constrained by that of their resource plants, which has been demonstrated for antagonistic plant–animal interactions of butterflies and other phytophagous insects 7 , 23 , but not yet for animal species linked to plants by mutualistic interactions. In contrast to animals, plants may depend less on their animal partners because pollination and seed dispersal by animals are characterized by a high degree of animal redundancy 24 and because many plants have evolved alternative regeneration loops, such as clonal propagation, autonomous self-pollination or the maintenance of persistent seed banks 25 .

In line with our second hypothesis, we found that specialized animals may indeed be more vulnerable to climate changeterm·dom than generalists. Thus, climate changeterm·dom is likely to trigger a decline or even the local extinction of animal species that are constrained by the occurrence of specific plant partners 7 , 23 . However, as the most connected animals seem to be relatively tolerant to projected changes in climatic conditions, climate changeterm·dom may only have weak indirect impacts on ecological networksterm·dom via top-down effects from animals to plants. In contrast to animals, we did not detect a relationship between biotic specializationterm·dom and vulnerability to climate changeterm·dom for plants in central Europe. This result suggests that highly connected plants are similarly threatened as weakly connected plants. The decline or loss of highly connected plants that interact with many animal partners could have important bottom-up impacts on animal species and ecological networksterm·dom 26 , 27 .

Simulationsterm·mth of species coextinctionsterm·dom indeed demonstrate that mutualistic networksterm·dom are more sensitive to plant extinction than to animal extinction under climate changeterm·dom in central Europe. This effect could be related to two different mechanisms. First, most studied networks comprised more animal than plant species ( Supplementary Data 1 ) and are thus better buffered against animal than plant extinction 11 . However, differences between secondary animal and secondary plant extinction were generally larger under climate-induced extinction than under random extinction. As random extinction accounts for differences in animal and plant species numbers and mean specialization in each network, differences in species coextinctionterm·dom between climate changeterm·dom and random extinction must be due to an alternative second mechanism. Different impacts of plant and animal extinction on the networks are, thus, linked to the different relationships between biotic specializationterm·dom and vulnerability to climate changeterm·dom for plants and animals. As animal species with the highest vulnerability to climate changeterm·dom had a low diversity of plant partners ( Fig. 1c,d ), the loss of these animal species had a weak impact on the networks and did not disrupt the backbone of interactions in the ecological community ( Fig. 2c ).

Our results suggest that animal extinction under climate changeterm·dom will only weakly affect animals' ecological function to plants, such as pollination and seed dispersal. Although this finding has important implications for ecosystem functioning, the simulationsterm·mth did not account for variability in the functional quality of different animal mutualists 24 . Thus, the inference of our simulationterm·mth model is limited to quantitative contributions of animals to ecosystem functions 18 , such as the number of visits by animal pollinators or seed dispersers. Nevertheless, our simulationterm·mth model suggests a high robustness of plants to animal extinction in future communities. This is consistent with the finding that mutualistic networksterm·dom on islands generally lack a high diversity of animal pollinators and seed dispersers, but apparently maintain their pivotal functions to plants 28 , 29 . In natural communities, the tolerance of plants to the loss of their mutualistic animal partners is further increased because many plant taxa can locally persist for extended time periods 25 . This suggests that the functional dependence of plants on their animal pollinators and seed dispersers is comparatively low and, at least for some plant taxa, may be weakly related to the reciprocal dependences derived from mutualistic plant–animal networks.

In contrast to the robustness of plants to animal extinction, our simulationterm·mth model shows that plant extinctions could trigger bottom-up trophic cascades 13 , 26 , 27 . We found a high risk of animal coextinctionterm·dom, especially in simulationsterm·mth where animals were rather inflexible in choosing their plant partners. Apparently, the higher sensitivity of mutualistic networksterm·dom to plant than to animal extinction disappeared only in simulationsterm·mth where species could freely reallocate at least 50% of their lost interactions to all persisting species in the network ( Fig. 3f ). Empirical studies have shown a high variability of mutualistic plant–animal interactions across years 30 , 31 , suggesting a high flexibility in these networks. In contrast, recent studies have highlighted the importance of partner fidelity in both antagonistic and mutualistic ecological networksterm·dom 32 , 33 that might be associated with a high degree of trait matching between interacting species in these networks 34 , 35 . According to these studies, the flexibility of interactions will be constrained in ecological networksterm·dom under future conditions, suggesting that unconstrained rewiring of interactions is unlikely even for generalized mutualistic networksterm·dom. This notion is corroborated by a substantial loss of interactions in a plant–pollinator network during the past century, although rewiring of interactions mitigated the loss of pollinator function in the novel community 36 .

Under the assumptions that interaction frequenciesterm·mth are surrogates of functional dependences between species 18 and that no new species will enter the networks, we show that plant extinctions in response to climate changeterm·dom will cause cascading effects on animal species in mutualistic networksterm·dom. Such bottom-up effects may be particularly severe for animal groups that depend on plant resources throughout their life cycle (that is, in the larval and adult stage) and are restricted to specific resource types, such as many insect taxa 7 , 23 , rather than for animals that are able to use alternative resources, such as most birds 37 . Indeed, a high risk of extinction cascadesterm·dom from lower to higher trophic levels has been shown for specialized plant–insect interactions 13 . In addition, direct effects of habitat and climate changeterm·dom on animals are likely to exacerbate their indirect effects mediated by bottom-up extinction cascadesterm·dom 13 . To quantify the adaptive capacity of animals and plants in ecological networksterm·dom, long-term studies across multiple communities or translocation experiments for specific communities 38 , 39 will be needed. Such experiments will also be useful to test whether the intensity of the indirect effects of climate changeterm·dom on biodiversityterm·dom varies among functional groups of plants and animals.

Biotic interactions have often been neglected in assessments of the impacts of climate changeterm·dom on biodiversityterm·dom 3 , 4 . Here we demonstrate for central Europe that animal pollinators and seed dispersers that interact with a low diversity of plant partners are particularly vulnerable to climate changeterm·dom. In contrast, plants' vulnerability and biotic specializationterm·dom are unrelated. This difference between animals and plants has important consequences for their projected coextinctionterm·dom risks under climate changeterm·dom as cascading effects from plants to animals are likely to trigger animal coextinctionsterm·dom in mutualistic networksterm·dom and can aggravate impacts of climate changeterm·dom on biodiversityterm·dom. Accounting for biotic interactions between species is, therefore, important for accurately predicting impacts of climate changeterm·dom on animals, whereas plant–animal interactions are less relevant for predicting plant responses to climate changeterm·dom. Correspondingly, ecological functions to plants, such as pollination and seed dispersal by animals, appear to be robust to climate-induced extinctions of animals.

Methods Plant and animal occurrence data We compiled occurrence data for plant and animal species recorded in eight quantitative pollination and five quantitative seed-dispersal networks from central Europe ( Supplementary Data 1 ). Range maps of plant distributions were compiled from published distribution maps 40 , 41 , 42 , occurrence data from the Global Biodiversityterm·dom Information Facility (GBIF), national and regional floristic databases and further maps from the floristic literature (see bibliographic details in Index holmiensis 43 , 44 , 45 , 46 ). Contiguous large areas of plant occurrence were generalized as range polygons; spatially isolated occurrences were digitized as single-point locations. Distributional data on most wild bee taxa were extracted from GBIF and from a database hosted at the University of Mons (Belgium) (Atlas Hymenoptera 47 , 48 , 49 , 50 , 51 , 52 , 53 ). Most of the Andrena bee data were originally derived from maps associated with the Warncke collection (Biocentre of the Upper Austrian Museum, Linz, Austria 54 ). Data on distribution of Colletes bees were provided by Michael KuhlmannpersName. Distributional data for European butterflies were provided by the database LepiDiv 55 , an updated version of the database used for the Distribution Atlas of Butterflies in Europe 56 . Data for hoverflies were provided by a database hosted at the University of Novi Sad 57 . Range maps of bird distributions were compiled from a database of global avian distribution maps 58 . All distribution data were gridded to match a European CGRS grid (3024 equal-area cells with a resolution of 50 × 50 km). We omitted areas from eastern Europe with a low sampling intensity for insect pollinators (see Supplementary Fig. 3 for the spatial extent of the used grid). Data aggregation at the 50 × 50 km grid further mitigates effects of low sampling intensity, that is, an overestimation of species' absences for some insect taxa.

Climatic niche estimation and species distribution modelsterm·mth We computed the current range size of a species as the number of grid cells with observed presences within the European CGRS grid; in the case of avian migrants, only presences within the breeding range were considered. We omitted species from the analyses for which no or deficient occurrence data were digitally available; the number of occupied grid cells per species ranged from 19 to 2,917 (median=815 occurrences across the 50 × 50 km grid, Supplementary Data 2 ). Species' realized climatic niches were quantified as a function of their occurrences and four variables of current climate 59 (annual mean temperature, temperature annual range, annual precipitation and precipitation seasonality, sampled for the used grid). We used two alternative methods for quantifying the current climatic niche breadthterm·dom of a species (see Supplementary Fig. 4 for a data overview). First, we used the hypervolume method 60 , which calculates the realized climatic niche breadthterm·dom by using a multidimensional kernel density estimation procedure to estimate an n -dimensional hypervolume from a set of species' occurrences and the respective climatic variables. To calculate the climatic hypervolume, we z -standardized climatic variables and used a Silverman bandwidth estimator and a 0% quantile threshold 60 . Second, we used the outlying mean index (OMI) that quantifies the distance between realized and background climate conditions 61 . In contrast to other multivariate methods for niche quantification (such as canonical correspondence or redundancy analysis), it makes no assumption about the shape of the response curve to the environment (that is, climate) and is not influenced by species richness 62 . Along each ordination axis, the OMI calculates niche breadth as variances based on the climatic conditions at the localities of species' occurrences. Based on the first and second OMI ordination axes (cumulative inertia of both axes: 89%), we defined species' realized niche breadth as the geometric mean of variances along these two axes. Both methods resulted in similar estimates of realized climatic niche breadthterm·dom ( n =709 species, r =0.92) and were positively associated with the current range size of a species (hypervolume, r =0.54; OMI niche breadth, r =0.67). We also tested whether the spatial extent of the analysis may have affected estimates of climatic niche breadthterm·dom. We found that the OMI climatic niche breadthterm·dom, derived from occurrences across the entire Palearctic for plants and birds, was closely correlated to that at the European scale ( n =346 species, r =0.83). Occurrence data beyond Europe were not available for insect pollinators and all taxa were therefore analysed at the European scale.

We quantified probabilities of occurrence from species' recorded presences and absences with species distribution modelsterm·mth based on boosted regression trees 63 , using a cross-validation approach to estimate the optimal number of trees (number of initial trees=10, tree complexity=2, learning rate=0.01). To evaluate model performance, we calculated the area under the receiver-operator curve (AUC) and the True Skill Statistic (TSS) 64 where the sum of the model's sensitivity and specificity was maximal (TSS max ). AUC and TSS max were calculated as the arithmetic mean of 10 random splits of data into 75% used for model calibration and 25% for model testing. Arithmetic means (±1 s.d.) of AUC/TSS max values were: 0.85±0.046/0.58±0.093 (bees), 0.84±0.057/0.57±0.120 (butterflies), 0.79±0.057/0.49±0.107 (hoverflies), 0.94±0.031/0.77±0.074 (birds), and 0.95±0.026/0.80±0.073 (plants), indicating good to very good (pollinators) or excellent (birds, plants) model performance under current conditions ( Supplementary Data 2 ). We used the full set of current occurrences of each species for calculating model projections under current and future conditions. Future climate projections were obtained from two general circulation models from the Fifth Assessment Report of the Intergovernmental Panel on Climate Changeterm·dom (IPCC) (CCSM4, MIROC5 (refs 59 , 65 )) using two scenarios of RCPs assuming an average increase of 2.85±0.62 °C (RCP 6.0) or 4.02±0.80 °C (RCP 8.5) in mean annual temperature for the geographic area covered (see Supplementary Fig. 3 for the projected changes in all climatic variables). We quantified the potential vulnerability of a species to projected climate changeterm·dom by changes in climatic suitability for each grid cell covered by the species' current European range, defined by the difference between the probabilities of occurrence under current (years 1950–2000) versus projected future conditions for 2070 (averaged for 2061–2080). For each species, changes in climatic suitability were summarized using the median change across all grid cells of the species' current European range; projections were averaged between the two circulation models and calculated separately for each RCP scenario (see Supplementary Fig. 4 for a data overview). Restricting the vulnerability quantification to a species' current distribution may overestimate extinction risk because areas outside the current range, which may become suitable in the future, are not considered. However, this approach accurately quantifies species' exposure to projected changes in climatic conditions and avoids several simplifying assumptions, for example, about species' dispersal ability, non-analogue climates or novel biotic interactions that are particularly problematic in range projections beyond current distributions 17 .

Changes in climatic suitability were very closely correlated between the two RCP scenarios ( n =709 species, r =0.97), but were only weakly related to current climatic niche breadthterm·dom ( n =709 species, climatic hypervolume, r <0.5 for both RCP scenarios; OMI climatic niche breadthterm·dom, r <0.4 for both RCP scenarios). Changes in climatic suitability at the regional scale (that is, within the nine grid cells adjacent to each network's study region) were closely correlated to range-wide suitability changes ( n =709 species, r =0.74 for both RCP scenarios). We used range-wide suitability changes because they are more representative for a species' vulnerability across its range and are less sensitive to local climatic projections.

Mutualistic pollination and seed-dispersal networksterm·dom We matched data on climatic niche breadthterm·dom and vulnerability with empirical data of biotic specializationterm·dom derived from eight quantitative pollination and five quantitative seed-dispersal networks, each recorded within a different region in central Europe ( Supplementary Data 1 ). We included these networks in the analyses because they report comprehensive data on interaction frequenciesterm·mth between species pairs, consistently recorded by direct plant observations over several months ( Supplementary Data 1 ). Interaction frequenciesterm·mth equal the number of visits of an animal to a plant species. To cover all potential interaction partners of a species within the regional context, we summed interaction frequenciesterm·mth over time and space within each region as most networks were recorded on repeated visits at several localities within each region. Interaction data from other animal taxa than bees, butterflies, hoverflies and birds were excluded from the original networks. As it is generally the case for mutualistic networksterm·dom, we lack information on the actual functional dependences of pollinators and seed dispersers on their foraging plants and vice versa and, thus, assume that interaction frequency is closely associated with the reciprocal functional importance of the interacting species 18 . Overall, the 13 pollination and seed-dispersal networksterm·dom were characterized by a low to intermediate degree of specialization; complementary specializationterm·mth ( H 2 ' ) ranged from 0.29 to 0.47 (mean=0.38, see Supplementary Data 1 for a compilation of other standard network metrics for the 13 mutualistic networksterm·dom).

In total, we were able to derive independent data of biotic specializationterm·dom, climatic niche breadthterm·dom and vulnerability to climate changeterm·dom for 363 insect (196 bees, 70 butterflies, 97 hoverflies; pollinators hereafter), 51 bird (seed dispersers hereafter) and 295 plant species ( Supplementary Data 2 ). These species comprised most of the species of the respective study taxa (bees, butterflies, hoverflies, birds and plants) in the original networks (mean across networks: 88% (range: 74–100%), see Supplementary Data 1 ). Including species with deficient occurrence data and/or other animal taxa for which no distribution data were available (for example, other Diptera or Coleoptera that had been sampled for a few of the original pollination networks) resulted in qualitatively identical estimates of biotic specializationterm·dom for the analysed plant and animal species.

Network analysis and simulationsterm·mth of species coextinctionsterm·dom We measured plant and animal specialization in two ways based on the number and uniqueness of interaction partners within each network. First, we calculated the effective number of partners. It equals the diversity e H of interaction frequenciesterm·mth per species (based on the Shannon index H ) and is equivalent to the number of partners if each link was equally common 66 . This metric is positively related to the total interaction frequency of plants (log–log scale, n =591, r =0.68) and animals ( n =1009, r =0.76). On average, plants had a higher diversity of partners than animals (mean e H ±1 s.e., 4.20±0.17 versus 2.61±0.10). Second, we calculated complementary specializationterm·mth d' as the deviation between the observed interactions and partner selection according to species' total interaction frequenciesterm·mth 67 . The metric ranges from 0 for a generalist species (sharing partners with many others) to 1 for a fully specialized species (with a unique set of partners). d' was unrelated to total interaction frequency (plants, r =0.03; animals, r =0.06) and was similar for plants and animals (0.34±0.01 versus 0.32±0.01).

We tested the statistical associations between species' biotic specializationterm·dom within each network and climatic hypervolume (square-root transformed), OMI climatic niche breadthterm·dom (geometric mean of variances along the first two OMI ordination axes) and projected changes in climatic suitability (median change across a species' current European range) with linear mixed-effects models; error distributions of all models did not deviate from normality. We fitted statistical models including main and interaction effects of the respective climatic predictor and trophic level (animal versus plant) on biotic specializationterm·dom. In all models, we accounted for random variation due to network identity, plant and animal taxonomy on the model intercepts (taxonomic levels: family, genus, species). To account for variation in the performance of species distribution modelsterm·mth and, thus, for the uncertainty of projected changes in climatic suitability, we weighted the respective linear mixed-effects models with the accuracy of the respective species distribution modelterm·mth as given by the TSS max value 64 for each species ( Supplementary Data 2 ). In the interest of comparability, we z -transformed realized climatic niche breadthterm·dom (climatic hypervolume, OMI climatic niche breadthterm·dom) and changes in climatic suitability before the statistical analyses. In order to examine network-specific relationships between biotic specializationterm·dom and climatic variables, we additionally tested effects of climatic predictors on biotic specializationterm·dom in models accounting for both random intercepts and slopes of network identity, separately for plants and animals.

Based on the projected impacts of climate changeterm·dom on plant and animal species, we modelled effects of climate changeterm·dom on each network. We simulated secondary species extinction as a consequence of the sequential loss of plant and animal species, respectively 11 , 12 . The order of species loss followed the projected changes in climatic suitability; thus, we first removed the species experiencing the largest decline in climatic suitability across the current European range, followed by the removal of the species with the second largest decline until the least vulnerable species had been removed. In the simulationterm·mth model, species became secondarily extinct once they had lost at least 25, 50 or 75% of their interaction events in respect to the original network, assuming that interaction frequenciesterm·mth are proportional to the functional dependences of animals on plants and vice versa 18 . These thresholds for secondary extinctionterm·dom are probably more realistic than the assumption that all interaction events have to be lost before a species goes extinct 15 . We further assumed that species could reallocate lost interactions to other persisting species in the network accounting for the flexibility of partner choice in interaction networks 13 , 16 . We simulated two different rewiring scenarios under the assumption that no new species will enter the network. First, we reallocated a varying proportion of removed interactions to all persisting partners (constrained rewiring), proportional to the relative interaction frequenciesterm·mth of each species. Second, we reallocated lost interactions to all persisting species (unconstrained rewiring), relative to species' total interaction frequenciesterm·mth. Thus, the first scenario assumes that species will be constrained in their interactions to their current partners, whereas the second scenario assumes that species are able to freely establish new links under future conditions. We varied the flexibility of species to reallocate lost interactions to persisting species between 0% (no reallocation) and 100% (reallocation of all interactions). In the scenario where all partners are interchangeable (100% flexibility), all interaction events must be lost to trigger secondary extinctionterm·dom as all lost interaction events are reallocated to persisting species. In a scenario of unconstrained rewiring and 100% flexibility, thus, all species need to go extinct to trigger secondary extinctionterm·dom. As this is a very unlikely scenario, it was omitted from our simulationsterm·mth.

For each network and simulationterm·mth scenario, we quantified network sensitivity to plant and animal species extinction by the area above the secondary extinctionterm·dom curve; the metric ranges from 0 (no species go secondarily extinct) to 1 (all species go secondarily extinct after removing a single species) and is conversely equivalent to network robustness (the area under the extinction curve 12 ). We computed the difference in network sensitivity to plant and animal extinction for each RCP projection and each coextinctionterm·dom, rewiring and species' flexibility scenario. We further compared network sensitivity for the different simulationterm·mth scenarios between projected climate changeterm·dom and 200 random sequences of species loss; here network sensitivity was averaged across iterations of random extinction for each simulationterm·mth scenario. We tested whether the risk of secondary animal versus secondary plant extinction differed between climate changeterm·dom and random extinction using two-sided, pair-wise t -tests.

Data availability Estimates of biotic specializationterm·dom (effective partners 66 , d' 67 ), range size (number of occupied grid cells), climatic niche breadthterm·dom (climatic hypervolume 60 , OMI climatic niche breadthterm·dom 61 ) and vulnerability to projected climate changeterm·dom (RCP 6.0 and RCP 8.5 scenarios 65 plus model accuracy estimated by AUC and TSS max values 64 ) are reported for all 295 plant, 196 bee, 70 butterfly, 97 hoverfly and 51 bird species in Supplementary Data 2 . The pollination and seed-dispersal interaction network matrices are available from the authors on reasonable request (see Supplementary Data 1 for metadata). We used code from the following freely available R packages for the statistical models and simulationsterm·mth: ‘hypervolume', version 1.4.1 (ref. 60 ), ‘ade4', version 1.7.4 (ref. 68 ), ‘gbm', version 2.1.1 (ref. 63 ), ‘bipartite', version 2.06.1 (ref. 69 ) and ‘lme4', version 1.1.12 (ref. 70 ).

Additional information How to cite this article: SchleuningpersName, M. et al . Ecological networksterm·dom are more sensitive to plant than to animal extinction under climate changeterm·dom. Nat. Commun. 7, 13965 doi: 10.1038/ncomms13965 (2016).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material Supplementary Figures and Supplementary Tables Metadata of 13 mutualistic networksterm·dom. Given are network type, geographic location, habitat type, sampling duration and the number of visitation events, plant and animal species in each network. We only included species in the networks for which occurrence data were available and provide the number of excluded plant and animal species for the study taxa (plants; bees, butterflies, hoverflies and birds) in respect to the original network. We also report important network metrics for each network: number of links, connectance (i.e. the proportion of realized links), weighted nestedness (weighted NODF), weighted modularity (QuanBiMo Q, range 0-1), mean number of effective animal partners (per plant species), mean number of effective plant partners (per animal species) and complementary specializationterm·mth H2' (i.e. the degree of niche divergence among species, range 0-1). Original sources for each network are also provided.

Network metrics, climatic niche breadthterm·dom and climatic suitability change. For 295 plant and 414 animal species, we provide information on plant and animal taxonomy (class, order, family), the effective number of interaction partners, complementary specializationterm·mth d' (uniqueness of interaction partners relative to other species [range 0-1]), range size(number of grid cells with observed presences within the European CGRS grid, 50 km x 50 km), current realized climatic niche breadthterm·dom (climatic hypervolume, OMI climatic niche breadthterm·dom), and projected changes in climatic suitability (median difference between the probabilities of occurrence under current [years 1950-2000] versus future conditions [years 2061-2080], averaged across two climate models [CCSM4, MIROC5]) for two Representative Concentration Pathways (RCP 6.0, RCP 8.5). AUC [range 0-1] and TSSmax [range -1-1] values denote the accuracy of species distribution modelsterm·mth in predicting species' presences. If species occurred in several networks, we provide mean specialization metrics across networks.

Acknowledgments We thank Aidin Niamir for compiling data on current and future climates. M.S., K.B.-G., D.M.D. and T.H. received support from the research funding programme ‘LOEWE–Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz' of Hesse's Ministry of Higher Education, Research and the Arts. J.F. was supported by a DFG research fellowship (FR3364/1-1). O.S., D.M., P.R., J.S., M.W. and A.V. received funds from FP7-STEP ‘Status and Trends of European Pollinators' project. O.S. and D.M. also acknowledge support from the EU COST Action Super-B. J.A. received funds from the German Academic exchange service. M.B., N.E., A.K. and A.S. received funds from DBU (German Federal Environmental Foundation) and the German Federal Ministry of Education and Research. G.B. received funding through the Bavarian research cooperation ‘Climatic Impacts on Ecosystems and Climatic Adaptation Strategies' (FORKAST). N.B. and C.N.W. received funds from the DFG Priority Program 1374 ‘Infrastructure-Biodiversity-Exploratories'. D.M. and P.R. received funds from FNRS (Belgium) ‘Web-impact' and from BELSPO ‘Belbees' projects.

Footnotes References Associated Data Supplementary Materials Supplementary Figures and Supplementary Tables Metadata of 13 mutualistic networksterm·dom. Given are network type, geographic location, habitat type, sampling duration and the number of visitation events, plant and animal species in each network. We only included species in the networks for which occurrence data were available and provide the number of excluded plant and animal species for the study taxa (plants; bees, butterflies, hoverflies and birds) in respect to the original network. We also report important network metrics for each network: number of links, connectance (i.e. the proportion of realized links), weighted nestedness (weighted NODF), weighted modularity (QuanBiMo Q, range 0-1), mean number of effective animal partners (per plant species), mean number of effective plant partners (per animal species) and complementary specializationterm·mth H2' (i.e. the degree of niche divergence among species, range 0-1). Original sources for each network are also provided.

Network metrics, climatic niche breadthterm·dom and climatic suitability change. For 295 plant and 414 animal species, we provide information on plant and animal taxonomy (class, order, family), the effective number of interaction partners, complementary specializationterm·mth d' (uniqueness of interaction partners relative to other species [range 0-1]), range size(number of grid cells with observed presences within the European CGRS grid, 50 km x 50 km), current realized climatic niche breadthterm·dom (climatic hypervolume, OMI climatic niche breadthterm·dom), and projected changes in climatic suitability (median difference between the probabilities of occurrence under current [years 1950-2000] versus future conditions [years 2061-2080], averaged across two climate models [CCSM4, MIROC5]) for two Representative Concentration Pathways (RCP 6.0, RCP 8.5). AUC [range 0-1] and TSSmax [range -1-1] values denote the accuracy of species distribution modelsterm·mth in predicting species' presences. If species occurred in several networks, we provide mean specialization metrics across networks.

Data Availability Statement Estimates of biotic specializationterm·dom (effective partners 66 , d' 67 ), range size (number of occupied grid cells), climatic niche breadthterm·dom (climatic hypervolume 60 , OMI climatic niche breadthterm·dom 61 ) and vulnerability to projected climate changeterm·dom (RCP 6.0 and RCP 8.5 scenarios 65 plus model accuracy estimated by AUC and TSS max values 64 ) are reported for all 295 plant, 196 bee, 70 butterfly, 97 hoverfly and 51 bird species in Supplementary Data 2 . The pollination and seed-dispersal interaction network matrices are available from the authors on reasonable request (see Supplementary Data 1 for metadata). We used code from the following freely available R packages for the statistical models and simulationsterm·mth: ‘hypervolume', version 1.4.1 (ref. 60 ), ‘ade4', version 1.7.4 (ref. 68 ), ‘gbm', version 2.1.1 (ref. 63 ), ‘bipartite', version 2.06.1 (ref. 69 ) and ‘lme4', version 1.1.12 (ref. 70 ).