Defaunation by humans causes a loss of large animals in many ecosystems globally. Recent work has emphasized the consequences of downsizing in animal communities for ecosystem functioning. However, ...no study so far has integrated network theory and life-history trade-offs to mechanistically evaluate the functional consequences of defaunation in plant–animal networks. Here, we simulated an avian seed-dispersal network and its derived ecosystem function seedling recruitment to assess the relative importance of different size-related mechanisms. Specifically, we considered size matching (between bird size and seed size) and size trade-offs, which are driven by differences in plant or animal species abundance (negative size–quantity relationship) as well as in recruitment probability and disperser quality (positive size–quality relationship). Defaunation led to impoverished seedling communities in terms of diversity and seed size, but only if models accounted for size matching. In addition, size trade-off in plants, in concert with size matching, provoked rapid decays in seedling abundance in response to defaunation. These results underscore a disproportional importance of large animals for ecosystem functions. Downsizing in ecological networks will have severe consequences for ecosystem functioning, especially in interaction networks that are structured by size matching between plants and animals.
Research linking biodiversity and ecosystem functioning (BEF) has been mostly centred on the influence of species richness on ecosystem functions in small-scale experiments with single trophic ...levels. In natural ecosystems, many ecosystem functions are mediated by interactions between plants and animals, such as pollination and seed dispersal by animals, for which BEF relationships are little understood. Largely disconnected from BEF research, network ecology has examined the structural diversity of complex ecological networks of interacting species. Here, we provide an overview of the most important concepts in BEF and ecological network research and exemplify their applicability to natural ecosystems with examples from pollination and seed-dispersal studies. In a synthesis, we connect the structural approaches of network analysis with the trait-based approaches of BEF research and propose a conceptual trait-based model for understanding BEF relationships of plant–animal interactions in natural ecosystems. The model describes the sequential processes that determine the BEF relationship, i.e. the responses of species to environmental filters, the matching of species in ecological networks and the functionality of species in terms of their quantitative and qualitative contributions to plant demography and ecosystem functioning. We illustrate this conceptual integration with examples from mutualistic interactions and highlight its value for predicting the consequences of biodiversity loss for multispecies interactions and ecosystem functions. We foresee that a better integration between BEF and network research will improve our mechanistic understanding of how biodiversity relates to the functioning of natural ecosystems. Our conceptual model is a step towards this integration between structural and functional biodiversity research.
Biodiversity is important for many ecosystem processes. Global declines in pollinator diversity and abundance have been recognized, raising concerns about a pollination crisis of crops and wild ...plants. However, experimental evidence for effects of pollinator species diversity on plant reproduction is extremely scarce. We established communities with 1-5 bee species to test how seed production of a plant community is determined by bee diversity. Higher bee diversity resulted in higher seed production, but the strongest difference was observed for one compared to more than one bee species. Functional complementarity among bee species had a far higher explanatory power than bee diversity, suggesting that additional bee species only benefit pollination when they increase coverage of functional niches. In our experiment, complementarity was driven by differences in flower and temperature preferences. Interspecific interactions among bee species contributed to realized functional complementarity, as bees reduced interspecific overlap by shifting to alternative flowers in the presence of other species. This increased the number of plant species visited by a bee community and demonstrates a new mechanism for a biodiversity-function relationship ("interactive complementarity"). In conclusion, our results highlight both the importance of bee functional diversity for the reproduction of plant communities and the need to identify complementarity traits for accurately predicting pollination services by different bee communities.
Network approaches have become a popular tool for understanding ecological complexity in a changing world. Many network descriptors relate directly or indirectly to specialization, which is a central ...concept in ecology and measured in different ways. Unfortunately, quantification of specialization and network structure using field data can suffer from sampling effects. Previous studies evaluating such sampling effects either used field data where the true network structure is unknown, or they simulated sampling based on completely generalized interactions. Here, we used a quantitative niche model to generate bipartite networks representing a wide range of specialization and evaluated potential sampling biases for a large set of specialization and network metrics for different network sizes. We show that with sample sizes realistic for species-rich networks, all metrics are biased towards overestimating specialization (and underestimating generalization and niche overlap). Importantly, this sampling bias depends on the true degree of specialization and is strongest for generalized networks. We show that methods used for empirical data may misrepresent sampling bias: null models simulating generalized interactions may overestimate bias, whereas richness estimators may strongly overestimate sampling completeness. Some network metrics are barely related between small and large sub-samples of the same network and thus may often not be meaningful. Small samples also overestimate interspecific variation of specialization within generalized networks. While new approaches to deal with these challenges have to be developed, we also identify metrics that are relatively unbiased and fairly consistent across sampling intensities and we identify a provisional rule of thumb for the number of observations required for accurate estimates. Our quantitative niche model can help understand variation in network structure capturing both sampling effects and biological meaning. This is needed to connect network science to fundamental ecological theory and to give robust quantitative answers for applied ecological problems.
Describing variation of species interaction networks across space and time promises a better understanding of how species communities respond to global change. To understand this variation, it has ...been suggested to partition network dissimilarity into one component driven by species turnover, that is, changes in community composition, and another component reflecting rewiring, that is, flexibility of interactions among shared species. The latter makes a strong case for investing the enormous effort in empirically recording interactions, instead of simply building networks based on community data. Here, I present a flexible R function (available in the R package bipartite) to calculate network dissimilarity and its components, with binary and quantitative networks. With this new tool, I compare two published methods for partitioning network dissimilarity, using conceptual examples, published plant–pollinator networks, and a set of simulations. This comparison highlights that the method that has received most attention overestimates the importance of rewiring for total network dissimilarity. In contrast, an earlier‐proposed method is derived from additive partitioning of the sets of interactions and thus accurately represents the two dissimilarity components. Furthermore, I argue that the term rewiring is not well defined in network ecology and that there are reasons why both methods overestimate the importance of rewiring, in particular with quantitative networks. The availability of a unified function to calculate multiple aspects of network dissimilarity will foster its critical application to characterize network dynamics and to identify underlying drivers. Studies on network dissimilarity and rewiring will have to be more careful in the choice of method and its interpretation.
Ecological networks depict the interactions between species, mainly based on observations in the field. The information contained in such interaction matrices depends on the sampling design, and ...typically, compounds preferences (specialization) and abundances (activity). Null models are the primary vehicles to disentangle the effects of specialization from those of sampling and abundance, but they ignore the feedback of network structure on abundances. Hence, network structure, as exemplified here by modularity, is difficult to link to specific causes. Indeed, various processes lead to modularity and to specific interaction patterns more generally. Inferring (co)evolutionary dynamics is even more challenging, as competition and trait matching yield identical patterns of interactions. A satisfactory resolution of the underlying factors determining network structure will require substantial additional information, not only on independently assessed abundances, but also on traits, and ideally on fitness consequences as measured in experimental setups.
Arable plants and wild bees are negatively affected by agricultural intensification, one of the major drivers of global biodiversity loss. However, it remains unclear how endangered and low ...competitive arable plants (rare arable plants) contribute to the persistence of flower-visiting wild bees by providing additional flower resources in agricultural landscapes. Thus, the effects of sowing 10 rare arable plant species on wild bees were investigated in an experimental field and on 10 different arable farms on nutrient-poor soils. Sowing of rare arable plants on cropped and uncropped plots was compared to annual and perennial wildflower strips. Results showed that rare arable plants on uncropped plots attracted as many wild bees as wildflower strips. Wild bee abundance and species richness increased in the autumn-sown crops in the second year, likely because winter annual rare arable plants were preferred. In particular, rare arable plants provided flowers preferred by long-tongued bumblebees, which are often lacking in intensively managed arable fields. Our study shows that sowing of rare arable plants can increase niche diversity and therefore resource availability for wild bees, and it can also conserve diversity of arable plants in degraded agricultural landscapes. Conservation of arable plants through sowing can also support wild bee communities and may become an important tool in pollinator-friendly management of arable land.
•Rare arable plants increase niche diversity and resource availability for wild bees.•Rare arable plants can attract as many wild bees as wildflower strips.•Flowers with diverse flower traits supplement the wild bee community.
Understanding how landscape characteristics affect biodiversity patterns and ecological processes at local and landscape scales is critical for mitigating effects of global environmental change. In ...this review, we use knowledge gained from human-modified landscapes to suggest eight hypotheses, which we hope will encourage more systematic research on the role of landscape composition and configuration in determining the structure of ecological communities, ecosystem functioning and services. We organize the eight hypotheses under four overarching themes. Section A: ‘landscape moderation of biodiversity patterns' includes (1) the landscape species pool hypothesis—the size of the landscape-wide species pool moderates local (alpha) biodiversity, and (2) the dominance of beta diversity hypothesis—landscape-moderated dissimilarity of local communities determines landscape-wide biodiversity and overrides negative local effects of habitat fragmentation on biodiversity. Section B: ‘landscape moderation of population dynamics' includes (3) the cross-habitat spillover hypothesis—landscape-moderated spillover of energy, resources and organisms across habitats, including between managed and natural ecosystems, influences landscape-wide community structure and associated processes and (4) the landscape-moderated concentration and dilution hypothesis—spatial and temporal changes in landscape composition can cause transient concentration or dilution of populations with functional consequences. Section C: ‘landscape moderation of functional trait selection’ includes (5) the landscape-moderated functional trait selection hypothesis—landscape moderation of species trait selection shapes the functional role and trajectory of community assembly, and (6) the landscape-moderated insurance hypothesis—landscape complexity provides spatial and temporal insurance, i.e. high resilience and stability of ecological processes in changing environments. Section D: ‘landscape constraints on conservation management' includes (7) the intermediate landscape-complexity hypothesis—landscape-moderated effectiveness of local conservation management is highest in structurally simple, rather than in cleared (i.e. extremely simplified) or in complex landscapes, and (8) the landscape-moderated biodiversity versus ecosystem service management.
Biodiversity can provide insurance against environmental change, but only if species differ in their response to environmental conditions (response diversity). Wild bees provide pollination services ...to wild and crop plants, and response diversity might insure this function against changing climate. To experimentally test the hypothesis that bee species differ in their response to increasing winter temperature, we stored cocoons of nine bee species at different temperatures during the winter (1.5–9.5 °C). Bee species differed significantly in their responses (weight loss, weight at emergence and emergence date). The developmental stage during the winter explained some of these differences. Bee species overwintering as adults generally showed decreased weight and earlier emergence with increasing temperature, whereas bee species overwintering in pre-imaginal stages showed weaker or even opposite responses. This means that winter warming will likely affect some bee species negatively by increasing energy expenditure, while others are less sensitive presumably due to different physiology. Likewise, species phenologies will respond differently to winter warming, potentially affecting plant–pollinator interactions. Responses are not independent of current flight periods: bees active in spring will likely show the strongest phenological advances. Taken together, wild bee diversity provides response diversity to climate change, which may be the basis for an insurance effect.
Most studies of plant–animal mutualistic networks have come from a temporally static perspective. This approach has revealed general patterns in network structure, but limits our ability to ...understand the ecological and evolutionary processes that shape these networks and to predict the consequences of natural and human‐driven disturbance on species interactions. We review the growing literature on temporal dynamics of plant–animal mutualistic networks including pollination, seed dispersal and ant defence mutualisms. We then discuss potential mechanisms underlying such variation in interactions, ranging from behavioural and physiological processes at the finest temporal scales to ecological and evolutionary processes at the broadest. We find that at the finest temporal scales (days, weeks, months) mutualistic interactions are highly dynamic, with considerable variation in network structure. At intermediate scales (years, decades), networks still exhibit high levels of temporal variation, but such variation appears to influence network properties only weakly. At the broadest temporal scales (many decades, centuries and beyond), continued shifts in interactions appear to reshape network structure, leading to dramatic community changes, including loss of species and function. Our review highlights the importance of considering the temporal dimension for understanding the ecology and evolution of complex webs of mutualistic interactions.
We review the growing literature on the temporal dynamics of plant–animal mutualistic networks, discuss potential mechanisms underlying such change and identify the research frontiers in the study of the temporal dynamics of mutualistic networks. Our synthesis highlights the importance of considering the temporal dimension for understanding the ecology and evolution of complex webs of mutualistic interactions.
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