Carsten F. Dormann

Specialization of mutualistic interaction networks decreases toward tropical latitudes.

Species-rich tropical communities are expected to be more specialized than their temperate counterparts. Several studies have reported increasing biotic specialization toward the tropics, whereas others have not found latitudinal trends once accounting for sampling bias or differences in plant diversity. Thus, the direction of the latitudinal specialization gradient remains contentious. With an unprecedented global data set, we investigated how biotic specialization between plants and animal pollinators or seed dispersers is associated with latitude, past and contemporary climate, and plant diversity. We show that in contrast to expectation, biotic specialization of mutualistic networks is significantly lower at tropical than at temperate latitudes. Specialization was more closely related to contemporary climate than to past climate stability, suggesting that current conditions have a stronger effect on biotic specialization than historical community stability. Biotic specialization decreased with increasing local and regional plant diversity. This suggests that high specialization of mutualistic interactions is a response of pollinators and seed dispersers to low plant diversity. This could explain why the latitudinal specialization gradient is reversed relative to the latitudinal diversity gradient. Low mutualistic network specialization in the tropics suggests higher tolerance against extinctions in tropical than in temperate communities.

Trading forage quality for quantity? Plant phenology and patch choice by Svalbard reindeer

Abstract Plant phenology of Luzula heathland plots in Spitsbergen (78°N) was manipulated by adding or removing snow, which altered the time for plots (2 m×2 m; n=10) to become snow-free. A 2-week difference in snowmelt, equivalent to approximately one-sixth of the growing season, was achieved between advanced (first to be snow-free) and delayed (last to be snow-free) treatments, which influenced plant biomass and plant quality. Nitrogen content of the forage species decreased with time after snowmelt, whereas C:N ratio increased. Manipulation of snowmelt led to a shift in ”phenological time”, without altering these plant quality parameters as such. Early in the growing season, Svalbard reindeer (Rangifer tarandus platyrhynchus) selected the advanced plots which had been snow-free for longest, presumably because of the greater biomass of both Luzula confusa and Salix polaris, major components of reindeer diet at that time of the year. Moreover, the proportion of live Luzula leaves was highest in advanced plots, relative to both unmanipulated control and delayed plots. In contrast, plant quality, measured as nitrogen content and C:N ratio of leaves, was lowest in the preferred plots. Phenolic content did not differ among treatments, and is therefore unlikely to play a role in reindeer selection for plots with early snowmelt. Unlike in temperate regions, where selection for plant quality seems to be of major importance, selection for plant quantity might be an outcome of generally low levels of plant biomass and high forage quality during the growing season in the high Arctic. Reindeer selection for high plant biomass is likely to lead to a more favourable nitrogen and energy return than selection for high plant quality.

A method for detecting modules in quantitative bipartite networks

Ecological networks are often composed of different sub-communities (often referred to as mod2 ules). Identifying such modules has the potential to develop a better understanding of the assem3 bly of ecological communities and to investigate functional overlap or specialisation. The most 4 informative form of networks are quantitative or weighted networks. Here we introduce an al5 gorithm to identify modules in quantitative bipartite (or two-mode) networks. It is based on the 6 hierarchical random graphs concept of Clauset et al. (2008 Nature 453: 98–101) and is extended 7 to include quantitative information and adapted to work with bipartite graphs. We define the al8 gorithm, which we call QuaBiMo, sketch its performance on simulated data and illustrate its 9 potential usefulness with a case study. 10Summary Ecological networks are often composed of different subcommunities (often referred to as modules). Identifying such modules has the potential to develop a better understanding of the assembly of ecological communities and to investigate functional overlap or specialization. The most informative form of networks are quantitative or weighted networks. Here, we introduce an algorithm to identify modules in quantitative bipartite (or two-mode) networks. It is based on the hierarchical random graphs concept of Clauset et al. (2008 Nature 453: 98–101) and is extended to include quantitative information and adapted to work with bipartite graphs. We define the algorithm, which we call QuanBiMo, sketch its performance on simulated data and illustrate its potential usefulness with a case study. Modules are detected with a higher accuracy in simulated quantitative networks than in their binary counterparts. Even at high levels of noise, QuanBiMo still classifies 70% of links correctly as within- or between-modules. Recursively applying the algorithm results in additional information of within-module organization of the network. The algorithm introduced here must be seen as a considerable improvement over the current standard of algorithms for binary networks. Due to its higher sensitivity, it is likely to lead to be useful for detecting modules in the typically noisy data of ecological networks.

COMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE

Sophisticated statistical analyses are common in ecological research, particularly in species distribution modeling. The effects of sometimes arbitrary decisions during the modeling procedure on the final outcome are difficult to assess, and to date are largely unexplored. We conducted an analysis quantifying the contribution of uncertainty in each step during the model-building sequence to variation in model validity and climate change projection uncertainty. Our study system was the distribution of the Great Grey Shrike in the German federal state of Saxony. For each of four steps (data quality, collinearity method, model type, and variable selection), we ran three different options in a factorial experiment, leading to 81 different model approaches. Each was subjected to a fivefold cross-validation, measuring area under curve (AUC) to assess model quality. Next, we used three climate change scenarios times three precipitation realizations to project future distributions from each model, yielding 729 projections. Again, we analyzed which step introduced most variability (the four model-building steps plus the two scenario steps) into predicted species prevalences by the year 2050. Predicted prevalences ranged from a factor of 0.2 to a factor of 10 of present prevalence, with the majority of predictions between 1.1 and 4.2 (inter-quartile range). We found that model type and data quality dominated this analysis. In particular, artificial neural networks yielded low cross-validation robustness and gave very conservative climate change predictions. Generalized linear and additive models were very similar in quality and predictions, and superior to neural networks. Variations in scenarios and realizations had very little effect, due to the small spatial extent of the study region and its relatively small range of climatic conditions. We conclude that, for climate projections, model type and data quality were the most influential factors. Since comparison of model types has received good coverage in the ecological literature, effects of data quality should now come under more scrutiny.

Species abundance distributions and richness estimations in fungal metagenomics – lessons learned from community ecology

Results of diversity and community ecology studies strongly depend on sampling depth. Completely surveyed communities follow log‐normal distribution, whereas power law functions best describe incompletely censused communities. It is arguable whether the statistics behind those theories can be applied to voluminous next generation sequencing data in microbiology by treating individual DNA sequences as counts of molecular taxonomic units (MOTUs). This study addresses the suitability of species abundance models in three groups of plant‐associated fungal communities – phyllosphere, ectomycorrhizal and arbuscular mycorrhizal fungi. We tested the impact of differential treatment of molecular singletons on observed and estimated species richness and species abundance distribution models. The arbuscular mycorrhizal community of 48 MOTUs was exhaustively sampled and followed log‐normal distribution. The ectomycorrhizal (153 MOTUs) and phyllosphere (327 MOTUs) communities significantly differed from log‐normal distribution. The fungal phyllosphere community in particular was clearly undersampled. This undersampling bias resulted in strong sensitivity to the exclusion of molecular singletons and other rare MOTUs that may represent technical artefacts. The analysis of abundant (core) and rare (satellite) MOTUs clearly identified two species abundance distributions in the phyllosphere data – a log‐normal model for the core group and a log‐series model for the satellite group. The prominent log‐series distribution of satellite phyllosphere fungi highlighted the ecological significance of an infrequent fungal component in the phyllosphere community.

Spatial and temporal trends of global pollination benefit.

Pollination is a well-studied and at the same time a threatened ecosystem service. A significant part of global crop production depends on or profits from pollination by animals. Using detailed information on global crop yields of 60 pollination dependent or profiting crops, we provide a map of global pollination benefits on a 5′ by 5′ latitude-longitude grid. The current spatial pattern of pollination benefits is only partly correlated with climate variables and the distribution of cropland. The resulting map of pollination benefits identifies hot spots of pollination benefits at sufficient detail to guide political decisions on where to protect pollination services by investing in structural diversity of land use. Additionally, we investigated the vulnerability of the national economies with respect to potential decline of pollination services as the portion of the (agricultural) economy depending on pollination benefits. While the general dependency of the agricultural economy on pollination seems to be stable from 1993 until 2009, we see increases in producer prices for pollination dependent crops, which we interpret as an early warning signal for a conflict between pollination service and other land uses at the global scale. Our spatially explicit analysis of global pollination benefit points to hot spots for the generation of pollination benefits and can serve as a base for further planning of land use, protection sites and agricultural policies for maintaining pollination services.

Food web structure and biocontrol in a four-trophic level system across a landscape complexity gradient

Decline in landscape complexity owing to agricultural intensification may affect biodiversity, food web complexity and associated ecological processes such as biological control, but such relationships are poorly understood. Here, we analysed food webs of cereal aphids, their primary parasitoids and hyperparasitoids in 18 agricultural landscapes differing in structural complexity (42–93% arable land). Despite little variation in the richness of each trophic group, we found considerable changes in trophic link properties across the landscape complexity gradient. Unexpectedly, aphid–parasitoid food webs exhibited a lower complexity (lower linkage density, interaction diversity and generality) in structurally complex landscapes, which was related to the dominance of one aphid species in complex landscapes. Nevertheless, primary parasitism, as well as hyperparasitism, was higher in complex landscapes, with primary parasitism reaching levels for potentially successful biological control. In conclusion, landscape complexity appeared to foster higher parasitism rates, but simpler food webs, thereby casting doubt on the general importance of food web complexity for ecosystem functioning.

Interannual variation in land-use intensity enhances grassland multidiversity

Significance Land-use intensification is a major threat to biodiversity. So far, however, studies on biodiversity impacts of land-use intensity (LUI) have been limited to a single or few groups of organisms and have not considered temporal variation in LUI. Therefore, we examined total ecosystem biodiversity in grasslands varying in LUI with a newly developed index called multidiversity, which integrates the species richness of 49 different organism groups ranging from bacteria to birds. Multidiversity declined strongly with increasing LUI, but changing LUI across years increased multidiversity, particularly of rarer species. We conclude that encouraging farmers to change the intensity of their land use over time could be an important strategy to maintain high biodiversity in grasslands. Although temporal heterogeneity is a well-accepted driver of biodiversity, effects of interannual variation in land-use intensity (LUI) have not been addressed yet. Additionally, responses to land use can differ greatly among different organisms; therefore, overall effects of land-use on total local biodiversity are hardly known. To test for effects of LUI (quantified as the combined intensity of fertilization, grazing, and mowing) and interannual variation in LUI (SD in LUI across time), we introduce a unique measure of whole-ecosystem biodiversity, multidiversity. This synthesizes individual diversity measures across up to 49 taxonomic groups of plants, animals, fungi, and bacteria from 150 grasslands. Multidiversity declined with increasing LUI among grasslands, particularly for rarer species and aboveground organisms, whereas common species and belowground groups were less sensitive. However, a high level of interannual variation in LUI increased overall multidiversity at low LUI and was even more beneficial for rarer species because it slowed the rate at which the multidiversity of rare species declined with increasing LUI. In more intensively managed grasslands, the diversity of rarer species was, on average, 18% of the maximum diversity across all grasslands when LUI was static over time but increased to 31% of the maximum when LUI changed maximally over time. In addition to decreasing overall LUI, we suggest varying LUI across years as a complementary strategy to promote biodiversity conservation.

Ecological, historical and evolutionary determinants of modularity in weighted seed-dispersal networks.

Modularity is a recurrent and important property of bipartite ecological networks. Although well-resolved ecological networks describe interaction frequencies between species pairs, modularity of bipartite networks has been analysed only on the basis of binary presence-absence data. We employ a new algorithm to detect modularity in weighted bipartite networks in a global analysis of avian seed-dispersal networks. We define roles of species, such as connector values, for weighted and binary networks and associate them with avian species traits and phylogeny. The weighted, but not binary, analysis identified a positive relationship between climatic seasonality and modularity, whereas past climate stability and phylogenetic signal were only weakly related to modularity. Connector values were associated with foraging behaviour and were phylogenetically conserved. The weighted modularity analysis demonstrates the dominating impact of ecological factors on the structure of seed-dispersal networks, but also underscores the relevance of evolutionary history in shaping species roles in ecological communities.

The potential for indirect effects between co-flowering plants via shared pollinators depends on resource abundance, accessibility and relatedness

Co-flowering plant species commonly share flower visitors, and thus have the potential to influence each others pollination. In this study we analysed 750 quantitative plant-pollinator networks from 28 studies representing diverse biomes worldwide. We show that the potential for one plant species to influence another indirectly via shared pollinators was greater for plants whose resources were more abundant (higher floral unit number and nectar sugar content) and more accessible. The potential indirect influence was also stronger between phylogenetically closer plant species and was independent of plant geographic origin (native vs. non-native). The positive effect of nectar sugar content and phylogenetic proximity was much more accentuated for bees than for other groups. Consequently, the impact of these factors depends on the pollination mode of plants, e.g. bee or fly pollinated. Our findings may help predict which plant species have the greatest importance in the functioning of plant-pollination networks.