Missing and forbidden links in mutualistic networks

Olesen, Jens M.; Bascompte, Jordi; Dupont, Yoko L.; Elberling, Heidi; Rasmussen, Claus; Jordano, Pedro

doi:10.1098/rspb.2010.1371

Cited by 291 publications

(406 citation statements)

References 32 publications

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“…Interestingly, some species pairs that are present in this site, but apparently not interacting, are known to interact elsewhere: e.g. Sylvia atricapilla with Daphne gnidium, Sylvia melanocephala with Smilax aspera (Olesen et al 2011). We believe that the independent study of species and link distributions (e.g.…”

Section: Sampling Completenessmentioning

confidence: 99%

“…Due to their high mobility, frugivorous birds are the main seed dispersers in most ecosystems (Herrera 1984;Traveset et al 2014). Avian seed dispersal data can be collected by: (1) recording fruit consumption by birds on focal plants (Olesen et al 2011), (2) identifying entire seeds in the droppings of mist-netted birds , and (3) identifying seeds in droppings collected in the field and identifying the disperser species with molecular techniques (González-Varo, Arroyo, & Jordano 2014).…”

Section: Introductionmentioning

confidence: 99%

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Sampling completeness in seed dispersal networks: When enough is enough

Costa

Silva

Ramos

et al. 2016

Basic and Applied Ecology

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Ecological networks are an increasingly popular tool to explore community assembly rules and frame practical conservation issues. However, most described networks vary largely in sampling effort, hampering the distinction of true biological patterns from artefacts caused by poor sampling. Identifying entire seeds in the droppings of mist-netted birds is generally considered a preferred sampling method for building unbiased, quantitative seed dispersal networks. We retrieved seeds from the droppings of 936 mist-netted birds captured during five days in seven sites in Portugal and estimated sampling completeness as the diversity of seed species, disperser species, and links detected with respect to those predicted by Chao 2 estimator. In one of those sites, sampling effort was extended to 25 days to evaluate the sensitivity of ten network structure descriptors to increasing sampling effort. After five sampling days we detected 93% of the seed species, 97% of the disperser species, and 79% of the links predicted by Chao 2, however sampling for 25 days resulted in the detection of more seeds, dispersers, and links than those estimated at day 5. Most network descriptors only began to stabilize around day 8, except for Connectance and Weighted Connectance that stabilized earlier. Similarly, only after 8 days most networks descriptors significantly departed from the confidence interval estimated by null models exclusively constrained by species abundances, thus reflecting independent ecological patterns. Nestedness was the only exception, as it never departed from the null models. We suggest that Chao 2 may slightly underestimate the real diversity and that in our case at least eight sampling days were needed to build a sound seed dispersal network as 67% of the seeds, 88% of the dispersers, and 71% of the links were detected. Our results have important implications for the interpretation of seed dispersal networks because under-sampled networks may produce biased descriptors that do not suitably characterize the focal communities. ZusammenfassungÖkologische Netzwerke werden immer beliebter als eine Methode, um die 'assembly rules' für Gemeinschaften zu untersuchen und praktische Naturschutzregeln zu entwerfen.

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Section: Sampling Completenessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sampling completeness in seed dispersal networks: When enough is enough

Costa

Silva

Ramos

et al. 2016

Basic and Applied Ecology

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“…These results agree with our analysis of the dynamical (local) stability of nested graphs, where we demonstrate that perfectly nested configurations are minimally stable. Within the restriction of interaction plausibility-whether an interaction is forbidden or not, and identifiable with a binary structure 16 -species preferences are partitioned to avoid competition. Thus, ecological systems are organized such that niches are exploited and the efficient use of available resources is promoted.…”

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confidence: 99%

The ghost of nestedness in ecological networks

2013

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Ecologists are fascinated by the prevalence of nestedness in biogeographic and community data, where it is thought to promote biodiversity in mutualistic systems. Traditionally, nestedness has been treated in a binary sense: species and their interactions are either present or absent, neglecting information on abundances and interaction frequencies. Extending nestedness to quantitative data facilitates the study of species preferences, and we propose a new detection method that follows from a basic property of bipartite networks: large dominant eigenvalues are associated with highly nested configurations. We show that complex ecological networks are binary nested, but quantitative preferences are non-nested, indicating limited consumer overlap of favoured resources. The spectral graph approach provides a formal link to local dynamical stability analysis, where we demonstrate that nested mutualistic structures are minimally stable. We conclude that, within the binary constraint of interaction plausibility, species preferences are partitioned to avoid competition, thereby benefiting system-wide resource allocation.

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“…Here we assume body size constraints lead to forbidden links [13,4,135]. To assess the role of these constraints in shaping empirical food-webs, we compared two probabilistic food-web models: one that does not include body size constraints (Model 1), and one that retains the same number and structure of forbidden links as the respective empirical food-web (Model 2).…”

Section: Body Size Constraints and Ecological Implicationsmentioning

confidence: 99%

Dedication. Acknowledgments

2011

The Acquisition of German

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xvii Dedication xixAcknowledgments xx . See panel C. for a scenario where one prey is preferred over another. C. A tri-trophic system, where a top-predator consumes a meso-predator, which in turn consumes two prey. The sizes of the nodes denote relative abundance, and the thickness of the arrows denote the magnitude of the biomass flow. The top-predator in the leftmost network is relatively abundant, depressing the meso-predator population, thereby limiting the meso-predator's e↵ect on the two prey populations. The two rightmost networks (boxed) have reduced top-predator populations. In both cases, the meso-predator has greater abundance due to decreased predation pressure. However, in one scenario, both prey are preferred equally, resulting in apparent competition dynamics. In the second scenario, one prey is preferred over another, resulting in an asymmetric cascade. Such preferences could result from specific anatomical constraints of the meso-predator, a scenario explored in Chapter 4. C 1 and C 2 , and 4 prey: r 1 r 4 ) is represented by both pairwise interaction distributions for each link, and a network-level interaction distribution for the system. Link thickness represents the median link-strength, shown with the vertical bar in each pairwise interaction distribution. B-C. Both model food-webs X and Y have link-strengths drawn from the NLID. In model food-web X, the weights are distributed similar to the median weights of the empirical web (i.e. equivalent structure), but they are distributed di↵erently among nodes (i.e. alternative interactions). In model Y , the weight structure is di↵erent than that of the empirical web (i.e. alternative structure), and necessarily, the distribution of weights among nodes di↵ers as well (i.e. alternative interactions). Dotted nodes indicate those with link-strengths that di↵er from the median link-strengths of the empirical food-web. D-F. Given a single niche axis based on a continuous resource trait (e.g. body size) where prey are ranked from r 1 to r 4 , distributions of resource use are shown for the two consumers. Representations of dietary overlap illustrate higher similarity in diet between predators of the empirical system and model X, whereas the consumers in model Y have little dietary overlap, despite sharing the same NLID. 5 A-C. Sensitivity analyses of the Saskatchewan, Amboseli, and Lake Naivasha food-web ensembles, respectively. Similarity values for each cuto↵ (s i ) for Model 2 are on the y-axis, and the x-axis ranges from a lower standard deviation (SD) than the empirical SD to a higher SD (such that the empirical SD is central to the range; stippled vertical line). The colored data points represent the median similarity indices for each cuto↵ value (legend), while the top and bottom whiskers denote the 25 th and 75 th percentiles, respectively. The underlined values mark the standard deviation corresponding to the highest average similarity across cuto↵ values. For all systems, the highest average similarity is close to or exactly matches ...

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Missing and forbidden links in mutualistic networks

Cited by 291 publications

References 32 publications

Sampling completeness in seed dispersal networks: When enough is enough

Sampling completeness in seed dispersal networks: When enough is enough

The ghost of nestedness in ecological networks

Dedication. Acknowledgments

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