The effect of network size and sampling completeness in depauperate networks

Sampling bias and the robustness of ecological metrics for plant-damage-type association networks

Plants and their insect herbivores have been a dominant component of the terrestrial ecological landscape for the past 410 million years and feature intricate evolutionary patterns and co-dependencies. A complex systems perspective allows for both detailed resolution of these evolutionary relationships as well as comparison and synthesis across systems. Using proxy data of insect herbivore damage (denoted by the damage type or DT) preserved on fossil leaves, functional bipartite network representations provide insights into how plant-insect associations depend on geological time, paleogeographical space, and environmental variables such as temperature and precipitation. However, the metrics measured from such networks are prone to sampling bias. Such sensitivity is of special concern for plant-DT association networks in paleontological settings where sampling effort is often severely limited. Here, we explore the sensitivity of functional bipartite network metrics to sampling intensity and identify sampling thresholds above which metrics appear robust to sampling effort. Across a broad range of sampling efforts, we find network metrics to be less affected by sampling bias and/or sample size than richness metrics, which are routinely used in studies of fossil plant-DT interactions. These results provide reassurance that cross-comparisons of plant-DT networks offer insights into network structure and function and support their widespread use in paleoecology. Moreover, these findings suggest novel opportunities for using plant-DT networks in neontological terrestrial ecology to understand functional aspects of insect herbivory across geological time, environmental perturbations, and geographic space.

Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty

Bipartite network analysis is a powerful tool to study the processes structuring interactions in ecological communities. In applying the method, it is assumed that the sampled interactions provide an accurate representation of the actual community. However, acquiring a representative sample may be difficult as not all species are equally abundant or easily identifiable. Two potential sampling issues can compromise the conclusions of bipartite network analyses: failure to capture the full range of interactions (sampling completeness) and use of a taxonomic level higher than species to evaluate the network (taxonomic resolution). We asked how commonly used descriptors of bipartite antagonistic communities (modularity, nestedness, connectance, and specialization [H₂ ']) are affected by reduced host sampling completeness, parasite taxonomic resolution, and their crossed effect, as they are likely to co-occur. We used a quantitative niche model to generate weighted bipartite networks that resembled natural host-parasite communities. The descriptors were more sensitive to uncertainty in parasite taxonomic resolution than to host sampling completeness. When only 10% of parasite taxonomic resolution was retained, modularity and specialization decreased by ~76% and ~12%, respectively, and nestedness and connectance increased by ~114% and ~345% respectively. The loss of taxonomic resolution led to a wide range of possible communities, which made it difficult to predict its effects on a given network. With regards to host sampling completeness, standardized nestedness, connectance, and specialization were robust, whereas modularity was sensitive (~30% decrease). The combination of both sampling issues had an additive effect on modularity. In communities with low effort for both sampling issues (50%-10% of sampling completeness and taxonomic resolution), estimators of modularity, and nestedness could not be distinguished from those of random assemblages. Thus, the categorical description of communities with low sampling effort (e.g., if a community is modular or not) should be done with caution. We recommend evaluating both sampling completeness and taxonomic certainty when conducting bipartite network analyses. Care should also be exercised when using nonrobust descriptors (the four descriptors for parasite taxonomic resolution; modularity for host sampling completeness) when sampling issues are likely to affect a dataset.

A multi-site method to capture turnover in rare to common interactions in bipartite species networks

Ecological network structure is maintained by a generalist core of common species. However, rare species contribute substantially to both the species and functional diversity of networks. Capturing changes in species composition and interactions, measured as turnover, is central to understanding the contribution of rare and common species and their interactions. Due to a large contribution of rare interactions, the pairwise metrics used to quantify interaction turnover are, however, sensitive to compositional change in the interactions of, often rare, peripheral specialists rather than common generalists in the network. Here we expand on pairwise interaction turnover using a multi-site metric that enables quantifying turnover in rare to common interactions (in terms of occurrence of interactions). The metric further separates this turnover into interaction turnover due to species turnover and interaction rewiring. We demonstrate the application and value of this method using a host-parasitoid system sampled along gradients of environmental modification. In the study system, both the type and amount of habitat needed to maintain interaction composition depended on the properties of the interactions considered, that is, from rare to common. The analyses further revealed the potential of host switching to prevent or delay species loss, and thereby buffer the system from perturbation. Multi-site interaction turnover provides a comprehensive measure of network change that can, for example, detect ecological thresholds to habitat loss for rare to common interactions. Accurate description of turnover in common, in addition to rare, species and their interactions is particularly relevant for understanding how network structure and function can be maintained.

Deforestation Simplifies Understory Bird Seed-Dispersal Networks in Human-Modified Landscapes

Global biodiversity is threatened by land-use changes through human activities. This is mainly due to the conversion of continuous forests into forest fragments surrounded by anthropogenic matrices. In general, sensitive species are lost while species adapted to disturbances succeed in altered environments. However, whether the interactions performed by the persisting species are also modified, and how it scales up to the network level throughout the landscape are virtually unknown in most tropical hotspots of biodiversity. Here we evaluated how landscape predictors (forest cover, total core area, edge density, inter-patch isolation) and local characteristics (fruit availability, vegetation complexity) affected understory birds seed-dispersal networks in 19 forest fragments along the hyperdiverse but highly depauperate northeast distribution of the Brazilian Atlantic Forest. Also, our sampled sites were distributed in two regions with contrasting land cover changes. We used mist nets to obtain samples of understory bird food contents to identify the plant species consumed and dispersed by them. We estimated network complexity on the basis of the number of interactions, links per species, interaction evenness, and modularity. Our findings showed that the number of interactions increased with the amount of forest cover, and it was significantly lower in the more deforested region. None of the other evaluated parameters were affected by any other landscape or local predictors. We also observed a lack of significant network structure compared to null models, which we attribute to a pervasive impoverishment of bird and plant communities in these highly modified landscapes. Our results demonstrate the importance of forest cover not only to maintain species diversity but also their respective mutualistic relationships, which are the bases for ecosystem functionality, forest regeneration and the provision of ecological services.

Contrasting Responses of Protistan Plant Parasites and Phagotrophs to Ecosystems, Land Management and Soil Properties

Functional traits are increasingly used in ecology to link the structure of microbial communities to ecosystem processes. We investigated two important protistan lineages, Cercozoa and Endomyxa (Rhizaria) in soil using Illumina sequencing and analyzed their diversity and functional traits along with their responses to environmental factors in grassland and forest across Germany. From 600 soil samples, we obtained 2,101 Operational Taxonomic Units representing ∼18 million Illumina reads (region V4, 18S rRNA gene). All major taxonomic and functional groups were present, dominated by small bacterivorous flagellates (Glissomonadida). Endomyxan plant parasites were absent from forests. In grassland, Cercozoa and Endomyxa were promoted by more intensive land use management. Grassland and forest strikingly differed in community composition. Relative abundances of bacterivores and eukaryvores were inversely influenced by environmental factors. These patterns provide new insights into the functional organization of soil biota and indications for a more sustainable land-use management.

Finding missing links in interaction networks

Documenting which species interact within ecological communities is challenging and labor intensive. As a result, many interactions remain unrecorded, potentially distorting our understanding of network structure and dynamics. We test the utility of four structural models and a new coverage-deficit model for predicting missing links in both simulated and empirical bipartite networks. We find they can perform well, although the predictive power of structural models varies with the underlying network structure. The accuracy of predictions can be improved by ensembling multiple models. Augmenting observed networks with most-likely missing links improves estimates of qualitative network metrics. Tools to identify likely missing links can be simple to implement, allowing the prioritization of research effort and more robust assessment of network properties.

Host ecology moderates the specialization of Neotropical bat-fly interaction networks

The transmission of diseases through parasites is a key mechanism in the regulation of plant and animal populations in ecosystems. Therefore, it is necessary to investigate the relative effect of the variables that can shape the specificity of host-parasite interactions. Previous studies have found that specialization of antagonistic interactions between fly ectoparasites and bats changes according to forest type, host richness, and roosting ecology of bats. In this study, we tested these hypotheses using data from 48 bat communities. In general, our results support previous findings that bat-fly interactions are specialized, resulting in lower niche overlap among bat flies species. In addition, we found that the specificity of bat-fly interactions is lower in tropical mountain forests and is positively related with the richness of bat host species of each study site. Finally, there was a higher bat flies niche overlap in smaller bat-fly interaction networks recorded in bat roosts in caves. We conclude that the roosting ecology of bats could be a key factor to understand the mechanisms related to the horizontal transmission of ectoparasitic flies among bats.

Measuring continuous compositional change using decline and decay in zeta diversity

Incidence, or compositional, matrices are generated for a broad range of research applications in biology. Zeta diversity provides a common currency and conceptual framework that links incidence-based metrics with multiple patterns of interest in biology, ecology, and biodiversity science. It quantifies the variation in species (or OTU) composition of multiple assemblages (or cases) in space or time, to capture the contribution of the full suite of narrow, intermediate, and wide-ranging species to biotic heterogeneity. Here we provide a conceptual framework for the application and interpretation of patterns of continuous change in compositional diversity using zeta diversity. This includes consideration of the survey design context, and the multiple ways in which zeta diversity decline and decay can be used to examine and test turnover in the identity of elements across space and time. We introduce the zeta ratio-based retention rate curve to quantify rates of compositional change. We illustrate these applications using 11 empirical data sets from a broad range of taxa, scales, and levels of biological organization-from DNA molecules and microbes to communities and interaction networks-including one of the original data sets used to express compositional change and distance decay in ecology. We show (1) how different sample selection schemes used during the calculation of compositional change are appropriate for different data types and questions, (2) how higher orders of zeta may in some cases better detect shifts and transitions, and (3) the relative roles of rare vs. common species in driving patterns of compositional change. By exploring the application of zeta diversity decline and decay, including the retention rate, across this broad range of contexts, we demonstrate its application for understanding continuous turnover in biological systems.