Extracting abundance information from DNA-based data

The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to diet analysis and food-web reconstruction, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, multiple sources of bias and noise in sampling and processing combine to inject error into DNA-based data sets. To understand how to extract abundance information, it is useful to distinguish two concepts. (i) Within-sample across-species quantification describes relative species abundances in one sample. (ii) Across-sample within-species quantification describes how the abundance of each individual species varies from sample to sample, such as over a time series, an environmental gradient or different experimental treatments. First, we review the literature on methods to recover across-species abundance information (by removing what we call “species pipeline biases”) and within-species abundance information (by removing what we call “pipeline noise”). We argue that many ecological questions can be answered with just within-species quantification, and we therefore demonstrate how to use a “DNA spike-in” to correct for pipeline noise and recover within-species abundance information. We also introduce a model-based estimator that can be used on data sets without a physical spike-in to approximate and correct for pipeline noise.     

Numerical response of small mustelids to vole abundance: delayed or not?

One of the most studied problems in population ecology has been to understand the relative roles of top–down and bottom–up forces in regulating animal populations. This has also been a key issue in studies of vole population dyna mics. Vole populations exhibit a wide variation of dynamics, from seasonal fluctuations to multiannual variations or cyclicity. One of the hypotheses to explain cyclic population dynamics is predation by the specialist predators. A common counterargument against the predation hypothesis has been the lack of conclusive observations of the time delay in the predators’ numerical response. We studied the interaction between voles and their specialist small mustelid predators, the stoat Mustela erminea and the least weasel Mustela n. nivalis, by modelling their interaction to data sets that cover large areas of Finland. Vole abundance was monitored with biannual trappings and their predators with snow‐tracking. Results show a high dependence of the predators on the voles, and this connection is generally tighter in weasels than in stoats. Weasel abundance is affected most strongly by the vole abundance in previous spring, 8.5– 10 months earlier, while in stoats the effect of autumn abundance of voles, 2.5–6 months earlier, was the strongest. These results, together with the observation that the weasels’ effects on voles are stronger after a time lag of 6–9.5 than 2–4.5 months, indicate the existence of a time lag in weasels’ numerical response. A time lag in the predators’ numerical response is a necessary condition for the predators to drive population cycles in its prey, and therefore our results support the specialist predation hypothesis.     

Quantitative analysis of protein interaction network dynamics in yeast

Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.     

Functional Roles of Centridini Oil Bees and Malpighiaceae Oil Flowers in Biome‐wide Pollination Networks

Interactions between oil‐collecting bees and oil‐producing flowers are a very specialized mutualism, whose natural history is well known at the organism and population levels. In this study, we assessed these interactions at the biome level with a network approach, and hypothesized that widespread bee and plant species would occupy different ecological functional roles (Eltonian niches) in different biomes. Furthermore, we expected the most important functional roles in each network to be occupied more frequently by Byrsonima oil flowers and Centris oil bees, which share the longest coevolutionary history in the Neotropics. By compiling data from 40 articles on oil flower interactions within the Malpighiaceae family, we built six networks for different Brazilian biomes. We assessed the ecological functional role of each species in pollination networks of oil flowers through the metric known as ‘network functional role’. Although 90 percent of the species occupied peripheral roles in each network, some were found to occupy highly central roles. Oil flowers of the genera Byrsonima and Banisteriopsis and oil bees of the genera Centris and Epicharis were the most important species in all networks, as they made a disproportionally high number of interactions (hubs), or helped bind together different modules (connectors). Our findings suggest that functional roles vary geographically and seem to be affected by local conditions in different biomes. Furthermore, coevolutionary history seems to play an important role in determining functional roles in oil flower networks, although other factors are probably also important, especially the degree of specialization in this kind of interaction.     

Volatility of network indices due to undersampling of intraspecific variation in plant–insect interactions

Appropriate sampling effort of interaction networks is necessary to extract robust indices describing the structure of species interactions. Here we show that time-invariant variation in the composition and diversity of interaction partners of plant individuals of the same species explains volatility in aggregate network statistics due to undersampling. Within a multi-species pollinator–plant interaction matrix, we replaced the interactions observed on multiple individuals of a single plant species (Sinapis arvensis, pooled interactions) with the plant–insect interactions observed on a single plant individual. In the resampling approach, we considered the interactions of 1 to 84 S. arvensis individuals in different combinations. For each resampled network, several commonly applied aggregated statistics were calculated to test how intraspecific variation affects the properties of a multi-species network. Our results showed that aggregate statistics are sensitive towards qualitative and quantitative intraspecific variation of flower–visitor interactions within a multi-species network, which may affect the ecological interpretation about the properties of a community. These findings challenge the robustness of commonly applied network indices, confirm the urge for a sufficient and representative sampling of interactions, and emphasize the significance of intraspecific variation in the context of communities and networks.     

Influence of taxonomic resolution on mutualistic network properties

1. Ecologists are increasingly interested in plant–pollinator networks that synthesize in a single object the species and the interactions linking them within their ecological context. Numerous indices have been developed to describe the structural properties and resilience of these networks, but currently, these indices are calculated for a network resolved to the species level, thus preventing the full exploitation of numerous datasets with a lower taxonomic resolution. Here, we used datasets from the literature to study whether taxonomic resolution has an impact on the properties of plant–pollinator networks.
2. For a set of 41 plant–pollinator networks from the literature, we calculated nine network index values at three different taxonomic resolutions: species, genus, and family. We used nine common indices assessing the structural properties or resilience of networks: nestedness (estimated using the nestedness index based on overlap and decreasing fill [NODF], weighted NODF, discrepancy [BR], and spectral radius [SR]), connectance, modularity, robustness to species loss, motifs frequencies, and normalized degree.
3. We observed that modifying the taxonomic resolution of these networks significantly changes the absolute values of the indices that describe their properties, except for the spectral radius and robustness. After the standardization of indices measuring nestedness with the Z‐score, three indices—NODF, BR, and SR for binary matrices—are not significantly different at different taxonomic resolutions. Finally, the relative values of all indices are strongly conserved at different taxonomic resolutions.
4. We conclude that it is possible to meaningfully estimate the properties of plant–pollinator interaction networks with a taxonomic resolution lower than the species level. We would advise using either the SR or robustness on untransformed data, or the NODF, discrepancy, or SR (for weighted networks only) on Z‐scores. Additionally, connectance and modularity can be compared between low taxonomic resolution networks using the rank instead of the absolute values.

     

Inferring host specificity and network formation through agent-based models: tick–mammal interactions in Borneo

Patterns of host–parasite association are poorly understood in tropical forests. While we typically observe only snapshots of the diverse assemblages and interactions under variable conditions, there is a desire to make inferences about prevalence and host-specificity patterns. We studied the interaction of ticks with non-volant small mammals in forests of Borneo. We inferred the probability of species interactions from individual-level data in a multi-level Bayesian model that incorporated environmental covariates and advanced estimates for rarely observed species through model averaging. We estimated the likelihood of observing particular interaction frequencies under field conditions and a scenario of exhaustive sampling and examined the consequences for inferring host specificity. We recorded a total of 13 different tick species belonging to the five genera Amblyomma, Dermacentor, Haemaphysalis, Ixodes, and Rhipicephalus from a total of 37 different host species (Rodentia, Scandentia, Carnivora, Soricidae) on 237 out of 1,444 host individuals. Infestation probabilities revealed most variation across host species but less variation across tick species with three common rat and two tree shrew species being most heavily infested. Host species identity explained ca. 75 % of the variation in infestation probability and another 8–10 % was explained by local host abundance. Host traits and site-specific attributes had little explanatory power. Host specificity was estimated to be similarly low for all tick species, which were all likely to infest 34–37 host species if exhaustively sampled. By taking into consideration the hierarchical organization of individual interactions that may take place under variable conditions and that shape host–parasite networks, we can discern uncertainty and sampling bias from true interaction frequencies, whereas network attributes derived from observed values may lead to highly misleading results. Multi-level approaches may help to move this field towards inferential approaches for understanding mechanisms that shape the strength and dynamics in ecological networks.     

Living in the branches: population dynamics and ecological processes in dendritic networks

Spatial structure regulates and modifies processes at several levels of ecological organization (e.g. individual/genetic, population and community) and is thus a key component of complex systems, where knowledge at a small scale can be insufficient for understanding system behaviour at a larger scale. Recent syntheses outline potential applications of network theory to ecological systems, but do not address the implications of physical structure for network dynamics. There is a specific need to examine how dendritic habitat structure, such as that found in stream, hedgerow and cave networks, influences ecological processes. Although dendritic networks are one type of ecological network, they are distinguished by two fundamental characteristics: (1) both the branches and the nodes serve as habitat, and (2) the specific spatial arrangement and hierarchical organization of these elements interacts with a species' movement behaviour to alter patterns of population distribution and abundance, and community interactions. Here, we summarize existing theory relating to ecological dynamics in dendritic networks, review empirical studies examining the population- and community-level consequences of these networks, and suggest future research integrating spatial pattern and processes in dendritic systems.     

Overcompensatory population dynamic responses to environmental stochasticity

1. To quantify the interactions between density-dependent, population regulation and density-independent limitation, we studied the time-series dynamics of an experimental laboratory insect microcosm system in which both environmental noise and resource limitation were manipulated. 2. A hierarchical Bayesian state-space approach is presented through which it is feasible to capture all sources of uncertainty, including observation error to accurately quantify the density dependence operating on the dynamics. 3. The regulatory processes underpinning the dynamics of two different bruchid beetles (Callosobruchus maculatus and Callosobruchus chinensis) are principally determined by environmental conditions, with fluctuations in abundance explained in terms of changes in overcompensatory dynamics and stochastic processes. 4. A general, stochastic population model is developed to explore the link between abundance fluctuations and the interaction between density dependence and noise. Taking account of time-lags in population regulation can substantially increase predicted population fluctuations resulting from underlying noise processes.     

The seasonal dynamic of ant‐flower networks in a semi‐arid tropical environment

1. Several studies have recently focused on the structure of ecological networks involving ants and plants with extrafloral nectaries; however, little is known about the effects of temporal variation in resource abundance on the structure of ant–plant networks mediated by floral nectar. 2. In this study, it was evaluated how strong seasonality in resource availability in a semi‐arid tropical environment affects the structure of ant–flower networks. We recorded ants collecting floral nectar during two seasons (from December 2009 to January 2013): dry and green seasons. Then, we built interaction networks for flower‐visiting ants in the Brazilian Caatinga separately for each combination of transect and season. 3. In general, strong seasonality directly influenced patterns of ant–flower interactions and the overall complexity of these ecological networks. During the dry season, networks were more connected, less modular, and exhibited greater niche overlap of flower‐visiting ants than during the green season. Moreover, resource utilisation by ants during the dry season tended to be more aggregated. These findings indicate that during the dry season, ant species tended to share many resource bases, probably owing to lower overall resource availability during this season. Species composition of the ant network component was highly season specific; however, a central core of highly generalised ants was present during both seasons. 4. The stability of this central core between seasons could strongly affect the ecological and evolutionary dynamics of these interaction networks. This study contributes to the understanding of the structure and dynamics of ant‐flower interactions in extremely seasonal environments.     

Inferring species interactions from ecological survey data: A mechanistic approach to predict quantitative food webs of seed feeding by carabid beetles

1. Ecological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, for example, predation, are short‐lived and difficult to observe or detect. While there are different methods for inferring the presence of interactions, they have rarely been used to predict the interaction strengths that are required to construct weighted, or quantitative, ecological networks.
2. Here, we develop a trait‐based approach suitable for inferring weighted networks, that is, with varying interaction strengths. We developed the method for seed‐feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other species and types of interaction.
3. Using existing literature data from experimental seed‐feeding trials, we predicted a per‐individual interaction cost index based on carabid and seed size. This was scaled up to the population level to create inferred weighted networks using the abundance of carabids and seeds from empirical samples and energetic intake rates of carabids from the literature. From these weighted networks, we also derived a novel measure of expected predation pressure per seed type per network.
4. This method was applied to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how network structure and predation pressure varied among fields.
5. Inferred networks are valuable because (a) they provide null models for the structuring of food webs to test against empirical species interaction data, for example, DNA analysis of carabid gut regurgitates and (b) they allow weighted networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This permits ecological network analysis even at times and in places when interactions were not directly assessed.

     

Persistence of plague outbreaks among great gerbils in Kazakhstan: effects of host population dynamics

Outbreaks of plague (Yersinia pestis) among great gerbils (Rhombomys opimus) generally require a high host abundance to be initiated. The duration of an outbreak is expected to depend on the subsequent development of this abundance; however, prediction is nontrivial due to the complexity of the gerbil–plague system. The aim of this study was to investigate how the duration of outbreaks depends on different types of host population dynamics generated from: a cyclic model; an autoregressive model giving irregular fluctuations; and a simple model with uncorrelated fluctuations. For each model, outbreak duration was studied under various levels of mean and variability of host abundance. Its focus on the effect of different gerbil dynamics sets this study apart from the few published studies on diseases in dynamic host populations. Plague outbreaks were simulated in a cellular automaton model based on statistical analysis of archived records of plague and host abundance. Temporal autocorrelation was found to make outbreak duration less sensitive to changes in mean abundance than uncorrelated fluctuations. Cyclicity had little effect on the mean duration of outbreaks, but resulted in a multimodal distribution. For all three types of gerbil dynamics, increased variability in gerbil abundance reduced the duration of outbreaks when the mean abundance was high (paralleling results on the risk of species extinction in fluctuating environments), but increased their duration when the mean abundance was lower. Spatial heterogeneity was briefly tested and produced longer outbreaks than the homogenous case. The results are relevant to predicting plague activity in populations of great gerbils.     

Functional genotypes are associated with commensal Escherichia coli strain abundance within‐host individuals and populations

The selective pressures that determine genotype abundance and distribution frequently vary between ecological levels. Thus, it is often unclear whether the same functional genotypes will become abundant at different levels and how selection acting at these different scales is linked. In this study, we examined whether particular functional genotypes, defined by the presence or absence of 34 genes, of commensal Escherichia coli strains were associated with within‐host abundance and/or host population abundance in a wild population of 54 adult mountain brushtail possums (Trichosurus cunninghami). Our results revealed that there was a positive correlation between a strain's relative abundance within individuals and the strain's abundance in the host population. We also found that strain abundance at both ecological levels was predicted by the same group of functional genes (agn43, focH, micH47, iroN, ygiL, ompT, kspmT2 and K1) that had associated patterns of occurrence. We propose that direct selection on the same functional genes at both levels may in part be responsible for the observed correlation between the ecological levels. However, a potential link between abundance within the host and excretion rate may also contribute.     

Scaling up: how do exogenous fluctuations in individual-based resource competition models re-emerge in aggregated stochastic population models?

In applied population dynamics the choice of stochastic per capita growth function has implications for population viability analyses, management recommendations, and pest control. This model choice is often based on statistical criteria, mathematical tractability or personal preferences, and general ecological guidelines are either too vague or entirely missing. To identify such guidelines, it is important to understand how exogenous and endogenous factors interact at the individual level and re-emerge at the aggregated population level. We therefore study different types of resource competition (contest vs. scramble competition) and different types of exogenous fluctuations (food and weather fluctuations) at the individual level in a simple individual-based simulation model. We statistically fit the resulting time series to find out (1) which functional form of the growth function (‘hyperbolic’ or ‘exponential’) better describes contest and scramble competition and (2) whether the pattern of population fluctuations resulting from the simulations can be assigned to vertical, lateral or nonlinear perturbations in the stochastic growth function (a classification scheme suggested by Royama 1992, Analytical Population Dynamics, Chapman and Hall, London). We found that the same type of competition can result in ‘hyperbolic’ or ‘exponential’ functional forms, depending on the type of exogenous fluctuations. So it is the interplay between exogenous variability and endogenous resource competition that affects model performance. In contrast to the widespread assumption of vertical (additive) perturbations, our findings highlight the importance of (non-additive) lateral and nonlinear perturbations and their combinations with vertical perturbations. The choice of the stochastic growth function should therefore consider not only statistical criteria but also ecological guidelines. We derive such ecological guidelines from our analysis.     

The effects of space and diversity of interaction types on the stability of complex ecological networks

The relationship between structure and stability in ecological networks and the effect of spatial dynamics on natural communities have both been major foci of ecological research for decades. Network research has traditionally focused on a single interaction type at a time (e.g. food webs, mutualistic networks). Networks comprising different types of interactions have recently started to be empirically characterized. Patterns observed in these networks and their implications for stability demand for further theoretical investigations. Here, we employed a spatially explicit model to disentangle the effects of mutualism/antagonism ratios in food web dynamics and stability. We found that increasing levels of plant-animal mutualistic interactions generally resulted in more stable communities. More importantly, increasing the proportion of mutualistic vs. antagonistic interactions at the base of the food web affects different aspects of ecological stability in different directions, although never negatively. Stability is either not influenced by increasing mutualism—for the cases of population stability and species’ spatial distributions—or is positively influenced by it—for spatial aggregation of species. Additionally, we observe that the relative increase of mutualistic relationships decreases the strength of biotic interactions in general within the ecological network. Our work highlights the importance of considering several dimensions of stability simultaneously to understand the dynamics of communities comprising multiple interaction types.     

Marine reserves with ecological uncertainty

To help manage the fluctuations inherent in fish populations scientists have argued for both an ecosystem approach to management and the greater use of marine reserves. Support for reserves includes empirical evidence that they can raise the spawning biomass and mean size of exploited populations, increase the abundance of species and, relative to reference sites, raise population density, biomass, fish size and diversity. By contrast, fishers often oppose the establishment and expansion of marine reserves and claim that reserves provide few, if any, economic payoffs. Using a stochastic optimal control model with two forms of ecological uncertainty we demonstrate that reserves create a resilience effect that allows for the population to recover faster, and can also raise the harvest immediately following a negative shock. The tradeoff of a larger reserve is a reduced harvest in the absence of a negative shock such that a reserve will never encompass the entire population if the goal is to maximize the economic returns from harvesting, and fishing is profitable. Under a wide range of parameter values with ecological uncertainty, and in the ‘worst case’ scenario for a reserve, we show that a marine reserve can increase the economic payoff to fishers even when the harvested population is not initially overexploited, harvesting is economically optimal and the population is persistent. Moreover, we show that the benefits of a reserve cannot be achieved by existing effort or output controls. Our results demonstrate that, in many cases, there is no tradeoff between the economic payoff of fishers and ecological benefits when a reserve is established at equal to, or less than, its optimum size.     

Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.     

The role of the endemic and critically endangered Colorful Puffleg Eriocnemis mirabilis in plant‐hummingbird networks of the Colombian Andes

Ecological network approaches may contribute to conservation practices by quantifying within‐community importance of species. In mutualistic plant‐pollinator systems, such networks reflect potential pollination of the plants and a considerable portion of the energy consumption by the pollinators, two key components for each party. Here, we used two different sampling approaches to describe mutualistic plant‐hummingbird networks from a cloud forest in the Colombian Western Andes, home to the Colorful Puffleg Eriocnemis mirabilis, an endemic and critically endangered hummingbird. We contrast networks between two localities (a protected area inside a National park vs. its buffer zone) and across sampling methods (floral visitation vs. pollen loads) to assess how the network structure and the importance of each hummingbird species within the networks may change. Visitation networks were characterized as having higher sampling completeness, yet pollen load network recorded more pollen types than plant species recorded by visitation. Irrespective of the sampling methods, the Colorful Puffleg was one of the most important hummingbird species in the network within the protected area inside the National park, but not in the buffer zone. Moreover, most species‐level network indices were related to hummingbirds’ abundance. This suggests that conservation initiatives aimed at the endangered Colorful Puffleg may both help on the survival of this endangered hummingbird, as well as on maintaining its key role in the mutualistic interaction network inside the National Park. Our study illustrates how conservation practitioners could assess the local importance of endangered species using interaction network approaches.     

Differing contributions of density dependence and climate to the population dynamics of three eruptive herbivores

1. Although both endogenous and exogenous processes regulate populations, the current understanding of the contributions from density dependence and climate to the population dynamics of eruptive herbivores remains limited. 2. Using a 17‐year time series of three cereal aphid species [Rhopalosiphum padi L., Metopolophium dirhodum (Walker), and Diuraphis noxia (Kurdumov)] compiled from a trapping network spanning the northwestern U.S.A., temporal and spatial patterns associated with population fluctuations, and modelled density dependence in aphid abundances were tested. These models were used to analyse correlations between climate and aphid abundances in the presence and absence of residual variance as a result of density‐dependent effects. 3. The temporal dynamics of aphid population fluctuations indicated periodicity, with no clear evidence for a spatial pattern underlying population fluctuations. 4. Aphid abundances oscillated in a manner consistent with delayed density dependence for all three aphid species, although the strength of these feedbacks differed among species. 5. Diuraphis noxia abundances were negatively correlated with increasing temperatures in the absence of density‐dependent effects, whereas M. dirhodum abundances were positively correlated with increasing cumulative precipitation in the presence of density‐dependent effects; yet, R. padi abundances were unrelated to climate variables irrespective of population feedbacks. 6. Our analysis suggests that endogenous feedbacks differentially regulate aphid populations in the northwestern U.S.A., and these feedbacks may operate at an expansive spatial scale. It is concluded that the contributions of density dependence and climate to aphid population dynamics are species‐specific in spite of similar ecological niches, with implications for assessing species responses to climate variability.