Ecological Interaction Networks Research Articles

Quilombola perceptions about plant-mediated ecological interactions

Background: Networks are useful tools to show ecological interactions because they allow to virtually represent natural structures. When applied to local knowledge this approach can reveal unnoticed perspectives, going beyond the species known and used, and showing the interdependence among them. We aim to investigate the ecological interactions between plants-and-animals and plants-and-plants perceived by people from three Quilombola groups in Southern Brazil. We also discuss how the proximity to urban areas can influence these perceptions. Methods: Through 141 ethnobotany interviews in three communities, we asked about the plants known and how each plant interacts with other plants and with animals. Results: The networks formed were similar in the three communities and had characteristics of free-scale networks. The main interactions perceived were between cultivated plants, and between plants and native animals, and were especially related to competition, facilitation, inquilinism, and herbivory. Manihot esculenta, Citrus sinensis, Psidium guajava, and Zea mays were species with the highest centrality. Ecological interactions among different species, especially the native ones, were more prone to occur in less urbanized areas, due to proximity to forested sites. Conclusions: The refined understanding of ecological interactions amidst traditional people reinforces the importance to preserve and maintain their knowledge beyond the simple species’ records, to assist traditional people to guide their claims to establish their rights. Keywords: Ecological interaction networks, local ecological knowledge, Quilombola communities, traditional people, ethnoecology.

Characterizing ecological interaction networks to support risk assessment in classical biological control of weeds.

A key element in weed biological control is the selection of a biological control agent that minimizes the risks of non-target attack and indirect effects on the recipient community. Network ecology is a promising approach that could help decipher tritrophic interactions in both the native and the invaded ranges, to complement quarantine-based host-specificity tests and gain insights on potential interactions of biological control agents. This review highlights practical questions addressed by networks, including 1) biological control agent selection, based on specialization indices, 2) risk assessment of biological control agent release into a novel environment, via particular patterns of association such as apparent competition between agent(s) and native herbivore(s), 3) network comparisons through structural metrics, 4) potential of network modelling and 5) limits of network construction methods.

Fossil fern rhizomes as a model system for exploring epiphyte community structure across geologic time: evidence from Patagonia.

BackgroundIn extant ecosystems, complex networks of ecological interactions between organisms can be readily studied. In contrast, understanding of such interactions in ecosystems of the geologic past is incomplete. Specifically, in past terrestrial ecosystems we know comparatively little about plant biotic interactions besides saprotrophy, herbivory, mycorrhizal associations, and oviposition. Due to taphonomic biases, epiphyte communities are particularly rare in the plant-fossil record, despite their prominence in modern ecosystems. Accordingly, little is known about how terrestrial epiphyte communities have changed across geologic time. Here, we describe a tiny in situ fossil epiphyte community that sheds light on plant-animal and plant-plant interactions more than 50 million years ago.MethodsA single silicified Todea (Osmundaceae) rhizome from a new locality of the early Eocene (ca. 52 Ma) Tufolitas Laguna del Hunco (Patagonia, Argentina) was studied in serial thin sections using light microscopy. The community of organisms colonizing the tissues of the rhizome was characterized by identifying the organisms and mapping and quantifying their distribution. A 200 × 200 µm grid was superimposed onto the rhizome cross section, and the colonizers present at each node of the grid were tallied.ResultsPreserved in situ, this community offers a rare window onto aspects of ancient ecosystems usually lost to time and taphonomic processes. The community is surprisingly diverse and includes the first fossilized leafy liverworts in South America, also marking the only fossil record of leafy bryophyte epiphytes outside of amber deposits; as well as several types of fungal hyphae and spores; microsclerotia with possible affinities in several ascomycete families; and evidence for oribatid mites.DiscussionThe community associated with the Patagonian rhizome enriches our understanding of terrestrial epiphyte communities in the distant past and adds to a growing body of literature on osmundaceous rhizomes as important hosts for component communities in ancient ecosystems, just as they are today. Because osmundaceous rhizomes represent an ecological niche that has remained virtually unchanged over time and space and are abundant in the fossil record, they provide a paleoecological model system that could be used to explore epiphyte community structure through time.

A novel coextinction model considering compensation and new interactions in ecological networks

The robustness of communities to species loss is a major topic in ecological research. Here, we introduce the Dependent random-search Coextinction Model (DCM), a new approach for modelling the consequences of primary extinctions in ecological networks. The DCM assumes that species may persist after a primary extinction through two non-exclusive mechanisms: compensation (i.e., by increasing the interaction strength with living partners) and new interactions. In the DCM, the strengths of the compensated interactions are proportional to the dependence on the lost partner, while the strengths of new interactions are proportional to the resemblance (e.g. phylogenetic, functional) between the extinct partners and the new ones. Using simulations with mutualistic webs as templates, we show that the probability of extinction cascades and the total number of extinctions decrease in highly connected networks, and the probability of extinction cascades increased in highly nested networks. The proposed model is simple and can be used in a variety of interaction networks (e.g. mutualistic, food webs, host-parasite), with any species-species resemblances representing ecological or evolutionary dissimilarities.

Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks

Abstract Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition.

Characterization and comprehensive analysis of the ecological interaction networks of bacterial communities in Paullinia cupana var. sorbilis by 16S rRNA gene metabarcoding.

Endophytes improve the host performance in areas of high plant endemicity. Paullinia cupana is an Amazonia plant species of economic and social importance due to the high caffeine concentration in its seeds. An interesting strategy to identify endophytic microorganisms with potential biotechnological application is to understand the factors that influence the endophytic community to rationalize the host management programs. We used the next-generation sequencing for bacterial 16S rRNA gene to examine how the P. cupana organ, genotype, and geographic location influenced its endophytic bacterial community. We obtained 1520 operational taxonomic units (OTUs) distributed in 19 phyla, 32 classes, 79 orders, 114 families and 174 genera. The P. cupana roots and leaves were specifically colonized by the bacterial genera Acidothermus and Porphyromonas, respectively, with high relative frequency. The plant organ type influenced the endophytic community's richness, diversity, OTUs composition, relative abundance of phyla and genera, and genera interaction network. However, the host plant genotype and geographic location influenced the composition and interaction among genera in the network analysis. Prevotella is a super-generalist genus in the interaction network of endophytic bacteria of P. cupana. This study revealed endophytic bacterial groups of importance to P. cupana and stressed that the host plant organ modulates the structure and interactions within this community. Our results indicated that the microbial community adapted to colonize P. cupana by adjusting to its composition and interaction network. The isolation of abundant and super-generalist bacterial genera shall help to examine their functionality to the composition and fitness of the endophytic community of P. cupana.

How agroforestry systems influence soil fauna and their functions - a review

Agroforestry systems have enhanced diversity of cultivated plants compared to monocultures, and are expected to affect associated biodiversity. Despite a growing body of literature on the importance of soil fauna, the known effects of different agroforestry types on soil fauna communities and functions have not yet been synthesized. We scanned publications on soil fauna in agroforestry systems. Our aim was to give an overview of strengths and weaknesses of the existing data, in terms of spatial coverage and representation of diverse agroforestry types and soil fauna groups and functions. Our database includes sixty-seven articles, mostly focusing on tropical regions and perennial crop agroforestry systems. Soil macrofauna are the most studied fauna group. The most common question addressed is the comparison of the effect of land use types on communities. Effects on fauna abundance and diversity are mainly positive when agroforestry is compared to cropland, and neutral or negative when compared to forests. Few publications actually measure soil fauna functions, or characterize their interactions and evolution in time and space depending on system design and management. Further work on soil fauna in agroforestry should harness ecological theory and address questions of spatial structure and scale, temporal dynamics and ecological interaction networks and how they determine ecosystem functioning.

Revealing biases in the sampling of ecological interaction networks.

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists’ abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network’s structure. To explore properties of large-scale ecological networks, we developed the software EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks.

Some theoretical notes on agrobiodiversity: spatial heterogeneity and population interactions

ABSTRACTEcological interactions are fundamental in ecological pest management, and these interactions form networks. The properties of these networks, where interactions of all possible nature (positive, neutral, negative) coexist, are key for management, but little is known about them. The main reasons for this lack of knowledge are the difficulties in obtaining empirical evidence. These problems may be partially bypassed using a theoretical approach. Here, by means of mathematical models that represent networks of ecological interactions in agroecosystems, we characterize some architectural features that promote the self-regulation of population densities in these networks. The results show that the key features are: spatial heterogeneity and a high proportion of positive interactions.

Implications of non-native species for mutualistic network resistance and resilience.

Resilience theory aims to understand and predict ecosystem state changes resulting from disturbances. Non-native species are ubiquitous in ecological communities and integrated into many described ecological interaction networks, including mutualisms. By altering the fitness landscape and rewiring species interactions, such network invasion may carry important implications for ecosystem resistance and resilience under continued environmental change. Here, I hypothesize that the tendency of established non-native species to be generalists may make them more likely than natives to occupy central network roles and may link them to the resistance and resilience of the overall network. I use a quantitative research synthesis of 58 empirical pollination and seed dispersal networks, along with extinction simulations, to examine the roles of known non-natives in networks. I show that non-native species in networks enhance network redundancy and may thereby bolster the ecological resistance or functional persistence of ecosystems in the face of disturbance. At the same time, non-natives are unlikely to partner with specialist natives, thus failing to support the resilience of native species assemblages. Non-natives significantly exceed natives in network centrality, normalized degree, and Pollination Service Index. Networks containing non-natives exhibit lower connectance, more links on average, and higher generality and vulnerability than networks lacking non-natives. As environmental change progresses, specialists are particularly likely to be impacted, reducing species diversity in many communities and network types. This work implies that functional diversity may be retained but taxonomic diversity decline as non-native species become established in networks worldwide.

The Importance of Ecological Networks in Multiple-Stressor Research and Management

Multiple stressors are increasingly affecting organisms and communities, thereby modifying ecosystems state and functioning. Raising awareness about the threat from multiple stressors has increased the number of experimental and observational studies specifically addressing consequences of stressor interactions on biota. Most studies measure the direct effects of multiple stressors and their interactions on biological endpoints such as abundance, biomass, or diversity of target organism groups. This yields invaluable information for the management and restoration of stressed ecosystems. However, as we argue in our perspective paper, this common approach ignores a fundamental characteristic of communities and ecosystems, i.e. that organisms in ecosystems are interlinked by biotic interactions in ecological networks. Examples from the literature show that biotic interactions can modify stressor effects, transfer stressor effects to distant groups of organisms, and create new stressor interactions. These examples also suggest that changes in biotic interactions can have effects of similar or greater magnitude than direct stressor effects. We provide a perspective on how to include network characteristics and biotic interactions into analyses of multiple-stressor effects on ecosystems. A strength of our approach is that it can make use of biomonitoring data produced with established and intercalibrated methods, and can combine it with novel metrics used to describe the functioning of ecosystems, such as trait information or stable-isotope measurements. The insights on network-mediated effects gained via the approach we propose can substantially increase mechanistic understanding of multiple-stressor effects, and in turn, the efficiency of ecosystem management and restoration.

Fire, grazing and climate shape plant–grasshopper interactions in a tallgrass prairie

Abstract Species interactions are integral to ecological community function, and the structure of species interactions has repercussions for the consequences of species extinctions. Few studies have examined the role of environmental factors in controlling species interaction networks across time. We examined variation in plant–grasshopper network structural properties in response to three major grassland drivers: periodic fire, ungulate grazing and climate. We sequenced a plant barcoding gene from extracted grasshopper gut contents to characterize diets of 26 grasshopper species. Resulting grasshopper species’ diets were combined with long‐term plant and grasshopper surveys to assemble plant–grasshopper networks across 13–19 years for six watersheds subjected to varying fire and grazing treatments. Network modularity, generality and predicted grasshopper community robustness to plant species loss all increased in grazed watersheds. Temperature decreased predicted grasshopper community robustness to plant species loss. Grasshopper communities were found to be vulnerable to climatic warming due to host plant loss. However, intermediate disturbance from ungulate grazers may maintain grasshopper diversity and buffer community robustness to species loss. Our results suggest that climate and disturbance shape the structure of ecological interaction networks and thus have many indirect effects on species persistence though direct effects on interaction partners. A plain language summary is available for this article.

Food Web Topology and Nested Keystone Species Complexes

Important species may be in critically central network positions in ecological interaction networks. Beyond quantifying which one is the most central species in a food web, a multinode approach can identify the key sets of the most central n species as well. However, for sets of different size n, these structural keystone species complexes may differ in their composition. If larger sets contain smaller sets, higher nestedness may be a proxy for predictive ecology and efficient management of ecosystems. On the contrary, lower nestedness makes the identification of keystones more complicated. Our question here is how the topology of a network can influence nestedness as an architectural constraint. Here, we study the role of keystone species complexes in 27 real food webs and quantify their nestedness. After quantifying their topology properties, we determine their keystone species complexes, calculate their nestedness, and statistically analyze the relationship between topological indices and nestedness. A better understanding of the cores of ecosystems is crucial for efficient conservation efforts, and to know which networks will have more nested keystone species complexes would be a great help for prioritizing species that could preserve the ecosystem’s structural integrity.

Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks

Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but only recently community ecologists realized the need to document their spatial and temporal variation. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time – i.e. a novel approach to interaction distribution modeling. We apply this framework to host–parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co‐occurrence, but not on species interactions. Even where species co‐occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host‐parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modeling and community ecology.

Studying the sensory ecology of frog‐biting midges (Corethrellidae: Diptera) and their frog hosts using ecological interaction networks

AbstractInvestigating species interaction networks has advanced the understanding of the robustness of ecosystems, the impact of species invasions, resource partitioning and the coevolution of interacting species. Here, we present a 10‐year study of the relationships between frog‐biting midges (Corethrella) and their frog hosts in tropical lowland forests across northern Borneo. We use quantitative bipartite host‐ectoparasite networks to explore this unusual relationship. Across northern Borneo, nine species of frog‐biting midges were found to bite 29 species of frogs. Overall, 378 individual Corethrella were found on 93 individual frogs. We compare the network structure between two forest types in Brunei Darussalam: a lowland mixed‐dipterocarp rainforest and a peatswamp forest. Results indicate that both antagonistic interaction networks were relatively specialized at the community level and show significant nestedness. The degree of specialization was higher in the rainforest than in the peatswamp suggesting higher diversity of midge sensory perception and frog avoidance strategies in the rainforest. At the species level, the specialization of midges as well as their frog hosts was highly variable in both networks suggesting that some frog species are better at avoiding being bitten by Corethrella than others. Frog species parasitized by midges had calls of low dominant frequencies suggesting that the spectral bandwidth of hearing in midges could influence host selection. Despite this, some frog species that call above 4 kHz have not escaped parasitism. We explore the role of habitat filtering, behavior and coevolution in shaping network structure.

Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient

Species’ functional traits set the blueprint for pair-wise interactions in ecological networks. Yet, it is unknown to what extent the functional diversity of plant and animal communities controls network assembly along environmental gradients in real-world ecosystems. Here we address this question with a unique dataset of mutualistic bird–fruit, bird–flower and insect–flower interaction networks and associated functional traits of 200 plant and 282 animal species sampled along broad climate and land-use gradients on Mt. Kilimanjaro. We show that plant functional diversity is mainly limited by precipitation, while animal functional diversity is primarily limited by temperature. Furthermore, shifts in plant and animal functional diversity along the elevational gradient control the niche breadth and partitioning of the respective other trophic level. These findings reveal that climatic constraints on the functional diversity of either plants or animals determine the relative importance of bottom-up and top-down control in plant–animal interaction networks.

Trophic structure alters consequences of environmental warming

Climate warming can directly affect traits and demographic rates of organisms. However, individuals are embedded in complex networks of ecological interactions with other members of the community, allowing for a range of direct and indirect effects that depend on the trophic structure of the community. Here we show that effects of warming (i.e. increase in mean temperature) on a given species can strongly depend on the community context and trophic complexity of the system. Specifically, we manipulated the presence/ absence of two competing tadpole species and their dragonfly predators to simulate different food webs of increasing complexity that were exposed to ambient or warmed conditions. We found that warming dramatically reduced herbivore (tadpole) survival in the absence of strong interspecific competition and predation, but it had no measureable effect on demographic rates on the dominant competitor in more complex communities where it was exposed to interspecific competition and predation. Conversely, our results also indicate that warming reduced the strength of interspecific competition and predation in our system. These results suggest that trophic complexity could potentially buffer climate change effects on populations and emphasize that we often cannot predict the effects of changes in abiotic conditions on a given population without accounting for the community context.

Morphological trait matching shapes plant–frugivore networks across the Andes

Interactions between resource and consumer species are organized in ecological networks. Species interactions in these networks are influenced by the functional traits of the interacting partners, but the generality of trait‐based interaction rules and the relationship between functional traits and a species’ specialization on specific interaction partners are not yet understood. Here we combine data on eight interaction networks between fleshy‐fruited plants and frugivorous birds sampled across the tropical and subtropical Andean range. We test which combinations of morphological plant and animal traits determine trait matching between resource and consumer species in these networks. In addition, we test which of the morphological traits influence functional specialization of plant and bird species. In a meta‐analysis across network‐specific fourth‐corner analyses, we found that plant–animal trait pairs related to size matching (fruit size–beak size) and avian foraging behavior (plant height–wing shape and crop mass–body mass) were positively related in these networks. The degree of functional specialization on specific interaction partners was positively related to crop mass in plants and to the pointedness of the wing in birds. Our findings show that morphological trait matching between fleshy‐fruited plants and frugivorous birds is a general phenomenon in plant–frugivore networks across the Andes and that specific plant and bird traits can be used to approximate the degree of functional specialization. These insights into the generality of interaction rules are the base for predictions of species interactions in ecological networks, for instance in novel communities in the future, and can be applied to identify plant and animal species that fulfill specialized functional roles in ecological communities.

Moving from frugivory to seed dispersal: Incorporating the functional outcomes of interactions in plant-frugivore networks.

There is growing interest in understanding the functional outcomes of species interactions in ecological networks. For many mutualistic networks, including pollination and seed dispersal networks, interactions are generally sampled by recording animal foraging visits to plants. However, these visits may not reflect actual pollination or seed dispersal events, despite these typically being the ecological processes of interest.Frugivorous animals can act as seed dispersers, by swallowing entire fruits and dispersing their seeds, or as pulp peckers or seed predators, by pecking fruits to consume pieces of pulp or seeds. These processes have opposing consequences for plant reproductive success. Therefore, equating visitation with seed dispersal could lead to biased inferences about the ecology, evolution and conservation of seed dispersal mutualisms.Here, we use natural history information on the functional outcomes of pairwise bird–plant interactions to examine changes in the structure of seven European plant–frugivore visitation networks after non‐mutualistic interactions (pulp pecking and seed predation) have been removed. Following existing knowledge of the contrasting structures of mutualistic and antagonistic networks, we hypothesized a number of changes following interaction removal, such as increased nestedness and lower specialization.Non‐mutualistic interactions with pulp peckers and seed predators occurred in all seven networks, accounting for 21%–48% of all interactions and 6%–24% of total interaction frequency. When non‐mutualistic interactions were removed, there were significant increases in network‐level metrics such as connectance and nestedness, while robustness decreased. These changes were generally small, homogenous and driven by decreases in network size. Conversely, changes in species‐level metrics were more variable and sometimes large, with significant decreases in plant degree, interaction frequency, specialization and resilience to animal extinctions and significant increases in frugivore species strength.Visitation data can overestimate the actual frequency of seed dispersal services in plant–frugivore networks. We show here that incorporating natural history information on the functions of species interactions can bring us closer to understanding the processes and functions operating in ecological communities. Our categorical approach lays the foundation for future work quantifying functional interaction outcomes along a mutualism–antagonism continuum, as documented in other frugivore faunas.

Metatranscriptome of human faecal microbial communities in a cohort of adult men.

The gut microbiome is intimately related to human health, but it is not yet known which functional activities are driven by specific microbes’ ecological configurations or transcription. We report a large-scale investigation of 372 human fecal metatranscriptomes and 929 metagenomes from a subset of 308 men in the Health Professionals Follow-up Study. We identified a metatranscriptomic “core” universally transcribed over time and across participants, often by different microbes. In contrast to the housekeeping functions enriched in this core, a “variable” metatranscriptome included specialized pathways that were differentially expressed both across participants and among microbes. Finally, longitudinal metagenomic profiles allowed ecological interaction network reconstruction, which remained stable over the six-month timespan, as did strain tracking within and between participants. These results provide an initial characterization of human fecal microbial ecology into core, subject-specific, microbe-specific, and temporally-variable transcription, and they differentiate metagenomically versus metatranscriptomically informative aspects of the human fecal microbiome.