Types Of Ecological Interactions Research Articles

Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, does the type of ecological interaction, such as mutualism or competition, change the average effect of a mutation (i.e., the mean of the distribution of fitness effects)? Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, we discovered that in the mutualism S. enterica obtained vitamins and more amino acids than previously known. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

Modeling the Role of Immune Cell Conversion in the Tumor-Immune Microenvironment

Tumors develop in a complex physical, biochemical, and cellular milieu, referred to as the tumor microenvironment. Of special interest is the set of immune cells that reciprocally interact with the tumor, the tumor-immune microenvironment (TIME). The diversity of cell types and cell–cell interactions in the TIME has led researchers to apply concepts from ecology to describe the dynamics. However, while tumor cells are known to induce immune cells to switch from anti-tumor to pro-tumor phenotypes, this type of ecological interaction has been largely overlooked. To address this gap in cancer modeling, we develop a minimal, ecological model of the TIME with immune cell conversion, to highlight this important interaction and explore its consequences. A key finding is that immune conversion increases the range of parameters supporting a co-existence phase in which the immune system and the tumor reach a stalemate. Our results suggest that further investigation of the consequences of immune cell conversion, using detailed, data-driven models, will be critical for greater understanding of TIME dynamics.

Flower colour and flowering phenology mediate plant–pollinator interaction assembly in a diverse co‐flowering community

Abstract Uncovering the role of competition and facilitation in community assembly is central for developing a predictive understanding of the forces that organize biodiversity. Standard trait‐based approaches however rely on detection of only one assembly mechanism (competition or facilitation) along a single trait even though pollinator‐mediated plant–plant interactions can be structured along multiple phenotypic, phenological and ecological traits. We evaluated plant species distribution along multiple phenotypic and ecological traits (flower colour, flowering time, pollinator sharing) and described an entire co‐flowering community as a set of modules with unique patterns of assembly, to test predictions regarding the relative contribution of competition and facilitation to the assembly of a diverse co‐flowering community. We show a modular pattern of flower colour assembly. Flower colour modules differ in their spectral reflectance patterns including colour hue and saturation. Within modules, however, species are differentially assembled along phenological and ecological traits (pollinator sharing) depending on the main pollinator group visiting plant species within each module. Results suggest different trait assembly patterns within individual trait‐modules in the same co‐flowering community and that different trait‐patterns can result from the same type of ecological interaction. This study reveals empirical evidence of community assembly along multiple axes of trait differentiation and raises caution when interpreting assembly patterns based on a single trait. Read the free Plain Language Summary for this article on the Journal blog.

In vitro modeling of tumor interclonal interactions using breast cancer cell lines.

To study the peculiarities of ecological relationships of breast cancer (BC) cell lines MCF-7, BT-474and MDA-MD-231under co-culturing conditions. Three BC cell lines: luminal A- MCF-7, luminal B- BT-474and triple-negative- MDA-MD-231were co-cultured pairwise. Immunocytochemistry was used to differentiate the cell lines in the wells. The effect of the cell-free culture medium on the growth rate of the alternate cell line in the pair was also evaluated. It was shown that when BT-474cells were co-cultured with MCF-7and BT-474cells were co-cultured with MDA-MD-231, two types of ecological interactions could be observed: commensalism and amensalism, respectively. While the cells do not interact with each other in contact, the supernatants of single cultures of MCF-7and MDA-MD-231exert the same effect on BT-474as co-cultivation of BT-474with these cells. The paracrine mechanism of intercellular interaction between different human BC cell lines has been demonstrated. The models used in population ecology can be applicable to identify the types of interaction between cell lines.

Global knowledge gaps in species interaction networks data

AbstractEcological networks are increasingly studied at large spatial scales, expanding their focus from a conceptual tool for community ecology into one that also addresses questions in biogeography and macroecology. This effort is supported by increased access to standardized information on ecological networks, in the form of openly accessible databases. Yet, there has been no systematic evaluation of the fitness for purpose of these data to explore synthesis questions at very large spatial scales. In particular, because the sampling of ecological networks is a difficult task, they are likely to not have a good representation of the diversity of Earth's bioclimatic conditions, likely to be spatially aggregated, and therefore unlikely to achieve broad representativeness. In this paper, we analyse over 1300 ecological networks in the mangal.io database, and discuss their coverage of climates, and the geographic areas in which there is a deficit of data on ecological networks. Taken together, our results suggest that while some information about the global structure of ecological networks is available, it remains fragmented over space, with further differences by types of ecological interactions. This causes great concerns both for our ability to transfer knowledge from one region to the next, but also to forecast the structural change in networks under climate change.

Hybrid networks reveal contrasting effects of agricultural intensification on antagonistic and mutualistic motifs

Abstract Anthropogenic‐driven perturbations such as agricultural intensification can affect simultaneously and distinctly several species groups and ecosystem functions. Unveiling these concurrent effects on interdependent species groups connected by different types of ecological interactions is a key challenge for ecologists. To this endeavour, hybrid ecological networks arise as a promising tool. In this study, we used bee trap nests to sample hybrid networks that combined mutualistic and antagonistic interactions to explore agricultural intensification effects on the representation of network motifs (i.e. subnetworks showing different interaction types between a small number of species). Also, we assessed the variability of network motif's frequencies on farms under similar management regimes and the dissimilarity between farms under different ones. For this, we implemented a novel approach, calculating network functional spaces based on probability density estimates of network motif's frequencies, using network motifs as traits. Results showed that environmentally friendly practices maximize the representation of mutualistic (cavity nesting bees–plants) and predation (wasps–prey and bees/wasps–antagonists) motifs. In contrast, intensive agriculture favoured generalist and intraguild predation interactions. Lastly, the frequency of motifs representing antagonistic interactions was more inconsistent and unpredictable across sites than mutualistic motifs, especially on intensified farms. Our novel approach, dissecting hybrid networks into their motifs and analysing the functional space defined by these, reported detailed and contrasting effects of agricultural intensification on network motifs that represent the mutualistic and antagonistic interactions in this system. A free plain language summary can be found within the Supporting Information of this article.

Reciprocal interactions between tumour cell populations enhance growth and reduce radiation sensitivity in prostate cancer

Intratumoural heterogeneity (ITH) contributes to local recurrence following radiotherapy in prostate cancer. Recent studies also show that ecological interactions between heterogeneous tumour cell populations can lead to resistance in chemotherapy. Here, we evaluated whether interactions between heterogenous populations could impact growth and response to radiotherapy in prostate cancer. Using mixed 3D cultures of parental and radioresistant populations from two prostate cancer cell lines and a predator-prey mathematical model to investigate various types of ecological interactions, we show that reciprocal interactions between heterogeneous populations enhance overall growth and reduce radiation sensitivity. The type of interaction influences the time of regrowth after radiation, and, at the population level, alters the survival and cell cycle of each population without eliminating either one. These interactions can arise from oxygen constraints and from cellular cross-talk that alter the tumour microenvironment. These findings suggest that ecological-type interactions are important in radiation response and could be targeted to reduce local recurrence.

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.

Floral volatiles and visitors: A meta‐network of associations in a natural community

Abstract Chemosensory communication between flowers and pollinators is a fundamental component of terrestrial biodiversity, given the importance of olfaction to foraging animals. In this respect, exploring chemically mediated interspecific interactions in natural assemblies may provide novel insights into the ecofunctional significance of volatile organic compounds (VOCs) for plant–insect co‐evolution. However, multispecies datasets of associations between plant semiochemicals and arthropods are still very rare and tend to lack community context. Here, we present the first insect–floral VOC meta‐network using plant–pollinator visitation data and the plants’ floral scent blends, collected in a Mediterranean scrubland. We assembled the insect–VOC meta‐network by substituting each plant species in the plant–pollinator network with the blend of VOCs it emits. Furthermore, we identified the modules of the network that is the most densely connected insect–VOC groups. After describing the role of the species in the network, we focused on the bees of the community, and by building phylogenetically informed GLS models, we found the species traits predicting the degree of chemical specialization. Modularity analysis of the meta‐network revealed tight associations between several classes of VOCs and pollinator groups. Linkage patterns suggest positive associations between (a) Megachilidae bees and sesquiterpenes, (b) Apidae and Andrenidae bees and benzenoids/phenylpropanoids, and (c) wasps, C6 green‐leaf volatiles and specific terpenoids. Benzenoids were found to be the least influential and most specialized chemical class in the community, whereas sesquiterpenes represented the most influential one. Furthermore, the degree of chemical generalization of the bees in the meta‐network was significantly associated with their ecological generalization, body mass and phenology, whereas their contribution to the network's structure was related to their level of sociality. Synthesis. Our findings help to disclose the ecofunctional significance of the floral volatile landscape and contribute novel testable hypotheses on the behavioural trends and chemical niches of pollinators in a natural community. The insect–volatilome meta‐network is thus shown to be advantageous for detecting and visualizing patterns of chemically mediated interspecific interactions. Given the ubiquity of chemosensory biocommunication, our approach can be applied for investigating various types of ecological interactions in community contexts.

Economic value of ecological information in ecosystem-based natural resource management depends on exploitation history

Ecosystem approaches to natural resource management are seen as a way to provide better outcomes for ecosystems and for people, yet the nature and strength of interactions among ecosystem components is usually unknown. Here we characterize the economic benefits of ecological knowledge through a simple model of fisheries that target a predator (piscivore) and its prey. We solve for the management (harvest) trajectory that maximizes net present value (NPV) for different ecological interactions and initial conditions that represent different levels of exploitation history. Optimal management trajectories generally approached similar harvest levels, but the pathways toward those levels varied considerably by ecological scenario. Application of the wrong harvest trajectory, which would happen if one type of ecological interaction were assumed but in fact another were occurring, generally led to only modest reductions in NPV. However, the risks were not equal across fleets: risks of incurring large losses of NPV and missing management targets were much higher in the fishery targeting piscivores, especially when piscivores were heavily depleted. Our findings suggest that the ecosystem approach might provide the greatest benefits when used to identify system states where management performs poorly with imperfect knowledge of system linkages so that management strategies can be adopted to avoid those states.

Climate drives phenological reassembly of a mountain wildflower meadow community

Spatial community reassembly driven by changes in species abundances or habitat occupancy is a well-documented response to anthropogenic global change, but communities can also reassemble temporally if the environment drives differential shifts in the timing of life events across community members. Much like spatial community reassembly, temporal reassembly could be particularly important when critical species interactions are temporally concentrated (e.g., plant-pollinator dynamics during flowering). Previous studies have documented species-specific shifts in phenology driven by climate change, implying that temporal reassembly, a process we term "phenological reassembly," is likely. However, few studies have documented changes in the temporal co-occurrence of community members driven by environmental change, likely because few datasets of entire communities exist. We addressed this gap by quantifying the relationship between flowering phenology and climate for 48 co-occurring subalpine wildflower species at Mount Rainier (Washington, USA) in a large network of plots distributed across Mt. Rainier's steep environmental gradients; large spatio-temporal variability in climate over the 6 yr of our study (including the earliest and latest snowmelt year on record) provided robust estimates of climate-phenology relationships for individual species. We used these relationships to examine changes to community co-flowering composition driven by 'climate change analog' conditions experienced at our sites in 2015. We found that both the timing and duration of flowering of focal species was strongly sensitive to multiple climatic factors (snowmelt, temperature, and soil moisture). Some consistent responses emerged, including earlier snowmelt and warmer growing seasons driving flowering phenology earlier for all focal species. However, variation among species in their phenological sensitivities to these climate drivers was large enough that phenological reassembly occurred in the climate change analog conditions of 2015. An unexpected driver of phenological reassembly was fine-scale variation in the direction and magnitude of climatic change, causing phenological reassembly to be most apparent early and late in the season and in topographic locations where snow duration was shortest (i.e., at low elevations and on ridges in the landscape). Because phenological reassembly may have implications for many types of ecological interactions, failing to monitor community-level repercussions of species-specific phenological shifts could underestimate climate change impacts.

Marine Ecosystem Considerations and Second-Best Management

We compare how the long-run distribution of fishing activities is affected in multispecies fisheries when facing different second-best control rules: (1) species-specific landing regulation, and (2) global input regulation. We show how this depends on the economic returns and on the type of ecological interaction considered. We highlight specifically that fishing effort does not necessarily increase on nontargeted species and decrease on targeted species, and that the characterization of second-best efficient instruments may differ drastically depending on the nature of the interaction.

The friendship paradox in species-rich ecological networks: Implications for conservation and monitoring

A great challenge in ecology and conservation biology is to deal with the inherent complexity of ecological systems. Because species are embedded in species-rich systems characterized by multiple interactions, it is often hard to identify which species are really important for ecological processes such as pollination. Here we show that species-rich networks describing plant-pollinator interactions share a property with networks depicting social relationships, the friendship paradox, which allows identifying highly-connected species without detailed information on the whole network of interactions. Numerical simulations support that the identified species are those more likely to affect community structure and ecological dynamics. A sampling protocol taking into account the friendship paradox property could be adapted to field studies, helping in the search for conservation surrogates or to monitor changes in the communities, such as functional extinction or the increase in ecological importance of invasive species. We hypothesize that the friendship paradox is likely to arise in networks describing other types of ecological interactions. Besides being useful for conservation and ecosystem management, the friendship paradox may have relevant implications in other areas of biology as well.

On the integration of biotic interaction and environmental constraints at the biogeographical scale

Biogeography is primarily concerned with the spatial distribution of biodiversity, including performing scenarios in a changing environment. The efforts deployed to develop species distribution models have resulted in predictive tools, but have mostly remained correlative and have largely ignored biotic interactions. Here we build upon the theory of island biogeography as a first approximation to the assembly dynamics of local communities embedded within a metacommunity context. We include all types of interactions and introduce environmental constraints on colonization and extinction dynamics. We develop a probabilistic framework based on Markov chains and derive probabilities for the realization of species assemblages, rather than single species occurrences. We consider the expected distribution of species richness under different types of ecological interactions. We also illustrate the potential of our framework by studying the interplay between different ecological requirements, interactions and the distribution of biodiversity along an environmental gradient. Our framework supports the idea that the future research in biogeography requires a coherent integration of several ecological concepts into a single theory in order to perform conceptual and methodological innovations, such as the switch from single‐species distribution to community distribution.

Joining the dots: An automated method for constructing food webs from compendia of published interactions

Food webs are important tools for understanding how complex natural communities are structured and how they respond to environmental change. However their full potential has yet to be realised because of the huge amount of resources required to construct them de novo. Consequently, the current catalogue of networks that are suitable for rigorous and comparative analyses and theoretical development still suffers from a lack of standardisation and replication.Here, we present a novel R function, WebBuilder, which automates the construction of food webs from taxonomic lists, and a dataset of trophic interactions. This function works by matching species against those within a dataset of trophic interactions, and ‘filling in’ missing trophic interactions based on these matches. We also present a dataset of over 20,000 freshwater trophic interactions, and use this and four well-characterised freshwater food webs to test the method.The WebBuilder function facilitates the generation of food webs of comparable quality to the most detailed published food webs, but at a fraction of the research effort or cost. Furthermore, it matched and often outperformed a selection of predictive models, which are currently among the best, in terms of capturing key properties of empirical food webs. The method is simple to use, systematic and, perhaps most importantly, reproducible, which will facilitate (re-) analysis and data sharing. Although developed and tested on a sample of freshwater food webs, this method could easily be extended to cover other types of ecological interactions (such as mutualistic interactions).

Speciation, diversification, and coexistence of sessile species that compete for space.

Speciation, diversification, and competition between species challenge the stability of complex ecosystems. Laboratory experiments often focus on one or two species competing under conditions where they may grow exponentially. Field studies, in contrast, emphasize multi-species communities characterized by many types of ecological interactions. A general problem is to understand conditions that support a dynamically maintained coexistence of many species in an ecosystem over a long time span. In the present paper we propose a lattice model of multiple competing and evolving sessile species. When allowing the interspecies interactions to mutate, we obtain coexistence of many species in a complex ecosystem, provided that there is a cost for each interaction. The diversity reached by the model incorporating speciation is found to be substantially higher than in the case when entirely new species appear due to immigration from outside of the considered ecosystem. The species self-organize their spatial distribution through competitive interactions to create many patches, implicitly protecting each other from competitively superior species, and speciation in each patch leads the system to high diversity. We also show that species that exist a long time tend to have a relatively small population, as this allows them to avoid encounter with competitive invaders.

Simulating microbial community patterning using Biocellion.

Mathematical modeling and computer simulation are important tools for understanding complex interactions between cells and their biotic and abiotic environment: similarities and differences between modeled and observed behavior provide the basis for hypothesis formation. Momeni et al. (Elife 2:e00230, 2013) investigated pattern formation in communities of yeast strains engaging in different types of ecological interactions, comparing the predictions of mathematical modeling, and simulation to actual patterns observed in wet-lab experiments. However, simulations of millions of cells in a three-dimensional community are extremely time consuming. One simulation run in MATLAB may take a week or longer, inhibiting exploration of the vast space of parameter combinations and assumptions. Improving the speed, scale, and accuracy of such simulations facilitates hypothesis formation and expedites discovery. Biocellion is a high-performance software framework for accelerating discrete agent-based simulation of biological systems with millions to trillions of cells. Simulations of comparable scale and accuracy to those taking a week of computer time using MATLAB require just hours using Biocellion on a multicore workstation. Biocellion further accelerates large scale, high resolution simulations using cluster computers by partitioning the work to run on multiple compute nodes. Biocellion targets computational biologists who have mathematical modeling backgrounds and basic C++ programming skills. This chapter describes the necessary steps to adapt the original Momeni et al.'s model to the Biocellion framework as a case study.

The role of eco-evolutionary experience in invasion success

Invasion ecology has made considerable progress in identifying specific mechanisms that potentially determine success and failure of biological invasions. Increasingly, efforts are being made to interrelate or even synthesize the growing number of hypotheses in order to gain a more comprehensive and integrative understanding of invasions. We argue that adopting an eco-evolutionary perspective on invasions is a promising approach to achieve such integration. It emphasizes the evolutionary antecedents of invasions, i.e. the species’ evolutionary legacy and its role in shaping novel biotic interactions that arise due to invasions. We present a conceptual framework consisting of five hypothetical scenarios about the influence of so-called ‘eco-evolutionary experience’ in resident native and invading non-native species on invasion success, depending on the type of ecological interaction (predation, competition, mutualism, and commensalism). We show that several major ecological invasion hypotheses, including ‘enemy release’, ‘EICA’, ‘novel weapons’, ‘naïve prey’, ‘new associations’, ‘missed mutualisms’ and ‘Darwin’s naturalization hypothesis’ can be integrated into this framework by uncovering their shared implicit reference to the concept of eco-evolutionary experience. We draft a routine for the assessment of eco-evolutionary experience in native and non-native species using a food web-based example and propose two indices (xpFocal index and xpResidents index) for the actual quantification of eco-evolutionary experience. Our study emphasizes the explanatory potential of an eco-evolutionary perspective on biological invasions.

A new parameterization for estimating co‐occurrence of interacting species

Models currently used to estimate patterns of species co-occurrence while accounting for errors in detection of species can be difficult to fit when the effects of covariates on species occurrence probabilities are included. The source of the estimation problems is the particular parameterization used to specify species co-occurrence probability. We develop a new parameterization for estimating patterns of co-occurrence of interacting species that allows the effects of covariates to be specified quite naturally without estimation problems. In our model, the occurrence of one species is assumed to depend on the occurrence of another, but the occurrence of the second species is not assumed to depend on the presence of the first species. This pattern of co-occurrence, wherein one species is dominant and the other is subordinate, can be produced by several types of ecological interactions (predator-prey, parasitism, and so on). A simulation study demonstrated that estimates of species occurrence probabilities were unbiased in samples of 50-100 locations and three surveys per location, provided species are easily detected (probability of detection > or = 0.5). Higher sample sizes (>200 locations) are needed to achieve unbiasedness when species are more difficult to detect. An analysis of data from treefrog surveys in southern Florida indicated that the occurrence of Cuban treefrogs, an invasive predator species, was highest near the point of its introduction and declined with distance from that location. Sites occupied by Cuban treefrogs were 9.0 times less likely to contain green treefrogs and 15.7 times less likely to contain squirrel treefrogs compared to sites without Cuban treefrogs. The detection probabilities of native treefrog species did not depend on the presence of Cuban treefrogs, suggesting that the native treefrog species are naive to the introduced species.

Evolutionary Branching and Sympatric Speciation Caused by Different Types of Ecological Interactions.

Evolutionary branching occurs when frequency-dependent selection splits a phenotypically monomorphic population into two distinct phenotypic clusters. A prerequisite for evolutionary branching is that directional selection drives the population toward a fitness minimum in phenotype space. This article demonstrates that selection regimes leading to evolutionary branching readily arise from a wide variety of different ecological interactions within and between species. We use classical ecological models for symmetric and asymmetric competition, for mutualism, and for predator-prey interactions to describe evolving populations with continuously varying characters. For these models, we investigate the ecological and evolutionary conditions that allow for evolutionary branching and establish that branching is a generic and robust phenomenon. Evolutionary branching becomes a model for sympatric speciation when population genetics and mating mechanisms are incorporated into ecological models. In sexual populations with random mating, the continual production of intermediate phenotypes from two incipient branches prevents evolutionary branching. In contrast, when mating is assortative for the ecological characters under study, evolutionary branching is possible in sexual populations and can lead to speciation. Therefore, we also study the evolution of assortative mating as a quantitative character. We show that evolution under branching conditions selects for assortativeness and thus allows sexual populations to escape from fitness minima. We conclude that evolutionary branching offers a general basis for understanding adaptive speciation and radiation under a wide range of different ecological conditions.