Species In Ecological Networks Research Articles

The Interplay of Binary and Quantitative Structure on the Stability of Mutualistic Networks.

Understanding how the structure of biological systems impacts their resilience (broadly defined) is a recurring question across multiple levels of biological organization. In ecology, considerable effort has been devoted to understanding how the structure of interactions between species in ecological networks is linked to different broad resilience outcomes, especially local stability. Still, nearly all of that work has focused on interaction structure in presence-absence terms and has not investigated quantitative structure, i.e., the arrangement of interaction strengths in ecological networks. We investigated how the interplay between binary and quantitative structure impacts stability in mutualistic interaction networks (those in which species interactions are mutually beneficial), using community matrix approaches. We additionally examined the effects of network complexity and within-guild competition for context. In terms of structure, we focused on understanding the stability impacts of nestedness, a structure in which more-specialized species interact with smaller subsets of the same species that more-generalized species interact with. Most mutualistic networks in nature display binary nestedness, which is puzzling because both binary and quantitative nestedness are known to be destabilizing on their own. We found that quantitative network structure has important consequences for local stability. In more-complex networks, binary-nested structures were the most stable configurations, depending on the quantitative structures, but which quantitative structure was stabilizing depended on network complexity and competitive context. As complexity increases and in the absence of within-guild competition, the most stable configurations have a nested binary structure with a complementary (i.e., anti-nested) quantitative structure. In the presence of within-guild competition, however, the most stable networks are those with a nested binary structure and a nested quantitative structure. In other words, the impact of interaction overlap on community persistence is dependent on the competitive context. These results help to explain the prevalence of binary-nested structures in nature and underscore the need for future empirical work on quantitative structure.

Trophic rewilding benefits a tropical community through direct and indirect network effects

Species reintroductions can be used as a conservation strategy to restore ecological interactions and the functionality of impoverished ecosystems. The ecological effects of reintroductions go beyond restoring pairwise interactions, because reintroductions can change how extant species are indirectly linked to each other in an ecological community. These indirect pathways, in turn, may shape a myriad of ecological and evolutionary processes operating in ecological systems. Here, we investigated how reintroductions may affect the direct and indirect pathways connecting species in ecological networks. We modeled the potential effects of the reintroduction of four frugivore species (channel‐billed toucans, red‐humped agoutis, brown howler monkeys and yellow‐footed tortoises) to the local seed dispersal network in an Atlantic Forest site, the Tijuca National Park (Rio de Janeiro, Brazil). We used a seed dispersal interaction dataset together with data on species occurrences in Tijuca to build network models. Then, we calculated how network structure and the total amount of indirect effects varied across simulated networks with and without the reintroduced species. Using random reintroduction simulations, we tested if the observed network changes were expected merely from the increase in species richness. The reintroduction of the frugivore species increased network connectance, nestedness, robustness, number of pathways and total amount of indirect effects in all simulated networks. The increase in number of pathways with the addition of the four reintroduced species was greater than the sum of isolated effects for each species, because some interaction pathways contained several reintroduced species. These changes in network metrics were significantly greater than if a randomly chosen set of four species was reintroduced. Furthermore, our results indicate that multiple reintroductions in the same area, known as refaunation, may have an even greater restoration effect than single species reintroductions through increased indirect connections in the network.

Reinterpreting the relationship between number of species and number of links connects community structure and stability.

For 50 years, ecologists have examined how the number of interactions (links) scales with the number of species in ecological networks. Here, we show that the way the number of links varies when species are sequentially removed from a community is fully defined by a single parameter identifiable from empirical data. We mathematically demonstrate that this parameter is network-specific and connects local stability and robustness, establishing a formal connection between community structure and two prime stability concepts. Importantly, this connection highlights a local stability-robustness trade-off, which is stronger in mutualistic than in trophic networks. Analysis of 435 empirical networks confirmed these results. We finally show how our network-specific approach relates to the classical across-network approach found in literature. Taken together, our results elucidate one of the intricate relationships between network structure and stability in community networks.

Interspecific interaction strength influences population density more than carrying capacity in more complex ecological networks

Understanding the mechanisms determining population density of species in communities and ecological networks is an important task in ecological studies. Interactions and carrying capacity largely determine population density of species and then community structure. However, their impacts on population density have not been fully investigated in ecological networks. In this study, we examined the associations of interspecific interaction strength and carrying capacity with population density in three kinds of theoretical and empirical ecological networks with different complexity. We firstly demonstrated both the net direct and indirect interaction strength of a species received from the other species showed positive associations with population density of the species in all ecological networks (except for in predation networks), particularly in more complex ecological networks. Direct interaction was more important than indirect interaction in determining population density. Carrying capacity showed a positive association with population density, particularly in less complex ecological networks. Our results suggest that interspecific interaction strength is more important than carrying capacity in determining species dominance in more complex networks.

Native and Non-Native Supergeneralist Bee Species Have Different Effects on Plant-Bee Networks.

Supergeneralists, defined as species that interact with multiple groups of species in ecological networks, can act as important connectors of otherwise disconnected species subsets. In Brazil, there are two supergeneralist bees: the honeybee Apis mellifera, a non-native species, and Trigona spinipes, a native stingless bee. We compared the role of both species and the effect of geographic and local factors on networks by addressing three questions: 1) Do both species have similar abundance and interaction patterns (degree and strength) in plant-bee networks? 2) Are both species equally influential to the network structure (nestedness, connectance, and plant and bee niche overlap)? 3) How are these species affected by geographic (altitude, temperature, precipitation) and local (natural vs. disturbed habitat) factors? We analyzed 21 plant-bee weighted interaction networks, encompassing most of the main biomes in Brazil. We found no significant difference between both species in abundance, in the number of plant species with which each bee species interacts (degree), and in the sum of their dependencies (strength). Structural equation models revealed the effect of A. mellifera and T. spinipes, respectively, on the interaction network pattern (nestedness) and in the similarity in bee’s interactive partners (bee niche overlap). It is most likely that the recent invasion of A. mellifera resulted in its rapid settlement inside the core of species that retain the largest number of interactions, resulting in a strong influence on nestedness. However, the long-term interaction between native T. spinipes and other bees most likely has a more direct effect on their interactive behavior. Moreover, temperature negatively affected A. mellifera bees, whereas disturbed habitats positively affected T. spinipes. Conversely, precipitation showed no effect. Being positively (T. spinipes) or indifferently (A. mellifera) affected by disturbed habitats makes these species prone to pollinate plant species in these areas, which are potentially poor in pollinators.

Predicting ecosystem functions from biodiversity and mutualistic networks: an extension of trait‐based concepts to plant–animal interactions

Research linking biodiversity and ecosystem functioning (BEF) has been mostly centred on the influence of species richness on ecosystem functions in small‐scale experiments with single trophic levels. In natural ecosystems, many ecosystem functions are mediated by interactions between plants and animals, such as pollination and seed dispersal by animals, for which BEF relationships are little understood. Largely disconnected from BEF research, network ecology has examined the structural diversity of complex ecological networks of interacting species. Here, we provide an overview of the most important concepts in BEF and ecological network research and exemplify their applicability to natural ecosystems with examples from pollination and seed‐dispersal studies. In a synthesis, we connect the structural approaches of network analysis with the trait‐based approaches of BEF research and propose a conceptual trait‐based model for understanding BEF relationships of plant–animal interactions in natural ecosystems. The model describes the sequential processes that determine the BEF relationship, i.e. the responses of species to environmental filters, the matching of species in ecological networks and the functionality of species in terms of their quantitative and qualitative contributions to plant demography and ecosystem functioning. We illustrate this conceptual integration with examples from mutualistic interactions and highlight its value for predicting the consequences of biodiversity loss for multispecies interactions and ecosystem functions. We foresee that a better integration between BEF and network research will improve our mechanistic understanding of how biodiversity relates to the functioning of natural ecosystems. Our conceptual model is a step towards this integration between structural and functional biodiversity research.

Optimizing size thresholds in a plant–pollinator interaction web: towards a mechanistic understanding of ecological networks

Using functional traits together with abundance effects strengthens the prediction of interactions between pairs of species in ecological networks. Insights into the way species interact as well as prediction accuracy can be gained when thresholds for trait value combinations that make interactions possible are optimized through model selection. I present novel data of two subalpine plant-pollinator communities and build several stochastic models integrating flower abundance and morphological threshold rules that allow or restrict interactions between species. The number of correctly predicted interactions was highest when thresholds were set so that the insect's proboscis was not shorter than the nectar-holder depth minus 1-1.6 mm, and not wider than the nectar-holder width minus 0.5 mm. In comparison with models based solely on plant abundance effects, the model incorporating optimized size thresholds better predicted the distribution of the trait differences between plants and insects. This indicates that a mechanistic approach of interaction webs based on optimized size thresholds provides valuable information on community structure. The possible implications for community functioning are discussed.

Succinctly assessing the topological importance of species in flower–pollinator networks

The topological importance of species within networks is an important way of bringing a species-level consideration to the study of whole ecological networks. There are many different indices of topological importance, including centrality indices, but it is likely that a small number are sufficient to explain variation in topological importance. We used 14 indices to describe topological importance of plants and pollinators in 12 quantitative mutualistic (plant–pollinator) networks. The 14 indices varied in their consideration of interaction strength (weighted versus unweighted indices) and indirect interactions (from the local measure of degree to meso-scale indices). We use principal components approximation to assess how well every combination of 1–14 indices approximated to the results of principal components analysis (PCA). We found that one or two indices were sufficient to explain up to 90% of the variation in topological importance in both plants and pollinators. The choice of index was crucial because there was considerable variation between the best and the worst approximating subsets of indices. The best single indices were unweighted degree and unweighted topological importance (Jordán's TI index) with two steps (a measurement of apparent competition). The best pairs of indices consisted of a measure of a TI index and one of closeness centrality (weighted or unweighted) or d′ (a standardised species-level measure of partner diversity). Although we have found indices that efficiently explain variation in topological importance, we recommend further research to discover the real-world relevance of different aspects of topological importance to species in ecological networks.

Centrality measures and the importance of generalist species in pollination networks

Studies of complex networks show that nodes with high centrality scores are important to network structure and stability. Following this rationale, centrality measures can be used to (i) identify keystone species in ecological networks, a major issue in community ecology, and (ii) differentiate the keystone species concept, e.g. species may play a key role in a network for different topological reasons. In 34 pollination communities we examine the relationship between the generalization level of species ( ND) and two complementary centrality indices: closeness ( CC) and betweenness centrality ( BC). CC measures the proximity of a species to all other species in the community, while BC describes the importance of a species as a connector. Most networks had a linear ND– CC relationship with a minimum CC value of 0.41. Hence, species were close to each and will be likely to be rapidly affected by disturbances. Contrarily, in most networks, the ND– BC relationships were power-law distributed with exponents larger than one. Only 59% of the species were connectors ( BC > 0). In particular, there was a connector threshold value of ND = 0.46. Species above this threshold represent ∼40%, almost all of which were connectors. These results indicate that in pollination systems the most generalized species are usually network keystone species, playing at least two roles: (i) interact closely with most other species (high CC) and (ii) connect otherwise unconnected subnetworks (high BC). We discuss the implications of centrality measures to community-based conservation ecology.

Trophic field overlap: A new approach to quantify keystone species

It is a current challenge to better understand the relative importance of species in ecosystems, and the network perspective is able to offer quantitative tools for this. It is plausible to assume, in general, that well-linked species, being key interactors, are also more important for the community. Recently a number of methods have been suggested for quantifying the network position of species in ecological networks (like the topological importance metric, TI). Most of them are based on node centrality indices and it may happen that the two most important species in a food web have very similar interaction structure and they can essentially replace each other if one becomes extinct. For conservation considerations it is a challenge to identify species that are richly connected and, at the same time, have a relatively unique and irreplaceable interaction pattern. We present a new method and illustrate our approach by using the Kuosheng Bay trophic network in Taiwan. The new method is based on the interaction matrix, where the strength of the interaction between nodes i and j depends only on topology. By defining a threshold separating weak and strong interactors, we define the effective range of interactions for each graph node. If the overlaps between pairs of these ranges are quantified, we gain a metric expressing how unique is the interaction pattern of a focal node (TO). The combination of centrality (TI) and uniqueness (TO) is called topological functionality (TF). We compare the nodal importance rank provided by this metric to others based on a variety of centrality measures. The main conclusion is that shrimps seem to have the most unique interaction pattern despite that their structural importance has been underestimated by all conventional centrality indices. Also, our network analysis suggests that fisheries disturb the ecosystem in a more critical network position than the impingement by the local power plant.