Effects of sampling completeness on the structure of plant–pollinator networks (original) (raw)
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Abstract. Plant–animal interaction networks provide important information on community organization. One of the most critical assumptions of network analysis is that the observed interaction patterns constitute an adequate sample of the set of interactions present in plant–animal communities. In spite of its importance, few studies have evaluated this assumption and, in consequence, there is no consensus on the sensitivity of network metrics to sampling methodological shortcomings. In this study we examined how variation in sampling completeness influences the estimation of six network metrics frequently used in the literature (connectance, nestedness, modularity, robustness to species loss, path length, and centralization). We analyzed data of 186 flowering plants and 336 pollinator species in 10 networks from a forest-fragmented system in central Chile. Using species-based accumulation curves, we estimated the deviation of network metrics in undersampled communities with respect to exhaustively sampled communities and the effect of network size and sampling evenness on network metrics. Our results indicate that: (1) most metrics were affected by sampling completeness, but differed in their sensitivity to sampling effort; (2) nestedness, modularity, and robustness to species loss were less influenced by insufficient sampling than connectance, path length, and centralization; (3) robustness was mildly influenced by sampling evenness. These results caution studies that summarize information from databases with high, or unknown, heterogeneity in sampling effort per species, and stimulate researchers to report sampling intensity to standardize its effects in the search for broad patterns in plant–pollinator networks. Key words: accumulation curves; Clench model; ecological networks; Los Ruiles National Reserve, Chile; network size; plant–pollinator network metrics; sampling completeness; sampling effort; sampling evenness.
Community Ecology, 2017
Conservation of species is often focused either only on those that are endangered, or on maximising the number recorded on species lists. However, species share space and time with others, thus interacting and building frameworks of relationships that can be unravelled by community-level network analysis. It is these relationships that ultimately drive ecosystem function via the transfer of energy and nutrients. However interactions are rarely considered in conservation planning. Network analysis can be used to detect key species (“hubs”) that play an important role in cohesiveness of networks. We applied this approach to plant-pollinator communities on two montane Northern Apennine grasslands, paying special attention to the modules and the identity of hubs. We performed season-wide sampling and then focused the network analyses on time units consistent with plant phenology. After testing for significance of modules, only some modules were found to be significantly segregated from others. Thus, networks were organized around a structured core of modules with a set of companion species that were not organized into compartments. Using a network approach we obtained a list of important plant and pollinator species, including three Network Hubs of utmost importance, and other hubs of particular biogeographical interest. By having a lot of links and high partner diversity, hubs should convey stability to networks. Due to their role in the networks, taking into account such key species when considering the management of sites could help to preserve the greatest number of interactions and thus support many other species.
Community Ecology, 2017
Conservation of species is often focused either only on those that are endangered, or on maximising the number recorded on species lists. However, species share space and time with others, thus interacting and building frameworks of relationships that can be unravelled by community-level network analysis. It is these relationships that ultimately drive ecosystem function via the transfer of energy and nutrients. However interactions are rarely considered in conservation planning. Network analysis can be used to detect key species ("hubs") that play an important role in cohesiveness of networks. We applied this approach to plant-pollinator communities on two montane Northern Apennine grasslands, paying special attention to the modules and the identity of hubs. We performed season-wide sampling and then focused the network analyses on time units consistent with plant phenology. After testing for significance of modules, only some modules were found to be significantly segregated from others. Thus, networks were organized around a structured core of modules with a set of companion species that were not organized into compartments. Using a network approach we obtained a list of important plant and pollinator species, including three Network Hubs of utmost importance, and other hubs of particular biogeographical interest. By having a lot of links and high partner diversity, hubs should convey stability to networks. Due to their role in the networks, taking into account such key species when considering the management of sites could help to preserve the greatest number of interactions and thus support many other species.
A method for under-sampled ecological network data analysis: plant-pollination as case study
In this paper, we develop a method, termed the Interaction Distribution (ID) method, for analysis of quantitative ecological network data. In many cases, quantitative network data sets are under-sampled, i.e. many interactions are poorly sampled or remain unobserved. Hence, the output of statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. The ID method can support assessment and inference of under-sampled ecological network data. In the current paper, we illustrate and discuss the ID method based on the properties of plant-animal pollination data sets of flower visitation frequencies. However, the ID method may be applied to other types of ecological networks. The method can supplement existing network analyses based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th p...
Oecologia, 2009
This study characterizes the structure of a plant-pollinator network in a temperate rain forest of Chiloé Island, southern Chile, where woody species are strongly dependent on biotic pollinators, and analyzes its robustness to the loss of participating species. Degree distribution, nestedness, and expected species persistence were evaluated. In addition, we assessed the roles of predefined subsets of plants (classified by life forms) and pollinators (grouped by taxonomic orders) in the network's structure and dynamics. For this, we simulated the complete removal of each plant and pollinator subset and analyzed the resultant connectivity patterns, as well as the expected long-term species losses by running a stochastic model. Finally, we evaluated the sensitivity of the network structure to the loss of single species in order to identify potential targets for conservation. Our results show that the plant-pollinator network of this Chilean temperate rain forest exhibits a nested structure of interactions, with a degree distribution best described by a power law model. Model simulations revealed the importance of trees and hymenopterans as pivotal groups that maintain the core structure of the pollination network and guarantee overall species persistence. The hymenopterans Bombus dahlbomii and Diphaglossa gayi, the shrubs Tepualia stipularis and Ugni molinae, the vines Mitraria coccinea and Asteranthera ovata, and the entire set of tree species exerted a disproportionately large influence on the preservation of network structure and should be considered as focal species for conservation programs given current threats from selective logging and habitat loss.
How to monitor ecological communities cost-efficiently: The example of plant–pollinator networks
Biological Conservation, 2010
Conservation practitioners often lack tools to monitor functioning of communities because time and monetary constraints create a gap between the optimal monitoring methods and the practical needs in conservation. Interaction networks provide a framework that has proven useful in ecological research. However, they are considered time consuming and too expensive for conservation purposes. We investigate whether it is possible to sample interaction networks cost-efficiently and whether a compromise exists between data quality and amount of resources required to sample the data by using a highly resolved mutualistic plant-pollinator network sampled over two years in Norway. The dataset was resampled with decreasing sampling intensity to simulate decreasing monitoring costs and we investigated the cost-efficiency of these monitoring regimes. The success in monitoring community structure varied largely with sampling intensity and the descriptor investigated. One major result was that a large proportion of the functionally most important species in the community, both plants and insects, could be identified with relatively little sampling. For example, monitoring only in ''peak-season", which costs ca. 20% relative to full monitoring, resulted in recording of 70% (in 2003) or 85% (in 2004) of the top 20 most functionally important pollinator species. Also, peak-season monitoring resulted in relatively precise estimates of several network descriptors. We present a first estimation of the full cost (travel time, sampling time and taxonomic services) of constructing pollination networks with different sampling effort. We recommend monitoring plant-pollinator networks in temperate regions during peak-season to cost-efficiently collect data for practical habitat management of ecosystem functioning.
Rareness and specialization in plant-pollinator networks
Ecology, 2011
Most rare species appear to be specialists in plant-pollinator networks. This observation could result either from real ecological processes or from sampling artifacts. Several methods have been proposed to overcome these artifacts, but they have the limitation of being based on visitation data, causing interactions involving rare visitor species to remain undersampled. We propose the analysis of food composition in bee trap nests to assess the reliability of network specialization estimates. We compared data from a plant-pollinator network in the Monte Desert of Villavicencio Nature Reserve, Argentina, sampled by visit observation, and data from trap nests sampled at the same time and location. Our study shows that trap nest sampling was good for estimating rare species degree. The rare species in the networks appear to be more specialized than they really are, and the bias in the estimation of the species degree increases with the rareness. The low species degree of these rare species in the visitation networks results from insufficient sampling of the rare interactions, which could have important consequences for network structure.
Acta Oecologica
The decline of pollinators may alter the complex system of interactions that they establish with flowering plants, with potential negative consequences on both partners. Within this context, network analysis may be a useful tool to study ecological properties of plant-pollinator interactions and to evaluate the outcomes of conservation actions. Three conservation measures were implemented within the European LIFE+ PP-ICON project to support the local pollinator community of a population of the rare plant Dictamnus albus in a protected area near Bologna, Italy. Artificial nesting sites were installed to support solitary bees, populations of native plants were reinforced to increase foraging resources for pollinators, and colonies of bumblebees reared from wild queens were released in the study area. In this work we evaluate the effects of these conservation actions on plant-pollinator networks over a period of four years, comparing a pre-(2011-2012) and a post-conservation (2013-2014) action period. The overall network generalisation increased after the implementation of conservation measures and interactions were more evenly distributed. Module composition significantly changed between the two periods, showing a marked rewiring of interactions. D. albus was a module hub both before and after conservation actions, thus emerging as an important node within its own module. In addition, some plant and pollinator species directly targeted by conservation measures became module connectors, highlighting their increased importance in linking different modules. Finally, the reinforcement of plant and pollinator populations led to increased flower visitation. These results indicate that conservation actions affected species both directly and indirectly and that the network of interactions has potentially increased its robustness and resilience towards possible species loss. This study highlights ways in which network analysis can be used to measure changes in plant-pollinator interactions in response to conservation actions.
Evaluating sampling completeness in a desert plantpollinator network
Journal of Animal …, 2011
1. The study of plant-pollinator interactions in a network context is receiving increasing attention. This approach has helped to identify several emerging network patterns such as nestedness and modularity. However, most studies are based only on qualitative information, and some ecosystems, such as deserts and tropical forests, are underrepresented in these data sets. 2. We present an exhaustive analysis of the structure of a 4-year plant-pollinator network from the Monte desert in Argentina using qualitative and quantitative tools. We describe the structure of this network and evaluate sampling completeness using asymptotic species richness estimators. Our goal is to assess the extent to which the realized sampling effort allows for an accurate description of species interactions and to estimate the minimum number of additional censuses required to detect 90% of the interactions. We evaluated completeness of detection of the community-wide pollinator fauna, of the pollinator fauna associated with each plant species and of the plant-pollinator interactions. We also evaluated whether sampling completeness was influenced by plant characteristics, such as flower abundance, flower life span, number of interspecific links (degree) and selectiveness in the identity of their flower visitors, as well as sampling effort. 3. We found that this desert plant-pollinator network has a nested structure and that it exhibits modularity and high network-level generalization. 4. In spite of our high sampling effort, and although we sampled 80% of the pollinator fauna, we recorded only 55% of the interactions. Furthermore, although a 64% increase in sampling effort would suffice to detect 90% of the pollinator species, a fivefold increase in sampling effort would be necessary to detect 90% of the interactions. 5. Detection of interactions was incomplete for most plant species, particularly specialists with a long flowering season and high flower abundance, or generalists with short flowering span and scant flowers. Our results suggest that sampling of a network with the same effort for all plant species is inadequate to sample interactions. 6. Sampling the diversity of interactions is labour intensive, and most plant-pollinator networks published to date are likely to be undersampled. Our analysis allowed estimating the completeness of our sampling, the additional effort needed to detect most interactions and the plant traits that influence the detection of their interactions.
Interaction strength in plant-pollinator networks: Are we using the right measure?
PLOS ONE
Understanding how ecological networks are assembled is important because network structure reflects ecosystem functioning and stability. Quantitative network analysis incorporates measures of interaction strength as an estimate of the magnitude of the effect of interaction partners on one another. Most plant-pollinator network studies use frequency of interaction between individual pollinators and individual plants (encounter) as a surrogate of interaction strength. However, the number of flowers visited per encounter may strongly vary among pollinator and plant species, and therefore not all encounters are quantitatively equivalent. We sampled plant-pollinator interactions in a Mediterranean scrubland and tested whether using a measure of interaction strength based on the number of flowers visited resulted in changes in species (species strength, interaction species asymmetry, specialization) and network descriptors (nestedness, H2', interaction evenness, plant generality, pollinator generality) compared to the encounter-based measure. Several species (including some of the most abundant ones) showed important changes in species descriptors, notably in specialization. These changes were especially important in plant species with large floral displays, which became less specialized with the visit-based measure of interaction strength. At the network level we found significant changes in all properties analysed. With the encounterbased approach plant generality was much higher than pollinator generality (high specialization asymmetry between trophic levels). However, with the visit-based approach plant generality was greatly reduced so that plants and pollinators had similar levels of generalization. Interaction evenness also decreased strongly with the visit-based approach. We conclude that accounting for the number of flowers visited per encounter provides a more ecologically relevant measure of interaction strength. Our results have important implications for the stability of pollination networks and the evolution of plant-pollinator interactions. The use of a visit-based approach is especially important in studies relating interaction network structure and ecosystem function (pollination and/or exploitation of floral resources).