There is a rich amount of information in co-occurrence (presence-absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co-occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.© 2020 John Wiley & Sons Ltd/CNRS.

Network analyses of plant-animal interactions hold valuable biological information. They are often used to quantify the degree of specialization between partners, but usually based on qualitative indices such as 'connectance' or number of links. These measures ignore interaction frequencies or sampling intensity, and strongly depend on network size.Here we introduce two quantitative indices using interaction frequencies to describe the degree of specialization, based on information theory. The first measure (d') describes the degree of interaction specialization at the species level, while the second measure (H2') characterizes the degree of specialization or partitioning among two parties in the entire network. Both indices are mathematically related and derived from Shannon entropy. The species-level index d' can be used to analyze variation within networks, while H2' as a network-level index is useful for comparisons across different interaction webs. Analyses of two published pollinator networks identified differences and features that have not been detected with previous approaches. For instance, plants and pollinators within a network differed in their average degree of specialization (weighted mean d'), and the correlation between specialization of pollinators and their relative abundance also differed between the webs. Rarefied sampling effort in both networks and null model simulations suggest that H2' is not affected by network size or sampling intensity.Quantitative analyses reflect properties of interaction networks more appropriately than previous qualitative attempts, and are robust against variation in sampling intensity, network size and symmetry. These measures will improve our understanding of patterns of specialization within and across networks from a broad spectrum of biological interactions.

Structural analysis of plant-pollinator networks has revealed remarkably high species and interaction diversity and highlighted the species important for pollination services. Although techniques to analyze plant-pollinator networks began to emerge a decade ago, the characterization of spatiotemporal variation of interactions is still in its infancy. Understanding the ecological and evolutionary causes and consequences of spatial and temporal variation in plant-pollinator interactions is important for both basic and applied questions in community structure and function, the evolution of floral traits, and the development of optimal conservation strategies. Here we review observational, theoretical, and experimental studies of temporal and spatial variation in plant-pollinator interaction networks to establish a foundation for future studies to incorporate perspectives in spatiotemporal variation. Such perspectives are crucial given the rapid environmental changes associated with habitat loss, climate change, and biological invasions, which we discuss in this context. The inherent plasticity of plant-pollinator interactions and network structure suggests that many species should be able to persist by responding to environmental changes quickly, even though the identity of their mutualistic partners may change.

Geographic patterns of biodiversity have long inspired interest in processes that shape the assembly, diversity, and dynamics of communities at different spatial scales. To study mechanisms of community assembly, ecologists often compare spatial variation in community composition (beta-diversity) across environmental and spatial gradients. These same patterns inspired evolutionary biologists to investigate how micro- and macro-evolutionary processes create gradients in biodiversity. Central to these perspectives are species interactions, which contribute to community assembly and geographic variation in evolutionary processes. However, studies of beta-diversity have predominantly focused on single trophic levels, resulting in gaps in our understanding of variation in species-interaction networks (interaction beta-diversity), especially at scales most relevant to evolutionary studies of geographic variation.We outline two challenges and their consequences in scaling-up studies of interaction beta-diversity from local to biogeographic scales using plant-pollinator interactions as a model system in ecology, evolution, and conservation.First, we highlight how variation in regional species pools may contribute to variation in interaction beta-diversity among biogeographic regions with dissimilar evolutionary history. Second, we highlight how pollinator behavior (host-switching) links ecological networks to geographic patterns of plant-pollinator interactions and evolutionary processes. Third, we outline key unanswered questions regarding the role of geographic variation in plant-pollinator interactions for conservation and ecosystem services (pollination) in changing environments.We conclude that the largest advances in the burgeoning field of interaction beta-diversity will come from studies that integrate frameworks in ecology, evolution, and conservation to understand the causes and consequences of interaction beta-diversity across scales.© 2016 Botanical Society of America.

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.© 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

The structural patterns comprising bimodal pollination networks can help characterize plant–pollinator systems and the interactions that influence species distribution and diversity over time and space. We compare network organization of three plant–pollinator communities along the altitudinal gradient of the San Francisco Peaks in northern Arizona. We found that pollination networks become more nested, as well as exhibit lower overall network specialization, with increasing elevation. Greater weight of generalist pollinators at higher elevations of the San Francisco Peaks may result in plant–pollinator communities less vulnerable to future species loss due to changing climate or shifts in species distribution. We uncover the critical, more generalized pollinator species likely responsible for higher nestedness and stability at the higher elevation environment. The generalist species most important for network stability may be of the greatest interest for conservation efforts; preservation of the most important links in plant–pollinator networks may help secure the more specialized pollinators and maintain species redundancy in the face of ecological change, such as changing climate.

Community studies have shown that plant species are often pollinated by multiple pollinators; however, networks of heterospecific pollen transfer (HPT) in natural communities remain largely unexplored. We analyzed pollen deposition on stigmas of 57 flowering species to build a picture of plant-plant interactions via HPT in a biodiverse alpine meadow in southwest China. Plant species were categorized as pollen donors or recipients by their link numbers and link qualities. We identified 3609 heterospecific pollen grains, representing 410 links among 69 pollen species. Each plant species received on average 7.2 pollen species and donated its pollen to 5.5 species; only a few species donated or received large amounts of pollen or pollen from a large number of species. Compared to specialized plants, generalized plants tended to receive more heterospecific pollen but exported no more pollen to other species. Plant position in the network was related to both floral traits (stigma position) and pollinator generalization level. When different species share the same pollinator, bidirectional HPT may occur, but this was rarely observed in the species-rich community, indicating that interspecific pollen interference was largely unidirectional. Our study highlights the importance of understanding how sympatric flowering plants reduce deleterious effects of HPT, for example via stigma position. This study is the first to present a pollen transfer network for an entire community and to unravel its properties using directed network analysis.

Co-occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co-occurrence data from the rocky intertidal zone in central Chile to a well-resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co-occurrence data to better detect known trophic or non-trophic, positive or negative interactions. We found a poor correspondence between the co-occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non-interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non-trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co-occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co-occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche-expanding positive non-trophic interactions. Detection of links (sensitivity or specificity) was not higher for well-known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co-occurrence networks must be interpreted with caution, especially when extending interaction-based ecological theory to interpret network variability and stability. Co-occurrence networks may be particularly valuable for analysis of community dynamics that blends interactions and environment, rather than pairwise interactions alone.© 2018 by the Ecological Society of America.

The accurate estimation of interaction network structure is essential for understanding network stability and function. A growing number of studies evaluate under-sampling as the degree of sampling completeness (proportional richness observed). How the relationship between network structural metrics and sampling completeness varies across networks of different sizes remains unclear, but this relationship has implications for the within- and between-system comparability of network structure. Here, we test the combined effects of network size and sampling completeness on the structure of spatially distinct networks (i.e., subwebs) in a host-parasitoid model system to better understand the within-system variability in metric bias. Richness estimates were used to quantify a gradient of sampling completeness of species and interactions across randomly subsampled subwebs. The combined impacts of network size and sampling completeness on the estimated values of twelve unweighted and weighted network metrics were tested. The robustness of network metrics to under-sampling was strongly related to network size, and sampling completeness of interactions were generally a better predictor of metric bias than sampling completeness of species. Weighted metrics often performed better than unweighted metrics at low sampling completeness; however, this was mainly evident at large rather than small subweb size. These outcomes highlight the significance of under-sampling for the comparability of both unweighted and weighted network metrics when networks are small and vary in size. This has implications for within-system comparability of species-poor networks and, more generally, reveals problems with under-sampling ecological networks that may otherwise be difficult to detect in species-rich networks. To mitigate the impacts of under-sampling, more careful considerations of system-specific variation in metric bias are needed.© 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

The majority of flowering plants and crops rely in whole or part on animals for pollination. The mutualism between plants and pollinators has attracted ecologists and evolutionists to use this type of interspecific interaction as a model system to study species adaptation and diversification since Charles Darwin. Recent debate on the nature of pollination systems call for studies of this interaction at different levels, ranging from single species to entire communities in a given area. At the species level, detailed studies suggest that floral traits are under selection from mutualists and antagonists as well as the physical environment. In contrast, studies at community-level are rare, but recent analyses indicate considerable spatial and temporal variation in both generalized and specialized pollination systems. This special issue of Biodiversity Science focuses on plant-pollinator interaction, presenting current research status in this area from China. Papers include floral traits and pollinator behaviors addressed by phenotypic manipulation, estimates of pollen removal and receipt, anatomy of flowers, histochemistry analysis and spatial and temporal comparison. The taxa being investigated include wild orchid and cultivated legume, endemic, endangered and invasive species with diverse sexual systems. These thirteen experimental studies and three reviews show the development of pollination biology in China and expose how to facilitate our understanding of the critical ecological proc-esses underlying interspecific interaction in both natural and agricultural ecosystems.

Phylogenetically related species share a common evolutionary history and may therefore have similar traits. In terms of interaction networks, where traits are a major determinant, related species should therefore interact with other species which are also related. However, this prediction is challenged by current evidence that there is a weak, albeit significant, phylogenetic signal in species' taxonomic niche, i.e., the identity of interacting species. We studied mutualistic and antagonistic plant-insect interaction networks in species-rich alpine meadows and show that there is instead a very strong phylogenetic signal in species' functional niches-i.e., the mean functional traits of their interactors. This pattern emerges because related species tend to interact with species bearing certain traits that allow biotic interactions (pollination, herbivory) but not necessarily with species from all the same evolutionary lineages. Those traits define a set of potential interactors and show clear patterns of phylogenetic clustering on several portions of plants and insect phylogenies. Thus, this emerging pattern of low phylogenetic signal in taxonomic niches but high phylogenetic signal in functional niches may be driven by the interplay between functional trait convergence across plants' and insects' phylogenies and random sampling of the potential interactors.

Aim To assess whether the reduced nutritional resources available for pollinators due to plant community simplification along an elevational plant-diversity gradient changes pollinator niche breadth and richness. Additionally, we evaluated how body size and proboscis length of pollinators shifted along the gradient, and whether these changes were related to pollinator niche breadth. Location An elevational gradient (2,350-3,520 m a.s.l.) on the oceanic high-mountain strato-volcano of El Teide (Tenerife, Canary Islands). Taxon Flowering plant and pollinator species. Methods We compared quantitative plant-pollinator networks along the plant-diversity gradient. We calculated a set of niche-based topological metrics that capture the degree of specialization, niche breadth and niche overlap. Furthermore, we obtained beta-diversity measures and the proportion of replacement and richness components. Results There was an overall decline in species richness of pollinators with increasing elevation. This decline was mainly driven by the loss of species along the elevational gradient, which conformed a nested subset pattern. The whole network showed less specialization, greater connectance and lower modularity towards the summit. At high elevations, pollinators were more generalized and less selective in their flower choice, showing a greater trophic niche breadth compared to pollinators at lower elevations. Mean body size of pollinators increased with elevation, and species body size and proboscis length were positively associated with the number of plant species visited. Main conclusions Overall, results indicated that the elevational gradient filters pollinator species, probably according to their thermal tolerance and ability to exploit a wide range of trophic resources. The finding that pollinators become more generalized and opportunistic at higher elevations is a novel result, which may have implications for new research into how ecological networks vary over environmental gradients. From an applied perspective, our results highlight the importance of considering the spatial variation of species assemblages when aiming to construct functionally reliable interaction networks along environmental gradients.

生态群落中不同物种间发生多样化的相互作用, 形成了复杂的种间互作网络。复杂生态网络的结构如何影响群落的生态系统功能及稳定性是群落生态学的核心问题之一。种间互作直接影响到物质和能量在生态系统不同组分之间的流动和循环以及群落构建过程, 使得网络结构与生态系统功能和群落稳定性密切相关。在群落及生态系统水平上开展种间互作网络研究将为群落的构建机制、生物多样性维持、生态系统稳定性、物种协同进化和性状分化等领域提供新的视野。当前生物多样性及生态系统功能受到全球变化的极大影响, 研究种间互作网络的拓扑结构、构建机制、稳定性和生态功能也可为生物多样性的保护和管理提供依据。该文从网络结构、构建机制、网络结构和稳定性关系、种间互作对生态系统功能的影响等4个方面综述当前种间网络研究进展, 并提出在今后的研究中利用机器学习和多层网络等来探究环境变化对种间互作网络结构和功能的影响, 并实现理论和实证研究的有效整合。

To understand how plant-pollinator interactions respond to habitat fragmentation, we need novel approaches that can capture properties that emerge at broad scales, where multiple communities engage in metanetworks. Here we studied plant-pollinator interactions over 2 years on 29 calcareous grassland fragments selected along independent gradients of habitat size and surrounding landscape diversity of cover types. We associated network centrality of plant-pollinator interactions and grassland fragments with their ecological and landscape traits, respectively. Interactions involving habitat specialist plants and large-bodied pollinators were the most central, implying that species with these traits form the metanetwork core. Large fragments embedded in landscapes with high land cover diversity exhibited the highest centrality; however, small fragments harboured many unique interactions not found on larger fragments. Intensively managed landscapes have reached a point in which all remaining fragments matter, meaning that losing any further areas may vanish unique interactions with unknown consequences for ecosystem functioning.© 2021 The Authors. Ecology Letters published by John Wiley & Sons Ltd.

岛屿或者“生境岛”中的生物区系常常显示出一种嵌套结构，即物种较贫乏的岛屿中的物种是物种较丰富的岛屿中的物种的一个适当的子集，如果将各个岛屿中的生物区系排列起来就形成一个嵌套的序列。与种-面积关系一样，嵌套结构在很多生境类型和生物类群中也都存在。嵌套性对生物保护也有一定的意义，特别是与SLOSS争论（是单个大的还是几个小的保护区能保护更多的物种）有一定关系。在过去的十几年中，已经提出了一些方法，可以对嵌套性进行定量刻画和统计检验。同时，对嵌套性的形成机制也进行了大量的研究，其中选择性的迁移和选择性的灭绝是两个主要的原因。由于嵌套性分析只需要物种的存在) 不存在数据，使得很多调查数据都能够利用起来，因此，这是一个值得深入研究的领域。

Describing how ecological interactions change over space and time and how they are shaped by environmental conditions is crucial to understand and predict ecosystem trajectories. However, it requires having an appropriate framework to measure network diversity locally, regionally and between samples (α-, γ- and β-diversity). Here, we propose a unifying framework that builds on Hill numbers and accounts both for the probabilistic nature of biotic interactions and the abundances of species or groups. We emphasise the importance of analysing network diversity across different species aggregation levels (e.g. from species to trophic groups) to get a better understanding of network structure. We illustrate our framework with a simulation experiment and an empirical analysis using a global food-web database. We discuss further usages of the framework and show how it responds to recent calls on comparing ecological networks and analysing their variation across environmental gradients and time.© 2019 John Wiley & Sons Ltd/CNRS.

Interaction network structure reflects the ecological mechanisms acting within biological communities, which are affected by environmental conditions. In tropical forests, higher precipitation usually increases fruit production, which may lead frugivores to increase specialization, resulting in more modular and less nested animal-plant networks. In these ecosystems, El Niño is a major driver of precipitation, however, we still lack knowledge of how species interactions change under this influence. To understand bat-plant network structure during an extreme ENSO event, we determined the links between frugivorous bat species and the plants they consume by DNA barcoding seeds and pulp in bat faeces. These interactions were recorded in the dry forest and rainforest of Costa Rica, during the dry and the wet seasons of an extreme El Niño year. From these we constructed seasonal and whole-year bat-plant networks and analyzed their structures and dissimilarities. In general, networks had low nestedness, high modularity, and were dominated by one large compartment which included most species and interactions. Contrary to our expectations, networks were less nested and more modular in drier conditions, both in the comparison between forest types and between seasons. We suggest that increased competition, when resources are scarce during drier seasons and habitats, lead to higher resource partitioning among bats and thus higher modularity. Moreover, we have found similar network structures between dry and rainforests during El Niño and non El Niño years. Finally, most interaction dissimilarity among networks occurred due to interaction rewiring among species, potentially driven by seasonal changes in resource availability.This article is protected by copyright. All rights reserved.

In a context of global changes, and amidst the perpetual modification of community structure undergone by most natural ecosystems, it is more important than ever to understand how species interactions vary through space and time. The integration of biogeography and network theory will yield important results and further our understanding of species interactions. It has, however, been hampered so far by the difficulty to quantify variation among interaction networks. Here, we propose a general framework to study the dissimilarity of species interaction networks over time, space or environments, allowing both the use of quantitative and qualitative data. We decompose network dissimilarity into interactions and species turnover components, so that it is immediately comparable to common measures of β-diversity. We emphasise that scaling up β-diversity of community composition to the β-diversity of interactions requires only a small methodological step, which we foresee will help empiricists adopt this method. We illustrate the framework with a large dataset of hosts and parasites interactions and highlight other possible usages. We discuss a research agenda towards a biogeographical theory of species interactions.© 2012 Blackwell Publishing Ltd/CNRS.

Ecological networks of two interacting guilds of species, such as flowering plants and pollinators, are common in nature, and studying their structure can yield insights into their resilience to environmental disturbances. Here we develop analytical methods for exploring the strengths of interactions within bipartite networks consisting of two guilds of phylogenetically related species. We then apply these methods to investigate the resilience of a plant-pollinator community to anticipated climate change. The methods allow the statistical assessment of, for example, whether closely related pollinators are more likely to visit plants with similar relative frequencies, and whether closely related pollinators tend to visit closely related plants. The methods can also incorporate trait information, allowing us to identify which plant traits are likely responsible for attracting different pollinators. These questions are important for our study of 14 prairie plants and their 22 insect pollinators. Over the last 70 years, six of the plants have advanced their flowering, while eight have not. When we experimentally forced earlier flowering times, five of the six advanced-flowering species experienced higher pollinator visitation rates, whereas only one of the eight other species had more visits; this network thus appears resilient to climate change, because those species with advanced flowering have ample pollinators earlier in the season. Using the methods developed here, we show that advanced-flowering plants did not have a distinct pollinator community from the other eight species. Furthermore, pollinator phylogeny did not explain pollinator community composition; closely related pollinators were not more likely to visit the same plant species. However, differences among pollinator communities visiting different plants were explained by plant height, floral color, and symmetry. As a result, closely related plants attracted similar numbers of pollinators. By parsing out characteristics that explain why plants share pollinators, we can identify plant species that likely share a common fate in a changing climate.

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 should stimulate researchers to report sampling intensity to standardize its effects in the search for broad patterns in plant-pollinator networks.

Biological interactions are key drivers of ecological and evolutionary processes. The complexity of such interactions hinders our understanding of ecological systems and our ability to make effective predictions in changing environments. However, network analysis allows us to better tackle the complexity of ecosystems because it extracts the properties of an ecological system according to the number and distribution of links among interacting entities. The number of studies using network analysis to solve ecological and evolutionary questions in parasitology has increased over the past decade. Here, we synthesise the contribution of network analysis toward disentangling host-parasite processes. Furthermore, we identify current trends in mainstream ecology and novel applications of network analysis that present opportunities for research on host-parasite interactions.Copyright © 2021 Elsevier Ltd. All rights reserved.

Modularity is a recurrent and important property of bipartite ecological networks. Although well-resolved ecological networks describe interaction frequencies between species pairs, modularity of bipartite networks has been analysed only on the basis of binary presence-absence data. We employ a new algorithm to detect modularity in weighted bipartite networks in a global analysis of avian seed-dispersal networks. We define roles of species, such as connector values, for weighted and binary networks and associate them with avian species traits and phylogeny. The weighted, but not binary, analysis identified a positive relationship between climatic seasonality and modularity, whereas past climate stability and phylogenetic signal were only weakly related to modularity. Connector values were associated with foraging behaviour and were phylogenetically conserved. The weighted modularity analysis demonstrates the dominating impact of ecological factors on the structure of seed-dispersal networks, but also underscores the relevance of evolutionary history in shaping species roles in ecological communities. © 2014 John Wiley & Sons Ltd/CNRS.

合作的进化为研究植物–传粉者相互关系提供了新的视角。植物与传粉者通过“报酬换服务”建立种间合作关系。这一合作关系从建立、维持到解体面临着3个关键问题: (1)在植物和传粉者不了解对方质量信息时, 双方如何选择出最适伙伴, 进而建立合作关系; (2)合作方如何限制欺骗策略(比如, 盗蜜和欺骗性传粉)的扩散以维持合作关系; (3)什么过程可导致传粉合作关系的解体。植物与传粉者间信号博弈或筛选博弈可促进二者合作关系的建立。面对欺骗策略, 传粉者和植物分别采用伙伴选择机制和防御机制加以应对。合作者与欺骗者的稳定共存也有助于植物–传粉者合作的维持。从合作转向对抗、转向新的伙伴和合作放弃3个过程可导致植物–传粉者的合作关系的解体。植物与传粉者合作关系的理论预期已经得到了部分实验结果支持, 深化了我们对植物与传粉者合作过程中关键机制的理解。在今后的研究中, 需要进一步探讨以下问题: (1)传粉者对植物信号诚实性的选择作用和植物对传粉者的筛选作用; (2)植物与传粉者各自应对欺骗策略的可能机制及其相对重要性; (3)合作者与欺骗者稳定共存的机制; (4)植物与传粉者合作系统对全球变化的响应。

The main drivers of global environmental change (CO2 enrichment, nitrogen deposition, climate, biotic invasions and land use) cause extinctions and alter species distributions, and recent evidence shows that they exert pervasive impacts on various antagonistic and mutualistic interactions among species. In this review, we synthesize data from 688 published studies to show that these drivers often alter competitive interactions among plants and animals, exert multitrophic effects on the decomposer food web, increase intensity of pathogen infection, weaken mutualisms involving plants, and enhance herbivory while having variable effects on predation. A recurrent finding is that there is substantial variability among studies in both the magnitude and direction of effects of any given GEC driver on any given type of biotic interaction. Further, we show that higher order effects among multiple drivers acting simultaneously create challenges in predicting future responses to global environmental change, and that extrapolating these complex impacts across entire networks of species interactions yields unanticipated effects on ecosystems. Finally, we conclude that in order to reliably predict the effects of GEC on community and ecosystem processes, the greatest single challenge will be to determine how biotic and abiotic context alters the direction and magnitude of GEC effects on biotic interactions.

Network analysis helps reveal the details of community organization by holistically assessing species diversity and the relationships contained therein. In this study, we collected arboreal ants from their host trees at three sites (Nabanhe, Menglun, and Bubeng) in the Xishuangbanna National Nature Reserve. Following computation of species diversity, network metrics, and community metrics, we compared the ant-tree bipartite networks between the three sample sites. Network metrics were evaluated using Z values standardized according to two different null models. Tree species composition differed across the three experimental sites, and the ant communities were correspondingly diverse. Ant and tree species number and the tree heterogeneity index (Shannon-Wiener diversity index, Simpson diversity index) were highest in Menglun, and lowest in Bubeng. Extinction slopes showed the same trends as the indices for ant and tree species number and tree heterogeneity, but did not display the same pattern as ant species heterogeneity. Evaluated parameters included: weighted nestedness metrics (WNODF), links per species, specialization, modularity, connectance, extinction slope exponent, and niche overlap. The absolute Z values of these parameters were highest in Menglun and lowest in Bubeng. In conclusion, tree species number and heterogeneity determine the ant-tree network stability, which is measured by extinction slope. WNODF and links per species are positively correlated with community stability. In specialized and modular networks, species populating higher trophic levels experience extinction events in direct correlation with those in lower trophic levels.

网络分析(network analysis)可以同时分析群落中的物种多样性和种间关系, 为了解生态群落的稳定性机制提供了新的分析思路和方法。本研究从西双版纳国家级自然保护区的纳板河、勐仑和勐腊(补蚌)三个地点采集了树栖性蚂蚁及树木的种类和数量数据, 对蚂蚁-树组成的二分网络进行了分析, 探讨了3个采样点物种的多样性、网络指标以及群落指标之间的关系。我们采用零模型的方法比较了3个样点的标准化网络参数差异。结果表明: 蚂蚁和树木的物种数以及树的异质性指数(Shannon-Wiener多样性指数、Simpson多样性指数)都呈现出勐仑 > 纳板河 > 补蚌的趋势。树木-蚂蚁的灭绝曲线系数大小关系同样为勐仑 > 纳板河 > 补蚌, 灭绝曲线与树的物种数及异质性指数大小趋势一致, 而与蚂蚁的异质性指数并不吻合。根据Z值的绝对值来看, 网络参数(加权嵌套性、平均连接数、特化水平、模块性、连接度)与群落参数(灭绝曲线系数、生态位重叠)的大小趋势相同, 表现出勐仑 > 纳板河 > 补蚌的趋势。综上所述, 蚂蚁-树互作网络的稳定性(灭绝曲线系数)主要由树的数量和异质性指数决定。网络的加权嵌套性和网络中节点的平均连接数也能促进群落的稳定性。而在一个特化的(数值越大表示专性互作越多)和模块化(具有较多密切互作的节点单元)的网络中, 当低营养级物种灭绝时高营养级物种数量将迅速减少。

Biogeography has traditionally focused on the distribution of species, while community ecology has sought to explain the patterns of community composition. Species interactions networks have rarely been subjected to such analyses, as modeling tools have only recently been developed for interaction networks. Here, we examine beta diversity of ecological networks using pollination networks sampled along an urbanization and agricultural intensification gradient in east Leinster, Ireland. We show, for the first time, that anthropogenic gradients structure interaction networks, and exert greater structuring force than geographical proximity. We further showed that species turnover, especially of plants, is the major driver of interaction turnover, and that this contribution increased with anthropogenic induced environmental dissimilarity, but not spatial distance. Finally, to explore the extent to which it is possible to predict each of the components of interaction turnover, we compared the predictive performance of models that included site characteristics and interaction properties to models that contained species level effects. We show that if we are to accurately predict interaction turnover, data are required on the species-specific responses to environmental gradients. This study highlights the importance of anthropogenic disturbances when considering the biogeography of interaction networks, especially in human dominated landscapes where geographical effects can be secondary sources of variation. Yet, to build a predictive science of the biogeography of interaction networks, further species-specific responses need to be incorporated into interaction distribution modeling approaches.