A roadmap towards predicting species interaction networks (across space and time)

Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species—and to describe the structure, variation, and change of the ecological networks they form—we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

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A Primer on Predicting Species Interaction Networks (Across Space and Time)

10.32942/osf.io/eu7k3 ◽

2021 ◽

Author(s):

Tanya Strydom ◽

Michael David Catchen ◽

Francis Banville ◽

Dominique Caron ◽

Gabriel Dansereau ◽

...

Keyword(s):

Species Interactions ◽

Species Interaction ◽

Multiple Sources ◽

Modeling Tools ◽

Structure Variation ◽

Road Map ◽

Host Parasite Interactions ◽

Challenges And Opportunities ◽

Simple Neural Network ◽

Host Parasite

Networks of species interactions can capture meaningful information on the structure and functioning of ecosystems. Yet the scarcity of existing data, and the difficulty associated with comprehensively sampling interactions between species, means that to describe the structure, variation, and change of ecological networks over time and space, we need to rely on modeling tools with the capacity to make accurate predictions about how species interact. Here we provide a proof-of-concept, where we show a simple neural-network model makes accurate predictions about species interactions, and use this model to reconstruct a metaweb of host-parasite interactions across space, and assess the challenges and opportunities associated with improving interaction predictions. We then provide a primer on the relevant method and tools that will guide the development and integration of these tools, and provide a road map forward toward integration of multiple sources of data and methodlogical approaches (including statistical, dynamical, and inferential models) to sketch the path forward for this research program.

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Vulnerable species interactions are important for the stability of mutualistic networks

10.1101/604868 ◽

2019 ◽

Author(s):

Benno I. Simmons ◽

Hannah S. Wauchope ◽

Tatsuya Amano ◽

Lynn V. Dicks ◽

William J. Sutherland ◽

...

Keyword(s):

Species Interactions ◽

Species Coexistence ◽

Species Interaction ◽

Interaction Networks ◽

Ecological Communities ◽

Ecological Context ◽

Intrinsic Properties ◽

Network Stability ◽

Starting Point ◽

The Stability

AbstractSpecies are central to ecology and conservation. However, it is the interactions between species that generate the functions on which ecosystems and humans depend. Despite the importance of interactions, we lack an understanding of the risk that their loss poses to ecological communities. Here, we quantify risk as a function of the vulnerability (likelihood of loss) and importance (contribution to network stability in terms of species coexistence) of 4330 mutualistic interactions from 41 empirical pollination and seed dispersal networks across six continents. Remarkably, we find that more vulnerable interactions are also more important: the interactions that contribute most to network stability are those that are most likely to be lost. Furthermore, most interactions tend to have more similar vulnerability and importance across networks than expected by chance, suggesting that vulnerability and importance may be intrinsic properties of interactions, rather than only a function of ecological context. These results provide a starting point for prioritising interactions for conservation in species interaction networks and, in areas lacking network data, could allow interaction properties to be inferred from taxonomy alone.

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Towards an Essential Biodiversity Variable for Species Interactions

Biodiversity Information Science and Standards ◽

10.3897/biss.2.25409 ◽

2018 ◽

Vol 2 ◽

pp. e25409

Author(s):

Quentin Groom ◽

Robert Guralnick ◽

W. Daniel Kissling

Keyword(s):

Species Interactions ◽

Interaction Strength ◽

Species Traits ◽

Species Interaction ◽

Ecological Networks ◽

Microbial Interactions ◽

Ecosystem Structure ◽

State Variables ◽

Flower Visitation ◽

Wide Range

Can Essential Biodiversity Variables (EBVs) be developed to monitor changes in species interactions? That was the difficult question asked at the GLOBIS-B workshop in February, 2017 in which >50 experts participated. EBVs can be defined as harmonized measurements that allow us to inform policy about essential changes in biodiversity. They can be seen as biological state variables from which more refined indicators may be derived. They have been presented as a means to monitor global biodiversity change and as a concept to drive the gathering, sharing, and standardisation of data on our biota (Geijzendorffer et al. 2015, Kissling et al. 2017, Pereira et al. 2013). There are different classes of EBVs that characterize, for example, the state of species populations, species traits and ecosystem structure and function. It has also been proposed that there should be EBVs related to species interactions. However, until now there has been little progress formulating what these should be, even though species interactions are central to ecology. Species interactions cover a wide range of important processes, from mutualisms, such as pollination, to different forms of heterotrophic nutrition, such as the predator-prey relationship. Indeed, ecological interactions are critical to understand why an ecosystem is more than the sum of its parts. Nevertheless, direct observation of species interactions is often difficult and time consuming work, which makes it difficult to monitor them in the long-term. For this reason the workshop focused on those species interactions that are feasible to study and are most relevant to policy. To bring focus to our discussions we concentrated on pollination, predation and microbial interactions. Taking pollination as an example, there was recognition of the importance of ecological networks and that network metrics may be a sensitive indicator of change. Potential EBVs might be the number of pairwise interactions between species or the modularity and interaction diversity of the whole network. This requires standardised data collection and reporting (e.g. standardization of measures of interaction strength or minimum data specifications for ecological networks) and sufficient data across time to regularly calculate these metrics. Other simpler surrogates for pollination might also prove useful, such as flower visitation rates or the proportion of fruit set. Finally, there was a recognition that we do not yet have enough tools to monitor some important interactions. Many interactions, particular among microbes, can currently only be inferred from the co-occurrence of taxa. However, technology is rapidly developing and it is possible to foresee a future where even these interactions can be monitored efficiently. Species interactions are essential to understanding ecology, but they are also difficult to monitor. Yet, delegates at the workshop left with a positive outlook that it is valuable to develop standardisation and harmonization of species interaction data to make them suitable for EBV production.

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Analyzing ecological networks of species interactions

10.1101/112540 ◽

2017 ◽

Cited By ~ 8

Author(s):

Eva Delmas ◽

Mathilde Besson ◽

Marie-Hélène Brice ◽

Laura A. Burkle ◽

Giulio V. Dalla Riva ◽

...

Keyword(s):

Community Ecology ◽

Species Interactions ◽

Empirical Studies ◽

Ecological Networks ◽

Ecological Communities ◽

Good Practices ◽

Future Developments ◽

General Overview

Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.

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Network ecology in dynamic landscapes

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2020.1889 ◽

2021 ◽

Vol 288 (1949) ◽

Author(s):

Marie-Josée Fortin ◽

Mark R. T. Dale ◽

Chris Brimacombe

Keyword(s):

Species Interactions ◽

Environmental Changes ◽

Complex Dynamics ◽

Landscape Heterogeneity ◽

Species Interaction ◽

Ecological Networks ◽

Species Dispersal ◽

Trade Offs ◽

Dynamic Landscapes ◽

Network Topologies

Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the ‘ecological network dynamics framework’. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.

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Classification of Biological Interactions: Challenges in the field and in analysis

Biodiversity Information Science and Standards ◽

10.3897/biss.5.74375 ◽

2021 ◽

Vol 5 ◽

Author(s):

Rafael Pinheiro ◽

Leonardo Jorge ◽

Thomas Lewinsohn

Keyword(s):

Species Interactions ◽

Biotic Interactions ◽

Field Studies ◽

Species Interaction ◽

Ecological Communities ◽

Biological Interactions ◽

Web Database ◽

Online Databases ◽

Living Organisms ◽

Taxonomic Groups

Within biological communities, species interact in a wide variety of ways. Species interactions have always been noted and classified by naturalists in describing living organisms and their ways. Moreover, they are essential to characterize ecological communities as functioning entities. Biodiversity databases, as a rule, are comprised of species records in certain localities and times. Many, if not most, originated as databases of museum specimens and/or published records. As such, they provide data on species occurrences and distribution, with little functional information. Currently, online databases for species interaction data are being formed or proposed. Usually, these databases set out to compile data from actual field studies, and their design reflects the singularities of particular studies that seed their development. In two online databases: the Web of Life (2021) and the Interaction Web DataBase (2020) (IWDB), the categories of interactions are quite heterogeneous (Table 1). For instance, they may refer explicitly to certain taxonomic groups (e.g., anemone-fish), or do so implicitly (host-parasitoid; parasitoids are all holometabolous insects with arthropod hosts); conversely, they may encompass almost any taxon (food webs). In another example, the Global Biotic Interactions database (Poelen et al. 2014) (GloBI) offers a choice of relational attributes when entering data, ranging from undefined to quite restricted (Table 2). Here we intend to contribute to the development of interaction databases, from two different points of view. First, what categories can be effectively applied to field observations of biotic interactions? Second, what theoretical and applied questions do we expect to address with interaction databases? These should be equally applicable to comparisons of studies of the same kind or mode of interaction, and to contrasts between interactions in multimodal studies.

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The balance of interaction types determines the assembly and stability of ecological communities

10.1101/643478 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jimmy J. Qian ◽

Erol Akçay

Keyword(s):

Species Interactions ◽

Community Dynamics ◽

Theoretical Work ◽

Species Interaction ◽

Ecological Communities ◽

External Stability ◽

Ecological Selection ◽

Interaction Types ◽

The Stability ◽

Almost All

What determines the assembly and stability of complex communities is a central question in ecology. Past work has suggested that mutualistic interactions are inherently destabilizing. However, this conclusion relies on assuming that benefits from mutualisms never stop increasing. Furthermore, almost all theoretical work focuses on the internal (asymptotic) stability of communities assembled all-at-once. Here, we present a model with saturating benefits from mutualisms and sequentially assembled communities. We show that such communities are internally stable for any level of diversity and any combination of species interaction types. External stability, or resistance to invasion, is thus an important but overlooked measure of stability. We demonstrate that the balance of different interaction types governs community dynamics. Mutualisms may increase external stability and diversity of communities as well as species persistence, depending on how benefits saturate. Ecological selection increases the prevalence of mutualisms, and limits on biodiversity emerge from species interactions. Our results help resolve longstanding debates on the stability, saturation, and diversity of communities.

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Food web networks shift across a precipitation gradient due to changes in community composition and species interactions

10.1101/353920 ◽

2018 ◽

Author(s):

Laura Melissa Guzman ◽

Bram Vanschoenwinkel ◽

Vinicius F. Farjalla ◽

Anita Poon ◽

Diane Srivastava

Keyword(s):

Community Composition ◽

Species Interactions ◽

Environmental Gradients ◽

Interaction Strength ◽

Species Interaction ◽

Ecological Networks ◽

Water Volume ◽

Brazilian Coast ◽

Markov Network ◽

Water Volumes

AbstractEcological networks change across spatial and environmental gradients due to (i) changes in species composition or (ii) changes in the frequency or strength of interactions. Here we use the communities of aquatic invertebrates inhabiting clusters of bromeliad phytotelms along the Brazilian coast as a model system for examining turnover in the properties of ecological networks. We first document the variation in the species pools of sites across a geographical climate gradient. Using the same sites, we also explored the geographic variation in species interaction strength using a newly developed Markov network approach. We found that community composition differed along a gradient of water volume within bromeliads due to the turnover of some species. From the Markov network analysis, we found that the top-down effects of certain predators differed geographically, which could also be explained by geographic differences in bromeliad water volumes. Overall, this study illustrates how a network can change across an environmental gradient through both changes in both species and their interactions.

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The role of serum periostin in the diagnosis of asthma: A meta-analysis

Allergy and Asthma Proceedings ◽

10.2500/aap.2020.41.200038 ◽

2020 ◽

Vol 41 (4) ◽

pp. 240-247

Author(s):

Lei Yang ◽

Qingtao Zhao ◽

Shuyu Wang

Keyword(s):

Diagnostic Accuracy ◽

Meta Analysis ◽

Characteristic Curve ◽

False Negative ◽

Summary Receiver Operating Characteristic ◽

True Negative ◽

Negative Results ◽

Noninvasive Biomarker ◽

Sensitivity Specificity ◽

Serum Periostin

Background: Serum periostin has been proposed as a noninvasive biomarker for asthma diagnosis and management. However, its accuracy for the diagnosis of asthma in different populations is not completely clear. Methods: This meta-analysis aimed to evaluate the diagnostic accuracy of periostin level in the clinical determination of asthma. Several medical literature data bases were searched for relevant studies through December 1, 2019. The numbers of patients with true-positive, false-positive, false-negative, and true-negative results for the periostin level were extracted from each individual study. We assessed the risk of bias by using Quality Assessment of Diagnostic Accuracy Studies 2. We used the meta-analysis to produce summary estimates of accuracy. Results: In total, nine studies with 1757 subjects met the inclusion criteria. The pooled estimates of sensitivity, specificity, and diagnostic odds ratios for the detection of asthma were 0.58 (95% confidence interval [CI], 0.38‐0.76), 0.86 (95% CI, 0.74‐0.93), and 8.28 (95% CI, 3.67‐18.68), respectively. The area under the summary receiver operating characteristic curve was 0.82 (95% CI, 0.79‐0.85). And significant publication bias was found in this meta‐analysis (p = 0.39). Conclusion: Serum periostin may be used for the diagnosis of asthma, with moderate diagnostic accuracy.

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A multicenter, hospital-based and non-inferiority study for diagnostic efficacy of automated whole breast ultrasound for breast cancer in China

Scientific Reports ◽

10.1038/s41598-021-93350-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yujing Xin ◽

Xinyuan Zhang ◽

Yi Yang ◽

Yi Chen ◽

Yanan Wang ◽

...

Keyword(s):

Breast Cancer ◽

False Negative ◽

Final Analysis ◽

Age Group ◽

Surgical Biopsy ◽

True Negative ◽

Diagnostic Efficacy ◽

Negative Results ◽

Informed Consent Form ◽

Magnetic Resonance Imaging Mri

AbstractThis study is the first multi-center non-inferiority study that aims to critically evaluate the effectiveness of HHUS/ABUS in China breast cancer detection. This was a multicenter hospital-based study. Five hospitals participated in this study. Women (30–69 years old) with defined criteria were invited for breast examination by HHUS, ABUS or/and mammography. For BI-RADS category 3, an additional magnetic resonance imaging (MRI) test was provided to distinguish the true negative results from false negative results. For women classified as BI-RADS category 4 or 5, either core aspiration biopsy or surgical biopsy was done to confirm the diagnosis. Between February 2016 and March 2017, 2844 women signed the informed consent form, and 1947 of them involved in final analysis (680 were 30 to 39 years old, 1267 were 40 to 69 years old).For all participants, ABUS sensitivity (91.81%) compared with HHUS sensitivity (94.70%) with non-inferior Z tests, P = 0.015. In the 40–69 age group, non-inferior Z tests showed that ABUS sensitivity (93.01%) was non-inferior to MG sensitivity (86.02%) with P < 0.001 and HHUS sensitivity (95.44%) was non-inferior to MG sensitivity (86.02%) with P < 0.001. Sensitivity of ABUS and HHUS are all superior to that of MG with P < 0.001 by superior test.For all participants, ABUS specificity (92.89%) was non-inferior to HHUS specificity (89.36%) with P < 0.001. Superiority test show that specificity of ABUS was superior to that of HHUS with P < 0.001. In the 40–69 age group, ABUS specificity (92.86%) was non-inferior to MG specificity (91.68%) with P < 0.001 and HHUS specificity (89.55%) was non-inferior to MG specificity (91.68%) with P < 0.001. ABUS is not superior to MG with P = 0.114 by superior test. The sensitivity of ABUS/HHUS is superior to that of MG. The specificity of ABUS/HHUS is non-inferior to that of MG. In China, for an experienced US radiologist, both HHUS and ABUS have better diagnostic efficacy than MG in symptomatic individuals.

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