SVD Entropy Reveals the High Complexity of Ecological Networks

Strydom, Tanya; Riva, Giulio Valentino Dalla; Poisot, Timothée

doi:10.3389/fevo.2021.623141

Cited by 24 publications

(16 citation statements)

References 39 publications

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“…The entropy value method is an important application of entropy theory in the field of determining weight [49,50]. The degree of dispersion and the importance of analysis of each evaluation index can be judged by the entropy value and the weight of the index [51,52].…”

Section: Entropy Value Methodsmentioning

confidence: 99%

Coordination of agricultural informatization and agricultural economy development: A panel data analysis from Shandong Province, China

Sun

Zhao

2022

PLoS ONE

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With the continuous development of the world economy, science, and technology, the era of intelligence and information is upon us. Through the implementation of the digital rural construction project in China, agriculture is developing rapidly in the direction of informatization. As a major agricultural province in China, Shandong Province has been ranked first in China for many years in terms of gross agricultural product and the import and export of agricultural products. According to the current situation of agricultural informatization in Shandong Province, this study uses relevant evaluation index data of Shandong Province from 2011 to 2019 and applies the entropy value method, coupling degree analysis model, and coupling coordination degree analysis model to deeply evaluate the mutual influence and coordination degree between the agricultural informatization level and agricultural economic development in Shandong province in each year. We identify the possible problems in the development of agricultural informatization in Shandong province. Finally, in terms of talent construction, infrastructure construction, the main role of government, policies, and farmers’ awareness of informatization, we propose some countermeasures and suggestions that are conducive to the coordinated development of agricultural informatization and the agricultural economy.

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Section: Entropy Value Methodsmentioning

confidence: 99%

Coordination of agricultural informatization and agricultural economy development: A panel data analysis from Shandong Province, China

Sun

Zhao

2022

PLoS ONE

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“…A network embedding projects each node of the network into a lowerdimensional latent space. Previous explorations of the dimensionality of food webs have revealed that a reduced number of dimensions (7) was sufficient to capture most of their structure [132]; however, recent quantifications of the complexity of the embedding space of bipartite ecological networks found a consistent high complexity [133], suggesting that the precise depth of embedding required may vary considerably across systems. Embedding enables us to represent the structure of a network, which previously required the S 2 dimensions of an adjacency matrix, with a smaller number of dimensions.…”

Section: (Ii) What Network Properties Should We Use To Inform Our Predictions Of Interactions?mentioning

confidence: 99%

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

Strydom

Catchen

Banville

et al. 2021

Phil. Trans. R. Soc. B

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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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“…The latent variables are created by performing a truncated singular value decomposition (t-SVD; Halko et al, 2011) on the adjacency matrix. SVD is an appropriate embedding of ecological networks, which has recently been shown to both capture their complex, emerging properties (Strydom, Dalla Riva, & Poisot, 2021) and to allow highly accurate prediction of the interactions within a single network (Poisot, Ouellet, et al, 2021).…”

Section: Step 1: Learning the Origin Network Representationmentioning

confidence: 99%

Food web reconstruction through phylogenetic transfer of low‐rank network representation

et al. 2022

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transfer learning ancestral character estimation biogeography 1. Despite their importance in many ecological processes, collecting data and information on ecological interactions is an exceedingly challenging task. For this reason, large parts of the world have a data deficit when it comes to species interactions, and how the resulting networks are structured. As data collection alone is unlikely to be sufficient, community ecologists must adopt predictive methods.2. We present a methodological framework that uses graph embedding and transfer learning to assemble a predicted list of trophic interactions of a species pool for which their interactions are unknown. Specifically, we 'learn' the information (latent traits) of species from a known interaction network and infer the latent traits of another species pool for which we have no a priori interaction data based on their phylogenetic relatedness to species from the known network. The latent traits can then be used to predict interactions and construct an interaction network.3. Here we assembled a metaweb for Canadian mammals derived from interactions in the European food web, despite only 4% of common species being shared between the two locations. The results of the predictive model are compared against databases of recorded pairwise interactions, showing that we correctly recover 91% of known interactions.4. The framework itself is robust even when the known network is incomplete or contains spurious interactions making it an ideal candidate as a tool for filling gaps when it comes to species interactions. We provide guidance on how this framework can be adapted by substituting some approaches or predictors in order to make it more generally applicable.

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SVD Entropy Reveals the High Complexity of Ecological Networks

Cited by 24 publications

References 39 publications

Coordination of agricultural informatization and agricultural economy development: A panel data analysis from Shandong Province, China

Coordination of agricultural informatization and agricultural economy development: A panel data analysis from Shandong Province, China

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

Food web reconstruction through phylogenetic transfer of low‐rank network representation

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