Inferring species interactions using Granger causality and convergent cross mapping

Barraquand, Frédéric; Picoche, Coralie; Detto, Matteo; Härtig, Florian

doi:10.1007/s12080-020-00482-7

Cited by 46 publications

(48 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Box 1, we write two definitions: one based on a linear regression which we call "linear Granger causality" [4,9,40,33] and a second, more general, definition which we call "general Granger causality" [43,44,41,42,39]. Linear Granger causality is a special case of general Granger causality.…”

Section: How Might Granger Causality Fail As An Indicator Of Direct Causality? Fig 3b Lists Four Conceptually Ormentioning

confidence: 99%

“…Linear Granger causality, popular in microbiome studies [4,9,40,33], uses standard parametric hypothesis tests to ask whether any coefficients are nonzero: if any of the ↵ k terms in Eq.1 is nonzero, then X linear well-documented software packages [48,31]. These tests assume that time series are "covariance-stationary", which means that certain statistical properties of the time series are time-independent [31] (see Appendix 1.5), and can fail when this assumption is violated [46,45,47].…”

Section: Box 1: Granger Causalitymentioning

confidence: 99%

“…In this article, when we use the term SSR, we refer only to SSR methods for causality detection. SSR enjoys widespread use in empirical ecology [54,55,69,68,67] and is often suggested as an alternative to model-based inference for microbial ecosystems [8,7,32,58,40,57]. SSR methods are intended to complement Granger causality: Whereas Granger causality has trouble with deterministic dynamics (Fig 3B ), the SSR approach is explicitly designed for systems that are primarily deterministic [5].…”

Section: State Space Reconstruction (Ssr): Intuition Pitfalls and Implementationsmentioning

confidence: 99%

“…cell births within a stool sample as the stool passes through the intestine). Unlike in linear Granger causality, there is no default test procedure for CCM causality criteria[15,40]. We therefore tested for CCM criteria using two different procedures (Fig7legend and Methods).Granger causality and CCM can perform well when their respective requirements are met, but both are fairly sensitive to the levels of process and measurement noise (Fig7D and E, correct inferences colored as green in pie charts).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Data-driven causal analysis of observational time series in ecology

Shou

2020

Preprint

View full text Add to dashboard Cite

Complex systems such as microbial communities play key roles in global processes and human life, yet are often challenging to understand. Although mechanistic knowledge in biology is generally rooted in manipulative experiments, perturbing these systems can encounter practical and ethical barriers. Thus, extensive attempts have been made to infer causal knowledge by analyzing observations of taxon abundance over time. When, and to what extent, does this strategy yield genuine insight? Unfortunately, the literature of causal inference can be formidable and controversial, as it draws from divergent fields such as philosophy, statistics, econometrics, and chaos theory. Most benchmarking papers focus on performance details of causal inference approaches, rather than fundamental issues such as the the underlying assumptions and their reasons, conceptual distinctions, and universal limitations. Here, we provide a synthesis of popular causal inference approaches including pairwise correlation and Reichenbach's common cause principle, Granger causality, and state space reconstruction. We find that each of these requires that certain properties of the data do not change with time (e.g. "IID", "stationarity", "reverting dynamics"). We provide new ways of visualizing key concepts, point out important issues that have been under-emphasized, and in some cases describe novel pathologies of causal inference methods. Although our synthesis is motivated by microbial communities, all arguments apply to other types of dynamic systems. We strive to balance precision with accessibility, and hope that our synthesis will motivate future development on causal inference approaches. To facilitate communication to a broad audience, we have made an accompanying video walkthrough (https://youtu.be/TZvEk3jXQfY).

show abstract

Section: How Might Granger Causality Fail As An Indicator Of Direct Causality? Fig 3b Lists Four Conceptually Ormentioning

confidence: 99%

Section: Box 1: Granger Causalitymentioning

confidence: 99%

Section: State Space Reconstruction (Ssr): Intuition Pitfalls and Implementationsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Data-driven causal analysis of observational time series in ecology

Shou

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Multivariate autoregressive models have been used to investigate temporal changes in abundance together with environmental effects and biotic interaction of pelagic and lake systems (Barraquand et al, 2018;Francis et al, 2012Francis et al, , 2014Griffiths et al, 2016;Ives et al, 2003). These models are especially convenient when the number of species is relatively small and potential causes and directions of biotic interaction effects are well known, as high numbers of species easily lead to high numbers of parameters to estimate and false positive effects can occur (Barraquand et al, 2019;Ives et al, 2003). In that regard, the Baltic Sea with its long history of marine research is an ideal study system with a relatively small number of species due to its short geological history and brackish environment (Elmgren & Hill, 1997;Reusch et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Identifying biotic drivers of population dynamics in a benthic–pelagic community

Forsblom

Lindén

Engström‐Öst

et al. 2021

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

A framework for identifying factors controlling cyanobacterium Microcystis flos‐aquae blooms by coupled CCM–ECCM Bayesian networks

Tal,

Ostrovsky,

Gal

2024

Ecology and Evolution

View full text Add to dashboard Cite

Cyanobacterial blooms in freshwater sources are a global concern, and gaining insight into their causes is crucial for effective resource management and control. In this study, we present a novel computational framework for the causal analysis of cyanobacterial harmful algal blooms (cyanoHABs) in Lake Kinneret. Our framework integrates Convergent Cross Mapping (CCM) and Extended CCM (ECCM) causal networks with Bayesian Network (BN) models. The constructed CCM–ECCM causal networks and BN models unveil significant interactions among factors influencing cyanoHAB formation. These interactions have been validated by domain experts and supported by evidence from peer‐reviewed publications. Our findings suggest that Microcystis flos‐aquae levels are influenced not only by community structure but also by ammonium, phosphate, oxygen, and temperature levels in the weeks preceding bloom occurrences. We demonstrated a non‐parametric computational framework for causal analysis of a multivariate ecosystem. Our framework offers a more comprehensive understanding of the underlying mechanisms driving M. flos‐aquae blooms in Lake Kinneret. It captures complex interactions and provides an explainable prediction model. By considering causal relationships, temporal dynamics, and joint probabilities of environmental factors, the proposed framework enhances our understanding of cyanoHABs in Lake Kinneret.

show abstract

Inferring species interactions using Granger causality and convergent cross mapping

Cited by 46 publications

References 80 publications

Data-driven causal analysis of observational time series in ecology

Data-driven causal analysis of observational time series in ecology

Identifying biotic drivers of population dynamics in a benthic–pelagic community

A framework for identifying factors controlling cyanobacterium Microcystis flos‐aquae blooms by coupled CCM–ECCM Bayesian networks

Contact Info

Product

Resources

About