Dataset Information

Seeing the forest for the trees through metabolic scaling.

ABSTRACT: We demonstrate that when power scaling occurs for an individual tree and in a forest, there is great resulting simplicity notwithstanding the underlying complexity characterizing the system over many size scales. Our scaling framework unifies seemingly distinct trends in a forest and provides a simple yet promising approach to quantitatively understand a bewilderingly complex many-body system with imperfectly known interactions. We show that the effective dimension, D _tree , of a tree is close to 3, whereas a mature forest has D _forest approaching 1. We discuss the energy equivalence rule and show that the metabolic rate-mass relationship is a power law with an exponent D/(D + 1) in both cases leading to a Kleiber's exponent of 3/4 for a tree and 1/2 for a forest. Our work has implications for understanding carbon sequestration and for climate science.

SUBMITTER: Volkov I

PROVIDER: S-EPMC9802057 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Seeing the forest for the trees through metabolic scaling.

Volkov Igor I Tovo Anna A Anfodillo Tommaso T Rinaldo Andrea A Maritan Amos A Banavar Jayanth R JR

PNAS nexus 20220310 1

We demonstrate that when power scaling occurs for an individual tree and in a forest, there is great resulting simplicity notwithstanding the underlying complexity characterizing the system over many size scales. Our scaling framework unifies seemingly distinct trends in a forest and provides a simple yet promising approach to quantitatively understand a bewilderingly complex many-body system with imperfectly known interactions. We show that the effective dimension, D tree</su ...[more]

PMID: 36712800

Similar Datasets

Project description:Purpose of the reviewOver the past decade, lipoprotein(a) [Lp(a)] made it to several consensus and guideline documents. This review aims to summarize the literature which underlies the various recommendations and compares recent European and North American consensus and guideline documents of the recent 3-4 years.Recent findingsMultiple large epidemiological and genetic studies have provided strong evidence for a causal association between Lp(a) concentrations and atherosclerotic cardiovascular disease (ASCVD) and aortic valve stenosis. There is a dose-dependent linear relationship between Lp(a) and ASCVD risk advocating to consider Lp(a) on a continuous scale rather than using thresholds. The best way to implement this in the clinic is by individualizing the Lp(a)-related risk using tools such as the 'Lp(a) risk calculator' ( http://www.lpaclinicalguidance.com ) that takes into account the Lp(a) level in the context of an individual's traditional risk factors and global risk for ASCVD. There is growing agreement across the guidelines regarding the clinical utility of measuring Lp(a) and more recent expert groups advocate for a general screening approach applied to all adults. As long as the cardiovascular outcomes trials for specific Lp(a)-lowering drugs are in progress, the current management of patients with high Lp(a) should focus on the comprehensive management of all other modifiable ASCVD risk factors which can be therapeutically addressed as per guideline recommendations.SummarySince the contribution of high Lp(a) concentrations to global ASCVD risk has been underestimated in the past, a clear recommendation to measure Lp(a) at least once in a person's lifetime is imperative. Recent expert consensus recommendations provide clinicians with direction on how to manage the excess risk associated with elevated Lp(a) concentration by comprehensive and individualized management of modifiable ASCVD risk factors while awaiting the results of clinical trials of Lp(a) targeted therapies.

Dataset Information

Seeing the forest for the trees through metabolic scaling.

Publications

Seeing the forest for the trees through metabolic scaling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets