The amount of carbon stored in deadwood is equivalent to about 8% of global forest carbon stocks 1 . Deadwood decomposition is largely governed by climate [2][3][4][5] with decomposer groups, such as microbes and insects, contributing to variations in decomposition rates 2,6,7 . At the global scale, the contribution of insects to deadwood decomposition and carbon release remains poorly understood 7 . Here we present a field experiment of wood decomposition across 55 forest sites on six continents. We find that deadwood decomposition rates increase with temperature, with the strongest temperature effect at high precipitation levels. Precipitation affects decomposition rates negatively at low temperature and positively at high temperatures. As net effect, including direct consumption and indirect effects via interactions with microbes, insects accelerate decomposition in tropical forests (3.9% median mass loss per year).In temperate and boreal forests we find weak positive and negative effects with a median mass loss of 0.9% and -0.1% per year, respectively. Furthermore, we apply the experimentally derived decomposition function to a global map of deadwood carbon synthesised from empirical and remote sensing data. This allows for a first estimate of 10.9 ± 3.2 Pg yr -1 of carbon released from deadwood globally, with 93% originating from tropical forests. Globally, the net effect of insects accounts for a carbon flux of 3.2 ± 0.9 Pg yr -1 or 29% of the total carbon released from deadwood, which highlights the functional importance of insects for deadwood decomposition and the global carbon cycle.

The rule-based search of chemical space can generate an almost infinite number of molecules, but exploration of known molecules as a function of the minimum number of steps needed to build up the target graphs promises to uncover new motifs and transformations. Assembly theory is an approach to compare the intrinsic complexity and properties of molecules by the minimum number of steps needed to build up the target graphs. Here, we apply this approach to prebiotic chemistry, gene sequences, plasticizers, and opiates. This allows us to explore molecules connected to the assembly tree, rather than the entire space of molecules possible. Last, by developing a reassembly method, based on assembly trees, we found that in the case of the opiates, a new set of drug candidates could be generated that would not be accessible via conventional fragment-based drug design, thereby demonstrating how this approach might find application in drug discovery.

Explaining the origin of life requires us to elucidate how self-replication arises. To be specific, how can a self-replicating entity develop spontaneously from a chemical reaction system in which no reaction is self-replicating? Previously proposed mathematical models either supply an explicit framework for a minimal living system or consider only catalyzed reactions, and thus fail to provide a comprehensive theory. Here, we set up a general mathematical model for chemical reaction systems that properly accounts for energetics, kinetics, and the conservation law. We found that 1) some systems are collectively catalytic, a mode whereby reactants are transformed into end products with the assistance of intermediates (as in the citric acid cycle), whereas some others are self-replicating, that is, different parts replicate each other and the system self-replicates as a whole (as in the formose reaction, in which sugar is replicated from formaldehyde); 2) side reactions do not always inhibit such systems; 3) randomly chosen chemical universes (namely random artificial chemistries) often contain one or more such systems; 4) it is possible to construct a self-replicating system in which the entropy of some parts spontaneously decreases, in a manner similar to that discussed by Schrödinger; and 5) complex self-replicating molecules can emerge spontaneously and relatively easily from simple chemical reaction systems through a sequence of transitions. Together, these results start to explain the origins of prebiotic evolution.

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The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory and exobiology. Many measures of these quantities are either difficult to compute, rely on the statistical notion of information, or can only be applied to strings. Based on assembly theory, we propose the notion of a ladderpath, which describes how an object can be decomposed into hierarchical structures using repetitive elements. From the ladderpath, two measures naturally emerge: the ladderpath-index and the order-index, which represent two axes of complexity. We show how the ladderpath approach can be applied to both strings and spatial patterns and argue that all systems that undergo evolution can be described as ladderpaths. Further, we discuss possible applications to human language and the origin of life. The ladderpath approach provides an alternative characterization of the information that is contained in a single object (or a system) and could aid in our understanding of evolving systems and the origin of life in particular.