Biology subjects

Maritan, A.

Publications and source records attributed to Maritan, A..

4 recordsLinked to original sources

bioRxiv2018

Inferring macro-ecological patterns from local species’ occurrences

O_LIBiodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns.\nC_LIO_LIMany databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework to infer species richness and abundances at large spatial scales in biodiversity-rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling).\nC_LIO_LIThis framework is based on the scale-invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well-known biodiversity patterns of ecological theory, such as the Species Accumulation Curve (SAC) and the Relative Species Abundance (RSA) as well as a new emergent pattern, which is the Relative Species Occupancy (RSO).\nC_LIO_LIOur estimates are robust and accurate, as confirmed by tests performed on both in silico-generated and real forests. We demonstrate the accuracy of our predictions using data from two well-studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence-absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.\nC_LI

ecology↗

bioRxiv2018

Adaptive metabolic strategies explain diauxic shifts and promote species coexistence

Bacteria are able to adapt to different environments by changing their \"metabolic strategies\", i.e. the ways in which they uptake available resources from the environment. For example, in a celebrated experiment Jacques Monod showed that bacteria cultured in media containing two different sugars consume them sequentially, resulting in bi-phasic growth curves called \"diauxic shifts\". From the theoretical point of view, microbial communities are commonly described using MacArthurs consumer-resource model, which describes the population dynamics of species competing for a given set of resources. In this model, however, metabolic strategies are treated as constant parameters. Here, we introduce adaptive metabolic strategies in the framework of consumer-resource models, allowing the strategies to evolve to maximize each species relative fitness. By doing so, we are able to describe quantitatively, and without invoking any specific molecular mechanisms for the metabolism of the microbial species, growth curves of the bakers yeast Saccharomyces cerevisiae measured in a controlled experimental set-up, with galactose as the primary carbon source. We also show that metabolic adaptation enables the community to self-organize, allowing species to coexist even in the presence of few resources, and to respond optimally to a time-dependent environment. A connection between the Competitive Exclusion Principle and the metabolic theory of ecology is also discussed.

ecology↗

bioRxiv2018

Archetypes in human behavior and their brain correlates: An evolutionary trade-off approach

Organisms perform multiple tasks and in doing so face critical trade-offs. According to Pareto optimality theory, such trade-offs lead to the evolution of phenotypes that are distributed in a portion of the trait-space resembling a polytope, whose vertices represent the specialists at one of the traits (archetypes).\n\nWe applied this theory to the variability of cognitive and behavioral scores measured in 1206 individuals from the Human Connectome Project. Among all possible 300 combinations of pairs of traits, we found the best fit to Pareto optimality when individuals were plotted in the trait-space of time preferences for reward, evaluated with the Delay Discounting task. This task requires choosing either immediate smaller rewards or delayed larger rewards. Time preference for reward identified three archetypes, which accounted for variability on many cognitive, personality, and socio-economic status scores, differences in brain structure, as well as in functional connectivity between prefrontal cortex, basal ganglia, and amygdala, regions associated with reward and their regulation. There was only a weak association with genetics. In summary, time preference for reward reflects a core variable that biases human phenotypes via natural and cultural selection.

neuroscience↗

bioRxiv2016

Biodiversity of Tropical Forests

The quantification of tropical tree biodiversity worldwide remains an open and challenging problem. In fact, more than two-fifths of the global tree population can be found either in tropical or sub-tropical forests1, but species identities are known only for {approx} 0.000067% of the individuals in all tropical forests2. For practical reasons, biodiversity is typically measured or monitored at fine spatial scales. However, important drivers of ecological change tend to act at large scales. Conservation issues, for example, apply to diversity at global, national or regional scales. Extrapolating species richness from the local to the global scale is not straightforward. Indeed, a vast number of different biodiversity estimators have been developed under different statistical sampling frameworks3-7, but most of them have been designed for local/regional-scale extrapolations, and they tend to be sensitive to the spatial distribution of trees8, sample coverage and sampling methods9. Here, we introduce an analytical framework that provides robust and accurate estimates of species richness and abundances in biodiversity-rich ecosystems, as confirmed by tests performed on various in silico-generated forests. The new framework quantifies the minimum percentage cover that should be sampled to achieve a given average confidence in the upscaled estimate of biodiversity. Our analysis of 15 empirical forest plots shows that previous methods10,11 have systematically overestimated the total number of species and leads to new estimates of hyper-rarity10 at the global scale11, known as Fishers paradox2. We show that hyper-rarity is a signature of critical-like behavior12 in tropical forests13-15, and it provides a buffer against mass extinctions16. When biotic factors or environmental conditions change, some of these rare species are more able than others to maintain the ecosystems functions, thus underscoring the importance of rare species.

ecology↗