arXiv subjects

Jorge Hidalgo

Publications and source records attributed to Jorge Hidalgo.

At least 19 recordsLinked to original sources

arXiv2025

Fluctuating Environments Favor Extreme Reproductive Delays and Penalize Intermediate Ones

The timing of reproduction is crucial for biological fitness. Reproductive delays carry an inherent demographic cost: an individual that takes longer to reproduce contributes later to population growth, a priori, without any compensating benefit. However, natural selection favors delayed reproduction under certain environmental conditions, a phenomenon that remains poorly understood. Here, we investigate how the environment can determine the evolution of particular life histories using a parsimonious delayed-logistic model. Individuals become reproductive only after a fixed lag whereas birth rates fluctuate under temporally correlated stochasticity. Numerical integration, analytical calculations and evolutionary simulations reveal that the joint effect of demographic memory and colored multiplicative noise generate a strongly nonmonotonic dependence of fitness on life history, with three distinct regimes of population performance: rapid reproduction maximizes linear growth but amplifies fluctuations and extinction risk; delayed reproduction buffers environmental variability, substantially increasing mean extinction times despite slower growth; strikingly, and central to our results, there is a broad band of maladaptive intermediate life histories that simultaneously reduce both growth and persistence, an effect that arises generically from the mismatch between reproductive timing and environmental autocorrelation. Our results show that reproductive timing is not merely a life-history parameter but an adaptive mechanism tuned to environmental time scales, and that "dangerous middle" maturation times can be inherently disfavored, with implications for understanding reproductive strategies across the tree of life.

q-bio.PE↗

arXiv2025

Holomorphic null curves in the special linear group

In this paper we develop the theory of approximation for holomorphic null curves in the special linear group ${\rm SL}_2(\mathbb{C})$. In particular, we establish Runge, Mergelyan, Mittag-Leffler, and Carleman type theorems for the family of holomorphic null immersions $M\to{\rm SL}_2(\mathbb{C})$ from any open Riemann surface $M$. Our results include jet interpolation of Weierstrass type and approximation by embeddings, as well as global conditions on the approximating curves. As application, we show that every open Riemann surface admits a proper holomorphic null embedding into ${\rm SL}_2(\mathbb{C})$, and hence also a proper conformal immersion of constant mean curvature $1$ into hyperbolic 3-space. This settles a problem posed by Alarcon and Forstneric in 2015.

math.DG↗

arXiv2024

$\mathrm{CMC\text{-}1}$ surfaces in hyperbolic and de Sitter spaces with Cantor ends

We prove that on every compact Riemann surface $M$ there is a Cantor set $C \subset M$ such that $M \setminus C$ admits a proper conformal constant mean curvature one ($\mathrm{CMC\text{-}1}$) immersion into hyperbolic $3$-space $\mathbb{H}^3$. Moreover, we obtain that every bordered Riemann surface admits an almost proper $\mathrm{CMC\text{-}1}$ face into de Sitter $3$-space $\mathbb{S}_1^3$, and we show that on every compact Riemann surface $M$ there is a Cantor set $C \subset M$ such that $M \setminus C$ admits an almost proper $\mathrm{CMC\text{-}1}$ face into $\mathbb{S}_1^3$. These results follow from different uniform approximation theorems for holomorphic null curves in $\mathbb{C}^2 \times \mathbb{C}^*$ that we also establish in this paper.

math.DG↗

arXiv2024

Complete CMC-1 surfaces in hyperbolic space with arbitrary complex structure

We prove that every open Riemann surface $M$ is the complex structure of a complete surface of constant mean curvature 1 (CMC-1) in the 3-dimensional hyperbolic space $\mathbb{H}^3$. We go further and establish a jet interpolation theorem for complete conformal CMC-1 immersions $M\to \mathbb{H}^3$. As a consequence, we show the existence of complete densely immersed CMC-1 surfaces in $\mathbb{H}^3$ with arbitrary complex structure. We obtain these results as application of a uniform approximation theorem with jet interpolation for holomorphic null curves in $\mathbb{C}^2\times\mathbb{C}^*$ which is also established in this paper.

math.DG↗

arXiv2024

A sub-Riemannian Gauss-Bonnet theorem for surfaces in contact manifolds

We obtain a sub-Riemannian version of the classical Gauss-Bonnet theorem. We consider subsurfaces of a three dimensional contact sub-Riemannian manifolds, and using a family of taming Riemannian metric, we obtain a pure sub-Riemannian result in the limit. In particular, we are able to recover topological information of the surface from the geometry around the characteristic set, i.e., the points where the tangent space to the surface and contact structure coincide. We both give a version for surfaces without boundary and surfaces with boundary.

math.DG↗

arXiv2019

Entropy production for coarse-grained dynamics

Systems out of equilibrium exhibit a net production of entropy. We study the dynamics of a stochastic system represented by a Master Equation that can be modeled by a Fokker-Planck equation in a coarse-grained, mesoscopic description. We show that the corresponding coarse-grained entropy production contains information on microscopic currents that are not captured by the Fokker-Planck equation and thus cannot be deduced from it. We study a discrete-state and a continuous-state system, deriving in both the cases an analytical expression for the coarse-graining corrections to the entropy production. This result elucidates the limits in which there is no loss of information in passing from a Master Equation to a Fokker-Planck equation describing the same system. Our results are amenable of experimental verification, which could help to infer some information about the underlying microscopic processes.

cond-mat.stat-mech↗

arXiv2018

How neutral and niche forces contribute to speciation processes?

The evolutionary and ecological processes behind the origin of species are among the most fundamental problems in biology. In fact, many theoretical hypothesis on different type of speciation have been proposed. In particular, models of sympatric speciation leading to the formation of new species without geographical isolation, are based on the niche hypothesis: the diversification of the population is induced by the competition for a limited set of the available resources. On the other hand, neutral models of evolution have shown that stochastic forces are sufficient to generate coexistence of different species. In this work, we bring this dichotomy to the context of species formation, and we study how neutral and niche forces contribute to sympatric speciation in a model ecosystem. In particular, we study the evolution of a population of individuals with asexual reproduction whose inherited characters or phenotypes are specified by both niche-based and neutral traits. We analyse the stationary state of the dynamics, and study the distribution of individuals in the whole space of possible phenotypes. We show, both by numerical simulations and analytics, that there is a non-trivial coupling between neutral and niche forces induced by stochastic effects in the evolution of the population that allows the formation of clusters (i.e., species) in the phenotypic space. Our framework can be generalised also to sexual reproduction or other type of population dynamics.

q-bio.PE↗

arXiv2018

Eco-evolutionary Model of Rapid Phenotypic Diversification in Species-Rich Communities

Evolutionary and ecosystem dynamics are often treated as different processes --operating at separate timescales-- even if evidence reveals that rapid evolutionary changes can feed back into ecological interactions. A recent long-term field experiment has explicitly shown that communities of competing plant species can experience very fast phenotypic diversification, and that this gives rise to enhanced complementarity in resource exploitation and to enlarged ecosystem-level productivity. Here, we build on progress made in recent years in the integration of eco-evolutionary dynamics, and present a computational approach aimed at describing these empirical findings in detail. In particular we model a community of organisms of different but similar species evolving in time through mechanisms of birth, competition, sexual reproduction, descent with modification, and death. Based on simple rules, this model provides a rationalization for the emergence of rapid phenotypic diversification in species-rich communities. Furthermore, it also leads to non-trivial predictions about long-term phenotypic change and ecological interactions. Our results illustrate that the presence of highly specialized, non-competing species leads to very stable communities and reveals that phenotypically equivalent species occupying the same niche may emerge and coexist for very long times. Thus, the framework presented here provides a simple approach --complementing existing theories, but specifically devised to account for the specificities of the recent empirical findings for plant communities-- to explain the collective emergence of diversification at a community level, and paves the way to further scrutinize the intimate entanglement of ecological and evolutionary processes, especially in species-rich communities.

q-bio.PE↗

arXiv2017

Impact of environmental colored noise in single-species population dynamics

Variability on external conditions has important consequences for the dynamics and the organization of biological systems. In many cases, the characteristic timescale of environmental changes as well as their correlations play a fundamental role in the way living systems adapt and respond to it. A proper mathematical approach to understand population dynamics, thus, requires approaches more refined than, e.g., simple white-noise approximations. To shed further light onto this problem, in this paper we propose a unifying framework based on different analytical and numerical tools available to deal with "colored" environmental noise. In particular, we employ a "unified colored noise approximation" to map the original problem into an effective one with white noise, and then we apply a standard path integral approach to gain analytical understanding. For the sake of specificity, we present our approach using as a guideline a variation of the contact process--which can also be seen as a birth-death process of the Malthus-Verhulst class--where the propagation or birth rate varies stochastically in time. Our approach allows us to tackle in a systematic manner some of the relevant questions concerning population dynamics under environmental variability, such as determining the stationary population density, establishing the conditions under which a population may become extinct, and estimating extinction times. We focus on the emerging phase diagram and its possible phase transitions, underlying how these are affected by the presence of environmental noise time-correlations.

q-bio.PE↗

arXiv2017

Entropy production in systems with random transition rates

We study the entropy production of a system with a finite number of states connected by random transition rates. The stationary entropy production, driven out of equilibrium both by asymmetric transition rates and by an external probability current, is shown to be composed of two contributions whose exact distributions are calculated in the large system size and close to equilibrium. The first contribution is related to Joule's law for the heat dissipated in a classical electrical circuit whereas the second one has a Gaussian distribution with an extensive average and a finite variance.

cond-mat.stat-mech↗

arXiv2017

Neutral theory and scale-free neural dynamics

Avalanches of electrochemical activity in brain networks have been empirically reported to obey scale-invariant behavior --characterized by power-law distributions up to some upper cut-off-- both in vitro and in vivo. Elucidating whether such scaling laws stem from the underlying neural dynamics operating at the edge of a phase transition is a fascinating possibility, as systems poised at criticality have been argued to exhibit a number of important functional advantages. Here we employ a well-known model for neural dynamics with synaptic plasticity, to elucidate an alternative scenario in which neuronal avalanches can coexist, overlapping in time, but still remaining scale-free. Remarkably their scale-invariance does not stem from underlying criticality nor self-organization at the edge of a continuous phase transition. Instead, it emerges from the fact that perturbations to the system exhibit a neutral drift --guided by demographic fluctuations-- with respect to endogenous spontaneous activity. Such a neutral dynamics --similar to the one in neutral theories of population genetics-- implies marginal propagation of activity, characterized by power-law distributed causal avalanches. Importantly, our results underline the importance of considering causal information --on which neuron triggers the firing of which-- to properly estimate the statistics of avalanches of neural activity. We discuss the implications of these findings both in modeling and to elucidate experimental observations, as well as its possible consequences for actual neural dynamics and information processing in actual neural networks.

q-bio.NC↗

arXiv2017

Explorability and the origin of Network Sparsity in Living Systems

The increasing volume of ecologically and biologically relevant data has revealed a wide collection of emergent patterns in living systems. Analyzing different datasets, ranging from metabolic gene-regulatory to species interaction networks, we find that these networks are sparse, i.e. the percentage of the active interactions scales inversely proportional to the system size. This puzzling characteristic has been neither yet considered nor explained. Herein, we introduce the new concept of explorability, a measure of the ability of the system to adapt to newly intervening changes. We show that sparsity is an emergent property resulting from a variational principle aiming at the optimization of both explorability and dynamical robustness, the capacity of the system to remain stable after perturbations of the underlying dynamics. Networks with higher connectivities lead to an incremental difficulty to find better values for both the explorability and dynamical robustness, associated with the fine-tuning of the newly added interactions. A relevant characteristic of our solution is its scale invariance, that is, it remains optimal when several communities are assembled togheter. Connectivity is also a key ingredient determining ecosystem stability and our proposed solution contributes to solving May's celebrated complexity-stability paradox.

physics.soc-ph↗

arXiv2016

Species coexistence in a neutral dynamics with environmental noise

Environmental fluctuations have important consequences in the organization of ecological communities, and understanding how such a variability influences the biodiversity of an ecosystem is a major question in ecology. In this paper, we analyze the case of two species competing for the resources within the framework of the neutral theory in the presence of environmental noise, devoting special attention on how such a variability modulates species fitness. The environment is dichotomous and stochastically alternates between periods favoring one of the species while disfavoring the other one, preserving neutrality on the long term. We study two different scenarios: in the first one species fitness varies linearly with the environment, and in the second one the effective fitness is re-scaled by the total fitness of the individuals competing for the same resource. We find that, in the former case environmental fluctuations always reduce the time of species coexistence, whereas such a time can be enhanced or reduced in the latter case, depending on the correlation time of the environment. This phenomenon can be understood as a direct consequence of Chesson's storage effect.

q-bio.PE↗

arXiv2016

Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks

Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent trade off between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way --even for asynchronous updating rules-- and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity.

q-bio.MN↗

arXiv2016

Cooperation, competition and the emergence of criticality in communities of adaptive systems

The hypothesis that living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. Criticality may confer an optimal balance between exceedingly ordered and too noisy states. We here present a model, based on information theory and statistical mechanics, illustrating how and why a community of agents aimed at understanding and communicating with each other converges to a globally coherent state in which all individuals are close to an internal critical state, i.e. at the borderline between order and disorder. We study --both analytically and computationally-- the circumstances under which criticality is the best possible outcome of the dynamical process, confirming the convergence to critical points under very generic conditions. Finally, we analyze the effect of cooperation (agents try to enhance not only their fitness, but also that of other individuals) and competition (agents try to improve their own fitness and to diminish those of competitors) within our setting. The conclusion is that, while competition fosters criticality, cooperation hinders it and can lead to more ordered or more disordered consensual solutions.

q-bio.PE↗

arXiv2016

Complex synchronization patterns in the human connectome network

A major challenge in neuroscience is posed by the need for relating the emerging dynamical features of brain activity with the underlying modular structure of neural connections, hierarchically organized throughout several scales. The spontaneous emergence of coherence and synchronization across such scales is crucial to neural function, while its anomalies often relate to pathological conditions. Here we provide a numerical study of synchronization dynamics in the human connectome network. Our purpose is to provide a detailed characterization of the recently uncovered broad dynamic regime, interposed between order and disorder, which stems from the hierarchical modular organization of the human connectome. In this regime -similar in essence to a Griffiths phase- synchronization dynamics are trapped within metastable attractors of local coherence. Here we explore the role of noise, as an effective description of external perturbations, and discuss how its presence accounts for the ability of the system to escape intermittently from such attractors and explore complex dynamic repertoires of locally coherent states, in analogy with experimentally recorded patterns of cerebral activity.

q-bio.NC↗

arXiv2015

Environmental unpredictability and inbreeding depression select for mixed dispersal syndromes

Mixed dispersal syndromes have historically been regarded as bet-hedging mechanisms that enhance survival in unpredictable environments, ensuring that some propagules stay in the maternal environment while others can potentially colonize new sites. However, this entails paying the costs of both dispersal and non-dispersal. Propagules that disperse are likely to encounter unfavorable conditions for establishment, while non-dispersing propagules might form populations of close relatives burdened with inbreeding. Here, we investigate the conditions under which mixed dispersal syndromes emerge and are evolutionarily stable, taking into account the risks of both environmental unpredictability and inbreeding. Using mathematical and computational modeling we show that high dispersal propensity is favored whenever temporal environmental unpredictability is low and inbreeding depression high, whereas mixed dispersal syndromes are adaptive under conditions of high environmental unpredictability, but more particularly if also inbreeding depression is small. Although pure dispersers can be selected for under some circumstances, mixed dispersal provides the optimal strategy under most parameterizations of our models, indicating that this strategy is likely to be favored under a wide variety of conditions. Furthermore, populations exhibiting any single phenotype go inevitably extinct when environmental and genetic costs are high, whilst mixed strategies can maintain viable populations even under such conditions. Our models support the hypothesis that the interplay between inbreeding depression and environmental unpredictability shapes dispersal syndromes, often resulting in mixed strategies. Moreover, mixed dispersal seems to facilitate persistence whenever conditions are critical or nearly critical for survival.

q-bio.PE↗

arXiv2015

Stochasticity enhances the gaining of bet-hedging strategies in contact-process-like dynamics

In biology and ecology, individuals or communities of individuals living in unpredictable environments often alternate between different evolutionary strategies to spread and reduce risks. Such behavior is commonly referred to as "bet-hedging." Long-term survival probabilities and population sizes can be much enhanced by exploiting such hybrid strategies. Here, we study the simplest possible birth-death stochastic model in which individuals can choose among a poor but safe strategy, a better but risky alternative, or a combination of both. We show analytically and computationally that the benefits derived from bet-hedging strategies are much stronger for higher environmental variabilities (large external noise) and/or for small spatial dimensions (large intrinsic noise). These circumstances are typically encountered by living systems, thus providing us with a possible justification for the ubiquitousness of bet-hedging in nature.

q-bio.PE↗