Biology subjects

Shirani, F.

Publications and source records attributed to Shirani, F..

4 recordsLinked to original sources

bioRxiv2026

Coevolution of Species' Borders: Interactions Between Interspecific Competition, Gene Flow, and Matching Habitat Choice

Existing theory examining the coevolutionary dynamics of species range borders assumes random dispersal, which causes maladaptive gene flow from the range core to the range margins and contributes to the formation of range limits. However, dispersal is unlikely to be random for many organisms in nature, calling into question existing theoretical predictions. For example, if individuals exhibit phenotype-dependent adaptive dispersal strategies such as matching habitat choice, then the resulting adaptive gene flow toward species range margins could facilitate range expansions and potentially prevent the formation of range limits by interspecific competition. To test this idea, we use a comprehensive mathematical model to develop a quantitative theory of range border coevolution that incorporates phenotype-optimal dispersal--a particular form of matching habitat choice in which individuals follow the gradient in an environmental optimum phenotype to settle in the habit best suited for their phenotype. We find that instead of preventing competitively formed range limits, adaptive dispersal leads to sharper range limits and reduced character displacement in sympatry. These differences are particularly remarkable when natural selection is weak, when individuals are specialized in their resource use, or when individuals are highly sensitive to environmental conditions. We show that matching habitat choice causes backward edge-to-core movements which dynamically interact with the effects of interspecific competition to establish the range limits. Thus, the formation of range limits by interspecific competition is robust to assumptions about individual dispersal. Further, our results identify the competitive advantage of evolving matching habitat choice in steep environmental gradients, especially for slowly-growing species in rapidly fluctuating climates.

ecology↗

bioRxiv2024

Environmental "Wiggles" as Stabilizers of Species Range Limits Set by Interspecific Competition

Whether interspecific competition is a major contributing factor to setting species range limits has been debated for a long time. Theoretical studies using evolutionary models have proposed that the interaction between interspecific competition and disruptive gene flow along an environmental gradient can halt range expansion of ecologically related species where they meet. However, the stability of such range limits has not been well addressed. We use a deterministic PDE model of adaptive range evolution over a continuous habitat to show that the range limits set by interspecific competition between two closely related species are unlikely to be evolutionarily stable if the environmental optima for fitness-related traits vary linearly in space. That is, in a (almost) linear environment without a dispersal barrier or a third (or more) related species, the range limits formed at the interface of two competing species constantly move towards the weaker species. Through extensive numerical computations, we then demonstrate that environmental nonlinearities such as "knees" and "wiggles"--wherein an isolated sharp change or a step-like change occurs in the steepness of a trait optimum--can strongly stabilize competitively formed range limits. The stabilization mechanism relies on the contrast that such nonlinearities create in the level of disruptive gene flow to the peripheral population of each species. We show that the stability of the range limits established at these nonlinearities, which are likely prevalent in nature, is robust against moderate environmental disturbances. Whether or not strong disturbances such as rapid high-amplitude changes in climate can destabilize such range limits depends on how the competitive dominance of the competing species changes across the environmental nonlinearity. Therefore, our results identify habitat regions where species ranges are fairly insensitive to climate change, and highlight the importance of measuring the competitive ability of species when predicting their response to climate change.

ecology↗

bioRxiv2024

Matching Habitat Choice and the Evolution of a Species' Range

Natural selection is not the only mechanism that promotes adaptation of an organism to its environment. Another mechanism is matching habitat choice, in which individuals sense and disperse toward habitat best suited to their phenotype. This can in principle facilitate rapid adaptation, enhance range expansion, and promote genetic differentiation, reproductive isolation, and speciation. However, empirical evidence that confirms the evolution of matching habitat choice in nature is limited. Here we obtain theoretical evidence that phenotype-optimal dispersal, a particular form of matching habitat choice, is likely to evolve only in the presence of a steep environmental gradient. Such a gradient may be steeper than the gradient the majority of species typically experience in nature, adding to the collection of possible explanations for the scarcity of evidence for matching habitat choice. We draw this conclusion from numerical solutions of a system of deterministic partial differential equations for a populations density along with the mean and variance of a fitness-related quantitative phenotypic trait such as body size. In steep gradients, we find that phenotype-optimal dispersal facilitates rapid adaptation on single-generation time scales, reduces within-population trait variation, increases range expansion speed, and enhances the chance of survival in rapidly changing environments. Moreover, it creates a directed gene flow that compensates for the maladaptive core-to-edge effects of random gene flow caused by random movements. These results suggest that adaptive gene flow to range margins, together with substantially reduced trait variation at central populations, may be hallmarks of phenotype-optimal dispersal in natural populations. Further, slowly-growing species under strong natural selection may particularly benefit from evolving phenotype-optimal dispersal.

ecology↗

bioRxiv2023

On the physiological and structural contributors to the dynamic balance of excitation and inhibition in local cortical networks

Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a networks baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. SummaryWe leverage computational tractability of a biologically plausible conductance-based meanfield model to perform a comprehensive bifurcation and sensitivity analysis that demonstrates how variations in key synaptic and structural parameters of a local cortical network affect networks stability and overall excitation-inhibition balance. Our results reveal optimality and criticality of baseline biological values for several of these parameters, and provide predictions on their effects on networks dynamics which can inform identifying pathological conditions and guide future experiments.

neuroscience↗