Showing papers by "Nadav M. Shnerb published in 2021"

Species coexistence and temporal environmental fluctuations: a quantitative comparison between stochastic and seasonal variations.

Abstract: Temporal environmental variations may promote diversity in communities of competing populations. Here we compare the effect of environmental stochasticity with the effect of periodic (e.g., seasonal) cycles, using analytic solutions and individual-based Monte-Carlo simulations. Even when stochasticity facilitates coexistence it still allows for rare sequences of bad years that may drive a population to extinction, therefore the stabilizing effect of periodic variations is stronger. Correspondingly, the mean time to extinction grows exponentially with community size in periodic environment and switch to power-law dependence under stochastic fluctuations. On the other hand, the number of temporal niches in periodic environment is typically lower, so as diversity increases stochastic temporal variations may support higher species richness.

Quantifying invasibility

Abstract: Invasibility, the chance of a population to grow from rarity and to establish a large-abundance colony, plays a fundamental role in population genetics, ecology, and evolution. For many decades, the mean growth rate when rare has been employed as an invasion criterion. Recent analyses have shown that this criterion fails as a quantitative metric for invasibility, with its magnitude sometimes even increasing while the invasibility decreases. Here we employ a new large-deviations (Wentzel-Kramers-Brillouin, WKB) approach and derive a novel and easy-to-use formula for the chance of invasion in terms of the mean growth rate and its variance. We also explain how to extract the required parameters from abundance time series. The efficacy of the formula, including its accompanying data analysis technique, is demonstrated using synthetic and empirically-calibrated time series from a few canonical models.

Alternative States in Plant Communities Driven by a Life-History Trade-Off and Demographic Stochasticity.

Abstract: Life-history trade-offs among species are major drivers of community assembly. Most studies investigate how trade-offs promote deterministic coexistence of species. It remains unclear how t...

Stochasticity-induced stabilization weakens in diverse communities.

Abstract: Environmental stochasticity and the temporal variations of demographic rates associated with it are ubiquitous in nature. The ability of these fluctuations to stabilize a coexistence state of competing populations (sometimes known as the storage effect) is a counterintuitive feature that has aroused much interest. Here we consider the performance of environmental stochasticity as a stabilizer in diverse communities. We show that the effect of stochasticity is buffered because of the differential response of populations to environmental variations, and its stabilizing effect disappears as the number of populations increases. Of particular importance is the ratio between the autocorrelation time of the environment and the generation time. Species richness grows with stochasticity only when this ratio is smaller than the inverse of the fundamental biodiversity parameter. In an opposite regime, when stochasticity impedes coexistence and lowers the species richness, its effect is determined by the ratio between the strength of environmental variations and the rate at which new types are added to the community via speciation, mutation or immigration.

A framework for quantifying deviations from dynamic equilibrium theory

Abstract: Community assembly is governed by colonization and extinction processes, and the simplest model describing it is Dynamic Equilibrium (DE) theory, which assumes that communities are shaped solely by stochastic colonization and extinction events. Despite its potential to serve as a null model for community dynamics, there is currently no accepted methodology for measuring deviations from the theory and testing it. Here we propose a novel and easily applicable methodology for quantifying deviations from the predictions and assumptions of DE by comparing observed community time-series to a randomization-based null model. We show that this methodology has good statistical properties on simulated data, and it can detect deviations from both the assumptions and predictions of DE in the classical Florida Keys experiment. We discuss alternative methods and present guidelines for practical use of the methodology, hoping it will enhance the applicability of DE as a reference for studying changes in ecological communities.

The time-identity tradeoff

Abstract: Distinguishability plays a major role in quantum and statistical physics. When particles are identical their wave function must be either symmetric or antisymmetric under permutations and the number of microscopic states, which determines entropy, is counted up to permutations. When the particles are distinguishable, wavefunctions have no symmetry and each permutation is a different microstate. This binary and discontinuous classification raises a few questions: one may wonder what happens if particles are almost identical, or when the property that distinguishes between them is irrelevant to the physical interactions in a given system. Here I sketch a general answer to these questions. For any pair of non-identical particles there is a timescale, $\tau_d$, required for a measurement to resolve the differences between them. Below $\tau_d$, particles seem identical, above it - different, and the uncertainty principle provides a lower bound for $\tau_d$. Thermal systems admit a conjugate temperature scale, $T_d$. Above this temperature, the system equilibrates (or appears to equilibrate) before it resolves the differences between particles, below this temperature the system identifies these differences before equilibration. As the physical differences between particles decline towards zero, $\tau_d \to \infty$ and $T_d \to 0$.

A metric driving local action for global biodiversity conservation

Abstract: Difficulties identifying appropriate biodiversity impact metrics remain a major barrier to inclusion of biodiversity considerations in environmentally conscious investment. Here we propose a science-based metric with a simple definition that supports a narrative of biodiversity protection. It links mathematically to a widely used global biodiversity indicator, the Living Planet Index, for which we propose an improved formula to overcome long-standing conceptual issue. We show that, in an ideal market, trade in our metric would lead to near optimal allocation of resources to global biodiversity conservation. Barriers to adoption are low, use of the metric does not require an institutional certification system. We propose its use in biodiversity related financial disclosures and in voluntary or legislated policies of no net biodiversity loss.