Biology subjects

Abbott, K. C.

Publications and source records attributed to Abbott, K. C..

6 recordsLinked to original sources

bioRxiv2024

How do body size and habitat fragmentation influence extinction in lizards? A long-term case study on artificial islands in the Brazilian Cerrado

Habitat fragmentation is known to cause extinctions and species turnover, but the factors that allow some species to persist while others become locally extinct are not well understood. Landscape flooding following the construction of hydroelectric dams causes a particularly dramatic form of fragmentation disturbance, where former terrestrial habitats become aquatic and former hilltops become land-bridge islands. As such, reservoir land-bridge islands have become a successful model for accessing the impacts of fragmentation on biodiversity. We used the lizard community, to assess species sensitivity to habitat structural change during land-bridge island formation. We monitored the lizard community for 23 years before, during, and after the flooding of the Serra da Mesa Dam reservoir. Over the course of our study, the diversity of the lizard community on land-bridge islands and mainland sites along the shores of the newly formed reservoir declined from 19 to six species. We found that in Serra da Mesa islands, lizards with large body sizes (e.g., Teiidae and Tropiduridae) decreased in abundance along the flooding process, thereby increasing their extinction risk. In contrast, we found a high abundance of small-bodied lizards (Gekkonidae, Gymnophthalmidae, Scincidae, and Sphaerodactylidae) on Serra da Mesa islands. Richness on the islands declined dramatically, resulting in communities currently with one highly abundant species, Gymnodactylus amarali. For the sake of biodiversity conservation, island or fragment sizes must be considered for maintaining a reasonable number of species and our characterization of the local extinction patterns may provide relevant information to mitigate wildlife depletion due to habitat fragmentation.

ecology↗

bioRxiv2022

A general theory for the dynamics of social populations: Within-group density dependence and between-group processes.

Despite the importance of population structures throughout ecology, relatively little theoretical attention has been paid to understanding the implications of social groups for population dynamics. The dynamics of socially structured populations differ substantially from those of unstructured or metapopulation-structured populations, because social groups themselves may split, fuse, and compete. These "between-group processes" have been suggested to be important drivers of the dynamics of socially-structured populations, but no general theoretical framework exists that can handle various density-dependent between-group processes within a single model. Here, we develop a general framework for the dynamics of socially-structured populations that considers births, deaths, migration, group extinction, fissions, fusions, and between-group competition within a single model. Both logistic growth and an Allee effect are considered for within-group density dependence. We show that the effect of various between-group processes is mediated by their influence on the stable distribution of group sizes, with the ultimate impact on the population determined by the interaction between the altered group size distribution and within-group density dependence. The group level is important to the dynamics of the entire population, since it drives extinction risk, impacts population growth rates, and leads to the emergence of population-level density dependence (even if birth and death rates depend only on group size and not population size). We conclude with a series of case studies that illustrate different ways that age, sex, and class structure impact the dynamics of social populations. In sum, our results make clear the importance of within-group density dependence, between-group dynamics, and the interactions between them for the population dynamics of social species and provide a general, flexible framework for modeling social populations.

ecology↗

bioRxiv2022

Noise source importance in linear stochastic models of biological systems that grow, shrink, wander, or persist

While noise is an important factor in biology, biological processes often involve multiple noise sources, whose relative importance can be unclear. Here we develop tools that quantify the importance of noise sources in a network based on their contributions to variability in a quantity of interest. We generalize the edge importance measures proposed by Schmidt and Thomas [1] for first-order reaction networks whose steady-state variance is a linear combination of variance produced by each directed edge. We show that the same additive property extends to a general family of stochastic processes subject to a set of linearity assumptions, whether in discrete or continuous state or time. Our analysis applies to both expanding and contracting populations, as well as populations obeying a martingale ("wandering") at long times. We show that the original Schmidt-Thomas edge importance measure is a special case of our more general measure, and is recovered when the model satisfies a conservation constraint ("persists"). In the growing and wandering cases we show that the choice of observables (measurements) used to monitor the process does not influence which noise sources are important at long times. In contrast, in the shrinking or persisting case, which noise sources are important depends on what is measured. We also generalize our measures to admit models with affine moment update equations, which admit additional limiting scenarios, and arise naturally after linearization. We illustrate our results using examples from cell biology and ecology: (i) a model for the dynamics of the inositol trisphospate receptor, (ii) a model for an endangered population of white-tailed eagles, and (iii) a model for wood frog dispersal. Author summaryBiological processes are frequently subject to an ensemble of independent noise sources. Noise sources produce fluctuations that propagate through the system, driving fluctuations in quantities of interest such as population size or ion channel configuration. We introduce a measure that quantifies how much variability each noise source contributes to any given quantity of interest. Using these methods, we identify which binding events contribute significantly to fluctuations in the state of a molecular signalling channel, which life history events contribute the most variability to an eagle population before and after a successful conservation effort rescued the population from the brink of extinction, and which dispersal events, at what times, matter most to variability in the recolonization of a series of ponds by wood frogs after a drought.

ecology↗

bioRxiv2021

Effects of stochasticity on the length and behaviour of ecological transients

There is a growing recognition that ecological systems can spend extended periods of time far away from an asymptotic state, and that ecological understanding will therefore require a deeper appreciation for how long ecological transients arise. Recent work has defined classes of deterministic mechanisms that can lead to long transients. Given the ubiquity of stochasticity in ecological systems, a similar systematic treatment of transients that includes the influence of stochasticity is important. Stochasticity can of course promote the appearance of transient dynamics by preventing systems from settling permanently near their asymptotic state, but stochasticity also interacts with deterministic features to create qualitatively new dynamics. As such, stochasticity may shorten, extend, or fundamentally change a systems transient dynamics. Here, we describe a general framework that is developing for understanding the range of possible outcomes when random processes impact the dynamics of ecological systems over realistic time scales. We emphasize that we can understand the ways in which stochasticity can either extend or reduce the lifetime of transients by studying the interactions between the stochastic and deterministic processes present, and we summarize both the current state of knowledge and avenues for future advances.

ecology↗

bioRxiv2020

Dynamical Ising model of spatially-coupled ecological oscillators

Long-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of "universal" properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a systems dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes, and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.

ecology↗

bioRxiv2017

Population dynamics of mutualism and intraspecific density dependence:how θ-logistic-like density dependence affects mutualistic positive feedback

Mutualism describes the biological phenomenon where two or more species are reciprocally beneficial, regardless of their ecological intimacy or evolutionary history. Classic theory shows that mutualistic benefit must be relatively weak, or else it overpowers the stabilizing influence of intraspecific competition and leads to unrealistic, unbounded population growth. Interestingly, the conclusion that strong positive interactions lead to runaway population growth is strongly grounded in the behavior of a single model. This model [-] the Lotka-Volterra competition model with a sign change to generate mutualism rather than competition between species [-] assumes logistic growth of each species plus a linear interaction term to represent the mutualism. While it is commonly held that the linear interaction term is to blame for the models unrealistic behavior, we show here that a linear mutualism added to a{theta} -logistic model of population growth can prevent unbounded growth. We find that when density dependence is decelerating, the benefit of mutualism at equilibrium is greater than when density dependence is accelerating. Although there is a greater benefit, however, decelerating density dependence tends to destabilize populations whereas accelerating density dependence is always stable. We interpret these findings tentatively, but with promise for the understanding of the population ecology of mutualism by generating several predictions relating growth rates of mutualist populations and the strength of mutualistic interaction.

ecology↗