Biology subjects

Kajin, M.

Publications and source records attributed to Kajin, M..

5 recordsLinked to original sources

bioRxiv2023

From disturbances to nonlinear fitness and back.

Disturbances can occur as short-lived pulses (e.g., storms) or sustained presses (e.g., chronic drought). Much work in ecology has developed methods to help predict how natural populations respond to disturbances, but analyses of pulse and press disturbances has been largely disconnected. We present a unified matrix framework that links presses and pulses within the same analytical approach, showing how transient nonlinearities and demography shape fitness. We find that transient responses to pulse disturbances accumulate to determine the long-term response to press disturbances. For structured-population models, this cumulative change is given by a new Transient Response Matrix (TRM). Strikingly, the TRM also yields the second derivatives of population growth rate with respect to matrix elements. Thus, there is an intimate but unexpected relationship between nonlinear selection pressures on demographic rates, and the transient dynamics of populations. This relationship yields a strong correlation between TRM and generation time across 439 unique plant and animal species (2690 population models). We also show that the TRM is directly related to Cohens cumulative distance measure for populations converging to stability. Our framework provides ecologists with a general tool to predict population responses to diverse environmental changes.

ecology↗

bioRxiv2023

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Environmental stochasticity is a key determinant of population viability. Decades of work exploring how environmental stochasticity influences population dynamics have highlighted the ability of some natural populations to limit the negative effects of environmental stochasticity, one of these strategies being demographic buffering. Whilst various methods exist to quantify demographic buffering, we still do not know which environment factors and demographic characteristics are most responsible for the demographic buffering observed in natural populations. Here, we introduce a framework to quantify the relative effects of three key drivers of demographic buffering: environment components (e.g., temporal autocorrelation and variance), population structure, and demographic rates (e.g., progression and fertility). Using Integral Projection Models, we explore how these drivers impact the demographic buffering abilities of three plant species with different life histories and demonstrate how our approach successfully characterises a populations capacity to demographically buffer against environmental stochasticity in a changing world.

ecology↗

bioRxiv2023

A unified framework to identify demographic buffering in natural populations

The Demographic Buffering Hypothesis (DBH) predicts that natural selection reduces the temporal fluctuations in demographic processes (such as survival, development, and reproduction), due to their negative impacts on population dynamics. However, a comprehensive approach that allows for the examination of demographic buffering patterns across multiple species is still lacking. Here, we propose a three-step framework aimed at identifying and quantifying demographic buffering. Firstly, we categorize species along a continuum of variance based on their stochastic elasticities. Secondly, we examine the linear selection gradients, followed by the examination of nonlinear selection gradients as the third step. With these three steps, our framework overcomes existing limitations of conventional approaches to identify and quantify demographic buffering, allows for multi-species comparisons, and offers an insight into the evolutionary forces that shape demographic buffering. We apply this framework to mammal species and discuss both the advantages and potential of our framework.

ecology↗

bioRxiv2023

A standard protocol to report discrete stage-structured demographic information

O_LIStage-based demographic methods, such as matrix population models (MPMs), are powerful tools used to address a broad range of fundamental questions in ecology, evolutionary biology, and conservation science. Accordingly, MPMs now exist for over 3,000 species worldwide. These data are being digitised as an ongoing process and periodically released into two large open-access online repositories: the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. During the last decade, data archiving and curation of COMPADRE and COMADRE, and subsequent comparative research, have revealed pronounced variation in how MPMs are parameterized and reported. C_LIO_LIHere, we summarise current issues related to the parameterisation and reporting of MPMs that arise most frequently and outline how they affect MPM construction, analysis, and interpretation. To quantify variation in how MPMs are reported, we present results from a survey identifying key aspects of MPMs that are frequently unreported in manuscripts. We then screen COMPADRE and COMADRE to quantify how often key pieces of information are omitted from manuscripts using MPMs. C_LIO_LIOver 80% of surveyed researchers (n=60) state a clear benefit to adopting more standardised methodologies for reporting MPMs. Furthermore, over 85% of the 300 MPMs assessed from COMPADRE and COMADRE omitted one or more elements that are key to their accurate interpretation. Based on these insights, we identify fundamental issues that can arise from MPM construction and communication and provide suggestions to improve clarity, reproducibility, and future research utilising MPMs and their required metadata. To fortify reproducibility and empower researchers to take full advantage of their demographic data, we introduce a standardized protocol to present MPMs in publications. This standard is linked to www.compadre-db.org, so that authors wishing to archive their MPMs can do so prior to submission of publications, following examples from other open-access repositories such as DRYAD, Figshare, and Zenodo. C_LIO_LICombining and standardising MPMs parameterized from populations around the globe and across the tree of life opens up powerful research opportunities in evolutionary biology, ecology, and conservation research. However, this potential can only be fully realised by adopting standardised methods to ensure reproducibility. C_LI

ecology↗

bioRxiv2021

To buffer or to be labile? A framework to disentangle demographic patterns and evolutionary processes

Until recently, natural selection was assumed to reduce temporal fluctuation in vital rates due to its negative effects on population dynamics - the so-called Demographic Buffering Hypothesis (DBH). After several failures to support the DBH in the two decades since it was first posited, an alternative hypothesis was suggested; the Demographic Lability Hypothesis (DLH), where population vital rates should track rather than buffer the environmental conditions. Despite the huge contribution of both hypotheses to comprehend the demographic strategies to cope the environmental stochasticity, it remains unclear if they represent two competing patterns or the extreme ends of a continuum encompassing all demographic strategies. To solve this historical debate, we unify several methods with an integrative theoretical approach where: i) using the sum of stochastic elasticity with respect to mean and variance - a first-order derivative approach - we rank species on a Buffering-Lability (DB-DL) continuum and ii) using the second-order derivative, we examine how vital rates are shaped by natural selection. Our framework, applied to 40 populations of 34 mammals, successfully placed the species on the DB-DL continuum. We could also link the species' position on the DB-DL continuum to their generation time and time to recovery. Moreover, the second-order derivative unveiled that vital rates with lower temporal variation are not necessarily under a strong pressure of stabilizing selection, as predicted by DBH and DLH. Our framework provides an important step towards unifying the different perspectives of DBH and DLH with key evolutionary concepts.

ecology↗