Biology subjects

Evers, S.

Publications and source records attributed to Evers, S..

4 recordsLinked to original sources

bioRxiv2022

Spatial replication should be prioritized to advance our understanding of population responses to climate

Understanding the responses of plant populations dynamics to climatic variability is frustrated by the need for long-term datasets. Here, we advocate for new studies that estimate the effects of climate by sampling replicate populations in locations with similar climate. We first use data analysis on spatial locations in the conterminous USA to assess how far apart spatial replicates should be from each other to minimize temporal correlations in climate. We find that on average spatial locations separated by 316 Km (SD = 149Km) have moderate (0.5) correlations in annual precipitation. Second, we use simulations to demonstrate that spatial replication can lead to substantial gains in the range of climates sampled during a given set of years so long as the climate correlations between the populations are at low to moderate levels. Third, we use simulations to quantify how many spatial replicates would be necessary to achieve the same statistical power of a single-population, long-term data set under different strengths and directions of spatial correlations in climate between spatial replicates. Our results indicate that spatial replication is an untapped opportunity to study the effects of climate on demography and to rapidly fill important knowledge gaps in the field of population ecology.

ecology↗

bioRxiv2022

Rpadrino: an R package to access and use PADRINO, an open access database of Integral Projection Models

O_LIDiscrete time structured population projection models are an important tool for studying population dynamics. Within this field, Integral Projection Models (IPMs) have become a popular method for studying populations structured by continuously distributed traits (e.g. height, weight). Databases of discrete time, discrete state structured population models, for example DATLife (life tables) and COMPADRE & COMADRE (matrix population models), have made quantitative syntheses straightforward to implement. These efforts allow researchers to address questions in both basic and applied ecology and evolutionary biology. There are now over 300 peer-reviewed publications containing IPMs. We describe a novel framework to quickly reconstruct these models for subsequent analyses using Rpadrino R package, which serves as an interface to PADRINO, a new database of structured population models. C_LIO_LIWe introduce an R package, Rpadrino, which enables users to download, subset, reconstruct, and extend published IPMs. Rpadrino makes use of recently created software, ipmr, to provide an engine to reconstruct a wide array of IPMs from their symbolic representations and conduct subsequent analyses. Rpadrino and ipmr are extensively documented to help users learn their usage. C_LIO_LIRpadrino currently enables users to reconstruct 280 IPMs from 40 publications that describe the demography of 14 animal and 26 plant species. All of these IPMs are tested to ensure they reproduce published estimates. Rpadrino provides an interface to augment PADRINO with external data and modify parameter values, creating a platform to extend models beyond their original purpose while retaining full reproducibility. C_LIO_LIPADRINO and Rpadrino provide a toolbox for asking new questions and conducting syntheses with peer-reviewed published IPMs. Rpadrino provides a user-friendly interface so researchers do not need to worry about the database structure or syntax, and can focus on their research questions and analyses. Additionally, Rpadrino is thoroughly documented, and provides numerous examples of how to perform analyses which are not included in the packages functionality. C_LI

ecology↗

bioRxiv2021

ipmr: Flexibly implement Integral Projection Models in R

O_LIIntegral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, color). Researchers use IPMs to investigate questions ranging from linking drivers to plant population dynamics, planning conservation and management strategies, and quantifying selective pressures in natural populations. The popularity of stage-structured population models has been supported by R scripts and packages (e.g. IPMpack, popbio, popdemo, lefko3) aimed at ecologists, which have introduced a broad repertoire of functionality and outputs. However, pressing ecological, evolutionary, and conservation biology topics require developing more complex IPMs, and considerably more expertise to implement them. Here, we introduce ipmr, a flexible R package for building, analyzing, and interpreting IPMs. C_LIO_LIThe ipmr framework relies on the mathematical notation of the models to express them in code format. Additionally, this package decouples the model parameterization step from the model implementation step. The latter point substantially increases ipmrs flexibility to model complex life cycles and demographic processes. C_LIO_LIipmr can handle a wide variety of models, including density dependence, discretely and continuously varying stochastic environments, and multiple continuous and/or discrete traits. ipmr can accommodate models with individuals cross-classified by age and size. Furthermore, the package provides methods for demographic analyses (e.g. asymptotic and stochastic growth rates) and visualization (e.g. kernel plotting). C_LIO_LIipmr is a flexible R package for integral projection models. The package substantially reduces the amount of time required to implement general IPMs. We also provide extensive documentation with six vignettes and help files, accessible from an R session and online. C_LI

ecology↗

bioRxiv2020

Local PI(4,5)P2 pool dynamics detected by the coincidence biosensor tubbyCT maintain the integrity of ER-PM junctions during PLC signaling

Phosphoinositides (PIs) are important signaling molecules and determinants of membrane identity in the eukaryotic plasma membrane, where they multi-task in divergent signaling pathways. Signaling pleiotropy likely depends on distinct PI pools in the same membrane, although the physical definition of such pools has remained ambiguous. PI(4,5)P2, specifically, is also the precursor for the second messengers in the Gq/PLC pathway, IP3 and DAG, and is broken down by PLC{beta} during signaling. Endoplasmic reticulum-plasma membrane contact sites (ER-PM junctions) have emerged as central hubs for lipid transport between both membranes, and specifically for PI homeostasis by supplying the PM with phosphatidylinositol. Here we show that the tubby protein, by virtue of its C-terminal tubby-domain, preferentially localizes to ER-PM junctions by binding to both PI(4,5)P2 and the ER-PM tether E-Syt3. Under conditions of vigorous PI(4,5)P2 consumption by PLC{beta}, additional recruitment of tubby revealed an increase of a local PI(4,5)P2 pool fed by local synthesis through PI kinases. Inhibition of this pool-filling process led to the release of the ER-PM tethers, E-Syts, from the membrane and hence to loss of integrity of the ER-PM contact sites. We conclude that spatiotemporal metabolic channeling of PI synthesis initiated by non-vesicular transport in the ER-PM junctions specifies a local pool of PI(4,5)P2 that is pivotal for the maintenance of homeostatic functions during global depletion of PI(4,5)P2. The findings further suggest that the tubby-like proteins (TULPs), so far known to impact on energy homeostasis and obesity through primary cilia signaling, have an additional function at ER-PM junctions. HIGHLIGHTSO_LIThe tubby domain preferentially assembles into ER-PM junctions due to coincidence detection of PI(4,5)P2 and E-Syt3 C_LIO_LITubby recruitment reveals an increase of a local pool of PI(4,5)P2 in ER-PM junctions during PLC{beta} signaling C_LIO_LIJunctional PI(4,5)P2 dynamics require local synthesis of PI(4,5)P2 C_LIO_LILocal PI(4,5)P2 supply is required for integrity of ER-PM junctions during PLC{beta} activity. C_LI

cell biology↗