Over the last century, a predominant number of biological investigations utilized either model systems or laboratory populations for experimentation. While model organisms are extensively studied from diverse perspectives (genetics, behaviour, life-history, etc.) it would be imprudent to assume new organism-oriented discoveries are behind us. Most recently, Stewart et al. (2018) revealed the existence of a new male type in the laboratory model organism, the bulb mite Rhizoglyphus robini.
We seek out ecologists with diverse backgrounds and perspectives to highlight their work and share their stories and experiences. Check out this week’s Ecologist Spotlight: Judith Weis.
This is a series of posts entitled “Reflections on the Past”, a series by Hari Sridhar. Hari interviews authors of well-known papers in ecology for first-hand accounts of the ins-and-outs of high-impact research. Posts in this series are archived at reflectionsonpaperspast.wordpress.com.
We seek out ecologists with diverse backgrounds and perspectives to highlight their work and share their stories and experiences. Check out this week’s Ecologist Spotlight: Earyn McGee.
We are in great need of an integrative framework that allows ecologists to predict life history strategies from functional traits that inform on population performance. The aim of a recent British Ecological Society cross-journal Special Feature is to link organismal functions, life history strategies and population performance. Here I discuss a test published within this Special Feature that shows how a recently developed dynamic energy budget population model can be used to infer from life history traits the population performance of bulb mites (Rhizoglyphus robini) in the lab.
Are you about to embark on your graduate school journey? Exciting times indeed! Here’s a few tips on what I found made me feel successful during my first year.
The bright spots approach aims to assess the performance of managed ecosystems by comparing certain ecological outcomes, while controlling for other known drivers of the outcome via a statistical model; in effect, ranking sites based on their residuals from the fitted model. While the method has the potential to reduce bias in comparing different sites, the resulting assessment may come with high variance.