Interspecific trait variation has long served as a conceptual foundation for our understanding of ecological patterns and dynamics. In particular, ecologists recognise the important role that animal behaviour plays in shaping ecological processes. An emerging area of interest in animal behaviour, the study of behavioural syndromes (animal personalities) considers how limited behavioural plasticity, as well as behavioural correlations affects an individualÕs fitness in diverse ecological contexts. In this article we explore how insights from the concept and study of behavioural syndromes provide fresh understanding of major issues in population ecology. We identify several general mechanisms for how population ecology phenomena can be influenced by a species or populationÕs average behavioural type, by within-species variation in behavioural type, or by behavioural correlations across time or across ecological contexts. We note, in particular, the importance of behavioural type-dependent dispersal in spatial ecology. We then review recent literature and provide new syntheses for how these general mechanisms produce novel insights on five major issues in population ecology: (1) limits to speciesÕ distribution and abundance; (2) species interactions; (3) population dynamics; (4) relative responses to human-induced rapid environmental change; and (5) ecological invasions.

Undergraduate researchers are often mentored by graduate or postdoctoral researchers who are in turn mentored by faculty, creating a “mentoring triad.” This study characterizes the prevalence of different mentoring triads at research universities and the relationships between undergraduates’ membership in specific triads and their research outcomes.

Undergraduate research with mentorship from faculty may be particularly important for ensuring the persistence of women and minority students in science. This study examines whether undergraduate researchers’ outcomes differ in relation to their gender or race/ethnicity and whether the mentoring structures they experience explain the differences.

Most college science, technology, engineering, and mathematics faculty members could benefit from more feedback about implementing evidence-based teaching strategies. The goals of this essay are to summarize best practices for providing feedback, to describe the current state of instructional feedback, to recommend strategies for providing feedback, and to highlight areas for research.

Undergraduate science, technology, engineering, and mathematics (STEM) students’ motivations have a strong influence on whether and how they will persist through challenging coursework and into STEM careers. Proper conceptualization and measurement of motivation constructs, such as students’ expectancies and perceptions of value and cost (i.e., expectancy value theory [EVT]) and their goals (i.e., achievement goal theory [AGT]), are necessary to understand and enhance STEM persistence and success. Research findings suggest the importance of exploring multiple measurement models for motivation constructs, including traditional confirmatory factor analysis, exploratory structural equation models (ESEM), and bifactor models, but more research is needed to determine whether the same model fits best across time and context. As such, we measured undergraduate biology students’ EVT and AGT motivations and investigated which measurement model best fit the data, and whether measurement invariance held, across three semesters. Having determined the best-fitting measurement model and type of invariance, we used scores from the best performing model to predict biology achievement. Measurement results indicated a bifactor-ESEM model had the best data-model fit for EVT and an ESEM model had the best data-model fit for AGT, with evidence of measurement invariance across semesters. Motivation factors, in particular attainment value and subjective task value, predicted small yet statistically significant amounts of variance in biology course outcomes each semester. Our findings provide support for using modern measurement models to capture students’ STEM motivations and potentially refine conceptualizations of them. Such future research will enhance educators’ ability to benevolently monitor and support students’ motivation, and enhance STEM performance and career success.