Using Active Learning to Teach Concepts and Methods in Quantitative Biology.

This article provides a summary of the ideas discussed at the 2015 Annual Meeting of the Society for Integrative and Comparative Biology society-wide symposium on Leading Students and Faculty to Quantitative Biology through Active Learning. It also includes a brief review of the recent advancements in incorporating active learning approaches into quantitative biology classrooms. We begin with an overview of recent literature that shows that active learning can improve students' outcomes in Science, Technology, Engineering and Math Education disciplines. We then discuss how this approach can be particularly useful when teaching topics in quantitative biology. Next, we describe some of the recent initiatives to develop hands-on activities in quantitative biology at both the graduate and the undergraduate levels. Throughout the article we provide resources for educators who wish to integrate active learning and technology into their classrooms.

Thermal sensitivity of growth rate in hatchling Sceloporus lizards: environmental, behavioral and genetic aspects.

To investigate the physiological, behavioral, and genetic contributions to growth rate, we studied the thermal sensitivity of growth rate in hatchlings of the iguanid lizards Sceloporus occidentalis and S. graciosus in the laboratory. We used a cycling thermal regime patterned after thermal environments found in nature. Growth rates increased with duration of access to radiant heat. Thus, variation in the thermal environment can cause phenotypic variation in growth rate and hence body size. The two species differed in both the magnitude and thermal sensitivity of growth rate, and these differences were associated with differences in behavioral thermoregulation. Thus, growth is determined interactively by both behavior and physiology. We found evidence of among-family variation in the growth rates of S. occidentalis, suggesting that growth rate has the genetic potential to evolve. In S. occidentalis, both growth rate and egg size affected body size of hatchlings at several weeks of age. In turn, hatchling size may affect fitness: for example, larger S. occidentalis hatchlings had higher sprint speeds and may therefore be more adept at capturing prey or evading predators. Our results demonstrate that growth rate has genetic, behavioral, and physiological components, and that the resulting effects on body size may have important consequences for ecological performance e.g., sprint speed.

Foraging by Passerine birds and Anolis lizards on St. Eustatius (Neth. Antilles): implications for interclass competition, and predation.

Observations of foraging and diet for eight Passerine bird, two Anolis lizard, and one Ameiva lizard species in dry sclerophyll scrub on St. Eustatius (Neth. Antilles) show that none of the bird species competes as much with either anole as does the other anole, or the Ameiva. Anoles feed on insects, primarily on the ground and in low vegetation; on St. Eustatius no mainly insectivorous bird species feeds primarily in these places. Instead, the main presentday interaction between birds and anoles is predation. The abundant pearly-eyed thrasher (Margarops fuscatus) and the sparrow hawk (Falco sparverius) are the major predators on anoles. This predation has little effect on the niche relations of the anoles, according to a model for the coevolution of competing species.

Latitudinal and climatic variation in body size and dorsal scale counts in Sceloporus lizards:a phylogenetic perspective.

Squamates often follow an inverse Bergmann's rule, with larger-bodied animals occurring in warmer areas or at lower latitudes. The size of dorsal scales in lizards has also been proposed to vary along climatic gradients, with species in warmer areas exhibiting larger scales, putatively to reduce heat load. We tested for these patterns in the diverse and widespread lizard genus Sceloporus. Among 106 species or populations, body size was associated positively with maximum temperature (consistent with the inverse of Bergmann's rule) and aridity, but did not covary with latitude. Scale size (inferred from the inverse relation with numbers of scales) was positively related to body size. Controlling for body size via multiple regression, scale size was associated negatively with latitude (best predictor), positively with minimum temperature, and negatively with aridity (similar results were obtained using scores from a principal components analysis of latitude and climatic indicators). Thus, lizards with larger scales are not necessarily found in areas with higher temperatures. Univariate analyses indicated phylogenetic signal for body size, scale counts, latitude, and all climate indicators. In all cases, phylogenetic regression models fit the data significantly better than nonphylogenetic models; thus, residuals for log(10) number of dorsal scale rows exhibited phylogenetic signal.

Estimating maximum performance: effects of intraindividual variation.

Researchers often estimate the performance capabilities of animals using a small number of trials per individual. This procedure inevitably underestimates maximum performance, but few studies have examined the magnitude of this effect. In this study we explored the effects of intraindividual variation and individual sample size on the estimation of locomotor performance parameters. We measured sprint speed of the lizard Sceloporus occidentalis at two temperatures (20 degrees C and 35 degrees C), obtaining 20 measurements per individual. Speed did not vary temporally, indicating no training or fatigue effects. About 50% of the overall variation in speed at each temperature was due to intraindividual variation. While speed was repeatable, repeatability decreased slightly with increasing separation between trials. Speeds at 20 degrees C and 35 degrees C were positively correlated, indicating repeatability across temperatures as well. We performed statistical sampling experiments in which we randomly drew a subset of each individual's full set of 20 trials. As expected, the sample's maximum speed increased with the number of trials per individual; for example, five trials yielded an estimate averaging 89% of the true maximum. The number of trials also influenced the sample correlation between mean speeds at 20 degrees C and 35 degrees C; for example, five trials yielded a correlation coefficient averaging 90% of the true correlation. Therefore, intraindividual variation caused underestimation of maximal speed and the correlation between speeds across temperatures. These biases declined as the number of trials per individual increased, and depended on the magnitude of intraindividual variation, as illustrated by running sampling experiments that used modified data sets.