Undergraduate Honors Theses

Thesis Defended

Spring 2011

Document Type



Ecology and Evolutionary Biology

First Advisor

Dr. Christy McCain


Understanding patterns of biodiversity is one of the pressing research areas in ecology given global conservation demands. The mechanisms that produce biodiversity are still debated; however, environmental productivity is often thought to be responsible for generating biodiversity since species richness and environmental productivity are generally positively correlated. Species richness is the measurement of the number of species within a given area. Few studies have examined the relationship between species richness of small mammals and environmental productivity across elevational gradients with multiple measures of environmental productivity. For ten sites along an elevational gradient in Colorado’s Front Range, we examined several factors as possible measures of environmental productivity, including temperature, precipitation, food resource abundance (arthropod and understory plant biomass), and small mammal abundance. Small mammal populations were estimated from mark-and-recapture data from the summer of 2010, and we evaluated four estimation methods, including minimum number of individuals known alive (MNKA), modified Lincoln-Peterson and Schnabel methods, and the Jackknife estimator (Program CAPTURE). Mark-and-recapture is a trapping technique that allows for population sizes to be mathematically estimated according to the number of individuals marked, and the number of individuals recaptured. The population estimate of MNKA, is the number of individuals marked in a trapping effort. The modified Lincoln-Peterson, Schnabel methods, and Jackknife estimators use markand-recapture data to mathematically derive population estimates. All population estimates were highly correlated (average r2 = 0.9800). Small mammal diversity was strongly positively correlated to understory plant biomass (r2 = 0.6404, p-value = 0.0033), temperature (r2 = 0.6212, p = 0.0041), precipitation (r2 = 0.6438, p-value = 0.0032), and small mammal abundance (MNKA; r2 = 0.5142, p-value = 0.0118). However, multivariate regression models for small mammal diversity and small mammal abundance only included understory plant biomass (r2 = 0.7005 and 0.6695, respectively) as the single necessary predictor among the various measurements of environmental productivity. In our preliminary analysis of the first year of sampling, understory plant biomass seems to be a good predictor of local small mammal diversity in the Front Range, Colorado.