Overview
Curiosity, passion, and focus: all toward understanding nature's wild things.
Wildlife ecology, conservation, and management comprise my ultimate passions. In my early career, I worked on a variety of projects that primarily focused on raptor ecology and conservation; whereas, today, my research foci involve projects with new taxa in unique ecological communities. I have even spent a period working with soils' data in order to advance understanding of organic carbon pools. The kinds of scientific questions I pursue tend to center around species population dynamics, their distributions, and the organizational characteristics of their communities. Each of the following projects has been pursued with great purpose and determination for I intend to foster a lifelong career in quantitative ecology, species conservation, and population-level management. With my broadened skills and interests in tow, I aim to earn appointment as a research ecologist for one of the natural resource agencies in the U.S. federal government.
Wildlife ecology, conservation, and management comprise my ultimate passions. In my early career, I worked on a variety of projects that primarily focused on raptor ecology and conservation; whereas, today, my research foci involve projects with new taxa in unique ecological communities. I have even spent a period working with soils' data in order to advance understanding of organic carbon pools. The kinds of scientific questions I pursue tend to center around species population dynamics, their distributions, and the organizational characteristics of their communities. Each of the following projects has been pursued with great purpose and determination for I intend to foster a lifelong career in quantitative ecology, species conservation, and population-level management. With my broadened skills and interests in tow, I aim to earn appointment as a research ecologist for one of the natural resource agencies in the U.S. federal government.
Projects
Integrating allometric-scaling relationships with spatiotemporal variability of abundance for terrestrial avifauna in North America.
At Michigan State University, my Ph.D. research will focus on the predictive modeling of bird populations on continental (spatial) and interannual/decadal (temporal) scales in North America. I will be using data on bird populations collected by contributors to the Breeding Bird Survey as well as various forms of environmental data (e.g., variables on climate, species body mass, habitat-use, landscape shape and complexity, etc.) from other sources. The primary goal of this project is to develop hierarchical models that predict population abundance on geographic scales for use in management. In order to make a new scientific contribution, my models will capitalize on the spatiotemporal variation in species abundance over time such that body mass and other (localized) environmental covariates can be used to predict abundance in a desired location for a given time period.
The statistical thermodynamics of photosynthesis predict increasing order of size distributions with increasing primary production.
Recent advances in the statistical physics of self replicating systems indicate that order can increase in such systems when the amount of energy processed by the system during replication is sufficient to overcome the degree of irreversibility of the movement of components of the system between non-ordered and ordered states. For a plant community, work is performed by energy captured by photosynthesis. England’s (2013; Journal of Chemical Physics 139: 121923) statement of the Second Law of Thermodynamics predicts that as the photosynthetic capacity of plant communities increases, the degree of organization of the size distribution of the plant community should increase. Our laboratory, under the direction of Brian Maurer, evaluated this prediction with the Gentry (forest transect) dataset and found it to be confirmed. We further examined the hypothesis that increases in organization of biomass distributions with increasing capacity are consistent with neutral models of community structure. We found that for the Gentry dataset, evidence indicated that the decrease in entropy with increasing biomass was not consistent with random allocation among ecologically equivalent species. My contribution to this project was to assess this random allocation hypothesis via permutation tests on species biomass within the sampled communities.
Assessing population dynamics of Lake Huron fish communities using Dynamic Factor Analysis.
An important component of understanding how human activities impact the biodiversity of Lake Huron is assessing how fish communities change over long time periods at different locations in the basin. For this project, I am working with collaborators at the Quantitative Fisheries Center (Michigan State University), Ontario Ministry of Natural Resources and Forestry, and Great Lakes Aquatic Habitat Framework (University of Michigan). Though trends in abundance for species have been previously mapped, these trends have not yet been assessed for their association with various environmental factors in Lake Huron. This is where I come in. To assess how populations respond to environmental correlates, I am using a multivariate tool for time series' data called Dynamic Factor Analysis. From the Ontario Ministry, I have received time series' data on abundance for 12 fish species, collected in five areas across Lake Huron. To date, I have fit commercial harvest data (i.e. species-specific catch in kg) to the fish time series, and I have found that lakewide harvest measures explain little variation in local fish communities, except for those species harvested in large quantities annually (e.g., lake whitefish). After receiving additional covariate data, and adjusting the analysis for more localized evaluations, I will assess additional factor models and summarize the various environmental influences on fish communities in the lake for use in regional management decisions.
Evaluating rodent population responses to silvicultural treatments on industrialized forests in northern California.
Understanding species responses to common silvicultural practices is important for management of commercial forests. Perhaps no other area in North America is as significant to industrial forest production as is the Pacific Northwest. In addition to its economic value, this area also holds great ecological value, especially in terms of local biodiversity and ecosystem function and services. For federally-threatened species like the northern spotted owl, rodents are important primary food resources. Therefore, quantifying densities of rodent communities in response to commercial forest practices is essential for a basic understanding of human impacts on local food webs. In this project, I am collaborating with individuals of the Applied Forest and Wildlife Ecology Lab at Michigan State University. During 2011 - 2013, capture-mark-recapture data were collected on a suite of rodent species inhabiting commercial forests owned by Sierra Pacific Industries, Inc. in northern California, USA. On sampled properties, four silvicultural treatments were applied at differing lengths of time, between 3 and 20 years. To quantify population densities and rodent responses to treatments, I am applying hierarchical spatial-capture-recapture models to these data. With limited captures for several species, I am aggregating rodent populations to the taxonomic level of genera. Following the summer of 2015, I expect to complete this analysis and, with our team of collaborators, provide recommendations to foresters for managing rodent communities by means of silvicultural practices on their economically (as well as ecologically) important properties.
Applying Granger-Ramanathan ensemble models to improve predictions of soil organic Carbon stocks in the United States.
Integrating modeled outcomes of ecological resources into more accurate and precise predictions thereof is useful for their future conservation and management. One resource that is important to the ecology of soils, and the organisms that depend on them, is stocks of organic Carbon. In the soil sciences, multiple methods exist for evaluating these stocks; however, few estimates of soil organic Carbon are integrated across different modeled outcomes. In 2014, soil scientists in Australia assessed different ensemble modeling approaches for predictive soil mapping, including Granger-Ramanathan model averaging (GRA), and applied those ensemble models to digital property maps of soil pH. After comparing the accuracies of the different approaches, researchers found that GRA achieved greater, if not better, predictive performance than complex ensemble modeling approaches (e.g., Bayesian model averaging), which are computationally challenging and sometimes inefficient to implement. For this project, I worked with members of the Geospatial Research Unit and U.S. Department of Agriculture's Natural Resources Conservation Service affiliated with the Division of Plant and Soil Sciences at West Virginia University. I analyzed a subset of two data sources (i.e. STATSGO2 and SSURGO products) in order to synthesize estimates of soil organic Carbon using GRA. The STATSGO2 and SSURGO databases exemplify two large archives of soil records modeled over nationwide (spatial) and multi-annual (temporal) scales. After fitting GRA models, I produced a computer program and tutorial for the methodology and also composed a guide for new users to implement GRA in a step-by-step procedure. I completed this project under the primary direction of Dr. Jim Thompson as well as the secondary direction of Drs. Sharon Waltman and Travis Nauman at West Virginia University.
Integrating citizen-science data with movement models to estimate raptor populations: a case study with golden eagles in eastern North America.
Estimating population size is fundamental to conservation and management. Population size is typically estimated using survey data, computer models, or both. Some of the most extensive and often least expensive survey data are those collected by citizen-scientists. A challenge to citizen-scientists is that the vagility of many organisms can complicate data collection. As a result, animal-movement effects on data collection can adversely affect modeling of those data. Thus, it would be helpful to develop methods that integrate citizen-science datasets with models that account for animal movement. In my Master's research, I used hawk-count data collected by citizen-scientists to estimate the number of golden eagles migrating through Pennsylvania, USA. To do this, I designed a computer model to simulate migratory flights of eagles to estimate what proportion of the population is available (i.e., within visible range or close enough) to be counted at migration monitoring sites in Pennsylvania. I then conducted a multi-state mark-recapture analysis to estimate detection probability (i.e., the rate at which birds within visible range are actually observed) of migrating eagles. Finally, I used availability rates and detection probabilities to adjust raw hawk-count data to produce estimates of population size. My models suggest that 24% (± 14; mean ± SE) of migrating golden eagles are available to be counted at hawk-count sites, and that 55% (± 1.6) of the available eagles are detected by hawk-count observers in Pennsylvania. Furthermore, I estimate that 5,122 (± 1,338) golden eagles migrate annually through the commonwealth. This analysis provides the first quantitative estimate of the size of the eastern golden eagle population, and with it, I demonstrate the utility of one approach to use (widely available) citizen-science data to address a pressing conservation goal—that of population size estimation. I completed this project at West Virginia University under the primary direction of Todd Katzner as well as the secondary direction of Drs. Adam Duerr, David Brandes, and George Merovich, Jr.
Effective dispersal of peregrine falcons in the Midwestern United States.
Dispersal is a significant life-history trait of vagile species that affects the distribution and genetic structure of populations. Natal dispersal in birds is the movement of an individual from its hatch site to a new location where its first reproductive effort occurs. In my undergraduate research, I assessed the influence of sex, hack-status (hacked or wild-fledged), and hatch site (cliff or human-made) on natal dispersal distance, and I also evaluated directional trends of dispersal in the Midwestern peregrine falcon subpopulation. I found that mean dispersal distance of female peregrines was >2 times farther than that of males. Dispersal distance did not differ between hacked females and wild-fledged females; however, hacked males dispersed significantly farther than wild-fledged males. Dispersal distance among urban-hatched females and cliff-hatched females did not differ, nor did dispersal distance of urban-hatched males and cliff-hatched males, probably because the sample size for cliff-hatched birds was so small and underrepresented in the dataset. As a whole, the direction of dispersal in Midwestern peregrines was nonuniformly distributed and skewed to the northwest and southeast cardinal directions. This research may, in fact, benefit future studies of peregrine demographics and population viability analyses by providing wildlife managers with information about the patterns and natal movements of peregrine falcons in the Midwestern United States, toward their regional recovery. I completed this project at Southern Illinois University under the primary direction of Sarah Wakamiya as well as the secondary direction of Dr. Eric Hellgren.
