Our planet is undergoing rapid change due to the expanding human population and climate
change, which leads to extreme weather events and habitat loss. It is more important than
ever to develop methods which can monitor the impact we are having on the biodiversity of
our planet. To influence policy changes in wildlife and resource management practices we
need to provide measurable evidence of how we are affecting animal health and fitness and
the ecosystems needed for their survival. We also need to pool our resources and work in
interdisciplinary teams to find common threads which can help preserve biodiversity and
vital habitats. This dissertation showcases how improved molecular biology assays and data
analysis approaches can help monitor the fitness of animal populations within changing ecosystems.
Chapter 1 details the development of a universal telomere assay for vertebrates. Recent
work has shown the utility of telomere assays in tracking animal health. Telomere lengths
can predict extinction events in animal populations, life span, and fitness consequences of
anthropogenic activity. Telomere length assays are an improvement over other methods of
measuring animal stress, such as cortisol levels, since they are stable during capture and
sampling of animals. This dissertation provides a telomere length assay which can be used
for any vertebrate. The assay was developed using a quantitative polymerase chain reaction
platform which requires low DNA input and is rapid. This dissertation also demonstrates how
this assay improves on current telomere assays developed for mice and can be used in a
vertebrate not previously assayed for telomere lengths, the American kestrel. This work has
the potential to propel research in vertebrate systems forward as it alleviates the need to
develop new reference primers for each species of interest. This improved assay has shown
promise in studies in mouse cell line studies, American kestrels, golden eagles, five species
of passerine birds, osprey, northern goshawks and bighorn sheep.
Chapter 2 presents a machine learning analysis, using a topic model approach, to integrate
big data from remote sensing, leaf area index surveys, metabolomics and metagenomics to analyze
community composition in cross-disciplinary datasets. Topic models were applied to understand
community organization across a range of distinct, but connected, biological scales within the
sagebrush steppe. The sagebrush steppe is home to several threatened species, including the
pygmy rabbit (Brachylagus idahoensis) and sage-grouse (Centrocercus urophasianus).
It covers vast swaths of the western United States and is subject to habitat fragmentation and
land use conversion for both farming and rangeland use. It is also threatened by increases in
fire events which can dramatically alter the landscape. Restoration efforts have been hampered
by a lack of resources and often by inadequate collaboration between stakeholders and scientists.
This work brought together scientists from four disciplines: remote sensing, field ecology,
metabolomics and metagenomics, to provide a framework for how studies can be designed and
analyzed that integrate patterns of biodiversity from multiple scales, from the molecular to
the landscape scale. A topic model approach was used which groups features (chemicals, bacterial
and plant taxa, and light spectrum) into “communities” which in turn can be analyzed for their
presence within individual samples and time points. Within the landscape, I found communities
which contain encroaching plant species, such as juniper (Juniperus spp.) and cheatgrass
(Bromus tectorum). Within plants, I found chemicals which are known toxins to herbivores.
Within herbivores, I identified differences in bacterial taxonomical communities associated with
changes in diet. This work will help to inform restoration efforts and provide a road map for
designing interdisciplinary studies.
Mapping wildfire danger at regional scale with an index model integrating coarse spatial resolution remote sensing data
