Forecasting Natural Regeneration in the Great Basin with Ecological Theory
and Remote Sensing
Authors: Zaiats Andrii 
Published: January 2025

Natural regeneration is the foundation of resilient ecosystems. Anthropogenic
pressures across the globe have caused the decline of ecosystem diversity,
distribution, and functional properties essential to global and local
communities. An example of the Great Basin ecosystems in the Western United
States demonstrates how foundational shrub species declines due to intensified
wildfires and other anthropogenic pressures, undermining the resilience across
ecological and social-ecological systems. Ecological restoration aims to assist
ecosystems in recovery after wildfires. However, restoration efforts in the
Great Basin have had limited success in great part due to highly unpredictable
rates of natural regeneration and spatially variable ecosystem functions that
underlie resilience. This work approaches the problem by developing forecasting
capacity and understanding natural regeneration using satellite and airborne
remote sensing technology. It further demonstrates how remote sensing, including
unoccupied aerial systems (UAS), provides the means to improve the monitoring of
extensive landscapes by capitalizing on population abundance estimates. Chapter
1 aimed to estimate the post-wildfire recovery of Artemisia
spp., foundational shrub species in the region, using a decadal remote sensing
data source at a relatively coarse resolution. Using a spatially explicit
approach to find similarities between > 400 wildfires undergoing natural
regeneration, we found that a simple population model can forecast the recovery
trajectory without using local data. In other words, insights about natural
regeneration are transferrable among ecologically similar landscapes. Chapter 2
focused on high resolution UAS imagery as a data source to understand the
relationship between functional (resistance and recovery) and structural
properties of shrubland communities. In establishing this relationship,
structural properties based on 3D vegetation reconstruction depend on the
spatial scale of the summary metric. We used scale decomposition of structural
heterogeneity to show how resistance and recovery manifest scale-specific
associations with structure. The results establish a scale-explicit dependence
on key functional ecosystem properties and structural patterns. Chapter 3
extends the utility of aerial imagery as a source for population abundance
estimates of Artemisia tridentata. The key challenge to
estimating abundance from aerial imagery is the presence of observation errors
that can significantly bias predictions and inference. Observation errors can
include missed and false detections, which require different analytical
solutions to avoid biased results. We use a hierarchical state-space model that
accounts for both types of observation errors to evaluate the landscape drivers
of observation errors and abundance. Applying state-space models to aerial
imagery allowed the pursuit of predictive and inferential questions that may
otherwise be impossible due to observation errors. Overall, this work advances
our capacity to better forecast, understand, and monitor natural regeneration in
the Great Basin. By expanding the scales of data acquisition, remotely sensed
data can be as valuable in meeting management-oriented monitoring and predictive
goals as they are in advancing ecological theory.