Browsing by Author "Koons, David, committee member"

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A case for context in quantitative ecology: statistical techniques to increase efficiency, accuracy, and equity in biodiversity research
(Colorado State University. Libraries, 2024) McCaslin, Hanna M., author; Bombaci, Sara, advisor; Hooten, Mevin, committee member; Koons, David, committee member; Hoeting, Jennifer, committee member
The current era of ecological research is characterized by rapid technological innovation, large datasets, and numerous computational and quantitative techniques. Together, big data and advanced computing are expanding our understanding of natural systems, allowing us to capture more complexity in our models, and helping us find solutions for salient challenges facing modern ecology and conservation, including climate change and biodiversity loss. However, large datasets are often characterized by noise, complex observational processes, and other challenges that can impede our ability to apply these data to address ecological research gaps. In each chapter of this dissertation, I seek to address a data problem inherent to the 'big data' that characterizes modern ecological research. Together, they extend the strategies available for addressing a problem facing many ecologists – how to make use of the large volumes of data we are collecting given (1) current computational limitations and (2) specific sampling biases that characterize various methods for data collection. In the first chapter, I present a recursive Bayesian computing (RB) method that can be used to fit Bayesian hierarchical models in sequential MCMC stages to ease computation and streamline hierarchical inference. I also demonstrate the application of transformation-assisted RB (TARB) to a hierarchical animal movement model to create unsupervised MCMC algorithms and obtain inference about individual- and population-level migratory characteristics. This recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. Transformation-assisted RB is a relatively accessible method for reducing the computational demands of fitting complex ecological statistical models, like those for animal movement, multi-species systems, or large spatial and temporal scales. Biodiversity monitoring projects that rely on collaborative, crowdsourced data collection are characterized by huge volumes of data that represent a major facet of 'big data ecology,' and quantitative methods designed to use these data for ecological research and conservation represent a leading edge of contemporary quantitative ecology. However, because participants select where to observe biodiversity, crowdsourced data are often influenced by sampling bias, including being biased toward affluent, white neighborhoods in urban areas. Despite the growing evidence of social sampling bias, research has yet to explore how socially driven sampling bias impacts inference and prediction informed by crowdsourced data, or if existing data pre-processing or analytical methods can effectively mitigate this bias. Thus, in Chapters 2 and 3, I explored social sampling bias in data from the crowdsourced avian biodiversity platform eBird. In Chapter 2, I studied patterns of social sampling bias in the locations of eBird "hotspots" to determine whether hotspots in Fresno, California, U.S.A. are more biased by social factors than the locations of Fresno eBird observations overall. My findings support previous work showing that eBird locations are biased by demographics. Further, I found that demographic bias is most pronounced in the locations of hotspots specifically, with hotspots being more likely to occur in areas with higher proportions of non-Hispanic white residents than eBird locations overall. This relationship is reinforced because hotspots in these predominantly white areas also amass more eBird checklists overall than hotspots in areas with more demographic diversity. These findings raise concerns that the eBird hotspot system may be exacerbating spatial bias in sampling and reinforcing patterns of inequity in data availability and eBird participation, by leading to datasets and user-facing maps of birding hotspots that mostly represent predominantly white neighborhoods. Then, in Chapter 3, I investigated the impacts of not accounting for socially biased sampling when using eBird data to study patterns of urban biodiversity. The luxury effect has emerged as a prominent hypothesis in urban ecology, describing a pattern of higher biodiversity associated with greater socioeconomic status observed in many cities. Using eBird data from 2015-2019, I tested whether an avian luxury effect is observed in Raleigh-Durham, North Carolina, U.S.A. before and after accounting for social sampling bias. By jointly modeling sampling intensity and species richness, I found that sampling intensity and species richness are positively correlated and sampling bias influences the estimated relationship between species richness and income. Thus, failing to account for sampling bias can hinder our ability to accurately observe social-ecological dynamics. Additionally, I found that randomly spatially subsampling eBird data prior to analysis, as recommended by existing guidelines to mitigate sampling bias in eBird data, does not reduce biased sampling related to demographics, because there are data gaps in communities of color and low-income communities that cannot be addressed via spatial subsampling. Therefore, it is paramount that crowdsourced and contributory science projects prioritize more equitable participation in their platforms, both for more ethical, equitable practice and because current sampling inequity negatively impacts data quality and project goals. Quantitative techniques can help us understand the complex observational processes influencing ecological data, and each chapter of this dissertation highlights how tailoring statistical or computing methods to these observational contexts can advance ecological knowledge – either by extending the complexity of models we can feasibly fit, as in Chapter 1, or by acknowledging and accounting for sampling inequity, in Chapters 2 and 3. We are all participants actively shaping the ecological processes we observe, and the actions, approaches, and assumptions used in our research reflect societal systems and biases. Data are never objective, and it is dangerous and false to assume that quantitative techniques can take data out of the contexts in which they were collected. Instead, quantitative frameworks that embrace, reflect, and seek to improve the ways in which social and observational contexts inform what is observed can elevate analytical techniques to tools towards more just, inclusive, and transparent ecological research and conservation.
Open Access
Avian influenza takes flight: host mobility, viral prevalence, and transmission at large spatial scales
(Colorado State University. Libraries, 2024) Berger, Brooke, author; Webb, Colleen, advisor; Miller, Ryan, committee member; Pepin, Kim, committee member; Gorsich, Erin, committee member; Koons, David, committee member
Many pathogens have large geographic distributions, but we currently have little ability to predict how they may change over time. Understanding what mechanisms drive the large-scale distributions and movements of pathogens is critical to designing effective surveillance programs, disease interventions, and predictive models of disease spread. Additionally, we lack information on what spatial scale these mechanisms are most important. In this dissertation we address one of the fundamental problems in ecology, the "problem of pattern and scale", in the context of disease prevalence and spatial transmission. Are the large-scale patterns we see emergent properties of many small-scale processes, or are they a product of large-scale processes themselves? We focused on the spatial distribution, prevalence and spatial transmission of influenza A virus in its endemic host, wild waterfowl. We used a zero-inflated Bayesian CAR model to determine if local environmental persistence of the virus or regional host migration were better predictors of large-scale patterns of prevalence. We found that an unweighted host migration network better predicted high and low values of prevalence than did local drivers. To understand how these factors impacted where IAV moves in the United States (US) we investigated how local-scale transmission and regional-scale host movements influence large-scale spatial transmission and our ability to detect these transmissions. We developed a Bayesian zero-inflated binomial network model to estimate the probability of spatial transmission between watersheds pairs. We found that regional host movement was the best predictor of spatial transmission and that Mallard ducks likely play a special role in moving the virus throughout the US. Viral movement patterns were closely associated with important waterfowl breeding and wintering habitats rather than flyways, as has been previously shown. In order to extend these analyses to other geographic areas and host species we need to construct continental scale host movement networks from movement data with differing spatial and temporal resolutions. We developed a method to simulate host movements from very few observations allowing us to match mark-recovery data to highly detailed satellite telemetry data. We used the biological information in the detailed movement data to estimate population posterior distributions of travel speed, turning angle, and direction. These quantities and an approximately Bayesian rejection scheme were used to simulate missing locations in the mark-recovery data with estimates of uncertainty. The method was validated with a telemetry dataset tracking the movement of Northern Pintail ducks, an important host of IAV. Movement networks constructed from simulated locations captured known population scale migration patterns of Pintail and exhibited similar higher order community structure. More broadly, this research contributes to our understanding of how host mobility impacts the prevalence and movement patterns of pathogens, and the spatial scale at which this mechanism is important. Our findings suggest that predicting the spread and spillover risk of IAV requires an understanding of where hosts move at the regional scale. In the future, as climate and land-use change alter the migration patterns of wild waterfowl, we can expect the distribution and movement patterns of IAV to shift as well.
Open Access
Bayesian methods for spatio-temporal ecological processes using imagery data
(Colorado State University. Libraries, 2021) Lu, Xinyi, author; Hooten, Mevin, advisor; Kaplan, Andee, committee member; Fosdick, Bailey, committee member; Koons, David, committee member
In this dissertation, I present novel Bayesian hierarchical models to statistically characterize spatio-temporal ecological processes. I am motivated by the volatility of Alaskan ecosystems in the face of global climate change and I demonstrate methods for emerging imagery data as survey technologies advance. For the nearshore marine ecosystem, I developed a model that combines ecological diffusion and logistic growth to quantify colonization dynamics of a population that establishes long-term equilibrium over a heterogeneous environment. I also unified modeling concepts from entity resolution and capture-recapture to identify unique individuals of the population from overlapping images and infer total abundance. For the terrestrial ecosystem, I developed a stochastic state-space model to quantify the impact of climate change on the structural transformation of land cover types. The methods presented in this dissertation provide interpretable inference and employ statistical computing strategies to achieve scalability.
Open Access
Home range estimates, habitat selection, and nesting behavior of Ferruginous Hawks (Buteo regalis) in western Wyoming
(Colorado State University. Libraries, 2024) Ramirez, Sarah Kathleen, author; Pejchar, Liba, advisor; Koons, David, committee member; Angeloni, Lisa, committee member
Oil and gas development has the potential to negatively impact wildlife, but the consequences for some raptor species are less well understood. Ferruginous Hawks could be particularly susceptible to negative effects due to their large habitat requirements and sensitivity to anthropogenic disturbance. Given the rapid expansion of oil and gas development in many parts of the range of Ferruginous Hawks, it is critical to evaluate habitat use in both a pre-construction and post-construction environment. Understanding selection of habitat resources and nest sites, as well as the factors that contribute to home range estimates, nest success and nest productivity could help inform efforts to mitigate against potential negative effects of land use change. In my first chapter, I aimed to investigate factors associated with breeding Ferruginous Hawk home range estimates and habitat selection in a landscape slated for energy development. In a sagebrush-steppe study site in western Wyoming, I captured breeding hawks and used radio and satellite-telemetry to collect location data, estimate home range estimates, and model habitat selection. Home range estimates were smaller for females and hawks with egg-laying breeding status, and larger with increasing numbers of producing wells. Ferruginous Hawks selected habitat with high terrain ruggedness, low shrub cover, and areas closer to primary prey, and avoided areas with high density of wells. The relationship between lagomorph density and distance to development was dependent on scale. My findings show that home range estimates are smaller in my study relative to other parts of the species' range, and that future energy development is likely to reduce habitat quality and availability for Ferruginous Hawks. In my second chapter, I investigated the factors associated with nest site selection, success, and productivity in the same study site in western Wyoming. I used an existing dataset on nest site locations, nest success, and productivity, and collected new data on these response variables between 2019 and 2023. I used a resource selection function model (RSF) to evaluate nest site selection and used generalized linear mixed models (GLMMs) to evaluate nest success and productivity. Ferruginous Hawks selected nest sites in developed-open space landcover (e.g., areas cleared of vegetation with little or no infrastructure), higher topographic position index (TPI), and in closer proximity to producing wells (km). In contrast, breeding hawks avoided nest sites in areas with higher densities of producing wells (per km2) and more shrub cover (%). Nest success and productivity of egg-laying pairs was positively associated with artificial nesting platforms (ANPs) and negatively associated with anthropogenic structures and rocky outcrops, developed-open space landcover, TPI and year. These findings suggest that Ferruginous Hawks may be subject to an ecological trap when they nest on anthropogenic structures, but that ANPs are a potentially viable tool for mitigation.
Open Access
The effect of multiple stressors on Kaua'i's forest birds: demography, health, and trophic cascades
(Colorado State University. Libraries, 2024) Romero, Ashley Cozette, author; Pejchar, Liba, advisor; Koons, David, committee member; Hart, Sarah, committee member
Island ecosystems harbor unparalleled biodiversity, providing habitat for a disproportionate share of the world's species relative to their area. Nevertheless, the vulnerability of islands to disturbances that result in negative consequences renders them among the most endangered environments. Bird populations on islands are particularly susceptible and often strongly affected by invasive species, habitat loss, climate change, and disease, all of which has resulted in widespread decline and extinction. In the Hawaiian Islands, native forest bird populations confront numerous threats, with the majority of remaining native bird populations classified as threatened, endangered, or teetering on the brink of extinction. To sustain and recover island bird populations, it is essential to understand the direct and indirect impacts of anthropogenic changes on forest bird populations, and to what degree each driver of change influences bird health, demographics, and habitat quality. The goals of this project were to quantify how various stressors influence the body condition and sex ratios of Kaua'i's forest birds, and to assess if primary and secondary forest regeneration processes that affect forest bird resource and habitat availability are influenced by invasive rodent control. To address these questions, I collected and analyzed three years of forest bird banding data in the Alaka'i Wilderness Preserve on Kaua'i's Island (Hawai'i, U.S.A.) from 2021 to 2023. Birds were banded in and outside of areas that have been subject to intensive rodent control for the past 5-9 years. I focused on four focal species of birds, three native and one non-native, and collected data to assess the body condition and sex of these captured birds. Mixed effects models were used to determine whether body condition was significantly associated with rodent control intensity on a local and landscape scale, malarial infection status, mean temperature during the peak mosquito breeding season, frequency of extreme rain events, canopy density, and topographic wetness index (TWI). Similarly, I used general linear models to test if rodent control intensity, malarial infection status, or date of capture was associated with a higher or lower likelihood of encountering a female. I found that body condition was negatively associated with positive malarial status for all but one of the focal species, and higher average temperatures and TWI were negatively associated with body condition for all species. None of the predictor variables evaluated were strong predictors of the sex of captured birds. To evaluate the effects of rodent control on forest regeneration processes that sustain bird habitat in the Alaka'i Wilderness Preserve, I examined whether rodent control was associated with changes in seed rain, fruiting density, and seedling emergence. Seed traps were established in February of 2023 and were checked periodically over a six-month period to obtain seed rain data from within and outside of areas of rodent control. Seeds collected from traps were identified to the lowest possible taxonomic level. During each visit to collect seed trap contents, the number of fruits on all fruiting plants within five meters of each seed rain trap was estimated. These estimates were used to calculate relative fruiting densities in treatment and reference plots. To examine seedling emergence, emergence plots in treatment and reference plots were cleared of all vegetation in July of 2022. I used Mixed Effects Models to compare seed rain, fruiting density, and seedling emergence between sites with and without rodent control and found no differences in seed rain, seedling emergence, or fruiting density within and outside of rodent control. My findings suggest that variables that are related to disease transmission may be more influential in predicting the health of forest birds than variables related to predation risk or food and resource availability. These findings also suggest that rodent control, at least at current levels of intensity, may not play a significant role in forest regeneration in Kaua'i's 'Alaka'i Plateau. Further research is needed to understand the effectiveness of rodent control in this system, but reducing the prevalence of avian malaria and mitigating climate change will be critical to alleviating sub-lethal effects on Kaua'i's forest birds. This study contributes to our understanding of the sometimes complex and synergistic effects of invasive predators, disease, forest structure, and climate on forest birds and the dynamics of insular ecosystems. My findings and approach could have implications for conservation and restoration in the many areas globally where rat invasion, climate change, and disease may interact to pose similar threats.
Open Access
Using population ecology to advance stream community assembly
(Colorado State University. Libraries, 2019) Pregler, Kasey C., author; Kanno, Yoichiro, advisor; Bailey, Larissa, committee member; Koons, David, committee member; Poff, LeRoy, committee member
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