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Browsing by Author "Hooten, Mevin, 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.
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    Big fish start small
    (Colorado State University. Libraries, 2020) Leach, Clinton, author; Webb, Colleen, advisor; Poff, LeRoy, committee member; Hooten, Mevin, committee member; Noon, Barry, committee member
    Individuals of the same species often participate in substantially different predator-prey interactions. In many species, these differences are driven by individual size and the ontogenetic niche shifts that occur as an individual grows. This intraspecific size-structure can have profound consequences for our understanding of food web structure and community dynamics. These consequences are particularly important in exploited marine ecosystems where fisheries often target the largest individuals and size-structured feedbacks have been implicated in preventing collapsed fisheries from recovering. In this dissertation, we explored the consequences of this size-structure for the Scotian Shelf and Gulf of Alaska ecosystems. To understand how the collapse of the cod stock on the Scotian Shelf may have fed back on the demographic landscape of cod, we developed a model to estimate how the length-dependent growth and survival of cod changed before and after the collapse. We found that forage fish, released from top-down control, likely played an important role in limiting cod access to food, with consequences for cod survival and the potential for long term recovery. To better understand the community context of these changes, we developed a multivariate autoregressive model to capture how shifts in species' size distributions may have driven changes in the interspecific interaction landscape on the Scotian Shelf. This study found further evidence for the role of forage fish in preventing cod recovery, and linked the corresponding changes in interaction structure to an increase in the overall instability of the system. Lastly, we explored the community structure of ontogenetic niche shifts in the Gulf of Alaska by developing a model to identify trophic groups — collections of individuals with similar interaction patterns — in an individual-level food web assembled from stomach contents data. The identified trophic groups revealed substantial overlap in the ontogenetic trajectories of Gulf of Alaska predator species and the low-dimensional structure of the individual-level food web. This work represents a step toward incorporating individual-level processes into modeling frameworks that can be used to both inform existing theory with data and to inform fisheries management. Specifically, this research highlights the different trophic roles that individuals of a species occupy as they grow, and the importance of growth in moving individuals up the food web and maintaining community structure and stability. Our findings suggest that disruptions to this flow and the resulting loss of large individuals can generate a cascade of effects through the system, leading to fundamental reorganization and increased instability.
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    Hierarchical Bayesian models for population ecology
    (Colorado State University. Libraries, 2017) Ketz, Alison C., author; Hobbs, N. Thompson, advisor; Hooten, Mevin, committee member; Wittemyer, George, committee member; Webb, Colleen, committee member
    Models, by their definition, are abstractions of the systems they describe and require a delicate balance of inclusion of information with reduction. Hierarchical Bayesian models are well suited for ecological problems, because they facilitate the decomposition of highly complex ecological systems into lower dimensional elements. We can partition variability that arises from the ecological processes separately from variability that arises from sampling error, thereby rigorously accounting for uncertainty. In this way, we can better answer questions pertaining to the ecology of populations and aid in better management of their ecosystems. Estimation of abundance is the central challenge in population ecology, and we begin this dissertation by addressing the problem of determining the population size of elk across multiple time and spatial scales during five winters. In Chapter 2, I build upon existing multi- state mark-recapture methods using a hierarchical Bayesian N-mixture model with multiple sources of commonly collected data on abundance, movement, and survival, to accurately estimate the abundance of a mobile population of elk on the winter range of Rocky Mountain National Park and Estes Park, CO. Classification data are used in ecology to examine population trends through model-based theoretical approaches. For ungulates such as elk, wildlife managers use sex-ratios and stable age or stage distributions to assess population growth or decline. However, physical ambiguities and observer skill can lead to biased results. In Chapter 3, I develop two hierarchical models to address the sample bias that results when data are missing-not-at-random, which occurs when individuals are observed but not classified. Forecasts are used to aid management to evaluate the probability that resource objectives will be met given different management actions. In Chapter 4, I develop a hierarchical model incorporating a discrete time, stage structured model assimilated with abundance and classification data, to provide forecasts under a variety of management actions to aid decision makers to meet objectives. I use Bayesian hierarchical models that incorporate multiple sources of information to address common estimation problems that arise in population ecology. We are frequently interested in constructs and latent processes that are not necessarily observable in ecological systems. I use theoretical models of the underlying processes to extract information pertaining to populations and management goals. Compounding the challenge is that we must rely upon survey samples rather than complete census. I illustrate the utility of hierarchical Bayesian models using data on the population of elk (Cervus elaphus nelsoni) on the winter range of Rocky Mountain National Park in Colorado, USA.
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    Jumping and swimming performance of burbot and white sucker: implications for barrier design
    (Colorado State University. Libraries, 2014) Gardunio, Eric, author; Myrick, Christopher, advisor; Bestgen, Kevin, committee member; Hooten, Mevin, committee member; Bledsoe, Brian, committee member; Zafft, David, committee member
    Chapter 1 - Illegally introduced burbot (Lota lota) populations have spread throughout the Green River drainage (GRD) of the upper Colorado River Basin in Wyoming and Utah, USA where they are having adverse effects on native and sport fisheries. We analyzed existing data to evaluate the status of burbot in southwestern Wyoming. Burbot appear to have been illegally introduced into Big Sandy Reservoir in the early- to mid-1990's, based on capture of burbot in 2003 that included one 16 year old fish and several between 7 and 12 years of age. Burbot began expanding throughout the Green River Drainage in the early 2000s and, with the assistance of a secondary introduction into Fontenelle Reservoir, have successfully invaded most portions of the GRD upstream of the Flaming Gorge Dam. Only one burbot has been captured downstream of Flaming Gorge Reservoir, but this detection indicates potential for downstream establishment in the future. Burbot are difficult to sample, especially in large rivers, so we recommend sampling techniques to monitor the expansion of burbot in lotic and lentic habitats of the upper Colorado River Basin as well as highlight research opportunities associated with this invasion. Chapter 2 - Burbot (Lota lota L.) and white suckers (Catostomus commersonii L.) are managed as invasive species in the upper Colorado River Basin and physical barriers to their upstream dispersal could be important tools for preventing their spread. A three-tiered lab-based experimental approach was used to define design parameters for both species utilizing a hybrid barrier that combines a vertical drop with a downstream velocity segment. The first tier of the study measured fish jumping ability over a range of waterfall height × plunge pool depth treatments to refine waterfall design parameters. Jumping attempt and waterfall exploration data were collected in each trial to allow a novel approach for examining the behavior associated with individual motivation to ascend the barrier, and to confirm that all height × depth treatments were challenged. The second tier of the study used constant acceleration trials (CATs) to define the length-specific burst transition (Bt) from aerobic (high-endurance; sustained) to anaerobic (rapid-fatigue; burst) swimming. Finally, the third tier of the study used fixed velocity trials at velocities > Bt to collect anaerobic endurance data that were used to solve Peake's equation to identify velocity × barrier length combinations that prevented upstream passage. To account for peak-performing individuals, upper 99% prediction intervals were used to determine design criteria that would prevent passage of fish of the total length (TL) in their system of interest. Minimum waterfall heights > 85% and 100% of the TL of the largest white sucker and burbot, respectively, in the system were found to prevent passage. Coupling these heights with plunge pools < 40% and 30% of white sucker and burbot TL increases the difficulty these species have ascending the fall. The CATs indicated that velocity barriers that deliver minimum velocities of 4.0 and 3.2 times the TL of the largest white sucker and burbot, respectively, in the system will ensure anaerobic swimming and thus fatigue fish prior to leaping attempts. A variety of velocity barrier length × velocity design parameters are defined for each species to prevent passage based on the FVTs and Peake's equation analysis.
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    Nonparametric tests of spatial isotropy and a calibration-capture-recapture model
    (Colorado State University. Libraries, 2017) Weller, Zachary D., author; Hoeting, Jennifer A., advisor; Cooley, Dan, committee member; Hooten, Mevin, committee member; Ahola, Jason, committee member
    In this dissertation we present applied, theoretical, and methodological advances in the statistical analysis of spatially-referenced and capture-recapture data. An important step in modeling spatially referenced data is choosing the spatial covariance function. Due to the development of a variety of covariance models, practitioners are faced with a myriad of choices for the covariance function. One of these choices is whether or not the covariance function is isotropic. Isotropy means that the covariance function depends only the distance between observations in space and not their relative direction. Part I of this dissertation focuses on nonparametric hypothesis tests of spatial isotropy. Statisticians have developed diagnostics, including graphical techniques and hypothesis tests, to assist in determining if an assumption of isotropy is adequate. Nonparametric tests of isotropy are one subset of these diagnostic methods, and while the theory for several nonparametric tests has been developed, the efficacy of these methods in practice is less understood. To begin part I of this dissertation, we develop a comprehensive review of nonparametric hypothesis tests of isotropy for spatially-referenced data. Our review provides informative graphics and insight about how nonparametric tests fit into the bigger picture of modeling spatial data and considerations for choosing a test of isotropy. An extensive simulation study offers comparisons of method performance and recommendations for test implementation. Our review also gives rise to a number of open research questions. In the second section of part I, we develop and demonstrate software that implements several of the tests. Because the tests were not available in software, we created the R package spTest, which implements a number of nonparametric tests of isotropy. The package is open source and available on the Comprehensive R Archive Network (CRAN). We provide a detailed demonstration of how to use spTest for testing isotropy on two spatially-referenced data sets. We offer insights into test limitations and how the tests can be used in conjunction with graphical techniques to evaluate isotropy properties. To conclude our work with spatially-referenced data in part I, we develop a new nonparametric test of spatial isotropy using the spectral representation of the spatial covariance function. Our new test overcomes some of the short-comings of other nonparametric tests. We develop theory that describes the distribution of our test statistic and explore the efficacy of our test via simulations and applications. We also note several difficulties in implementing the test, explore remedies to these difficulties, and propose several areas of future work. Finally, in part II of this dissertation, we shift our focus away from spatially-referenced data to capture-recapture data. Our capture-recapture work is motivated by methane concentration data collected by new mobile sensing technology. Because this technology is still in its infancy, there is a need to develop algorithms to extract meaningful information from the data. We develop a new Bayesian hierarchical capture-recapture model which we call the calibration-capture-recapture (CCR) model. We use our model and methane data to estimate the number and emission rate of methane sources within an urban sampling region. We apply our CCR model to methane data collected in two U.S. cities. Our new CCR model provides a framework to draw inference from data collected by mobile sensing technologies. The methodology for our capture-recapture model is useful in other capture-recapture settings, and the results of our model are important for informing climate change and infrastructure discussions.
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    Patents, knowledge creation, and spillovers in genetics for agriculture and natural resources
    (Colorado State University. Libraries, 2020) Samad, Ghulam, author; Graff, Gregory D., advisor; Maskus, Keith E., committee member; Weiler, Stephan, committee member; Hooten, Mevin, committee member
    Increasing food, energy, and resource demand by growing global population is putting unprecedented pressure on agriculture and natural resource systems. Innovation in agriculture, energy, and other resource intensive industries contributes enormously to productivity and sustainability gains. Innovation in genetic resources and biological systems is a particularly promising yet controversial area of such innovation. Generally, it has been observed that regional clustering (economies of agglomeration) plays an important role in driving innovation. To what extent do we observe regional clustering to play a role in innovation in these industries? Especially given that production is highly diffused geographically, and research and technology are seen as highly globalized (global public goods vs. global monopolies by MNCs). The overarching questions address by this study are the following: (1) What do patents reveal about geographic patterns of knowledge creation and spillovers? (2) What economic and policy factors drive invention activity at the regional scale? And indirectly, (3) What is the role of regional clustering in driving innovations for food security and sustainability? To address these overarching objectives this study is mainly separated into three parts. The first part delves into three related questions: (1) How have biological inventions for use in primary resource-intensive industries been spatially distributed across the United States? And, in particular, to what degree have they been geographically concentrated? (2) What are the time-space dynamics of biological inventions for these industries? To what extent does the concentration of previous inventions effect where new inventions arise? And, (3) based on these insights, can we identify primary innovation clusters in the U.S. for these industries? This study draws on detailed information on inventor address from about 34,000 patented inventions as indicators of innovation and entrepreneurship in three closely related industries: (1) agriculture, (2) bioenergy, and (3) environmental management. To address these questions three approaches are used mapping, Moran I and regression analysis. Results indicate these biological inventions are distributed across the U.S, but highly concentrated clusters are formed in urban regions. Moreover, a spatial clustering pattern clearly exists. In term of concentration of biological inventions for these industries, a rural-urban division exists. Inventions do not tend to concentrate near production activities but tend to concentrate in urban area. The number of inventions in an area in prior years has a significant impact on the number of current year inventions. This relationship represents the localized spillover phenomenon. While we do see inventions in rural areas, rural areas do not appear to be the hotspots of innovation in agricultural, energy, or environmental biotechnologies. The second part of this dissertation explores the covariates of regional concentration of these biological inventions for agriculture, energy, and environment in the United States. First, the geographic patterns of these inventions are analyzed using negative binomial panel regression of patented inventions by region, to identify the density of inventions overall as well as the space-time dynamics of invention cumulativeness. We find that inventions have been spatially concentrated in about 30 major metropolitan clusters, and that spatial distribution has remained remarkably stable over time. Factors of population, earnings, and farm income are correlated with their invention counts. As a first rule, these inventions are created in higher population urban regions. Although, among regions of similar population inventions are more likely closer to agricultural production. Results clearly show the emergence of largely urban innovation clusters in agriculture and resource industries. The third part of this dissertation broadens the scope to explore the spatial distribution and covariates of regional invention activity across Organization for Economic Cooperation and Development (OECD) countries. Three approaches are used mapping, Moran I and regression analysis to analyse the spatial distribution and covariates across OECD. The results showed that while inventions are distributed across the OECD, there again appear to be concentrated clusters in larger urban regions (another broader set of top 30 clusters). Moreover, the number of inventions made in prior years has significant explanatory power on the number of current year inventions, by region. This represents the localized spillover phenomenon. In addition, region size (as measured by population) and level of economic activity (as measured by regional income) do not appear to be related to the count of inventions for these industries. R&D expenditures (regional) and an IP index (which is national in nature but is applied to regions for this study) are strongly related to biotech invention activity for these industries. A rural-urban division does appear to exist. Finally, these invention counts appear to be negatively correlated with gross value added of agriculture by region across OECD countries.
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    The effects of climate change on high elevation lake ecosystems
    (Colorado State University. Libraries, 2019) Christianson, Kyle R., author; Johnson, Brett, advisor; Hooten, Mevin, committee member; Denning, Scott, committee member; Myrick, Christopher, committee member
    High elevation lakes are an important class of the world's fresh water. Nearly 10% of all lakes globally reside above 2,100 m ASL and almost half of the world's population relies on water from high elevation regions. Also, these lakes provide important cool water habitat refugia for aquatic biota. However, high elevation areas are sensitive to changes in climate and are changing faster than other regions. Likewise, secondary effects of a changing climate like drought, forest fire, and eutrophication threaten lake habitats, exacerbating changes from air warming. Despite the importance of high elevation lakes and their increased threat from climate change, little is known about high elevation lakes and their vulnerability to these threats. The goal of my dissertation was first (Chapter 1) to determine historic changes in lake surface temperatures for a set of high elevation lakes in the Southern Rocky Mountains, USA (SRM). Then, I determined potential future changes to thermal stratification (Chapter 2) and the length of the open water season (Chapter 3) for a subset of lakes in the Rawah Wilderness Area (RWA) within the SRM. For these future predictions, I estimated alterations in lake surface and bottom temperatures from multiple stressors, as well as how these changes may affect aquatic habitat for native and nonnative fish species that reside in the region. Although historic lake temperature trend analyses are numerous, remote lakes, including many high elevation lakes, are typically underrepresented due to limited availability of long-term datasets. In Chapter 1, I developed a Bayesian modeling technique to analyze sparse data from high elevation lakes that allowed me to estimate lake surface warming across a large region (SRM). The analysis allowed for inclusion of lakes with few repeated measurements, and observations made prior to 1980 when more intensive lake monitoring began. I accumulated the largest dataset of high elevation lake surface temperatures globally analyzed to date. Data from 590 high elevation lakes in the Southern Rocky Mountains showed a 0.13°C decade-1 increase in surface temperatures and a 14% increase in seasonal degree days since 1955. Like surface temperature trends, many studies have also examined the effects of climate warming on lake thermal stratification, but few have addressed environmental changes concomitant with climate change, such as alterations in water clarity and lake inflow. Although air temperature rise is a predominant factor linked to lake thermal characteristics, climate-driven changes at watershed scales can substantially alter lake clarity and inflow, exacerbating the effects of future air warming on lake thermal conditions. In Chapter 2, I employed the mechanistic General Lake Model (GLM) to simulate future thermal conditions of typical mountain lakes of the western United States. I found that after air temperature, alterations in inflow had the largest effect on lake thermal conditions, changes in wind had the least effect, and large lakes experienced more than double the increase in lake stability than small lakes. Assuming air temperature rise alone, summer stability of mountain lakes of the western United States was predicted to increase by 15-23% at +2°C air temperatures, and by 39-62% at +5°C air temperatures. When accounting for associated changes in clarity and inflow, lake stability was predicted to increase by 208% with +2°C air warming and 318% at +5°C air warming. Finally, the open water duration at high elevations is increasing at a higher rate than at lower elevations. Earlier snowmelt, resulting in decreased ice cover duration, is having a proportionally higher effect on mountain lakes than other regions. But the effect early melt and increased air temperatures have on mountain lake thermal characteristics and implications for fish is unclear. Mountain lakes exhibit a variety of thermal conditions, altering metabolic requirements for ectotherms. In Chapter 3, I coupled GLM with a fish bioenergetics model to assess potential thermal changes and energetic consequences for native Cutthroat Trout (Oncorhynchus clarkii spp.) and nonnative but present Brook Trout (Salvelinus fontinalis) in a continuously mixed polymictic and seasonally stratified dimictic mountain lake during early and nominal snowpack melt in the SRM. I found that early snowmelt alone had a larger consumptive demand for all species than an air temperature increase of 2°C, but combined these environmental changes are most effective. Early melt coupled with 5°C air warming could more than double the food requirements for Cutthroat Trout and Brook Trout. Ultimately, food availability may dictate the future success of fish in mountain regions. My dissertation research expanded the current knowledge of high elevation lake thermal conditions, developed a novel method to utilize sparse datasets, and provided valuable holistic insight to potential future changes in lake thermal structure and habitat suitability for fish while accounting for localized and watershed scale consequences of climate change.