Geology topics

Mevin Hooten

Publications and source records attributed to Mevin Hooten.

At least 19 recordsLinked to original sources

USGS2023

Multistage hierarchical capture–recapture models

Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual-level data. A variety of implementations exist for such models, including integrated likelihood, parameter-expanded data augmentation, and combinations of those. Capture–recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture–recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two datasets resulting from capture–recapture studies of different species.

Article↗

USGS2022

Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling

We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online.

Journal of Agricultural, Biological and Environmen↗

USGS2022

Searching for refuge: A framework for identifying site factors conferring resistance to climate-driven vegetation change

Climate change is occurring at accelerated rates in high latitude regions such as Alaska, causing alterations in woody plant growth and associated ecosystem patterns and processes. Our aim is to assess the magnitude and speed that climate-induced changes in woody plant distribution and volume may be reduced and/or slowed by relatively static landscape features like physical characteristics (e.g. depth to gravel, mineral cover percent and slope degree) and/or edaphic properties (e.g. soil organic matter, soil pH and site wetness rating) that resist climate-vegetation responses

Alaska↗

USGS2022

Bayesian inverse reinforcement learning for collective animal movement

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori, and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long-term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the selfpropelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.

Annals of Applied Statistics↗

USGS2021

Recursive Bayesian computation facilitates adaptive optimal design in ecological studies

Optimal design procedures provide a framework to leverage the learning generated by ecological models to flexibly and efficiently deploy future monitoring efforts. At the same time, Bayesian hierarchical models have become widespread in ecology and offer a rich set of tools for ecological learning and inference. However, coupling these methods with an optimal design framework can become computationally intractable. Recursive Bayesian computation offers a way to substantially reduce this computational burden, making optimal design accessible for modern Bayesian ecological models. We demonstrate the application of so-called prior-proposal recursive Bayes to optimal design using a simulated data binary regression and the real-world example of monitoring and modeling sea otters in Glacier Bay, Alaska. These examples highlight the computational gains offered by recursive Bayesian methods and the tighter fusion of monitoring and science that those computational gains enable.

Ecology↗

USGS2021

Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long-term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster-specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site-level cluster labels associated with assemblage-level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.

Alaska↗

USGS2021

Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator

Background Reintroducing predators is a promising conservation tool to help remedy human-caused ecosystem changes. However, the growth and spread of a reintroduced population is a spatiotemporal process that is driven by a suite of factors, such as habitat change, human activity, and prey availability. Sea otters ( Enhydra lutris ) are apex predators of nearshore marine ecosystems that had declined nearly to extinction across much of their range by the early 20th century. In Southeast Alaska, which is comprised of a diverse matrix of nearshore habitat and managed areas, reintroduction of 413 individuals in the late 1960s initiated the growth and spread of a population that now exceeds 25,000. Methods Periodic aerial surveys in the region provide a time series of spatially-explicit data to investigate factors influencing this successful and ongoing recovery. We integrated an ecological diffusion model that accounted for spatially-variable motility and density-dependent population growth, as well as multiple population epicenters, into a Bayesian hierarchical framework to help understand the factors influencing the success of this recovery. Results Our results indicated that sea otters exhibited higher residence time as well as greater equilibrium abundance in Glacier Bay, a protected area, and in areas where there is limited or no commercial fishing. Asymptotic spread rates suggested sea otters colonized Southeast Alaska at rates of 1–8 km/yr with lower rates occurring in areas correlated with higher residence time, which primarily included areas near shore and closed to commercial fishing. Further, we found that the intrinsic growth rate of sea otters may be higher than previous estimates suggested. Conclusions This study shows how predator recolonization can occur from multiple population epicenters. Additionally, our results suggest spatial heterogeneity in the physical environment as well as human activity and management can influence recolonization processes, both in terms of movement (or motility) and density dependence.

Alaska↗

USGS2021

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

Idaho, Montana↗

USGS2020

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

Proceedings of the Royal Society B: Biological Sci↗

USGS2020

Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling

Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti , depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus’ extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance.

Espírito Santo↗

USGS2020

Hierarchical computing for hierarchical models in ecology

Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade-offs between capturing ecological complexity in statistical models and implementing these models. We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation-assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual- and population-level migratory characteristics. Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm. For complex ecological statistical models, like those for animal movement, multi-species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation-assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena.

Article↗

USGS2020

Ecological forecasting—21st century science for 21st century management

Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the adverse effects of ecological stressors and optimize the effectiveness of management actions. To explore the potential for ecological forecasting to enhance natural resource management, the U.S. Geological Survey (USGS) convened a workshop titled "Building Capacity for Applied Short-Term Ecological Forecasting" on May 29—31, 2019, with participants from several Federal agencies, including the Bureau of Land Management, the U.S. Fish and Wildlife Service, the National Park Service, and the National Oceanic and Atmospheric Administration as well as all mission areas within the USGS. Participants broadly agreed that short-term ecological forecasting—on the order of days to years into the future—has tremendous potential to improve the quality and timeliness of information available to guide resource management decisions. Participants considered how ecological forecasting could directly affect their agency missions and specified numerous critical tools for addressing natural resource management concerns in the 21st century that could be enhanced by ecological forecasting. Given this breadth of possible applications for forecast products, participants developed a repeatable framework for evaluating potential value of a forecast product for enhancing resource management. Applying that process to a large list of forecast ideas that were developed in a brainstorming session, participants identified a small set of promising forecast products that illustrate the value of ecological forecasting for informing resource management. Workshop outcomes also include insights about important likely obstacles and next steps. In particular, reliable production and delivery of operational ecological forecasts will require a sustained commitment by research agencies, in partnership with resource management agencies, to maintain and improve forecasting tools and capabilities.

Open-File Report↗

USGS2020

Statistical implementations of agent-based demographic models

A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g., those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent-based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multistage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID-19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelized easily for additional computational eciency.

International Statistical Review↗

USGS2020

Model-based clustering reveals patterns in central place use of a marine top predator

Satellite telemetry data are commonly used to quantify habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated with important life history events and may exhibit unique characteristics; however, using satellite telemetry data to study central places is complicated by common nuances like locational error and animal movement. We coupled a novel modeling framework that accounts for these nuances with an Argos satellite telemetry dataset to examine the spatiotemporal behavior associated with harbor seal haul-out sites on Kodiak Island, Alaska, USA. The methodology incorporates an observation model that accommodates multiple sources of uncertainty in telemetry data and a flexible Bayesian nonparametric model to uncover latent clustering in the telemetry locations. We also contribute extensions to examine the effect of covariates on site selection and to obtain population-level inference concerning central place use. Harbor seal haul-out sites generally occurred in inlets and bays, areas that are isolated from the open water of the Gulf of Alaska. Most individuals selected haul-out sites that were protected from wave exposure. The effects of bathymetry and shoreline complexity on haul-out site selection were variable among individual seals, as were the effects of time of day, time since low tide, and day of year on temporal patterns of haul-out use. As repositories of satellite telemetry data on a wide variety of species accumulate, so do opportunities for using this information to learn about the locations of central places, as well as the temporal patterns in their use. The model-based approach we describe offers a practical and rigorous means for gaining insight concerning these sensitive locations, knowledge of which is important for the effective management and conservation of many species.

Alaska↗

USGS2020

Geary’s contiguity ratio (Geary’s c)

Geary's contiguity ratio (i.e., Geary's c ) is used to measure spatial autocorrelation in data with discrete spatial support. Calculation of Geary's c depends on the observed spatial data and a set of dyadic weights that are generally defined based on the proximity of observations to each other, but can be generalized to accommodate geographic distances among observations and the lengths of shared boundaries for the regions from which the data arise. Geary's c can be used to test hypotheses about spatial autocorrelation under both parametric and nonparametric assumptions. Also, components of Geary's c can be used to assess how autocorrelation may vary throughout the spatial domain. We demonstrate the application of Geary's c to assess spatial structure in vegetation data arising from a survey in Alaska, USA, using two different approaches for defining proximity.

Book chapter↗

USGS2020

Animal movement models with mechanistic selection functions

A suite of statistical methods are used to study animal movement. Most of these methods treat animal trajectory data in one of three ways: as discrete pro- cesses, as continuous processes, or as point processes. We brie y review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statis- tically treat animal trajectory data. However, most resource selection analyses provide inference based on approximations of point process models. The forms of these models have been limited to a few types of specications that provide infer- ence about relative resource use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis often proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specications related to resource selection. We then provide an example of a case where a spatio-temporal point process model coincides with that implied by a mechanistic model for movement expressed as a partial dier- ential equation derived from rst principles of movement. We demonstrate that inference from this form of point process model is intuitive (and could be useful for management and conservation) by analyzing a set of telemetry data from a mountain lion in Colorado, USA, to understand the eects of spatially explicit environmental conditions on movement behavior of this species.

Spatial Statistics↗

USGS2019

Compound effects of water clarity, inflow, wind and climate warming on mountain lake thermal regimes

Many studies have examined the effects of climate warming on lake stability, 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. Without accounting for potential changes in clarity and inflow, future thermal predictions could be inaccurate. We employed the General Lake Model to simulate future thermal conditions (relative thermal resistance to mixing; RTRM) of small (< 12 ha) mountain lakes of the western United States by calibrating the model to a set of lakes in the Southern Rocky Mountains, USA. We found that after air temperature, alterations in inflow had the largest effect on lake thermal conditions, changes in wind had the least effect, and larger lakes experienced more than double the increase in lake stability than smaller lakes. Generally, clear, high inflow lakes had the lowest stability now, and in the future, while the largest overall increase in thermal stability occurred in larger lakes with low inflows and high turbidity. 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% with at 5 °C air warming. Thus, ignoring the multivariate effects of climate change can substantially underestimate changes to mountain lake thermal and stratification regimes. Dimictic lakes may become more strongly stratified and polymictic lakes will experience more prolonged stratification. While predicted changes to lake temperatures may not be harmful to trout species that currently inhabit mountain lakes, longer and more intense stratification could cause indirect effects, such as hypoxia, that could reduce growth and survival of these organisms.

Colorado↗

USGS2019

Simultaneous autoregressive (SAR) model

Simultaneous autoregressive (SAR) models are useful for accommodating various forms of dependence among data that have discrete support in a space of interest. These models are often specified hierarchically as mixed-effects regression models with first-moment structure controlled by a conventional linear regression term and second-moment structure induced by correlated random effects. In their general form, SAR models resemble conditional autoregressive (CAR) models, and can be made equivalent but are often parameterized differently. Importantly, SAR models can be specified by simultaneously regressing a discrete spatial process on itself. Thus, they allow one to construct statistical models for processes with directional graphical properties that pertain to data generating mechanisms. Most commonly SAR models have been used to account for structure among data with areal spatial support in applications involving ecology, epidemiology, sociology, and environmental science.

Book chapter↗