Geology topics

Tammy L. Wilson

Publications and source records attributed to Tammy L. Wilson.

14 recordsLinked to original sources

USGS2026

Life-history stages and behavior influence demographic classification of moose captured on remote cameras

Obtaining accurate information on demographic states, such as the age and sex classes of animals, is an important step for monitoring wildlife populations. Traditionally, demographic data are collected from harvest, aerial surveys and telemetry studies. However, these methods can be expensive, limited to small spatial scales, or biased due to human behavior. Remote cameras have become a mainstay for studying and monitoring wildlife as they are relatively inexpensive, can be deployed over large spatial scales, and effort can be accounted for during surveys. For some species, a variety of demographic information, such as sex and age classes, can be obtained from pictures. Moose Alces alces are a photogenic species found across boreal and semi-boreal forests of the Northern Hemisphere. Previous studies have used demographic data from remote cameras to estimate demographic parameters and population dynamics. A primary assumption is that these age and sex classes are accurately classified. However, numerous factors can influence the ability of observers to identify age and sex classes of moose captured on cameras. We used data from 84 cameras from a 3-year period (2021–2024) in northern Maine, USA, to evaluate how temporal, environmental, site-level, and endogenous factors influence observers' ability to classify age and sex classes of moose. Using Bayesian categorical regression models, we found that temporal variability, position and proximity of moose from cameras, and the behavior of moose influenced our ability to identify age and sex classes. This information can be used to decide which periods to use data for population modeling and how to design studies to reduce the amount of uncertainty associated with different age and sex classes. We anticipate that our approach could also be used for other species whose age and sex classes can be differentiated using remote cameras.

Maine↗

USGS2026

Connecting the dots: Using multiple datasets to monitor the brown bear population in Katmai National Park and Preserve

Monitoring wide-ranging wildlife populations is challenging. Surveys are time consuming, expensive, and observation conditions need to be just right. But confidently assessing the number of animals, and if their numbers are increasing or decreasing is important for park management and ecological understanding. This article describes survey methods and how they can be combined for the most accurate estimates.

Alaska↗

USGS2024

Monitoring animal populations with cameras using open, multistate, N-mixture models

Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose states are known. However, this powerful class of models has been underutilized because they are computationally intensive, and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with multistate, Dail-Madsen (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated four commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, and 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose ( Alces alces ) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy, and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental states are clearly identifiable. We discuss several avenues for future research and caveats for using multistate DM models for large-scale population monitoring.

New Hampshire, Vermont↗

USGS2024

The effects of spatio-temporal variation in marine resources on the occupancy dynamics of a terrestrial avian predator

Identifying how species respond to system drivers such as weather, climate, habitat, and resource availability is critical in understanding population change. In coastal areas, the transfer of nutrients across the marine and terrestrial interface increases complexity. Nesting populations of bald eagles ( Haliaeetus leucocephalus ) along the Pacific coast of North America, although terrestrial, are largely dependent on marine resources during the breeding season and therefore represent a good focal species for understanding linkages of nutrients between terrestrial and marine systems. Due to their location, coastal eagle populations are susceptible to a variety of climate-induced perturbations, from both land and sea. The northeast Pacific Marine Heatwave (PMH) of 2014-2016 had wide-ranging impacts on the marine ecosystem and provided an opportunity to explore how marine conditions can impact terrestrial wildlife populations. We used a spatially-explicit multi-state occupancy modeling framework to analyze >30yrs of bald eagle nest occupancy data collected in four large national parks along a coastal-interior gradient in Alaska, USA. We assessed occupancy state in relation to weather conditions, salmon abundance, access to alternate prey resources, and the PMH event to help elucidate the factors affecting bald eagle occupancy dynamics over time. We found that occupancy probability was higher in areas where prey resources were concentrated (e.g., near seabird colonies, where bears facilitate access to salmon carcasses). We also found that the probability of reproductive success was higher during warmer, drier springs with higher-than-average salmon abundance. After the onset of the marine heatwave, success declined in the areas most dependent on non-salmon marine resources. These findings confirm the importance of spring weather conditions and access to salmon resources during the critical chick-rearing period, but also reveal that marine heatwaves may have important secondary effects through a reduction in the overall quantity or quality of prey available to bald eagles. Given ongoing warming at high latitudes and the expectation that marine heatwaves will become more common, our findings are useful for understanding ongoing and future changes in the transfer of nutrients from marine to terrestrial ecosystems and how such changes may impact terrestrial species such as bald eagles.

Alaska↗

USGS2024

Monitoring questing winter tick abundance on traditional moose hunting lands

An important symbolic and subsistence animal for many Native American Tribes, the moose ( Alces alces ; mos in Algonquin, Penobscot language) has been under consistent threat in the northeastern United States because of winter tick ( Dermacentor albipictus ) parasitism over the past several decades, causing declines in moose populations throughout the region. This decline has raised concern for Tribes and agencies that are invested in moose. Given this concern, it is increasingly important to effectively monitor and develop strategies to manage winter ticks to address consistent population declines of moose due to winter ticks. The Penobscot Nation developed a novel strategy to sample questing winter ticks (i.e., ticks that are actively seeking hosts) using a plot-based sampling protocol that may be suitable for heterogeneous habitats. We deployed this protocol in the northeastern United States in 2022 during the tick questing period (Sep–Dec) on Penobscot Nation sovereign trust lands, the White Mountain National Forest and Umbagog National Wildlife Refuge, and western-central Massachusetts, USA. We analyzed the data using occupancy and N-mixture models. Detection probability peaked during mid-October and tick occupancy and abundance were greatest at sites with intermediate understory vegetation height. The sampling protocol was successful at sampling ticks in Massachusetts, where abundances were expected to be low, indicating that it may be useful for studies planning to monitor winter tick distribution and abundance in areas with sub-optimal moose habitat and where winter tick abundance is expected to be low. This approach may also benefit managers or researchers intending to monitor many species of hard ticks, and where imperfect detection is expected.

Maine, Massachusetts, New Hampshire↗

USGS2024

Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies

Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.

Maine↗

USGS2023

Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring

Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector) to consider the impact of a ML model’s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January–September 2022) using three tagging methods (one with ML bounding box assistance and two without assistance). We used a generalized linear mixed model to examine the influence of ML model performance and tagging method on tagging efficiency. We found that ML bounding boxes offer significant improvement in tagging efficiency when labelling data compared to unassisted tagging. Additionally, the time taken to label with bounding boxes was not statistically different from an unassisted tagging approach. However, we found that gains in efficiency are contingent on the ML algorithm’s performance and that incorrect ML predictions, particularly the 4.2% false positive and 3.6% false negative predictions, can slow the tagging process compared to a non-hybrid approach. These findings indicate that although practitioners usually forgo the production of bounding boxes when selecting a data labelling process due to the increased effort, ML bounding box-assisted tagging can offer an efficient method for labeling. More broadly, ML-assisted data labelling offers an opportunity to accelerate the analysis of trail camera imagery, but an assessment of the ML model’s performance can illuminate whether the hybrid-tagging approach is ultimately a help or hinderance.

Maine, Vermont↗

USGS2023

Understanding drivers of mercury in lake trout (Salvelinus namaycush), a top-predator fish in southwest Alaska's parklands

Mercury (Hg) is a widespread element and persistent pollutant, harmful to fish, wildlife, and humans in its organic, methylated form. The risk of Hg contamination is driven by factors that regulate Hg loading, methylation, bioaccumulation, and biomagnification. In remote locations, with infrequent access and limited data, understanding the relative importance of these factors can pose a challenge. Here, we assessed Hg concentrations in an apex predator fish species, lake trout (Salvelinus namaycush), collected from 14 lakes spanning two National Parks in southwest Alaska, U.S.A. We then examined factors associated with the variation in fish Hg concentrations using a Bayesian hierarchical model. We found that total Hg concentrations in water were consistently low among lakes (0.11–0.50 ng L− 1). Conversely, total Hg concentrations in lake trout spanned a thirty-fold range (101–3046 ng g− 1 dry weight), with median values at 7 lakes exceeding Alaska’s human consumption threshold. Model results showed that fish age and, to a lesser extent, body condition best explained variation in Hg concentration among fish within a lake, with Hg elevated in older, thinner lake trout. Other factors, including plankton methyl Hg content, fish species richness, volcano proximity, and glacier loss, best explained variation in lake trout Hg concentration among lakes. Collectively, these results provide evidence that multiple, hierarchically nested factors control fish Hg levels in these lakes.

Alaska↗

USGS2022

The mysterious case of the missing razor clams

Oceans are changing and these changes are affecting animals that live there. Animals respond differently to changes in water temperature, food availability, or contaminants. Those responses can be seen in their genes. Gene transcription is a tool that allows scientists to see the response of an animal’s genes to its environment. We used gene transcription to compare two populations of Pacific razor clams in Alaska: one that has lots of clams and one that used to have lots but doesn’t anymore. We were surprised when we didn't find any differences in their gene responses! So, we had to think about what else might be influencing the number of clams in these two populations. As we “dug” for answers, we found out that there are differences between the populations that don‘t influence their genes but may impact their numbers, such as predation.

Frontiers for Young Minds↗

USGS2022

Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration

Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear ( Ursus arctos ) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km 2 during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.

Alaska↗

USGS2021

Gene expression profiles in two razor clam populations: Discerning drivers of population status

With rapidly changing marine ecosystems, shifts in abundance and distribution are being documented for a variety of intertidal species. We examined two adjacent populations of Pacific razor clams ( Siliqua patula ) in lower Cook Inlet, Alaska. One population (east) supported a sport and personal use fishery, but this has been closed since 2015 due to declines in abundance, and the second population (west) continues to support commercial and sport fisheries. We used gene expression to investigate potential causes of the east side decline, comparing razor clam physiological responses between east and west Cook Inlet. The target gene profile used was developed for razor clam populations in Alaska based on physiological responses to environmental stressors. In this study, we identified no differences of gene expression between east and west populations, leading to two potential conclusions: (1) differences in factors capable of influencing physiology exist between the east and west and are sufficient to influence razor clam populations but are not detected by the genes in our panel, or (2) physiological processes do not account for the differences in abundance, and other factors such as predation or changes in habitat may be impacting the east Cook Inlet population.

Alaska↗

USGS2020

Using a structured decision analysis to evaluate bald eagle vital signs monitoring in Southwest Alaska National Parks

1. Monitoring programs can benefit from an adaptive monitoring approach, where key decisions about why, where, what, and how to monitor are revisited periodically in order to ensure programmatic relevancy. 2. The National Park Service (NPS) monitors status and trends of Vital Signs to evaluate compliance with the NPS mission. Although abundant, The Southwest Alaska Network (SWAN) monitors bald eagles because of their inherent importance to park visitors and role as an important ecological indicator. Our goal is to identify an optimal monitoring program that may be standardized among participating parks. 3. We gathered an expert panel of scientists and managers, and implemented a Delphi Process to gather information about the bald eagle monitoring program. Panelists generated a list of means objectives for the monitoring program: minimizing cost, minimizing effort, maximizing the ability to detect change in bald eagle populations, and maximizing the amount of accurate information collected about bald eagles. 4. We used a swing-weighting technique to assign importance to each objective. Collecting accurate information about bald eagles was considered the most important means objective. 5. Combining panelist-generated information with objective importance, we analyzed the scenarios and defined the optimal decision using linear value modeling. Through our analysis, we found that a “Comprehensive” monitoring scenario, comprised of all feasible monitoring metrics is the optimal monitoring scenario. Even with greatly increased cost, the Comprehensive monitoring scenario remains the best solution. 6. We suggest further exploration of the cost and effort required for the Comprehensive scenario, to determine if it is in the parks’ best interest to begin monitoring additional metrics.

Alaska↗

USGS2020

Using the Delphi process to gather information from a Bald Eagle expert panel

Bald eagle ( Haliaeetus leucocephalus ) populations are classified by the Southwest Alaska Network (SWAN) of the National Park Service as a vital sign of biological integrity, largely because of their importance as an indicator species for environmental contaminants and human disturbance. Though Bald Eagles are plentiful in Alaska, it is still imperative to have a monitoring plan that allows for the estimation of population sizes and detection of significant changes in populations. Currently, Bald Eagles are monitored in Kenai Fjords National Park, Katmai National Park and Preserve, Lake Clark National Park and Preserve, and Wrangell – St. Elias National Park, but each park uses different monitoring procedures and evaluation criteria. This makes it difficult for scientists and managers to compare data, detect changes in overall populations, and make effective management decisions. Our research is using a formal structured decision-making process to ensure that the Bald Eagle monitoring conducted by the parks is standardized and meets programmatic goals and objectives. We implemented a Delphi process, which is an iterative survey technique that is used to gather expert opinion. We used online questionnaires to gather information and opinions from National Park Service scientists and managers, eagle experts, and other interested parties. We identified important stressors and feasible monitoring metrics, which were tied to the means objectives for the Bald Eagle monitoring program: minimize cost, minimize effort, maximize ability to detect change in populations, and maximize accurate information about Bald Eagles. We will also analyze monitoring metrics using a consequence table, which determines the performance of each objective in terms of the means objectives chosen by expert panelists. This information will help to create a more accurate conceptual model of the system to guide development of a Bald Eagle monitoring program that can be standardized among Southwest Alaska National Parks.

Alaska↗

USGS2012

Spatial ecology of refuge selection by an herbivore under risk of predation

Prey species use structures such as burrows to minimize predation risk. The spatial arrangement of these resources can have important implications for individual and population fitness. For example, there is evidence that clustered resources can benefit individuals by reducing predation risk and increasing foraging opportunity concurrently, which leads to higher population density. However, the scale of clustering that is important in these processes has been ignored during theoretical and empirical development of resource models. Ecological understanding of refuge exploitation by prey can be improved by spatial analysis of refuge use and availability that incorporates the effect of scale. We measured the spatial distribution of pygmy rabbit (Brachylagus idahoensis) refugia (burrows) through censuses in four 6-ha sites. Point pattern analyses were used to evaluate burrow selection by comparing the spatial distribution of used and available burrows. The presence of food resources and additional overstory cover resources was further examined using logistic regression. Burrows were spatially clustered at scales up to approximately 25 m, and then regularly spaced at distances beyond ~40 m. Pygmy rabbit exploitation of burrows did not match availability. Burrows used by pygmy rabbits were likely to be located in areas with high overall burrow density (resource clusters) and high overstory cover, which together minimized predation risk. However, in some cases we observed an interaction between either overstory cover (safety) or understory cover (forage) and burrow density. The interactions show that pygmy rabbits will use burrows in areas with low relative burrow density (high relative predation risk) if understory food resources are high. This points to a potential trade-off whereby rabbits must sacrifice some safety afforded by additional nearby burrows to obtain ample forage resources. Observed patterns of clustered burrows and non-random burrow use improve understanding of the importance of spatial distribution of refugia for burrowing herbivores. The analyses used allowed for the estimation of the spatial scale where subtle trade-offs between predation avoidance and foraging opportunity are likely to occur in a natural system.

Ecosphere↗