Geology topics

Joseph Michael Eisaguirre

Publications and source records attributed to Joseph Michael Eisaguirre.

12 recordsLinked to original sources

USGS2026

Ecological diffusion models are still useful in ecology

Context Connecting animal movement across scales—both among spatial scales and between individual and population level processes—is a central theme in ecology, including the sub-fields of movement and landscape ecology. However, most modeling frameworks operate at either the individual (Lagrangian) or population (Eulerian) level. Objectives We aimed to review how a partial differential equation (PDE) known as the ecological diffusion equation (EDE) offers connections among the scales of animal movement and how it can be embedded in commonly used statistical frameworks (e.g., hierarchical models) and fit to multiple types of data, such as count, presence-only, presence-absence, and telemetry data. In doing so, we also highlight emerging and promising avenues of research related to statistical EDE models. Methods We present how the EDE is derived from a simple set of first principles, can be used to represent both individual animal movement and spatiotemporal population dynamics, and can be fit to data by being applied in a hierarchical statistical framework. Finally, we review literature on recent applications and advances related to EDE models. Results In presenting the derivation of the EDE and reviewing recent advances and applications of EDE models, we established that the EDE can enable a mechanistic understanding of animal movement across scales, as well as the integration of multiple types of data. We also identified a number of topics where EDE models can be advanced to gain additional ecological insight. Conclusions Ecological diffusion is a process that emerges naturally from first principles governing animal movement and can be used to model both individual and population-level spatiotemporal dynamics. Statistical EDE models are related to other modeling frameworks, such as species distribution models, occupancy models, and step-selection analysis. The flexibility of hierarchical statistical modeling allows combining different types of data collected on movement and population dynamics, and the theoretical underpinnings of the EDE allow it to connect ecological processes occurring at fine and coarse spatial scales, as well as individual animal movement and population dynamics.

Landscape Ecology↗

USGS2025

Marginalizing time in habitat selection and species distribution models improves inference

Aim Recent methodological advances for studying how animals move and use space with telemetry data have focused on fine-scale, more mechanistic inference. However, in many cases, researchers and managers remain interested in larger scale questions regarding species distribution and habitat use across study areas, landscapes, or seasonal ranges. Point processes offer a unified framework for many methods applied in studies of species distribution and resource selection; however, challenges remain in terms of dealing with temporal autocorrelation common in many types of telemetry data collected from animal locations. Innovation Space–time point processes (STPPs) have a unique property, in that marginalising time offers a connection between individual animal movement and broader point processes, yet this property has seen little attention in both statistical and applied research. In this paper, we first present some of the details of this marginalisation property and methods for applying marginalised STPPs (mSTTPs) to autocorrelated telemetry data and then apply a mSTTP in a case study on the summer space use and habitat selection of female caribou ( Rangifer tarandus ) in Denali National Park and Preserve, Alaska. Main Conclusions The case study demonstrated that an mSTPP approach can improve inference over other commonly used methods in terms of its ability to account for temporal autocorrelation and offers greater precision in parameter estimates and improved predictions of space use. As this method fits conveniently into the existing point process frameworks, it offers a practical solution to dealing with temporal autocorrelation inherent to many types of telemetry data when research questions center around broader scale patterns of animal habitat selection and space use.

Alaska↗

USGS2025

Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models

Mortality risk for animals often varies spatially and can be linked to how animals use landscapes. While numerous studies collect telemetry data on animals, the focus is typically on the period when animals are alive, even though there is important information that could be gleaned about mortality risk. We introduce a thinned spatial point process (SPP) modelling framework that couples relative abundance and space use with a mortality process to formally treat the occurrence of mortality events across the landscape as a spatial process. We show how this model can be embedded in a hierarchical statistical framework and fit to telemetry data to make inferences about how spatial covariates drive both space use and mortality risk. We apply the method to two data sets to study the effects of roads and habitat on spatially explicit mortality risk: (1) VHF telemetry data collected for willow ptarmigan in Alaska, and (2) hourly GPS telemetry data collected for black bears in Colorado. These case studies demonstrate the applicability of this method for different species and data types, making it broadly useful in enabling inferences about the mechanisms influencing animal survival and spatial population processes while formally treating survival as a spatial process, especially as the development and implementation of joint analyses continue to progress.

Ecology Letters↗

USGS2025

Modeling individual-level and population-level nest success of California Condors from movement data

The California Condor ( Gymnogyps californianus ) is a critically endangered species with populations that are not currently self-sustaining. Although understanding nest success is key to understanding trends in their populations, field monitoring of condor nests has become increasingly challenging as the number of nesting condors has increased and their range has expanded. We investigated whether California Condor nest fate could be accurately estimated from telemetry data with limited field observations. Our study focused on the southern California population of California Condors (2015–2022), and we used a recently published Bayesian hierarchical modeling framework that combines movement data and occasional field observations to estimate individual-level and population-level nest success. The model detected shifts in space use to categorize if each nest failed or if a young fledged. Estimated model parameters suggested that after nest failure, condors shifted toward more expansive space use. Additional field observations, not included as data in the model, provided evidence that we accurately categorized nest fate for 63 out of 65 California Condor nesting attempts. Finally, we scaled individual-level reproductive success to estimate annual population-level nesting success. These methods offer managers a way to reduce field monitoring efforts while still allowing for estimation of nest success, which will be key as the breeding populations of California Condors continue to grow and become more widely spread across the landscape.

California↗

USGS2024

Assessing the sustainability of Pacific walrus harvest in a changing environment

Harvest sustainability is a primary goal of wildlife management and conservation, and in a changing world, it is increasingly important to consider environmental drivers of population dynamics alongside harvest in cohesive management plans. This is particularly pertinent for harvested species that acutely experience effects of climate change. The Pacific walrus ( Odobenus rosmarus divergens ), a crucial subsistence resource for Indigenous communities, is simultaneously subject to rapid habitat loss associated with diminishing sea ice and an increasing anthropogenic footprint in the Arctic. We developed a theta-logistic population modeling-management framework to evaluate various harvest scenarios combined with 4 potential climate and disturbance scenarios (ranging from optimistic to pessimistic, based largely on sea ice projections from general circulation models) to simulate Pacific walrus population dynamics to the end of the twenty-first century, focusing on the independent-aged female subset of the population. We considered 2 types of harvest strategies: 1) state-dependent harvest scenarios wherein we calculated harvest as a percentage of the population and updated annual harvests at set intervals as the population was reassessed, and 2) annually consistent harvest scenarios wherein annual harvest levels remain consistent into the future. All climate and disturbance scenarios indicated declines of varying severity in Pacific walrus abundance to the end of the twenty-first century, even in the absence of harvest. However, we found that a state-dependent annual harvest of 1.23% of the independent-aged female subset of the population (e.g., 1,280 independent-aged females harvested in 2020, similar to contemporary harvest levels) met our criterion for sustainability under all climate and disturbance scenarios, considering a medium risk tolerance level of 25%. This indicates that the present rate of Pacific walrus harvest is sustainable and will continue to be—provided the population is assessed at regular intervals and harvest is adapted to match changes in population dynamics. Our simulations indicate that a sustainable annually-consistent harvest is also possible but only at low levels if the population declines as expected. Applying a constant annual harvest of 1,280 independent-aged females failed to meet our criterion for sustainability under 3 of the 4 climate and disturbance scenarios we evaluated and had a higher probability of quasi-extinction than an equivalent state-dependent harvest scenario (1.23%). We highlight the importance of state-dependent management strategies and suggest our modeling framework is useful for managing harvest sustainability in a changing climate.

Journal of Wildlife Management↗

USGS2024

Assessing the population consequences of disturbance and climate change for the Pacific walrus

Climate change and anthropogenic disturbance are increasingly affecting wildlife at a global scale. Predicting how varying types and degrees of disturbance may interact to influence population dynamics is a key management challenge. Population consequences of disturbance (PCoD) models provide a framework to link effects of anthropogenic disturbance on an individual’s behavior and physiology to population-level changes. In the present study, we develop a Pacific walrus ( Odobenus rosmarus divergens ) PCoD model to encompass the population-level effects of both anthropogenic disturbance and climate change. As the Arctic becomes increasingly ice-free, walruses spend more time at coastal (vs. ice-based) haulouts, from which they must expend more energy to reach foraging areas and where they have an elevated risk of mortality. Concurrently, sea ice loss is increasing the anthropogenic footprint in the Arctic (e.g. fisheries, shipping, energy exploration), which creates additional disturbance. We applied the PCoD model to 4 scenarios (ranging from optimistic to pessimistic) which incorporate different global sea ice model projections along with varying degrees of anthropogenic disturbance. All scenarios indicated a decline in Pacific walrus vital rates by the end of the 21st century, but our results demonstrated that the intensity of that decline could be mitigated by global efforts to reduce carbon emissions, along with local management and conservation efforts to protect important coastal haulouts and foraging grounds. In summary, we introduce a flexible PCoD modeling framework in a novel context which will prove useful to researchers studying species threatened by rapid environmental change.

Marine Ecology Progress Series↗

USGS2024

Understanding sea otter population change in southeast Alaska

Introduction The Southeast Alaska (SE) stock of northern sea otters ( Enhydra lutris kenyoni ) ranges from Cape Yakataga on the north to the Dixon Entrance on the south. During the maritime fur trade, sea otters were commercially harvested to near extinction in SE for their pelts and were presumed unlikely to naturally repopulate the region.

Alaska↗

USGS2024

Rayleigh step-selection functions and connections to continuous-time mechanistic movement models

Background The process known as ecological diffusion emerges from a first principles view of animal movement, but ecological diffusion and other partial differential equation models can be difficult to fit to data. Step-selection functions (SSFs), on the other hand, have emerged as powerful practical tools for ecologists studying the movement and habitat selection of animals. Methods SSFs typically involve comparing resources between a set of used and available points at each step in a sequence of observed positions. We use change of variables to show that ecological diffusion implies certain distributions for available steps that are more flexible than others commonly used. We then demonstrate advantages of these distributions with SSF models fit to data collected for a mountain lion in Colorado, USA. Results We show that connections between ecological diffusion and SSFs imply a Rayleigh step-length distribution and uniform turning angle distribution, which can accommodate data collected at irregular time intervals. The results of fitting an SSF model with these distributions compared to a set of commonly used distributions revealed how precision and inference can vary between the two approaches. Conclusions Our new continuous-time step-length distribution can be integrated into various forms of SSFs, making them applicable to data sets with irregular time intervals between successive animal locations.

Movement Ecology↗

USGS2024

Assessing the sustainability of Pacific walrus harvest in a changing environment

Harvest sustainability is a primary goal of wildlife management and conservation, and in a changing world it is increasingly important to consider environmental drivers of population dynamics alongside harvest in cohesive management plans. This is particularly pertinent for harvested species that are acutely experiencing effects of climate change. The Pacific walrus ( Odobenus rosmarus divergens ), a critical traditional subsistence resource for indigenous communities, is simultaneously subject to rapid habitat loss associated with diminishing sea ice and an increasing anthropogenic footprint in the Arctic. We developed a theta-logistic population modeling-management framework to evaluate various harvest scenarios combined with four potential climate/disturbance scenarios (ranging from optimistic–pessimistic) which simulates Pacific walrus population dynamics to the end of the 21 st century. We considered two types of harvest strategies: (1) adaptive harvest scenarios wherein harvest is calculated as a percentage of the population and annual harvests are updated at set intervals as the population is reassessed, and (2) non-adaptive harvest scenarios wherein annual harvest remains constant. All climate/disturbance scenarios indicated declines of varying severity in Pacific walrus abundance to the end of the 21 st century, even in the absence of harvest. However, we found that an adaptive annual harvest of 1.23% of the independent-aged female subset of the population (e.g., 1,280 independent-aged females harvested in 2020, representing contemporary harvest levels) met our criterion for sustainability (>70% probability of maintaining population abundance above maximum net productivity level) under all climate/disturbance scenarios, accepting a medium risk tolerance level of 25%. This suggests that the present rate of Pacific walrus harvest is sustainable and will continue to be—provided the harvest adapts to match changes in population dynamics. Our simulations suggest that a sustainable non-adaptive harvest is also possible, but only at low levels if the population declines as expected. Applying a constant annual harvest of 1,280 independent-aged females (equivalent to contemporary harvest levels of 1.23) exceeded our criterion for sustainability and resulted in a >5% chance of quasi-extinction by the end of the 21 st century under three of the four climate/disturbance scenarios we evaluated. Our results highlight the importance of adaptive co-management strategies, and we suggest such modeling frameworks are useful for managing for harvest sustainability in a changing climate.

BioRxiv↗

USGS2023

Informing management of recovering predators and their prey with ecological diffusion models

The reintroduction and recovery of predators can be ecologically beneficial as well as socially and economically controversial. However, the growth and expansion of predator populations, and thus their ecological, social, and economic impacts, are not static but rather they vary in space and time. We propose a spatiotemporal statistical modeling framework based on ecological diffusion to better inform the ecology and management of recovering predators and their prey. We demonstrate its utility by applying it to a recovering sea otter ( Enhydra lutris ) population in Southeast Alaska, where sea otters were reintroduced in the late 1960s and have exhibited unprecedented population growth. Estimated parameters yield inferences about movement and population ecology, and our approach provides useful derived quantities, such as local abundance and carrying capacity as well as a quantity we term the equilibrium differential. We used our model to examine how density dependence and carrying capacity of sea otters vary spatially across a region. The diffusion modeling approach we present can be generalized for use in other instances of (re)colonization across taxa to inform management and conservation efforts.

Alaska↗

USGS2023

A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data

Rapidly advancing animal telemetry technologies paired with new statistical models can provide insight into the behaviour of otherwise unobservable free-living animals. Changes in behaviour apparent from pairing telemetry with statistical models often occur as animals undertake key life-history activities, such as reproduction. For many species that are secretive or occupy remote areas, these life-history events are difficult to detect with conventional survey techniques, and consequently, vital rates are difficult to estimate. We present a hierarchical modelling framework, which integrates movement data observed via animal-borne telemetry and optional, infrequent survey data, to estimate individual- and population-level reproductive success. The approach combines a mechanistic movement model and survival model, and allows for assessing the effects of hypothesized individual and environmental covariates on reproductive success. We first tested our approach with simulated data, and then applied it to movement data from migratory golden eagles ( Aquila chrysaetos) breeding in southcentral Alaska across four breeding seasons. We show that results supported our biological hypotheses that changes in movement coincided with the timing of reproductive failures, and that changes in movement could be used to assess breeding success (and failure) at the individual and population levels. The analysis also provided evidence of inter-annual variation in population-level nest success and the timing of nesting failures. This new approach is adaptable to many species that care for young and can be tracked with telemetry devices, and can provide not only individual-level information useful for testing ecological hypotheses, but estimates of demographic parameters that can directly inform conservation and management if tagged animals are representative of the population.

Methods in Ecology and Evolution↗

USGS2023

Northern Sea Otter (Enhydra lutris kenyoni) population abundance and distribution across the southeast Alaska stock, summer 2022

In the summer of 2022, the U.S. Fish & Wildlife Service and partners successfully completed the first single-year, aerial photo survey of sea otters across the entire Southeast stock of northern sea otters. This document presents the latest findings from the 2022 Southeast Alaska Sea Otter Survey, providing up-to-date information regarding occupancy, abundance and carrying capacity. We collected two independent data streams of sea otter observations during the survey using novel image sensor technologies for marine wildlife surveys and recently developed statistical methods. We calculated an abundance estimate of 22,359 (95% Bayesian credible interval: 19,595, 25,290, CV = 0.064) sea otters. Based on the these results, the population size of the Southeast stock of northern sea otters is still increasing and below the estimated carrying capacity for the region of 48,083 (95% Bayesian credible interval: 40,575, 58,570) sea otters. Results from this report include the first updated abundance estimates for the Southeast stock of sea otters in 10 years, which will help inform stakeholders from across the region and addresses the top priority identified by the 2019 Southeast Sea Otter Stakeholder Working Group.

Alaska↗