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USGS2025

Spatial close-kin mark-recapture models applied to terrestrial species with continuous natal dispersal

Close-kin mark–recapture (CKMR) methods use information on genetic relatedness among individuals to estimate demographic parameters. An individual's genotype can be considered a ‘recapture’ of each of its parent's genotype, and the frequency of kin-pair matches detected in a population sample can directly inform estimates of abundance. CKMR inference procedures require analysts to define kinship probabilities in functional forms, which inevitably involve simplifying assumptions. Among others, population structure can have a strong influence on how kinship probabilities are formulated. Many terrestrial species are philopatric or face barriers to dispersal, and not accounting for dispersal limitation in kinship probabilities, can create substantial bias if sampling is also spatially structured (e.g. via harvest). We present a spatially explicit formulation of CKMR that corrects for incomplete mixing by incorporating natal dispersal distances and spatial distribution of individuals into the kinship probabilities. We used individual-based simulations to evaluate the accuracy of abundance estimates obtained with one spatially naïve and two spatially explicit CKMR models across six scenarios with distinct spatial patterns of relative abundance and sampling probability. Estimates of abundance obtained with a CKMR model naïve to spatial structure were negatively biased when sampling was spatially biased. Incorporating patterns of natal dispersal in the kinship probabilities helped address this bias, but estimates were not always accurate depending on the model used and the scenario considered. Incorporating natal dispersal into spatially structured CKMR models can address the bias created by population structure and heterogeneous sampling but will often require additional assumptions and auxiliary data (e.g. relative abundance indices). The models shown here were designed for terrestrial species with continuous patterns of natal dispersal and high year-to-year site fidelity but could be extended to other species.

Methods in Ecology and Evolution↗

USGS2025

An analysis of the linked decisions in the confiscation of illegally traded turtles

Over the last few decades, freshwater turtles have become more common in the illegal wildlife trade because of growing global demand. Illegally traded turtles may be intercepted by several different agencies with separate jurisdictions. When turtles are confiscated, uncertainties may make releasing them back to the wild difficult. We used tools from decision analysis to achieve the following three objectives: (1) map elements of the decision process and their relationships in the illegal turtle trade using conceptual models, (2) outline the linked decisions for turtle confiscation and repatriation using decision trees, and (3) evaluate the decision trees for two example scenarios, one with moderate uncertainty and one with high uncertainty. We used the wood turtle ( Glyptemys insculpta ) as a case study, which is a species of conservation concern in part due to illegal wildlife trafficking. We conducted 23 semi-structured interviews of decision makers in law enforcement, biologists, land managers, and zoo staff. Interviews revealed that decisions regarding the disposition of confiscated turtles are complicated by uncertainty in disease status and origin. Decision makers that handle confiscated turtles also recognize that their decisions are often made in sequence and dependent on the outcome of antecedent decisions. In evaluating our decision trees, we found that the optimal decisions for example scenarios were similar and insensitive to uncertainty. Future applications of the decision trees by decision makers would involve a decision analyst to parameterize and interpret the choices and consequences involved in working through these decision trees. Collectively, our work shows how the use of decision trees can help structure and evaluate risky decisions for repatriating confiscated wildlife.

Conservation Science and Practice↗

USGS2003

Nonidentifiability of population size from capture-recapture data with heterogeneous detection probabilities

Heterogeneity in detection probabilities has long been recognized as problematic in mark-recapture studies, and numerous models developed to accommodate its effects. Individual heterogeneity is especially problematic, in that reasonable alternative models may predict essentially identical observations from populations of substantially different sizes. Thus even with very large samples, the analyst will not be able to distinguish among reasonable models of heterogeneity, even though these yield quite distinct inferences about population size. The problem is illustrated with models for closed and open populations.

Biometrics↗

USGS2005

Interpretation of transmissivity estimates from single-well pumping aquifer tests

Interpretation of single-well tests with the Cooper-Jacob method remains more reasonable than most alternatives. Drawdowns from 628 simulated single-well tests where transmissivity was specified were interpreted with the Cooper-Jacob straight-line method to estimate transmissivity. Error and bias as a function of vertical anisotropy, partial penetration, specific yield, and interpretive technique were investigated for transmissivities that ranged from 10 to 10,000 m2/d. Cooper-Jacob transmissivity estimates in confined aquifers were affected minimally by partial penetration, vertical anisotropy, or analyst. Cooper-Jacob transmissivity estimates of simulated unconfined aquifers averaged twice the known values. Transmissivity estimates of unconfined aquifers were not improved by interpreting results with an unconfined aquifer solution. Judicious interpretation of late-time data consistently improved estimates where transmissivity exceeded 250 m2/d in unconfined aquifers. ?? 2006 National Ground Water Association.

Ground Water↗

USGS2015

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Madre de Dios↗

USGS2006

Using self-organizing maps to determine observation threshold limit predictions in highly variant data

A significant data quality challenge for highly variant systems surrounds the limited ability to quantify operationally reasonable limits on the data elements being collected and provide reasonable threshold predictions. In many instances, the number of influences that drive a resulting value or operational range is too large to enable physical sampling for each influencer, or is too complicated to accurately model in an explicit simulation. An alternative method to determine reasonable observation thresholds is to employ an automation algorithm that would emulate a human analyst visually inspecting data for limits. Using the visualization technique of self-organizing maps (SOM) on data having poorly understood relationships, a methodology for determining threshold limits was developed. To illustrate this approach, analysis of environmental influences that drive the abundance of a target indicator species (the pink shrimp, Farfantepenaeus duorarum) provided a real example of applicability. The relationship between salinity and temperature and abundance of F. duorarum is well documented, but the effect of changes in water quality upstream on pink shrimp abundance is not well understood. The highly variant nature surrounding catch of a specific number of organisms in the wild, and the data available from up-stream hydrology measures for salinity and temperature, made this an ideal candidate for the approach to provide a determination about the influence of changes in hydrology on populations of organisms.

Conference Paper↗

USGS2019

Integrating management strategy evaluation into fisheries management: Advancing best practices for stakeholder inclusion based on an MSE for Northeast US Atlantic herring

The New England Fishery Management Council used management strategy evaluation (MSE) to evaluate possible harvest control rules for Atlantic herring ( Clupea harengus ), the first MSE in the US and perhaps globally to use open-invitation, public workshops for input. Stakeholder inclusion can increase both realism and likelihood of use by managers, but inclusivity is not achieved easily. Here, self-selected participants had diverse backgrounds and differing levels of interest and preparedness. We describe some challenges with directly engaging the public in MSE and offer broader insights for obtaining effective public participation during a decision-making process. Conducting an open MSE aligns well with publicly driven management but requires clear goals and communication. Investment in effective organizers, impartial facilitators, and knowledgeable analysts can improve communication and understanding of MSE to the betterment of fisheries management. We aim to further MSE best practices on integrating stakeholders and hope that our lessons learned on communication, engagement, and integration of MSE into an existing management arena will be useful to other practitioners.

Canadian Journal of Fisheries and Aquatic Sciences↗

USGS2021

Interlaboratory comparison of SARS-CoV2 molecular detection assays in use by U.S. veterinary diagnostic laboratories

The continued search for intermediate hosts and potential reservoirs for SARS-CoV2 makes it clear that animal surveillance is critical in outbreak response and prevention. Real-time RT-PCR assays for SARS-CoV2 detection can easily be adapted to different host species. U.S. veterinary diagnostic laboratories have used the CDC assays or other national reference laboratory methods to test animal samples. However, these methods have only been evaluated using internal validation protocols. To help the laboratories evaluate their SARS-CoV2 test methods, an interlaboratory comparison (ILC) was performed in collaboration with multiple organizations. Forty-four sets of 19 blind-coded RNA samples in Tris-EDTA (TE) buffer or PrimeStore transport medium were shipped to 42 laboratories. Results were analyzed according to the principles of the International Organization for Standardization (ISO) 16140-2:2016 standard. Qualitative assessment of PrimeStore samples revealed that, in approximately two-thirds of the laboratories, the limit of detection with a probability of 0.95 (LOD95) for detecting the RNA was ≤20 copies per PCR reaction, close to the theoretical LOD of 3 copies per reaction. This level of sensitivity is not expected in clinical samples because of additional factors, such as sample collection, transport, and extraction of RNA from the clinical matrix. Quantitative assessment of Ct values indicated that reproducibility standard deviations for testing the RNA with assays reported as N1 were slightly lower than those for N2, and they were higher for the RNA in PrimeStore medium than those in TE buffer. Analyst experience and the use of either a singleplex or multiplex PCR also affected the quantitative ILC test results.

Journal of Veterinary Diagnostic Investigation↗

USGS2023

Findings from a decade of ground motion simulation validation research and a path forward

Simulated ground motions have the potential to advance seismic hazard assessments and structural response analyses, particularly for conditions with limited recorded ground motions such as large magnitude earthquakes at short source-to-site distances. However, rigorous validation of simulated ground motions is needed for hazard analysts, practicing engineers, or regulatory bodies to be confident in their use. A decade ago, validation exercises were mainly limited to comparisons of simulated-to-observed waveforms and median values of spectral accelerations for selected earthquakes. The Southern California Earthquake Center (SCEC) Ground Motion Simulation Validation (GMSV) group was formed to increase coordination between simulation modelers and research engineers with the aim of devising and applying more effective methods for simulation validation. Here, we summarize what has been learned in over a decade of GMSV activities, principally reflecting the views of the SCEC research community but also extending our findings and suggestions for a path forward to broader United States and worldwide simulation validation efforts. We categorize different validation methods according to their approach and the metrics considered. Two general approaches are to compare validation metrics from simulations to those from historical records or to those from semi-empirical models. Validation metrics are categorized into ground motion characteristics and structural responses. We discuss example validation studies that have been impactful in the past decade and suggest future research directions. Key lessons learned are that validation is application-specific, our outreach and dissemination need improvement, and much validation-related research remains unexplored.

Earthquake Spectra↗

USGS2016

A decision framework for identifying models to estimate forest ecosystem services gains from restoration

Restoring degraded forests and agricultural lands has become a global conservation priority. A growing number of tools can quantify ecosystem service tradeoffs associated with forest restoration. This evolving “tools landscape” presents a dilemma: more tools are available, but selecting appropriate tools has become more challenging. We present a Restoration Ecosystem Service Tool Selector (RESTS) framework that describes key characteristics of 13 ecosystem service assessment tools. Analysts enter information about their decision context, services to be analyzed, and desired outputs. Tools are filtered and presented based on five evaluative criteria: scalability, cost, time requirements, handling of uncertainty, and applicability to benefit-cost analysis. RESTS uses a spreadsheet interface but a web-based interface is planned. Given the rapid evolution of ecosystem services science, RESTS provides an adaptable framework to guide forest restoration decision makers toward tools that can help quantify ecosystem services in support of restoration.

Forest Ecosystems↗

USGS2023

A simplified method for value of information using constructed scales

The value of information is a central concept in decision analysis, used to quantify how much the expected outcome of a decision would be improved if epistemic uncertainty could be resolved prior to committing to a course of action. One of the challenges, however, in quantitative analysis of the value of information is that the calculations are demanding, especially in requiring predictions of outcomes as a function of alternative actions and sources of uncertainty. However, the concept of value of information is important in early framing of some decisions, before such predictions are available. We propose a novel measure of the value of information based on constructed scales (CVOI), grounded in the algebra of the expected value of perfect information (EVPI), but requiring less of experts and analysts. The CVOI calculation decomposes EVPI into a contribution representing the relevance of the uncertainty to the decision and a contribution representing the magnitude of uncertainty; constructed ratio scales are then proposed for each contribution. We demonstrate the use of CVOI to identify research priorities related to migratory bird management in the face of climate change.

Decision Analysis↗

USGS2017

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders

Motivation Several spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data. Model and data analysis We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data. Benefits Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.

Western Ghats, Nagarahole Tiger Reserve↗

USGS2007

[Book review] The sound approach to birding: A guide to understanding bird sound

What is the “Sound Approach”? It is a trio of enthusiastic bird-sound recordists-analysts (Arnoud B. van den Berg, Mark Constantine, and Magnus Robb) who, with friends, traveled to 42 countries in a massive effort to record all the songs and calls of the birds of the Western Palearctic. The recordings in this guide are selected from the 30,000 or so recorded digitally with stereo microphones by the authors since the year 2000.

The Auk↗

USGS2020

Earthquake magnitude and Lg Q variations between the Grenville and northern Appalachian geologic provinces of eastern Canada

This article assesses the ability of regionally specific, frequency‐dependent crustal attenuation ( ⁠ 1 / Q "> 1 / Q ⁠ ) to reduce mean magnitude discrepancies between seismic stations in the northern Appalachian and Grenville provinces (NAP and GP) of Canada. Lg Q ( f ) "> Q ( f ) is an important parameter in ground‐motion models used in probabilistic seismic hazard analysis. Discrepancies in regional magnitude estimates have long been noted to exist between stations in the two provinces for common event origins. Such discrepancies could arise from systematic site condition variations between the geologic provinces or from varying crustal attenuative properties. To evaluate the effect of frequency‐dependent anelastic attenuation, Lg Q ( f ) "> Q ( f ) on estimated magnitudes, we analyze Lg amplitudes from > 6000 "> > 6000 waveforms recorded by Grenville and northern Appalachian receivers from 420 natural earthquakes of M N "> M N magnitude 3–5.6. Waveform analysis is strictly limited to analyst‐reviewed, vertical‐component waveforms in which Lg is clearly identified, ensuring that the datasets exhibit dominant, high‐frequency energy in the Lg velocity window. Lg Q ( f ) "> Q ( f ) is found to be higher in the GP than in the northern Appalachians. In the Grenville, Q ( f ) = 761 ( ± 145 ) f 0.25 ( ± 0.014 ) "> Q ( f ) = 761 ( ± 145 ) f 0.25 ( ± 0.014 ) ⁠ , and in the northern Appalachians, attenuation is higher: Q ( f ) = 506 ( ± 172 ) f 0.33 ( ± 0.310 ) "> Q ( f ) = 506 ( ± 172 ) f 0.33 ( ± 0.310 ) . Earthquake magnitude determined using the peak amplitude of the Lg phase ( ⁠ m b L g "> m b L g ) for eastern Canada is corrected to incorporate the frequency‐dependent, regionally specific Lg Q ( f ) "> Q ( f ) determined in this study. Using the new Lg Q ( f ) "> Q ( f ) values diminishes and nearly resolves magnitude discrepancies between the provinces. Correcting regional magnitude discrepancies between provinces is critical for reliable regional seismic hazard estimates because magnitude error in a particular region could lead to increased uncertainty in seismic hazard models.

Grenville Geologic Province, Northern Appalachian ↗

USGS2023

A detailed view of the 2020-2023 southwestern Puerto Rico seismic sequence with deep learning

The 2020–2023 southwestern Puerto Rico seismic sequence, still ongoing in 2023, is remarkable for its multiple‐fault rupture complexity and elevated aftershock productivity. We applied an automatic workflow to continuous data from 43 seismic stations in Puerto Rico to build an enhanced earthquake catalog with ∼180,000 events for the 3+ yr sequence from 28 December 2019 to 1 January 2023. This workflow contained the EQTransformer (EQT) deep learning model for event detection and phase picking, the EikoNet‐Hypocenter Inversion with Stein Variational Inference probabilistic earthquake location approach with a neural network trained to solve the eikonal wave equation, and relocation with event‐pair waveform cross correlation. EQT increased the number of catalog events in the sequence by about seven times, though its performance was not quite as good as thorough analyst review. The enhanced catalog revealed new structural details of the sequence space–time evolution, including sudden changes in activity, on a complex system of many small normal and strike‐slip faults. This sequence started on 28 December 2019 with an M 4.7 strike‐slip earthquake followed by 10 days of shallow strike‐slip foreshocks, including several M 5+ earthquakes, in a compact region. The oblique normal fault M w "> M w 6.4 mainshock then happened on 7 January 2020. Early aftershocks in January 2020, with several M 5+ earthquakes, quickly expanded into two intersecting fault zones with diffuse seismicity: one extending ∼35 km on a northward‐dipping normal fault and the other ∼60‐km‐long and oriented west‐northwest–east‐southeast on strike‐slip faults. Months to years later, aftershocks moved westward, deeper, and to outer reaches of the active fault zones, with abrupt rapid seismicity migration following larger M 4.7+ aftershocks in May, July, and December 2020. The observed seismicity evolution indicates cascading failure from stress transfer on multiple critically stressed faults. High aftershock productivity results from the complex multiple‐fault network hosting the sequence, which is characteristic of an immature fault system in the diffuse deformation zone around Puerto Rico, at the complicated North American–Caribbean plate boundary region.

Puerto Rico↗

USGS2018

Seismology with dark data: Image-based processing of analog records using machine learning for the rangely earthquake control experiment

Before the digital era, seismograms were recorded in analog form and read manually by analysts. The digital era represents only about 25% of the total time span of instrumental seismology. Analog data provide important constraints on earthquake processes over the long term, and in some cases are the only data available. The media on which analog data are recorded degrades with time and there is an urgent need for cost‐effective approaches to preserve the information they contain. In this study, we work directly with images by constructing a set of image‐based methods for earthquake processing, rather than pursue the usual approach of converting analog data to vector time series. We demonstrate this approach on one month of continuous Develocorder films from the Rangely earthquake control experiment run by the U.S. Geological Survey (USGS). We scan the films into images and compress these into low‐dimensional feature vectors as input to a classifier that separates earthquakes from noise in a defined feature space. We feed the detected event images into a short‐term average/long‐term average (STA/LTA) picker, a grid‐search associator, and a 2D image correlator to measure both absolute arrival times and relative arrival‐time differences between events. We use these measurements to locate the earthquakes using hypoDD. In the month that we studied, we identified 40 events clustered near the injection wells. In the original study, Raleigh et al. (1976) identified only 32 events during the same period. Scanning without vectorizing analog seismograms represents an attractive approach to archiving these perishable data. We demonstrated that it is possible to carry out precision seismology directly on such images. Our approach has the potential for wide application to analog seismograms.

Seismological Research Letters↗

USGS2022

DevelNet: Earthquake detection on Develocorder films with deep learning: Application to the rangely earthquake control experiment

There exists over a century of instrumental seismic data; however, most seismograms recorded before the 1980s are only available in analog form. Although analog seismograms are of great value, they are underutilized due to the difficulties of making quantitative measurements on the original media and in converting them to digital time series. In this study, we present an alternative workflow, based on deep learning, to reconstruct an earthquake catalog from images of analog data without conversion to vector time series. We trained a convolutional neural network—DevelNet, using synthetic analog data to detect earthquakes on scanned multichannel Develocorder film images. We then developed an image‐based processing workflow to measure arrival times, locate, and determine the magnitudes of earthquakes in the data. We demonstrate the performance of this approach on two years of continuous Develocorder film recordings from the Rangely earthquake control experiment in the mid‐1970s. Our approach detects twice the number of events reported in the original catalog ( Raleigh et al. , 1976 ). This demonstrates that DevelNet efficiently detects earthquakes from Develocorder film scans, performs consistently over time, and is robust to changes in network geometry. Our locations generally agree with the original study, although the automatically measured arrival times are less precise than manual reading, leading to increased location scatter. Our automatic workflow of Develocorder films rivals the performance of skilled analysts in earthquake detection, but with minimal human intervention. This image‐based processing offers a new approach for effectively and efficiently extracting earthquake information from analog seismic data.

Bulletin of the Seismological Society of America↗

USGS2025

Estimating earthquake source depth using teleseismic broadband waveform modeling at the USGS National Earthquake Information Center

The U.S. Geologic Survey National Earthquake Information Center (NEIC) monitors global seismicity, producing a catalog of earthquake source parameters in near-real-time to provide information that can help mitigate the societal impact of earthquakes. The NEIC commonly relies on teleseismic observations to constrain earthquake source parameters (e.g., location, depth, magnitude, and mechanism) due to a lack of local and regional observations. For these ‘teleseismic’ events, depth phase (i.e., pP , sP ) arrival time observations provide the best estimate on source depth. However, depth phases are often difficult to accurately identify and/or pick. Therefore, NEIC relies on waveform modeling, such as those determined from W-phase ( M ww ), body wave ( M wb ), and regional ( M wr ) moment tensor estimations, to provide constraints on source depth. While depth estimates from these approaches are informative, higher frequency observations provide more precise estimates because depth phases are more prominently observed at higher frequencies. Here, we present NEIC’s relatively high-frequency (~0.04 to 1 Hz) teleseismic waveform modeling approach, termed Synthetic Depth Phase Modeling (SynDepth), for determining source depth. SynDepth was developed to provide NEIC with a tool that enables rapid, accurate, and quantifiable estimates of earthquake source depth in cases where locator depths are not reliable. This relatively simple and fast procedure searches over 1 km-incremented source depths and an expanding triangular source-time function to find the best-fitting solution. We compare automatic SynDepth solutions for a dataset of 1,216 earthquakes (M5.5-M7.6) between 2017 and 2021 to NEIC-derived depth estimates from other methods. Our approach provides a robust depth estimate for earthquakes lacking local arrival time data, and it minimizes the need for analyst review of depth-phase picks ( pP , sP ) or using predefined ‘fixed’ depths.

Seismological Research Letters↗