Geology topics

Abigail Jean Lawson

Publications and source records attributed to Abigail Jean Lawson.

12 recordsLinked to original sources

USGS2026

Mass mortality of avian migrants in New Mexico, USA, that coincided with an extreme weather event

Many birds are migratory, and this life history strategy allows for maximized access to seasonally abundant resources and favorable climates. However, migration exposes birds to threats and stressors, resulting in high mortality during migration. Anthropogenic landscape alterations and climate change have intensified threats, and mass mortality events linked to extreme weather are more common in recent decades. Documenting mass mortality is critical for predicting future occurrences and implementing effective conservation. Here, we describe a mortality event that occurred throughout New Mexico, USA in fall 2020. Carcasses began appearing in the region in mid-August, during a period of extreme heat and drought. Following an extreme cold weather event on September 8–9 th , the number of carcasses increased dramatically and expanded throughout the state. In total, we collected 628 carcasses comprising 58 species within Doña Ana and Otero Counties in New Mexico. Necropsy determined emaciation was the cause of death for 74.6% of carcasses. Live birds captured during the period of peak mortality (n = 223) were in similarly poor condition. This event provides a striking example of how multiple types of extreme stressors, in this case widespread drought and unseasonal cold, coincided with a mortality event, indicating a possible synergistic relationship between these factors and the mass mortality. Mortality events are likely to increase in frequency with intensifying climate change. Establishment of networks of biologists and researchers could improve our ability to identify and communicate developing mortality events, organize data collection, and improve understanding of the causes and consequences of mortality.

New Mexico↗

USGS2026

Constructed value of information with iterative scoring and parametric uncertainty to identify management-relevant research priorities for a declining raptor species

Constructed value of information (CVoI) is an expert elicitation decision-analytic tool used to prioritize sources of uncertainty based on their potential to improve decision outcomes if resolved. Despite increased application of CVoI, the robustness of CVoI prioritization of sources of uncertainty relative to differences in expert elicitation and scoring methods has not been evaluated. We engaged a group of species experts in a decision-analytic process to elicit uncertainties, framed as alternative hypotheses, about current population declines of the American kestrel ( Falco sparverius ) in the United States. Participants scored 13 hypotheses across 3 CVoI criteria, which are defined as constructed scales. Rather than experts selecting a single score per criterion, we used a likelihood point method to incorporate parametric uncertainty in the scoring process, in which experts were given 100 points to distribute across possible score categories within the criterion-specific constructed scale. Experts provided scores over 2 scoring rounds, with an opportunity to review and discuss initial scores between rounds. We used a Shannon entropy calculation to quantify how evenly participants allotted their points. We used simulation to evaluate the robustness of our prioritization results relative to a scoring method in which participants selected a single score category for each criterion. Participants often spread their points across 2 adjacent scores, reflecting parametric uncertainty. For one third of the hypothesis-scoring round combinations, the prioritization results differed in approximately 50% of simulations. The highest scoring hypotheses related to how the use of artificial versus natural nest cavities affects fecundity or survival, whether winter roosting sites are a limiting factor for population growth, and whether gamebird habitat management may benefit kestrel populations. Our CVoI prioritization framework can be used to develop collaborative research that is directly relevant to a management decision and is an advance in eliciting more representative expert beliefs.

Conservation Biology↗

USGS2026

American kestrel population trends and vital rates at the continental scale

The American kestrel ( Falco sparverius , hereafter referred to as kestrel) has declined across much of its North American range since at least the mid-1960s. Kestrel population dynamics have been explored through a multitude of local studies and two broad reviews of available data. Across large geographic extents, however, the demographic cause(s) of kestrel population declines remain(s) largely unknown. As part of a collaborative effort to elucidate the drivers of kestrel population declines, we developed a continental-scale integrated population model using band-recovery data, productivity data, and Breeding Bird Survey indices from 1986 to 2019 to estimate indices of annual population sizes, survival, and productivity rates across the continental United States. We detected a decline in population size of ~1%–2% per year. Overall estimates of population growth from 1986 to 2019 suggest a 29% decline in population size (95% CI = −34% to −23%). There was little evidence of a trend in brood size. However, survival of juvenile birds (mean = −0.015, SD = 0.008 and mean = −0.024, SD = 0.010 for females and males, respectively) and adult males (mean = −0.016, SD = 0.010) in the summer declined, suggesting that these vital rates could be contributing to declines in populations over time. Winter adult survival rates (mean = −0.004, SD = 0.009 and mean = −0.009, SD = 0.010 for females and males, respectively) also declined but to a lesser extent than summer survival. For juvenile birds, winter survival increased (mean = 0.006, SD = 0.008 and mean = 0.002, SD = 0.009 for females and males, respectively); however, this was not enough to offset declines in summer survival and annual survival rates declined over the time series. Annual adult survival was also low relative to previous research on kestrel survival rates. Given the importance of survival to population trends, our findings provide support for several previously proposed broad classes of factors potentially contributing to observed population declines: declines in arthropod prey, second-generation rodenticides, neonicotinoid insecticides, and predation.

Ecosphere↗

USGS2026

Carcass size and ground substrate drive detection rates of avian carcasses by human surveyors and a dog team

Accurate avian mortality estimates are essential for understanding anthropogenic impacts to bird populations and informing conservation strategies. Carcass surveys are commonly conducted by human surveyors or by detection dogs, but the factors influencing surveyor detection abilities have not been fully explored. In this study, we conducted two years of detection trials in the semi-arid high desert of southern New Mexico, USA, testing 27 human surveyors and one conservation detection dog across 1096 trials with 238 carcasses representing 50 avian species. We directly compared detection abilities between surveyor types (human and dog) and identified key factors influencing detection probabilities. The conservation detection dog exhibited a significantly higher detection probability (mean = 0.87) than human surveyors (mean = 0.49, individuals ranged 0.25–0.71), consistent with previous studies. Detection probabilities for both surveyor types were influenced by carcass size and ground substrate; detection probability was higher for larger carcasses and areas with lower vegetative complexity. We discuss our results in the context of common tradeoffs faced by managers in designing carcass surveys and how guidance may vary under different scenarios. Broadly, our study provides valuable insight that can enhance wildlife mortality monitoring, ensuring more accurate mortality estimates to inform management and conservation efforts.

New Mexico↗

USGS2025

Nocturnal flight call monitoring reveals in-flight behavioral alteration by avian migrants in response to artificial light at night

The world in which birds evolved to migrate has been drastically altered in the Anthropocene by artificial light. Sources of light such as urban centers or bright upward-facing lights attract migrants, altering their behavior, especially during inclement weather, often leading to mortality. Seemingly less extreme sources, such as pole-mounted floodlighting, ubiquitous throughout much of the world, have received comparatively less study, and migrant responses to such sources are poorly understood. We studied migrant behavior in relation to light at White Sands Missile Range (New Mexico, USA) by recording nocturnal flight calls at sites with and without lights during non-inclement weather. We collected 103,424 h of recordings and detected 2,851,863 calls over three fall migration seasons. We assessed how temporal, weather, and lighting variables explain variability in call rates between light and dark sites, and examined how different taxonomic groups behave in relation to light. Contrary to predictions, call rates were higher at dark sites than at light sites, and this difference was strongest early in the migration season. We found illuminated sites with a greater proportion of shielded lights, or with lights of higher dominant wavelengths (warmer color temperatures), had higher call rates (closely resembling dark sites) than other light sites, indicating that these factors may reduce impact to migrants. Our taxonomic analyses revealed consistent differences in call rate between light and dark sites for warblers, but no difference for most sparrows. Our findings indicate that lights alter behavior, but the use of “bird-friendly” lighting strategies may reduce this impact.

New Mexico↗

USGS2025

Inferring Brown-Capped Rosy-Finch demography and breeding distribution trends from long-term wintering data in New Mexico

The three North American Rosy-Finch species (Brown-capped [ Leucosticte australis ], Black [ L. atrata ], and Gray-crowned [ L. tephrocotis ]) are among the most climate-threatened species in the United States. New Mexico is an important location for investigating the effects of climate change because it is the southernmost location in which Brown-capped Rosy-Finches breed and the southernmost location where all three Rosy-Finch species co-occur during winter. In the context of climate change, this range boundary is important to study because it is the first part of the range anticipated to cross a threshold of unsuitability for these species with increasing temperatures. Rosy-Finches are difficult to study during the breeding season due to the high elevation and remoteness of their breeding grounds; therefore, winter studies may lend insight into population trends and provide direction for conservation actions based on knowledge of the breeding origins of wintering birds. The goals of our study were to investigate long-term survival and migration trends from wintering Brown-capped Rosy-Finches in New Mexico and evaluate the efficacy of radio frequency identification (RFID)-equipped artificial feeders to monitor population trends. As of May 2025, we have conducted a robust design survival analysis on 22 years of mark-recapture data from a particular wintering site in New Mexico, assessed patterns in the breeding origins of individuals captured at this site using stable isotope analysis, and examined patterns in data collected via RFID. Our main findings from this study are that annual survival probability of Rosy-Finches wintering in New Mexico is low compared to that of other migratory passerines, that Brown-capped Rosy-Finches wintering in New Mexico likely originate from a variety of locations across their breeding range, and that RFID monitoring is useful in improving survival estimates in Rosy-Finches, particularly in short-term studies.

New Mexico↗

USGS2024

RE-ARMing salt marshes: A resilience-experimentalist approach to prescribed fire and bird conservation in high marshes of the Gulf of Mexico

Uncertainty, complexity, and dynamic changes present challenges for conservation and natural resource management. Evidence-based approaches grounded in reliable information and rigorous analysis can enhance the navigation of the uncertainties and trade-offs inherent in conservation problems. This study highlights the importance of collaborative efforts and evidence-based decision-making, specifically implementing the Resilience-Experimentalist school of adaptive management (RE-ARM), which emphasizes stakeholder involvement, shared understanding, and experimentation. Our goal was to develop an adaptive management framework to reduce the uncertainty around the use of prescribed fire to manage the habitat for eastern black rails ( Laterallus jamaicensis jamaicensis ) and mottled ducks ( Anas fulvigula ) in saltmarshes of the Gulf of Mexico. Supported by discussions at a series of workshops, we used a value of information analysis to select a fire management hypothesis to test, developed an influence diagram to represent the system under fire management, used the influence diagram to develop a Bayesian decision network (BDN), and conducted a power analysis to guide management experiments and monitoring. Value of information analysis identified fire return interval as the critical uncertainty. Our BDN provided valuable insight into how managers believe prescribed fire influences vegetation characteristics and how vegetation influences both eastern black rail occupancy and mottled duck abundance. The results of the power analysis indicated that a standard occupancy modeling framework was more useful to compare 2- and 5-year fire return intervals for black rails than two alternative designs (removal and conditional). Our BDN can be used to predict the probability of achieving the desirable vegetative response to increase the occupancy probability of black rails and abundance of mottled ducks, and monitoring data can be used to update the BDN (learn) and improve best management practices for prescribed burns (adapt). Linking the value of information, BDNs, and power analysis enhances our understanding of the system, improves management decision-making, and builds trust among scientists, interested parties, and decision-makers. This approach lays the groundwork for knowledge co-production and adaptive management.

Frontiers in Conservation Science↗

USGS2024

Accounting for multiple uncertainties in a decision-support population viability assessment

Conservation and management decisions often must be made on strict timelines, based on the “best available information” regarding a species’ current and expected future status. Simulation models are valuable tools for predicting a species’ future status but must incorporate multiple types of uncertainty in order to provide a complete understanding of plausible outcomes. Here we present a population viability analysis for a data-deficient species proposed for protection under the U.S. Endangered Species Act, the alligator snapping turtle. We used a matrix population model to simulate population trajectories, incorporating both parametric uncertainty and temporal variation into demographic parameters. We used expert elicitation to generate modified survival rates in the presence of specific anthropogenic threats, for which empirical estimates were unavailable. Because uncertainty in the expert elicited values was of particular interest to decision makers, we constructed a set of simulation scenarios to evaluate the sensitivity of model conclusions to the accuracy of expert elicited parameters. Our model predicted steep population declines under all scenarios with anthropogenic threats, indicating that under- or overestimation by experts would not change the overall conclusion that populations would decline. An additional sensitivity analysis revealed that a parameter related to nest survival for which there was high disagreement among experts had a negligible effect on model outcome, while other parameters (e.g., the effect of poaching) had more influence. Our analyses demonstrate the use of an expert-parameterized decision-support population viability analysis that explicitly evaluates the effects of multiple sources of uncertainty on model predictions.

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗

USGS2023

Qualitative value of information provides a transparent and repeatable method for identifying critical uncertainty

Conservation decisions are often made in the face of uncertainty because the urgency to act can preclude delaying management while uncertainty is resolved. In this context, adaptive management is attractive, allowing simultaneous management and learning. An adaptive program design requires the identification of critical uncertainties that impede the choice of management action. Quantitative evaluation of critical uncertainty, using the expected value of information, may require more resources than are available in the early stages of conservation planning. Here, we demonstrate the use of a qualitative index to the value of information (QVoI) to prioritize which sources of uncertainty to reduce regarding the use of prescribed fire to benefit Eastern Black Rails ( Laterallus jamaicensis jamaicensis ), Yellow Rails ( Coterminous noveboracensis ), and Mottled Ducks ( Anas fulvigula ; hereafter, focal species) in high marshes of the U.S. Gulf of Mexico. Prescribed fire has been used as a management tool in Gulf of Mexico high marshes throughout the last 30+ years; however, effects of periodic burning on the focal species and the optimal conditions for burning marshes to improve habitat remain unknown. We followed a structured decision-making framework to develop conceptual models, which we then used to identify sources of uncertainty and articulate alternative hypotheses about prescribed fire in high marshes. We used QVoI to evaluate the sources of uncertainty based on their magnitude, relevance for decision making, and reducibility. We found that hypotheses related to the optimal fire return interval and season were the highest priorities for study, whereas hypotheses related to predation rates and interactions among management techniques were lowest. These results suggest that learning about the optimal fire frequency and season to benefit the focal species might produce the greatest management benefit. In this case study, we demonstrate that QVoI can help managers decide where to apply limited resources to learn which specific actions will result in a higher likelihood of achieving the desired management objectives. Further, we summarize the strengths and limitations of QVoI and outline recommendations for its future use for prioritizing research to reduce uncertainty about system dynamics and the effects of management actions.

Ecological Applications↗

USGS2023

Strategic monitoring to minimize misclassification errors from conservation status assessments

Classifying species into risk categories is a ubiquitous process in conservation decision-making affecting regulatory procedures, conservation actions, and guiding resource allocation at global, national, and regional scales. However, monitoring programs often do not provide data required for accurate species classification decisions. Misclassification can lead to otherwise preventable species extinctions, undue regulatory burden, poor allocation of limited conservation resources, and can undermine species conservation legislation. We developed a framework that evaluates monitoring designs based on the ability to correctly inform a species classification decision, where minimizing the risk of misclassification is the central objective. We further evaluated monitoring designs by calculating the expected value of information and explored the relationship between statistical power to detect trends and misclassification. Our measure of misclassification risk, which can be tailored to the decision context, clarified the costs of over- and under-protection. High power to detect trends often corresponded to accurate species classification decisions. However, in several scenarios power to detect trends was low but the ability to correctly inform the classification decision was high. The value of information generally increased with monitoring intensity and quantified the tradeoffs between spatial and temporal replication. Our framework allows managers to assess monitoring program performance with direct implications for conservation decision-making. Our framework affords practitioners an opportunity to evaluate the effectiveness of monitoring programs a priori focusing on improving conservation decisions. We demonstrate that prioritizing monitoring to minimize misclassification errors can improve monitoring efficiency and conservation decision-making with considerable practical applications and benefits for species conservation.

Biological Conservation↗

USGS2022

Hidden in plain sight: Integrated population models to resolve partially observable latent population structure

Population models often require detailed information on sex-, age-, or size-specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage- or size-structured species subject to consumptive use. American alligators ( Alligator mississippiensis ; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class-specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state-space count model to derive estimates of size class-specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class-specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high-resolution output of latent population structure that is partially observed during the monitoring process.

South Carolina↗

USGS2021

Decision context as an essential component of population viability analysis

Population viability analysis (PVA) is a widely used tool that applies demographic data in simulation frameworks to assess extinction risk for species or populations. It is used in diverse conservation applications, including evaluating management effectiveness, relative risk of threats, and potential changes to protective status (Beissinger & McCullough, 2002 ), and can be a critical tool for making decisions with imperfect knowledge of the system state, often on limited timelines (Meine et al., 2006 ). Chaudhary and Oli ( 2020 ) recently developed a framework to appraise the quality of PVAs based on the presence of essential background, model, and analysis components. They evaluated 160 published PVAs and reported a decline in the quality of PVAs over time (1990−2017). We agree PVA studies should report unambiguous descriptions of their essential components (Table 1 in Chaudhary and Oli) and explicitly state the model's biological and statistical assumptions. The need for increased transparency in PVAs is evident. Morrison et al. ( 2016 ) reported that only 50% of PVAs published in peer-reviewed and gray literature were both reproducible and repeatable. Further, in an examination of 67 studies that used matrix population models (widely used in PVAs), Kendall et al. ( 2019 ) reported that models frequently contained misspecification errors. Given the rapid advancement of simulation techniques, updated guidance for PVA construction is warranted. However, we believe the essential PVA components identified by Chaudhary and Oli contain a critical omission: the decision context in which the PVA was created and its usefulness in that context. Quality and utility are not mutually exclusive; however, some models that do not meet idealized quality standards might still be valuable because they are useful and represent the best available science for a given decision context (hereafter, decision-support models). The definition of quality for decision-support models should be different than models developed for the purpose of learning (hereafter, heuristic models) and should incorporate how useful the model was, despite information gaps. We further argue that assessment questions should be used prospectively to guide modeling projects, rather than for retrospective comparison of model quality.

Conservation Biology↗