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Conor P. McGowan

Publications and source records attributed to Conor P. McGowan.

At least 19 recordsLinked to original sources
USGS2026

Using structured decision-making to develop a communications strategy for the U.S. Geological Survey Cooperative Research Units Program

Communication regarding the mission of the U.S. Geological Survey (USGS) Cooperative Research Units Program (CRU) can take many forms, yet clear and concise messaging for various audiences is critical to highlight program accomplishments and increase visibility. Before the work described in this report, CRU did not have a communication strategy; therefore, CRU leadership supported a structured decision-making (SDM) workshop to develop a comprehensive strategy for multiple audiences. The workshop was conducted in November 2024, in Nebraska City, Nebraska. The working group for this SDM process included CRU Program leadership, the CRU Communications Team lead, Unit scientists, a Unit administrative assistant, a representative of the Wildlife Management Institute (WMI), a member of the USGS Ecosystems Mission Area (EMA), Office of Communications and Publishing (OCAP) team, and the team lead for the CRU Program strategic planning process, as well as three facilitators who were also unit scientists as well as experts in SDM. Over the course of a week, the SDM team followed the PrOACT framework which identified the problem, objectives, alternatives, consequences, and tradeoffs to guide us toward a strategy for implementation of a set of actions for CRU communications. Results of the SDM workshop included the development of a problem statement, an objectives hierarchy, a suite of alternatives that were evaluated using a consequences table and a clear process for assessing tradeoffs among alternative communication actions and strategies. Through the evaluation of consequences of each action or campaign, the team developed both the assessment tool (for the future) and an immediate plan for communication product development and distribution. The consequences table for this problem was meant to be flexible to accommodate changes in CRU thematic priorities and can be easily updated with new objectives, measures, and alternatives. In addition, the weight placed on objectives may change as the Team moves forward; the ranking and scoring system used in the workshop can be easily updated. Overall, the working group identified three different actions or campaigns—Fact Sheets, Who Are We Campaign, and Alumni Campaign—that scored high in the prototype decision framework. However, the tradeoffs analysis indicated that each action(s) performed better on some objectives than others. The working group identified a need to therefore develop an implementation plan that is composed of individual actions that each target different objectives to potentially create a holistic and feasible communications strategy that performs well for all objectives. In addition, the SDM prototype developed a scalable, objective-based framework for effectively communication of the value and accomplishments of the CRU program.

Preprints.org↗
USGS2025

Denning black bear response to anthropogenic disturbance and implications for cub survival in Florida

Wildlife research and management can be disruptive to wildlife. By advancing our understanding of the impacts of these activities, we can reduce adverse effects, improve decision-making, and enhance the outcomes of research and management. During 2017–2019, we observed the responses of denning female American black bears ( Ursus americanus ) to 3 types of routine research and management activities in Florida, USA: (1) a low-level, nonintrusive human approach near the natal den ( n H 44); (2) a high-level, intrusive human approach involving cub handling ( n H 42); and (3) a prescribed burn within 1 km of the den during the denning season ( n H 11). We measured responses (flight distance, time away, and postdisturbance denning behavior) using Global Positioning System collars programmed to record a location every 2 hours. We observed minimal response from bears to low-level human disturbances. In contrast, all bears fled after high-level human disturbances, with responses ranging from staying nearby throughout the disturbance and quickly returning to cubs, to fleeing several kilometers and abandoning cubs. On average, bears fled approximately 380 m from the den and returned to their cubs 7 hours postdisturbance. After returning, most bears relocated their cubs to a new den site, on average 125 m away. Responses to prescribed fire ranged from no measurable response and no den relocation to den site abandonment with cub mortality. Through generalized linear modeling, we found that adult female time away was positively associated with cub age. We found that annual cub survival was negatively associated with fire exposure in the den and with continued denning at a disturbed den site following high-level disturbance. In areas where bears are easily displaced from dens, these results provide insights that may improve bear research and habitat management decisions.

Florida↗
USGS2024

Viability modeling for decision support with limited data: A lizard case study

Plateau spot-tailed earless lizards, Holbrookia lacerata, are a species of ground lizard in central Texas that are under review for listing as endangered under the US Endangered Species Act, but heretofore no predictive models of population dynamics or viability have been developed. We used limited available data and published demographic rates in a PVA model to predict future status of these lizards under parametric and ecological uncertainty and temporal variability. Even in cases where data are sparse and life history information are limited, viability models can help clarify the consequences of management choices given the uncertainty. Our model predicted that on average populations will decline in in the future. Quasi-extinction probability was low 20 years into the future but up to 0.60. Extinction risk was highly dependent on the road mortality effect and the proportion of the population exposed to roadways, both of which are currently uncertain quantities. Despite these unknowns, our model enables managers to consider the future abundance and extinction risk for the species and make decisions about management to project the populations and also identifies key uncertainties for future research and monitoring.

Texas↗
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↗
USGS2024

Strengths and opportunities in gopher tortoise population modeling: Reply to Loope et al.

We read the letter written by Loope et al. (2024) that evaluated the predictive population model for gopher tortoises from our previous paper ( Folt et al., 2022 ), and, despite criticism raised, we contend that our effort remains a valuable contribution. In our previous paper, we performed an unprecedented and exhaustive review of gopher tortoise population demography, which synthesized a large volume of literature about the current state of knowledge on gopher tortoise population dynamics and factors that influence demographic rates, including anthropogenic threats ( Folt et al., 2022 ). We then built a predictive population model that examined the relative influence of multiple anthropogenic stressors on tortoise populations into the future, while accounting for important geographic variation ( Folt et al., 2022 ).

Global Ecology and Conservation↗
USGS2023

Models for linking hunter retention and recruitment to regulations and game populations

Introduction: Declining hunter populations across North America present wildlife management agencies with the prospect of declining revenues for wildlife conservation and management and the need for new tools to evaluate management strategies and predict future status of game species and hunters. Methods: Here we present a modeling framework and potential decision support tool for managers to link future hunter population dynamics to regulatory restrictiveness, prey abundance, and harvest success. Our hunter model is parameterized based on the authors’ judgment and can be used for demonstration purposes. We simulated three scenarios of restricted harvest, moderate harvest and liberal harvest. Results: Our simulations show that even though liberal harvest predicts higher cumulative license sales revenue, it corresponds with a slight decline in buck abundance over 10 years. In contrast, highly restrictive harvest corresponds with deer population growth, but a near collapse of hunter populations. Our model demonstrates that managers might face tradeoffs between managing for deer population abundance and hunting revenue and clarifies how these factors might affect decision making. Discussion: The utility of our tool would be dependent on accessing data on hunter retention and recruitment, however, the strength of our paper is in highlighting a new way of thinking about and potentially addressing these potential tradeoffs. Further, these simulations demonstrate that these tools could be used to evaluate management strategies but also highlight uncertainties, establish research priorities, and potentially design an adaptive management framework.

Alabama↗
USGS2023

Estimating recruitment rate and population dynamics at a migratory stopover site using an integrated population model

Consideration of the full annual cycle population dynamics can provide useful insight for conservation efforts, but collecting data needed to estimate demographic parameters is often logistically difficult. For species that breed in remote areas, monitoring is often conducted during migratory stopover or at nonbreeding sites, and the recruitment rate of new breeding adults can be difficult to estimate directly. Here, we present an integrated population model that uses mark-resight and count data to estimate survival probability, population growth rate, and recruitment rate for an Arctic-breeding shorebird of conservation concern, the red knot ( Calidris canutus rufa ), from data collected during spring stopover in Delaware Bay, USA, from 2005 to 2018. At this site, red knots feed primarily on the eggs of spawning horseshoe crabs ( Limulus polyphemus ), a legally harvested species. We used this model to estimate the relationship between horseshoe crab abundance and red knot demographics, which informed a recent revision to the framework used to establish horseshoe crab harvest regulations. Our analysis indicates that the red knot population was most likely stable from 2005 to 2018 (average λ = 1.03, 95% credible interval [CRI]: 0.961, 1.15) despite low recruitment rates (average ρ = 0.088, 95% CRI: 0.012, 0.18). Adult survival probability was positively associated with horseshoe crab abundance in the same year (β = 0.35, 95% CRI: 0.09, 0.63), but we found no effect of horseshoe crab abundance two years previously on recruitment of new adults (β = −0.08, 95% CRI: −0.41, 0.38). Our approach demonstrates the utility of integrated population models for understanding population dynamics, even when data are only available from migratory stopover monitoring.

Delaware, New Jersey↗
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↗
USGS2023

Prioritization of species status assessments for decision support

Species status assessments are used to inform U.S. Fish and Wildlife Service (USFWS) decision making for Endangered Species Act (ESA) classification decisions, recovery planning, and more. The large number of species that require assessment and uncertainty in the data available impede the process of assigning and completing the assessments, which makes creating a multiyear work plan extremely difficult. An optimized triaging system that maximizes the use of the best available information while managing the complex ESA workload and meeting deadlines is necessary. We used a structured decision-making framework to approach the problem with the goal of creating a prioritization tool that would be effective at scheduling assessments, given the best information available and priorities of the USFWS. We collected data on the species awaiting assessment and developed a value function that incorporates existing deadlines, taxonomic uncertainty, controversy of the species, and population and habitat data availability and quality. We used a constrained linear optimization algorithm to maximize the value function and ensure that workload capacity was not exceeded. A comparison of model scenarios indicates that imposed deadlines impact the model more than capacity constraints. Additionally, differential weighting of the metrics significantly affected the outcome of the model. In the future, elicitation of metric weights should be done routinely before the model is run for use in official planning to ensure alignment with current USFWS priorities. Output from this optimization can be used to inform a five-year work plan, allocate resources, and discuss workforce decisions.

Decision Analysis↗
USGS2023

Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )

To predict future status of the northwestern pond turtle ( Actinemys marmorata ) and southwestern pond turtle ( Actinemys pallida ) species, we developed a stochastic stage-based matrix population model to simulate future population conditions. We constructed a demographic population viability analysis for each species based on a post-breeding, single sex, stage-based life history diagram elicited from taxa experts and derived from relevant literature. Demographic parameters were based on estimates from published literature and data provided to the U.S. Fish and Wildlife Service (USFWS). Using the most recent observations of turtles, available habitat, local abundances, and current threat conditions, we calculated spatially explicit initial abundances to initialize our stochastic projection. In order to incorporate multiple types of uncertainty (ecological, parametric, temporal), we built three embedded simulation loops within the simulation model. Representing ecological uncertainty, species status was projected into the future using multiple plausible future scenarios based on two representative concentration pathways (RCP 4.5, 8.5) and two shared socioeconomic pathways (SSP 2, 5) to reflect plausible alternative future trajectories of relevant environmental conditions. Parametric uncertainty was included for survival estimates of all life stages due the inconsistency of estimates across the species’ range. Temporal variability or environmental stochasticity was included in the form of randomized variation from the mean demographic parameter values in each year of the approximately 80-year simulation. The model output included probability of extinction and estimated abundance through 2100 for each unique Analysis Unit (AU) and for the full geographic range of the species except populations in the state of Washington. The AUs in Washington are conservation dependent and sustained by a head-starting and reintroduction program. Thus, the population dynamics do not match our model for the rest of the range and therefore the Washington AUs were included in this projection modeling effort. There is already pre-existing, detailed PVA for these specific populations (Pramuk et al. 2012, p.41-60), and the Status assessment report can use those results for inference about future status. We discuss the results of Pramuk et al. (2012, p.41-61) alongside our own. Probability of extinction was overall higher for the southwestern pond turtle as compared to the northwestern species and population growth rates were strongly negative for both species (approximately -3% annually for all AUs for all scenarios). This appendix is organized into three primary sections: 1) a description of the life history, the core population dynamics model, and demographic parameters, 2) a description of methods for establishing initial abundances of the populations for the future viability modeling, and 3) a description of the methods for modeling effects of various threats on future demographic rates and the results of future conditions scenarios.

Report↗
USGS2022

Using predictions from multiple anthropogenic threats to estimate future population persistence of an imperiled species

Imperiled species face numerous and diverse anthropogenic threats to their persistence, and wildlife managers charged with making conservation decisions benefit from a sound understanding of how populations, species, and ecosystems will respond to future changes in threats to biodiversity. In southeastern North America, the gopher tortoise ( Gopherus polyphemus ) is a keystone species in upland ecosystems; however, tortoise populations have declined strongly over the last century, and the species is a candidate for increased protection by the United States federal government under the Endangered Species Act (ESA). Here, we sought to support conservation decision making for G. polyphemus by developing a spatially-explicit predictive population model that linked four anthropogenic threats (climate warming, sea-level rise, urbanization, habitat degradation) to demographic vital rates and used the model to estimate future changes in the number of individuals, populations, and metapopulations across the species’ range. Using recent survey data, we projected 457 populations for 80 years into the future under scenarios varying in threat magnitude, management magnitude, and demographic uncertainty. Population projections predicted that the number of individuals, populations, and metapopulations would decline among all simulated scenarios in the next 80 years. Model predictions were more sensitive to variation in adult survival and immigration rates than to variation in threat magnitude. A scenario with decreased habitat management and threat effects from climate warming, sea-level rise, and urbanization predicted geographic variation in persistence probabilities for populations that might result in decreased genetic representation across the species' range. Our results can be used to support conservation listing decisions for the gopher tortoise as part of its federal Species Status Assessment and provide an analytical framework for how to link diverse threats to geographically-varying demographic rates during population viability analyses for wide-ranging imperiled species around the world.

Global Ecology and Conservation↗
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↗
USGS2021

Species-specific demographic and behavioral responses to food availability during migratory stopover

Understanding the effects of migratory stopover site conditions on both demographic rates and migratory behaviors is critical for interpreting changes in passage population sizes at stopover sites and predicting responses to future changes and conservation actions. We used a Bayesian formulation of the open robust design model to analyze mark-resight observations of three migratory shorebird species using Delaware Bay, USA during spring stopover from 2005 to 2018. We tested for an effect of stopover food availability and weather conditions on survival probability and the probability of returning to this site in the next year and found species differences in these relationships. After years with greater food availability, red knot Calidris canutus rufa had higher survival probability but ruddy turnstone Arenaria interpres were more likely to return to the site. Estimates of within-year probabilities of arrival and persistence at the stopover site showed relatively consistent migration schedules for ruddy turnstone, but more interannual variation for red knot and sanderling Calidris alba . Shorebird use of this site typically peaked during May 26–28, but the estimated proportion of the population present during this period varied dramatically among years for red knot (range: 0.07–0.59) but less so for ruddy turnstone and sanderling. This demonstrates that both the proportion of the flyway population using this stopover site and the proportion present during a given sampling period vary among years, and both should be considered in analyzing and interpreting monitoring data. Stopover conditions can influence both migratory behavior and demographics, underscoring the importance of flyway-wide monitoring.

Delaware, New Jersey↗
USGS2021

Contrasting patterns of demography and population viability among gopher tortoise (Gopherus polyphemus) populations at the species’ northern range edge

Population viability analyses are useful tools to predict abundance and extinction risk for imperiled species. In southeastern North America, the federally threatened gopher tortoise ( Gopherus polyphemus ) is a keystone species in the diverse and imperiled longleaf pine ( Pinus palustris ) ecosystem, and researchers have suggested that tortoise populations are declining and characterized by high extinction risk. We report results from a 30-year demographic study of gopher tortoises in southern Alabama (1991–2020), where 3 populations have been stable and 3 others have declined. To better understand the demographic vital rates associated with stable and declining tortoise populations, we used a multi-state hierarchical mark-recapture model to estimate sex- and stage-specific patterns of demographic vital rates at each population. We then built a predictive population model to project population dynamics and evaluate extinction risk in a population viability context. Population structure did not change significantly in stable populations, but juveniles became less abundant in declining populations over 30 years. Apparent survival varied by age, sex, and site; adults had higher survival than juveniles, but female survival was substantially lower in declining populations than in stable ones. Using simulations, we predicted that stable populations with high female survival would persist over the next 100 years but sites with lower female survival would decline, become male-biased, and be at high risk of extirpation. Stable populations were most sensitive to changes in apparent survival of adult females. Because local populations varied greatly in vital rates, our analysis improves upon previous demographic models for northern populations of gopher tortoises by accounting for population-level variation in demographic patterns and, counter to previous model predictions, suggests that small tortoise populations can persist when habitat is managed effectively. © 2021 The Wildlife Society.

Alabama↗
USGS2020

The Long-term effect of bleeding for Limulus amebocyte lysate on annual survival and recapture of tagged horseshoe crabs

In the U.S., 525,000 horseshoe crabs ( Limulus polyphemus ) per year have been captured during 2013–2017, brought to biomedical facilities, and bled to produce Limulus amebocyte lysate (LAL), then mostly released to the area of capture. The Atlantic States Marine Fisheries Commission estimates short-term bleeding-induced mortality to be 15% (4% to 30%), resulting in mortality of approximately 78,750 horseshoe crabs annually in recent years comprising a minor portion (<13%) of the up to one million annual coastwide landings dominated by harvest for bait. However, the long-term effect of bleeding for LAL on annual survival and spawning behavior is unknown; thus, results from short-term studies alone might underestimate bleeding effects at the population level. To address this knowledge gap, we analyzed data from the U.S. Fish and Wildlife horseshoe crab tagging database to estimate the differences in survival and recapture rates of bled and not bled horseshoe crabs tagged in the same years and geographic area. Contrary to expectation, survival was not lower for bled crabs compared to unbled crabs. Differences varied, but survival estimates tended to be higher for bled crabs than for unbled crabs. However, biomedical culling and selection for younger or healthier animals could have resulted in biomedically tagged individuals representing a healthier subset of the overall population with subsequent higher survival. Furthermore, the tagging analysis revealed a post-bleeding reduction in capture probability, which could indicate decreased spawning activity, evident in males more than females. Continued tagging of bled and unbled crabs in the same geographic area while recording age class and sex will contribute to the further resolution of LAL production’s effect on horseshoe crab populations.

Delaware, Maryland, New Jersey, Virginia↗
USGS2020

A demographic projection model to support conservation decision making for an endangered snake with limited monitoring data

Conservation planning for rare and threatened species is often made more difficult by a lack of research and monitoring data. In such cases, managers may rely on qualitative assessments of species risk that lack explicit acknowledgement of uncertainty. Snakes are a group of conservation concern that are also notoriously difficult to monitor. Here, we demonstrate a quantitative population projection for a data-deficient species, the Puerto Rican boa ( Chilabothrus inornatus ) using expert knowledge and published information about species life history and threats to persistence. Using this model, we simulated population dynamics over 30 years under four scenarios of future urbanization and found that there was an increased probability of population decline as urbanization rates increased. We conduct a sensitivity analysis to evaluate the sensitivity of outcomes to model inputs, a practice that may also be useful in recovery planning. The sensitivity analyses also provide insight into how the future trajectories would change if the elicited demographic rates are incorrect. Even when data are sparse, quantitative methods can often be used to produce rigorous and reproducible estimates of future status with quantifiable uncertainty.

Puerto Rico↗
USGS2020

Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment

The US Fish and Wildlife Service has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of a species’ current status and predicted future status. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, recovery planning, etc. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting species conservation decisions in current data and cutting edge analytical and modeling techniques. Here we describe a novel analysis of available data to assess current condition of eastern black rail across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future conditions under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With results of these data analyses and predictive modeling, the US Fish and Wildlife Service concluded that protections of the Endangered Species Act were warranted for this subspecies.

Endangered Species Research↗