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At least 1,117 records · Page 62Linked to original sources
USGS2021

Population structure, intergroup interaction, and human contact govern infectious disease impacts in mountain gorilla populations

Infectious zoonotic diseases are a threat to wildlife conservation and global health. They are especially a concern for wild apes, which are vulnerable to many human infectious diseases. As ecotourism, deforestation, and great ape field research increase, the threat of human-sourced infections to wild populations becomes more substantial and could result in devastating population declines. The endangered mountain gorillas ( Gorilla beringei beringei ) of the Virunga Massif in east-central Africa suffer periodic disease outbreaks and are exposed to infections from human-sourced pathogens. It is important to understand the possible risks of disease introduction and spread in this population and how human contact may facilitate disease transmission. Here we present and evaluate an individual-based, stochastic, discrete-time disease transmission model to predict epidemic outcomes and better understand health risks to the Virunga mountain gorilla population. To model disease transmission we have derived estimates for gorilla contact, interaction, and migration rates. The model shows that the social structure of gorilla populations plays a profound role in governing disease impacts with subdivided populations experiencing less than 25% of the outbreak levels of a single homogeneous population. It predicts that gorilla group dispersal and limited group interactions are strong factors in preventing widespread population-level outbreaks of infectious disease after such diseases have been introduced into the population. However, even a moderate amount of human contact increases disease spread and can lead to population-level outbreaks.

Virunga Massif↗
USGS2013

Crustal P-wave velocity structure from Altyn Tagh to Longmen mountains along the Taiwan-Altay geoscience transect

Based upon the seismic experiments along Geoscience Transect from the Altyn Tagh to the Longmen Mountains, the crustal P-wave velocity structure was derived to outline the characteristics of the crust in the eastern margin of Tibetan Plateau. The section shows a few significant features. The crustal thickness varies dramatically, and is consistent with the tectonic settings. The Moho boundary abruptly drops to 73km depth beneath the southern Altyn Tagh from 50km in the Tarim basin, then rises again to about 58km depth beneath the Qaidam basin. The Moho drops again to about 70km underneath the Songpan-Garzê Terrane, then rises stepwise to 60km near Longmen Mountains. To further southeast, the crust thins to 52km beneath the Sichuan basin in the southeast of Longmen Mountains. In the north of the Kunlun Fault, a low-velocity zone,which may be a layer of melted rocks due to high temperature and pressure in the depth,exists in the the bottom of the middle crust.The two depressions of the Moho correlate with the Qilian and Songpan-Garzê Terranes, implying that these two mountains have deep roots. According to our results, it is deduced that the thick crust of the northeastern Tibetan Plateau probably is a result of east-west and northwest-southeast crustal shortening since the Mesozoic during the collision of the Asian and Indian plates.

Taiwan-Altay geoscience transect↗
USGS2023

Growth, drought response, and climate-associated genomic structure in whitebark pine in the Sierra Nevada of California

Whitebark pine ( Pinus albicaulis Engelm.) has experienced rapid population declines and is listed as threatened under the Endangered Species Act in the United States. Whitebark pine in the Sierra Nevada of California represents the southernmost end of the species' distribution and, like other portions of its range, faces threats from an introduced pathogen, native bark beetles, and a rapidly warming climate. Beyond these chronic stressors, there is also concern about how this species will respond to acute stressors, such as drought. We present patterns of stem growth from 766 large (average diameter at breast height >25 cm), disease-free whitebark pine across the Sierra Nevada before and during a recent period of drought. We contextualize growth patterns using population genomic diversity and structure from a subset of 327 trees. Sampled whitebark pine generally had positive to neutral stem growth trends from 1970 to 2011, which was positively correlated with minimum temperature and precipitation. Indices of stem growth during drought years (2012 to 2015) relative to a predrought interval were mostly positive to neutral at our sampled sites. Individual tree growth response phenotypes appeared to be linked to genotypic variation in climate-associated loci, suggesting that some genotypes can take better advantage of local climatic conditions than others. We speculate that reduced snowpack during the 2012 to 2015 drought years may have lengthened the growing season while retaining sufficient moisture to maintain growth at most study sites. Growth responses may differ under future warming, however, particularly if drought severity increases and modifies interactions with pests and pathogens.

California, Nevada↗
USGS2018

The effect of isolation, fragmentation, and population bottlenecks on song structure of a Hawaiian honeycreeper

Little is known about how important social behaviors such as song vary within and among populations for any of the endemic Hawaiian honeycreepers. Habitat loss and non‐native diseases (e.g., avian malaria) have resulted in isolation and fragmentation of Hawaiian honeycreepers within primarily high elevation forests. In this study, we examined how isolation of Hawai'i ‘amakihi ( Chlorodrepanis virens ) populations within a fragmented landscape influences acoustic variability in song. In the last decade, small, isolated populations of disease tolerant ‘amakihi have been found within low elevation forests, allowing us to record ‘amakihi songs across a large elevational gradient (10–1800 m) that parallels disease susceptibility on Hawai'i island. To understand underlying differences among populations, we examined the role of geographic distance, elevation, and habitat structure on acoustic characteristics of ‘amakihi songs. We found that the acoustic characteristics of ‘amakihi songs and song‐type repertoires varied most strongly across an elevational gradient. Differences in ‘amakihi song types were primarily driven by less complex songs (e.g., fewer frequency changes, shorter songs) of individuals recorded at low elevation sites compared to mid and high elevation populations. The reduced complexity of ‘amakihi songs at low elevation sites is most likely shaped by the effects of habitat fragmentation and a disease‐driven population bottleneck associated with avian malaria, and maintained through isolation, localized song learning and sharing, and cultural drift. These results highlight how a non‐native disease through its influence on population demographics may have also indirectly played a role in shaping the acoustic characteristics of a species.

Hawai'i↗
USGS2019

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N ‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N ‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N ‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N ‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.

Ecology and Evolution↗
USGS2016

Using structural equation modeling to link human activities to wetland ecological integrity

The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity “unimpaired for future generations.”

Colorado↗
USGS2016

Marine ecoregion and Deepwater Horizon oil spill affect recruitment and population structure of a salt marsh snail

Marine species with planktonic larvae often have high spatial and temporal variation in recruitment that leads to subsequent variation in the ecology of benthic adults. Using a combination of published and unpublished data, we compared the population structure of the salt marsh snail, Littoraria irrorata , between the South Atlantic Bight and the Gulf Coast of the United States to infer geographic differences in recruitment and to test the hypothesis that the Deepwater Horizon oil spill led to widespread recruitment failure of L. irrorata in Louisiana in 2010. Size-frequency distributions in both ecoregions were bimodal, with troughs in the distributions consistent with a transition from sub-adults to adults at ~13 mm in shell length as reported in the literature; however, adult snails reached larger sizes in the Gulf Coast. The ratio of sub-adults to adults was 1.5–2 times greater in the South Atlantic Bight than the Gulf Coast, consistent with higher recruitment rates in the South Atlantic Bight. Higher recruitment rates in the South Atlantic Bight could contribute to higher snail densities and reduced adult growth in this region. The ratio of sub-adults to adults in Louisiana was lower in 2011 than in previous years, and began to recover in 2012–2014, consistent with widespread recruitment failure in 2010, when large expanses of spilled oil were present in coastal waters. Our results reveal an important difference in the ecology of a key salt marsh invertebrate between the two ecoregions, and also suggest that the Deepwater Horizon oil spill may have caused widespread recruitment failure in this species and perhaps others with similar planktonic larval stages.

Ecosphere↗
USGS2016

Structured decision making as a framework for large-scale wildlife harvest management decisions

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer ( Odocoileus virginianus ) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

Ecosphere↗
USGS2017

Model-based estimators of density and connectivity to inform conservation of spatially structured populations

Conservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter histories of individuals (e.g., a record of where individuals are detected across space, often sequenced over multiple sampling occasions), to estimate the relationship between space use and characteristics of a landscape, allowing simultaneous estimation of both local densities of individuals across space and connectivity at the scale of individual movement. We developed two model-based estimators derived from the SCR ecological distance model to quantify connectivity over a continuous surface: (1) potential connectivity—a metric of the connectivity of areas based on resistance to individual movement; and (2) density-weighted connectivity (DWC)—potential connectivity weighted by estimated density. Estimates of potential connectivity and DWC can provide spatial representations of areas that are most important for the conservation of threatened species, or management of abundant populations (i.e., areas with high density and landscape connectivity), and thus generate predictions that have great potential to inform conservation and management actions. We used a simulation study with a stationary trap design across a range of landscape resistance scenarios to evaluate how well our model estimates resistance, potential connectivity, and DWC. Correlation between true and estimated potential connectivity was high, and there was positive correlation and high spatial accuracy between estimated DWC and true DWC. We applied our approach to data collected from a population of black bears in New York, and found that forested areas represented low levels of resistance for black bears. We demonstrate that formal inference about measures of landscape connectivity can be achieved from standard methods of studying animal populations which yield individual encounter history data such as camera trapping. Resulting biological parameters including resistance, potential connectivity, and DWC estimate the spatial distribution and connectivity of the population within a statistical framework, and we outline applications to many possible conservation and management problems.

Ecosphere↗
USGS2021

Spatial network clustering reveals elk population structure and local variation in prevalence of chronic wasting disease

Spatial organization plays prominent roles in disease transmission, genetics, and demography of wildlife populations and is therefore an important consideration not only for wildlife management, but also for inference about populations and processes. We used hierarchical agglomerative clustering of a spatial graph network to partition Wind Cave National Park (WICA) into five regions used by 163 female elk ( Cervus elaphus ) marked with global positioning system collars during 2005–08 and 2011–13. We grouped elk based on differential use of the five regions, developed a priori models for inter-group variation in the occurrence of chronic wasting disease (CWD), and used Akaike's information criterion to compare models and stratify regions. Previous descriptions of elk population structure, which have been based on social contact or overlap of individual ranges, have distinguished spatially disjunct population subsets. Constructing hierarchical partitions of the landscape enabled us to also discern and describe overlapping and nested subsets. During 2016–18, apparent park-wide prevalence of CWD was 0.18 (90% CI = [0.146, 0.182]); however, prevalence within three spatial strata used primarily by different elk ranged from 0.03 ([0.008, 0.074]) to 0.29 ([0.211, 0.375]). In context with published estimates of recruitment, predation, and anthropogenic mortality, such differences in prevalence equate to increasing local abundance of elk in southwestern WICA, stable to declining abundance in the west/northwest, and rapidly declining abundance in the east. Despite the modest size of WICA (11,357 ha), park-wide averages conflate effects of elk distribution and disease, obscuring spatial patterns with profound implications for study and management of elk and CWD. Graph networks have been used widely in ecology to describe such phenomena as social relationships, connectivity of habitat patches, animal movements, and the spread of disease. Extension to partitioning of geographic range is straightforward but entails different considerations. We discuss allocation of sampling effort, construction of an initial partition, specification of a model for graph cohesion, selection of a clustering algorithm, and identification of useful partitions.

North Dakota↗
USGS1994

Use of benthic invertebrate community structure and the sediment quality triad to evaluate metal-contaminated sediment in the upper Clark Fork River, Montana

The upper Clark Fork River, above Flathead River, is contaminated with large amounts of As, Cd, Cu, Pb, Mn, and Zn ores from past mining activities. The contaminated area extends from the Butte and Anaconda area to at least 230 km downstream to Milltown Reservoir. Both the upper Clark Fork River and Milltown Reservoir have been designated as U.S. Environmental Protection Agency Superfund sites because of metal-contaminated bottom sediments. We evaluated the impacts of past mining activities on the Clark Fork River ecosystem using benthic invertebrate community assessment, residue chemistry, and toxicity testing. Oligochaeta and Chironomidae generally accounted for over 90% of the benthic invertebrate community in the soft sediment depositional areas. Taxa of Oligochaeta and Chironomidae were predominantly pollution tolerant. Higher numbers of Chironomidae genera were present at stations with higher concentrations of metals in sediment identified as toxic by the amphipod Hyalella azteca in 28-d exposures. Frequency of mouthpart deformities in genera of Chironomidae was low and did not correspond to concentrations of metals in sediment. Total abundance of organisms/m 2 did not correspond to concentrations of metals in the sediment samples. Chemical analyses, laboratory toxicity tests, and benthic community evaluations all provide evidence of metal-induced degradation to aquatic communities in both the reservoir and the river. Using a weight-of-evidence approach-the Sediment Quality Triad - provided good concurrence among measures of benthic community structure, sediment chemistry, and laboratory toxicity.

Montana↗
USGS2020

Hydrodynamics drive pelagic communities and food web structure in a tidal environment

Hydrodynamic processes can lead to the accumulation and/or dispersal of water column constituents, including sediment, phytoplankton, and particulate detritus. Using a combination of field observations and stable isotope tracing tools, we identified how hydrodynamic processes influenced physical habitat, pelagic communities, and food web structure in a freshwater tidal system. The pelagic habitat of a terminal channel differed spatially, likely aligning with differences in hydrodynamics. Three zones that we classified by exchange with downstream habitat had distinct water quality characteristics, supported different densities of zooplankton and nekton, and exhibited disparate support from benthic and pelagic trophic pathways to pelagic consumers. Hydrodynamically driven zones and their emergent characteristics appeared sensitive to hydrology, as elevated runoff was correlated with a shift in hydrodynamic habitat and organismal distributions. The results of our study highlight the relationship between hydrodynamic processes, biological responses, and climate, and suggest that understanding the physical process can improve understanding of pelagic habitats and communities.

California↗
USGS2015

Density, distribution, and genetic structure of grizzly bears in the Cabinet-Yaak Ecosystem

The conservation status of the 2 threatened grizzly bear ( Ursus arctos ) populations in the Cabinet-Yaak Ecosystem (CYE) of northern Montana and Idaho had remained unchanged since designation in 1975; however, the current demographic status of these populations was uncertain. No rigorous data on population density and distribution or analysis of recent population genetic structure were available to measure the effectiveness of conservation efforts. We used genetic detection data from hair corral, bear rub, and opportunistic sampling in traditional and spatial capture–recapture models to generate estimates of abundance and density of grizzly bears in the CYE. We calculated mean bear residency on our sampling grid from telemetry data using Huggins and Pledger models to estimate the average number of bears present and to correct our superpopulation estimates for lack of geographic closure. Estimated grizzly bear abundance (all sex and age classes) in the CYE in 2012 was 48–50 bears, approximately half the population recovery goal. Grizzly bear density in the CYE (4.3–4.5 grizzly bears/1,000 km 2 ) was among the lowest of interior North American populations. The sizes of the Cabinet ( n = 22–24) and Yaak ( n = 18–22) populations were similar. Spatial models produced similar estimates of abundance and density with comparable precision without requiring radio-telemetry data to address assumptions of geographic closure. The 2 populations in the CYE were demographically and reproductively isolated from each other and the Cabinet population was highly inbred. With parentage analysis, we documented natural migrants to the Cabinet and Yaak populations by bears born to parents in the Selkirk and Northern Continental Divide populations. These events supported data from other sources suggesting that the expansion of neighboring populations may eventually help sustain the CYE populations. However, the small size, isolation, and inbreeding documented by this study demonstrate the need for comprehensive management designed to support CYE population growth and increased connectivity and gene flow with other populations.

Idaho, Montana↗
USGS2016

Structured decision making for managing pneumonia epizootics in bighorn sheep

Good decision-making is essential to conserving wildlife populations. Although there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision-making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. To illustrate the application of this process to a management need, we present an SDM-based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep ( Ovis canadensis ) in Montana. Pneumonia epizootics are a major challenge for managers due to long-term impacts to herds, epistemic uncertainty in timing and location of future epizootics, and consequent difficulty knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd-specific objectives, and predicted costs and benefits of each alternative. Decision analyses for 2 example herds revealed that meeting management objectives necessitates specific approaches unique to each herd. The analyses showed how and under what circumstances the alternatives are optimal compared to other approaches and current management. Managers can be confident that these decisions are effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes, and risk tolerance.

Journal of Wildlife Management↗
USGS2017

Addressing wild turkey population declines using structured decision making

We present a case study from New York, USA, of the use of structured decision making (SDM) to identify fall turkey harvest regulations that best meet stakeholder objectives, in light of recent apparent declines in abundance of wild turkeys in the northeastern United States. We used the SDM framework to incorporate the multiple objectives associated with turkey hunting, stakeholder desires, and region-specific ecological and environmental factors that could influence fall harvest. We identified a set of 4 fall harvest regulations, composed of different season lengths and bag limits, and evaluated their relative achievement of the objectives. We used a stochastic turkey population model, statistical modeling, and expert elicitation to evaluate the consequences of each harvest regulation on each of the objectives. We conducted a statewide mail survey of fall turkey hunters in New York to gather the necessary information to evaluate tradeoffs among multiple objectives associated with hunter satisfaction. The optimal fall harvest regulation was a 2-week season and allowed for the harvest of 1 bird/hunter. This regulation was the most conservative of those evaluated, reflecting the concerns about recent declines in turkey abundance among agency wildlife biologists and the hunting public. Depending on the region of the state, the 2-week, 1-bird regulation was predicted to result in 7–32% more turkeys on the landscape after 5 years. The SDM process provided a transparent framework for setting fall turkey harvest regulations and reduced potential stakeholder conflict by explicitly taking the multiple objectives of different stakeholder groups into account.

New York↗
USGS2022

Structural and functional landscape connectivity for lesser prairie-chickens in the Sand Shinnery Oak Prairie Ecoregion

The lesser prairie-chicken ( Tympanuchus pallidicinctus ) is a species of conservation concern on the Southern High Plains of Texas and New Mexico, USA. Because fragmentation and isolation have increased since pre-settlement, dispersal through this heterogeneous landscape may be constrained, with serious implications for conservation and management of this species. Our objectives were to quantify landscape connectivity for lesser prairie-chickens within a patch network of potentially isolated leks (breeding display grounds), and examine effects of land use change on modeled lesser prairie-chicken movements through the landscape. We used graph theory to quantify structural landscape connectivity and circuit theory to quantify functional landscape connectivity for lesser prairie-chickens in the Sand Shinnery Oak Prairie Ecoregion of the Southern High Plains. There was a high degree of clustering among leks ( n = 1,023 leks), with a 41.9-km coalescence distance of the network. We identified 3 leks as cutpoints within the network, meaning if the habitat patches containing these leks were fragmented, the remaining leks would become isolated from each other. We also identified several leks that were important for maintaining overall population connectivity for lesser prairie-chickens on the Southern High Plains. Conservation Reserve Program land was important for maintaining connectivity among leks to the north and west of the main lek core area located in New Mexico, but wind energy development constrained pathways to the north and south of this main lek core area. Our results suggest that landscape connectivity was reduced by row-crop agriculture and energy production and facilitated by the Conservation Reserve Program within the Sand Shinnery Oak Prairie Ecoregion.

New Mexico, Texas↗
USGS2021

Quantifying the spatial structure of invasive lake trout in Yellowstone Lake to improve suppression efficacy

Invasive Lake Trout Salvelinus namaycush have altered the once-pristine Yellowstone Lake ecosystem through top-down effects by consuming Yellowstone Cutthroat Trout Oncorhynchus clarkii bouvieri . To conserve Yellowstone Cutthroat Trout and restore the ecosystem, a Lake Trout gillnetting program was implemented to suppress the invasive population. We evaluated the spatial structure of Lake Trout in Yellowstone Lake with the intent of increasing suppression efficiency. Specifically, we addressed questions related to adult Lake Trout aggregation and movement during summer and autumn (spawning) periods and how Lake Trout used locations in the context of suppression efforts. We tracked 373 Lake Trout (>500 mm TL) during the summer and autumn of 2016 and 2017. Based on kernel density estimates, Lake Trout were highly aggregated at 9 locations during summer and 22 locations during the spawning period. Using a novel metric, individual days (product of mean individuals per survey and mean length of stay), five summer locations and five spawning locations had at least 30 individual days. These locations are suggested as priority areas for targeting Lake Trout suppression. Lake Trout were less aggregated and moved less during the summer, making them less vulnerable to a passive gear in the summer than during the autumn spawning period. Lake Trout exhibited low spawning site fidelity compared to populations elsewhere, possibly due to decades of intensive gill netting at spawning locations. Given the aggregation and movement patterns observed in Yellowstone Lake, continuing to target adult Lake Trout during the spawning period is the most cost-effective approach to Lake Trout suppression.

Wyoming↗