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42 results for “Greater Yellowstone Ecosystem”
Rates and controls of nitrogen fixation in post-fire lodgepole pine forests, Greater Yellowstone Ecosystem, 2022
This dataset contains all the contents needed to reproduce the calculations and analyses done in the original paper associated with this dataset (Heumann et al. 2025 Ecology). The primary method used in this study was the Acetylene Reduction Assay (ARA) which measures the rate at which acetylene is reduced to ethylene in nitrogen-fixing organisms as a proxy for nitrogen fixation activity. We measured acetylene reduction rates in multiple cryptic niches (i.e., lichen, moss, pine litter, dead wood and mineral soil) in 34-year-old lodgepole pine stands in the Greater Yellowstone Ecosystem to explore the rates, temporal patterns, and climate controls on cryptic N fixation. Thus the foundation of this dataset is ethylene production rate measurements. All the data tables in this dataset contain either measured ethylene production rates or estimates of N fixation scaled from those ethylene production rates. Included with this are various physical measurements (e.g. dry mass, moisture content, incubation temperatures) that we included in our analyses in order to either scale up rates of N fixation using biomass estimates from field sites or explore temperature and moisture relationships with nitrogen fixation activity under controlled conditions. Included with this dataset are three R studio scripts used to run the calculations and analyses reported in the manuscript publication from this study.
Less fuel for the next fire? Short-interval fire delays forest recovery and interacting drivers amplify effects, Greater Yellowstone Ecosystem, Montana and Wyoming, USA
As 21st-century climate and disturbance dynamics depart from historical baselines, ecosystem resilience is uncertain. Multiple drivers are changing simultaneously, and interactions among drivers could amplify ecosystem vulnerability to change. We explored how interacting drivers affected post-fire recovery of subalpine forests, which Subalpine forests in Greater Yellowstone (Northern Rocky Mountains, USA) were historically resilient to infrequent (100-300 year), severe fire., in Greater Yellowstone (Northern Rocky Mountains, USA). We sampled paired short- (< 30 year) and long- (> 125 year) interval post-fire plots most recently last burned between 1988 and 2018 to address two questions: (1) How do short-interval fire, climate, topography, and distance to unburned live forest edge and other factors (topography, distance to live edge) interact to affect post-fire forest recoveryregeneration? (2) How do forest biomass and fuels vary following short- versus long-interval severe fires? Mean post-fire live stem density was an order of magnitude lower following short- versus long-interval fires (3,240 versus 28,741 stems ha-1, respectively). Differences between paired plots increased with greater climate water deficit normal (ρ = 0.67) and were amplified at longer distances to live forest edge. Surprisingly, warmer-drier climate was associated with higher seedling densities even after short-interval fire, likely relating to regional variation in serotiny of lodgepole pine (Pinus contorta var. latifolia). Unlike conifers, density of aspen (Populus tremuloides), a deciduous resprouter, increased with short- versus long-interval fire (mean 384 versus 62 stems ha-1, respectively). Live biomass and canopy fuels remained low nearly 30 years after short-interval fire, in contrast to rapid recovery after long-interval fire, suggesting that future burn severity may be reduced for several decades following reburns. Short-interval plots also had half as much dead woody biomass compar
Data for: Sparse subalpine forest recovery pathways, plant communities, and carbon stocks 34 years after stand-replacing fire (Greater Yellowstone Ecosystem, Wyoming, USA; 2022)
We assessed postfire forest recovery pathways, stem densities, understory plant communities, and carbon stocks across 55 plots in areas exhibiting sparse and reduced forest recovery 34 years after the 1988 Yellowstone Fires in the Greater Yellowstone Ecosystem, Wyoming, USA. Recovery pathways were identified using plot-level frequency distributions of tree ages and correlated with potentially important biotic and abiotic variables (e.g., elevation, seed source distance). Species- and age-specific stem densities were similarly regressed across environmental factors to determine variability in forest recovery across the sampled landscape. Understory plant communities were sampled in 0.25m-square quadrats and environmental drivers of individual species occurrence and whole compositional shifts were determined. Finally, carbon stock sizes were derived from field measures of tree characteristics, understory cover, and soil combined with regionally derived allometric equations. Data collection is complete and is part of a forthcoming manuscript at Ecological Monographs.
High elevation forest age structure across an elevational gradient in the Greater Yellowstone Ecosystem
<p>Dataset for Blomdahl et al. 2022. Drivers of forest change in the Greater Yellowstone Ecosystem. Journal of Vegetation Science. </p> <p>See publication for site description and methods. </p> <p>Descriptions for variables in “trees_seedlings.csv”:</p> <p><strong>Plot_ID: </strong>Plot identifier. Nomeclature follows transect name and plot number. ECO="Ecotone" transect, SBM="South Bird Mountain" transect.</p> <p><strong>Year_Sampled: </strong>Samples collected 2017-2019.</p> <p><strong>Tree_ID: </strong>Identifier for unique trees and seedlings. </p> <p><strong>Core: </strong>Tree core sample identifier. Applies only to trees (cores not taken from seedlings). Generally, 2 cores were taken per Tree >5 cm DCH, though sometimes up to 4 were collected if a sample was rotten.</p> <p><strong>Sample_ID: </strong>Identifier for unique samples, some of which come from the same tree (for unique individuals: "Tree_ID"). Applies to trees and seedlings.</p> <p><strong>Form: </strong>Stems >5 cm diameter at coring height (DCH), coring height=30 cm; Seedlings >30: Stems <5 cm DCH and >30 cm in height (sometimes referred to as "saplings"); Seedlings <30: Stems <30 cm in height</p> <p><strong>Species: </strong>ABLA=<em>Abies</em> <em>lasiocarpa</em>, PIAL=Pinus <em>albicaulis</em>, PICO=<em>Pinus</em> <em>contorta</em>, PIEN=<em>Picea</em> <em>engelmannii</em>, PSME=<em>Pseudotsuga</em> <em>menziesii</em></p> <p><strong>Diam_30_cm: </strong>Diameter (cm) at 30 cm sample height.</p> <p><strong>Diam_0_cm: </strong>Diameter (cm) at 0 cm sample height (i.e., the base). Only seedlings were measured at base, not trees.</p> <p><strong>Seedling_Ht_cm: </strong>Length of seedling stem (cm).</p> <p><strong>Bark_Thick_cm: </strong> Bark thickness (cm). Not recorded in 2018. Bark thickness assumed to be <0.1 cm for seedlings.</p> <p><strong>Live_Dead: </strong>Live/Dead status when sampled. L=Live, D=Dead.</p> <p><strong>Canopy: </strong>Canopy position. D=Dominant, C=Codominant. S=Suppressed. Not recorded in 2017. All seedlings assumed suppressed.</p> <p><strong>Outer_Ring: </strong>Last complete year of growth, generally one year prior to Year_Sampled for live trees. Mortality year for dead trees.</p> <p><strong>Inner_Ring:</strong> Year of innermost ring measured in tree core sample measured at 30 cm sample height. Does not apply to seedlings, which were sampled as cross sections, and therefore the pith was always measureable.</p> <p><strong>Pith_30: </strong>Year of the first ring of the tree or sapling, measured at 30 cm sampling height. </p> <p><strong>Pith_0: </strong>Year of the first ring of the seedling, measuring at 0 cm sampling height (i.e., the base). Applies only to seedlings, which were destructively sampled at the base.</p> <p><strong>Estab_Year: </strong>Estimated year of establishment for trees and saplings, same as Pith_0 for seedlings. See methods of Blomdahl et al., 2022, for how establishment year was estimated.</p> <p><strong>Age:</strong> Estimated age of the tree.</p>
Simulated future vertebrate habitat in the Greater Yellowstone Ecosystem
Aim Biodiversity conservation relies in part on enduring habitat in protected areas. In fire-prone ecosystems, shifts in species’ ranges will result both from changes in climate and fire-catalyzed vegetation change, which could lead to niche contraction and undermine protected-area efficacy. We explored these dynamics for three forest species with varied niches representative of other taxa and different hypothesized responses to fire-regime change (Black-backed Woodpecker, Picoides arcticus; North American marten, Martes spp.; red squirrel, Tamiasciurus hudsonicus). We asked: How do the extent and spatial pattern of these species’ distributions change during the 21st century based on the independent and joint effects of climate and vegetation? Location Greater Yellowstone Ecosystem (Wyoming, USA). Methods For each species, we developed separate distribution models based on climate and forest attributes, projected under four climate-fire scenarios (a 2x2 design with moderate and high temperature and precipitation change). A spatially explicit forest landscape model calibrated for Greater Yellowstone was used to project fire and forest dynamics through 2100, and climate suitability was estimated with Maxent. Results Suitable habitat for all three species based on climate or vegetation alone frequently did not overlap on the landscape, and habitat patches became simpler in shape and farther apart. Climatically suitable habitat for the Black-backed Woodpecker increased in all scenarios, and suitable forest structure expanded by a factor of 30 in dry scenarios with more fire. Climatically suitable habitat for martens declined with warming and drying; the area of suitable vegetation fell >80% with fire-driven losses of mature forest. Red squirrel habitat was maintained in all scenarios, but was sensitive to aridity, and patches were redistributed and compacted. Main conclusions Projections based on climate alone may misrepresent future species distributions, especially wh
Data from: Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem
<p>1. Conversion of land for settlements and agriculture is increasing globally and can influence wildlife space use. However, there is limited research to identify the thresholds of land use change that incur wildlife avoidance, and how these thresholds might vary across levels of selection.</p> <p>2. We evaluated multi-level avoidance thresholds of elk (<em>Cervus canadensis</em>) impacted by residential development and irrigated agriculture across the Greater Yellowstone Ecosystem in Idaho, Montana, and Wyoming. Using GPS data from 765 elk in 21 herds, we estimated habitat selection in relation to development and agriculture at 3 levels (home range selection, within home range selection, and movement path selection). Next, using individual selection covariates and associated measures of land use availability, we used functional-response models to evaluate how selection varied based on availability, and in turn, to estimate avoidance thresholds.</p> <p>3. We found individual and level-specific variation in elk responses to environmental factors. Elk exhibited stronger responses (either selection or avoidance) when selecting home range locations (i.e. second-order selection) than when selecting areas within home ranges (i.e. third-order selection) or selecting movement paths (i.e. fourth order selection). Importantly, elk avoidance of development and agriculture changed as the amount of land in these categories changed. Across all levels of selection, elk exhibited neutral selection for human development at low levels of availability (<1.1–2.2% developed) but avoided areas that were >1.1–2.2% developed. Conversely, elk selected positively for irrigated agriculture at low to moderate levels of availability (<52.0–66.2% agriculture) but exhibited neutral selection in areas that were > 52.0–66.2% agriculture.</p> <p>4. Synthesis and Applications: Elk avoidance of low levels of human development suggests conservation efforts such as restrictions on future development or conservation easements could focus on areas that are still below 2% developed. Additionally, because elk selection was strongest at the landscape scale, conservation actions that are based on information about the overall landscape structure may be most impactful. Our results highlight the importance of understanding variability in wildlife habitat selection at multiple levels, particularly in relation to land use change and highlight how functional response modelling can help inform landscape conservation.</p>
Data from: Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem
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Data from: Multiple anthropogenic interventions drive puma survival following wolf recovery in the Greater Yellowstone Ecosystem
Humans are primary drivers of declining abundances and extirpation of large carnivores worldwide. Management interventions to restore biodiversity patterns, however, include carnivore reintroductions, despite the many unresolved ecological consequences associated with such efforts. Using multistate capture-mark-recapture models, we explored age-specific survival and cause-specific mortality rates for 134 pumas (Puma concolor) monitored in the Greater Yellowstone Ecosystem during gray wolf (Canis lupus) recovery. We identified two top models explaining differences in puma survivorship, and our results suggested three management interventions (unsustainable puma hunting, reduction of a primary prey, reintroduction of a dominant competitor) have unintentionally impacted puma survival. Specifically, puma survival across age classes was lower in the 6-month hunting season than the 6-month non-hunting season; human-caused mortality rates for juveniles and adults, and predation rates on puma kittens, were higher in the hunting season. Predation on puma kittens, and starvation rates for all pumas, also increased as managers reduced elk (Cervus elaphus) abundance in the system, highlighting direct and indirect effects of competition between recovering wolves and pumas over prey. Our results emphasize the importance of understanding the synergistic effects of existing management strategies and the recovery of large, dominant carnivores to effectively conserve subordinate, hunted carnivores in human-dominated landscapes.
Data from: Multiple anthropogenic interventions drive puma survival following wolf recovery in the Greater Yellowstone Ecosystem
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Data from: Elk migration influences the risk of disease spillover in the Greater Yellowstone Ecosystem
<ol> <li>Wildlife migrations provide important ecosystem services, but they are declining. Within the Greater Yellowstone Ecosystem (GYE) some elk (<i>Cervus canadensis</i>) herds are losing migratory tendencies, which may increase spatiotemporal overlap between elk and livestock (domestic bison [<i>Bison bison</i>] and cattle [<i>Bos taurus</i>]), potentially exacerbating pathogen transmission risk.</li> <li>We combined disease, movement, demographic, and environmental data from eight elk herds in the GYE to examine the differential risk of brucellosis transmission (through aborted fetuses) from migrant and resident elk to livestock.</li> <li>For both migrants and residents, we found that transmission risk from elk to livestock occurred almost exclusively on private ranchlands as opposed to state or federal grazing allotments. Weather variability affected the estimated distribution of spillover risk from migrant elk to livestock, with a 7-12% increase in migrant abortions on private ranchlands during years with heavier snowfall. In contrast, weather variability did not affect spillover risk from resident elk.</li> <li>Migrant elk were responsible for the majority (68%) of disease spillover risk to livestock because they occurred in greater numbers than resident elk. On a per-capita basis, however, our analyses suggested that resident elk disproportionately contributed to spillover risk. In five of seven herds, we estimated that the per-capita spillover risk was greater from residents than from migrants. Averaged across herds, an individual resident elk was 23% more likely than an individual migrant elk to abort on private ranchlands.</li> <li>Our results demonstrate links between migration behavior, spillover risk, and environmental variability, and highlight the utility of integrating models of pathogen transmission and host movement to generate new insights about the role of migration in disease spillover risk. Further, they add to the accumulating body of evidence across taxa that suggests that migrants and residents should be considered separately during investigations of wildlife disease ecology. Finally, our findings have applied implications for elk and brucellosis in the GYE, and suggest that managers should prioritize actions that maintain spatial separation of elk and livestock on private ranchlands during years when snowpack persists into the risk period.</li> </ol>
Figure 20 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 20 - Lactarius barrowsii. Top collection EB008-15 (left and right) and bottom collection EB015-15 under Pinus flexilis, Story Hill, Bozeman, Montana, USA. Scale bars: 2 cm. Photos by E. Barge.
Figure 17 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 17 - Lactarius alnicola. Top and bottom collection EB0064-14 under Picea engelmannii, Gallatin Range, Montana, USA. Scale bars: 2 cm. Photos by E. Barge.
Figure 13 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 13 - Lactarius olympianus. Collection EB0070-14 under Picea engelmannii, Tobacco Root Mountains, Montana, USA. Scale bar: 2 cm. Photo by E. Barge.
Figure 10 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 10 - Lactarius aff. brunneoviolaceus. Collection CLC3098 near Salix reticulata (pictured), Salix planifolia, and krummholz Picea engelmannii, Beartooth Plateau, Montana, USA. Scale bar: 2 cm. Photo by C. Cripps.
Figure 14 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 14 - Lactarius pseudodelicatus. Collection CLC512 under Populus tremuloides, Teton Range, Idaho, USA. Scale bar: 2 cm. Photo by C. Cripps.
Figure 24 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 24 - Lactarius rufus. Collection EB0076-14 under Pinus contorta and Picea engelmannii, Gallatin Range, Montana, USA. Scale bar: 2 cm. Photo by E. Barge.
Figure 23 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 23 - Lactarius badiosanguineus. Top collection EB0069-14 under Picea engelmannii, Tobacco Root Mountains, Montana, USA. Bottom collection EB200-13 under Picea engelmannii and Abies lasiocarpa, Gallatin Range, Montana, USA. Scale bars: 2 cm. Photos by E. Barge.
Figure 1 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 1 - Map showing the location of the Greater Yellowstone Ecosystem (GYE). The GYE is located in the Central Rocky Mountains of North America and includes over 20 mountain ranges, with Yellowstone National Park at its center, Grand Teton National Park, and portions of surrounding national forests and other lands in Montana, Wyoming, and Idaho. The GYE is outlined by the black box, and the Rocky Mountains by the dotted line.
Figure 16 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 16 - Lactarius zonarius v. riparius. Collection CLC2933 under Populus trichocarpa, Bozeman, Montana, USA. Scale bar: 2 cm. Photo by E. Barge.
Figure 5 from: Barge EG, Cripps CL (2016) New reports, phylogenetic analysis, and a key to Lactarius Pers. in the Greater Yellowstone Ecosystem informed by molecular data. MycoKeys 15: 1-58. https://doi.org/10.3897/mycokeys.15.9587
Figure 5 - Lactarius glyciosmus. Collection EB111-15 under Betula glandulosa, Hellroaring Plateau, Montana, USA. Scale bar: 2 cm. Photo by E. Barge.
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OpenNeuro
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