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105 results for “Yellowstone”
Data for: Simulated postfire tree regeneration suggests reorganization of Greater Yellowstone forests during the 21st century
Tree regeneration underpins forest resilience, but how postfire tree regeneration will change with future climate and fire regimes is difficult to anticipate. Areas of sparse and failed postfire tree regeneration have been documented in western US forests, but how future recovery pathways will unfold is uncertain. We conducted a simulation study in the Greater Yellowstone Ecosystem (GYE; United States) using a process-based model, iLand, to ask how rates, composition, and spatial patterns of postfire tree regeneration vary with 21st-century climate. Subalpine forest and fire dynamics were simulated through 2100 under four climate scenarios, 2 × 2 factorial of aridity (wet and dry) and temperature (warm and hot), in five GYE landscapes. We tallied postfire tree seedling density by species in simulated fires (> 400 ha) at five years postfire. This data set contains three data sets to reproduce analyses for changes rates of regeneration, proportion of burned cells with regeneration failure, and postfire reorganization pathways. We include the data and R scripts used for these three analyses in the publication associated with these data.
Ice timing (formation or ice-on and clearance or ice-off) for Yellowstone Lake, Wyoming, USA (1927-2022)
Lakes are sentinels of environmental change. In cold climates, lake ice phenology–the timing and duration of ice cover during winter–is a key control on ecosystem function. Ice phenology appears to be driven by a complex interplay between physical characteristics and climatic conditions. Under climate change, lakes are generally freezing later, melting out earlier, and experiencing a shorter duration of ice cover; however, few long-term records exist for large, high-elevation lakes which may be particularly vulnerable to climate impacts. Here, we provide an ice phenology data over the last century (1927-2022) for North America’s largest high-elevation lake—Yellowstone Lake.
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.
Plot-level field data and model simulation results, archived to accompany Turner et al. manuscript; reports data from summer 2017 sampling of short-interval fires that burned during summer 2016 in Greater Yellowstone.
Subalpine forests in the northern Rocky Mountains have been resilient to stand-replacing fires that historically burned at 100–300-yr intervals. Fire intervals are projected to decline drastically as climate warms, and forests that reburn before recovering from previous fire may lose their ability to rebound. We studied recent fires in Greater Yellowstone (Wyoming, USA) and asked whether short-interval (less than 30 yrs) stand-replacing fires can erode lodgepole pine (Pinus contorta var. latifolia) forest resilience via increased burn severity, reduced early postfire tree regeneration, reduced carbon stocks, and slower carbon recovery. During 2016, fires reburned young lodgepole pine forests that regenerated after wildfires in 1988 and 2000. During 2017, we sampled 0.25-ha plots in stand-replacing reburns (n=18) and nearby young forests that did not reburn (n=9). We also simulated stand development with and without reburns to assess carbon recovery trajectories. Nearly all prefire biomass was combusted ("crown fire plus") in some reburns in which prefire trees were dense and small (≤ 4 cm basal diameter). Postfire tree seedling density was reduced six-fold relative to the previous (long-interval) fire, and high-density stands (greater than 40,000 stems ha-1) were converted to sparse stands (less than 1,000 stems ha-1). In reburns, coarse wood biomass and aboveground carbon stocks were reduced by 65% and 62%, respectively, relative to areas that did not reburn. Increased carbon loss plus sparse tree regeneration delayed simulated carbon recovery by greater than 150 yrs. Forests did not transition to nonforest, but extreme burn severity and reduced tree recovery foreshadow an erosion of forest resilience.
Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA
We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.
A catalog of associated, machine-learning-derived phase arrival times for ten days of seismic data in the Yellowstone region
<p>This dataset contains the associated phase picks and event information from applying a deep learning phase picker to continuous data recorded over March 25 – April 3, 2014, on 20 three-component stations and 14 vertical-component stations in the Yellowstone region. This 10-day period contains an M<sub>w</sub> 4.8 event, the largest earthquake in the Yellowstone region since 1980. The catalog and deep learning phase picker are described in Armstrong et al. (submitted).</p> <p>The arrivals were associated using the method described by Baker et al. (2021) and located using HypoInverse2000 (Klein, 2002). There are 1,053 events in this catalog, including 855 that were previously unidentified. Events that also appear in the University of Utah Seismograph Stations catalog have an event identifier (evid) beginning with “6”, while new events begin with “9”. </p> <p>Columns include:</p> <ul> <li>A simple event number</li> <li>the network, station, channel, and location code for the arrival time</li> <li>the arrival time in UTC (arrival_time) and Unix (arrival_time_epoch) format</li> <li>any static correction applied to the arrival time</li> <li>the P-pick first motion polarity as determined by a machine learning model - up (1), down (-1), or unknown (0)</li> <li>the arrival time residual </li> <li>the take off angle in degrees </li> <li>the event latitude and longitude in degrees</li> <li>the event depth in km</li> <li>the event origin time in UTC (origin_time) and Unix (origin_time_epoch) format</li> <li>the azimuthal gap of the event in degrees</li> <li>the root mean square error (RMS) of the event location</li> <li>the event identifier (evid) - begins with a “6” for events in the UUSS catalog and a “9” for new events</li> </ul> <p> </p>
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
Snag-fall patterns following stand-replacing fire vary with stem characteristics and topography in subalpine forests of Greater Yellowstone
We assessed the stem- and landscape-level drivers of snag persistence and snag-fall mode within the area burned as stand-replacing fire in the 1988 Yellowstone Fires in Yellowstone National Park, Wyoming, USA. Snags were sampled 14-15 years postfire (n = 131) and again in a separate set of plots 34 years postfire (n = 55). Stem characteristics such as species identity (e.g., lodgepole pine, whitebark pine, Engelmann spruce, subalpine fir, and Douglas-fir), diameter at breast height, whether the tree was alive or dead at the time of fire, and the mode of snag-fall (snapping or uprooting) were measured and used to explain patterns of snag persistence and modes of snag-fall. In addition, plot-level environmental variables (e.g., slope, aspect, elevation, stand density) were measured and related to the proportion of stems still standing as snags at 14-15 and 34 years postfire. Data collection is complete and is part of a forthcoming manuscript in revision at Forest Ecology and Management.
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.
Looking beyond the mean: Drivers of variability in postfire stand development of conifers in Greater Yellowstone
High-severity, infrequent fires in forests shape landscape mosaics of stand age and structure for decades to centuries, and forest structure can vary substantially even among same-aged stands. This variability among stand structures can affect landscape-scale carbon and nitrogen cycling, wildlife habitat availability, and vulnerability to subsequent disturbances. We used an individual-based forest process model (iLand) to ask: Over 300 years of postfire stand development, how does variation in early regeneration densities versus abiotic conditions influence among-stand structural variability for four conifer species widespread in western North America? We parameterized iLand for lodgepole pine (Pinus contorta var. latifolia), Douglas-fir (Pseudotsuga menziesii var. glauca), Engelmann spruce (Picea engelmannii), and subalpine fir (Abies lasiocarpa) in Greater Yellowstone (USA). Simulations were initialized with field data on regeneration following stand-replacing fires, and stand development was simulated under historical climatic conditions without further disturbance. Stand structure was characterized by stand density and basal area. Stands became more similar in structure as time since fire increased. Basal area converged more rapidly among stands than tree density for Douglas-fir and lodgepole pine, but not for subalpine fir and Engelmann spruce. For all species, regeneration-driven variation in stand density persisted for at least 105 years postfire, and for lodgepole pine, early regeneration densities dictated among-stand variation for 203 years. Over time, stands shifted from competition-driven convergence to environment-driven divergence, in which variability among stands was maintained or increased. The relative importance of drivers of stand structural variability differed between density and basal area and among species due to differential species traits, growth rates, and sensitivity to intraspecific competition versus abiotic conditions. Understanding dy
Assessing change in ecosystem processes twenty four years after the 1988 Yellowstone Wildfires, 2013
The extent of young postfire conifer forests is growing throughout western North America as the frequency and size of high-severity fires increase, making it important to understand ecosystem structure and function in early seral forests. Understanding nitrogen (N) dynamics during postfire stand development is especially important because northern conifers are often N limited. We re-sampled lodgepole pine (Pinus contorta var. latifolia) stands that regenerated naturally after the 1988 fires in Yellowstone National Park (Wyoming, USA) to ask: (1) How have N pools and fluxes changed over a decade (15 to 25 years postfire) of very rapid forest growth? (2) At postfire year 25, how do N pools and fluxes vary with lodgepole pine density and productivity? Lodgepole pine foliage, litter (annual litterfall, forest-floor litter), and mineral soils were sampled in 14 plots (0.25-ha) that varied in postfire lodgepole pine density (1,500 to 344,000 stems ha-1) and aboveground net primary production (ANPP; 1.4 to 16.1 Mg ha-1 yr-1). Previous data collected 15 and 17 years postfire (i.e., 2003 and 2005) provided a reference for assessing change in ecosystem process rates over time. At that time, lodgepole pine foliar nitrogen (N) concentrations had not yet suggested N limitation, and tree density and net primary production strongly influenced ecosystem carbon (C) and N stocks. These data were collected in 2012 and 2013 and are associated with the following publication: Turner, M. G., T. G. Whitby, and W. H. Romme. 2019. Feast not famine: Nitrogen pools recover rapidly in 25-yr old postfire lodgepole pine. Ecology (In press).
Video S1 - Native Cutthroat Trout and the Yellowstone Lake Ecosystem
<p><strong>Video S1.</strong> The Yellowstone Lake ecosystem in Yellowstone National Park. Following glacial recession, cutthroat trout evolved as the sole salmonid and dominant fish within Yellowstone Lake and its connected river network. Yellowstone Lake is a large aquatic system on the Yellowstone Plateau (2,357 m in elevation) with a highly protected watershed (> 3200 km2) located within Yellowstone National Park and the Bridger-Teton Wilderness of Wyoming, USA. Powerboat access is limited to only two locations, and most of the shoreline lies in protected (federally proposed) wilderness. Thermal structure of the lake is typically unstable with a weak and variable thermocline at a depth of 12–15 m during July-September. Surface water temperatures rarely exceed 18°C. The lake freezes over by late December and can remain frozen until late May or early June. In winter, ice about 1 m thick covers much of the lake except where shallow water covers active hot springs. During spring (May-July), cutthroat trout spawn in tributaries around Yellowstone Lake, where they are important prey for grizzly bears, black bears, river otters, and numerous avian predators.</p>
Video S1. Two Ocean Pass South of Yellowstone National Park, Wyoming
<p><strong>Video S1.</strong> <strong>Two Ocean Pass South of Yellowstone National Park, Wyoming.</strong> Two Ocean Pass connects the headwaters of the Atlantic and Pacific drainages in the Bridger-Teton Wilderness of Wyoming south of Yellowstone National Park. Here, a broad alpine meadow straddles the Continental Divide at 2,478 m elevation, and headwaters of the Columbia and Missouri drainages originate from a single perennial stream; North Two Ocean Creek flows along the Continental Divide and branches into Pacific Creek, a Snake River tributary flowing to the west, and Atlantic Creek, a Yellowstone River tributary flowing to the east. The pass is a nearly level meadow near the center of which is a marsh that becomes a small lake in times of wet weather or snowmelt runoff. No barrier prevents the movement and mixing of fish between Pacific Creek and Atlantic Creek. Following glacial recession from the region about 14,000 years ago, ancestral Yellowstone cutthroat trout colonized the Yellowstone River drainage from sources in the lower Snake River drainage over Two Ocean Pass. They dispersed downstream and were the only trout inhabiting Yellowstone Lake for thousands of years prior to the establishment of Yellowstone National Park. The watershed of the Yellowstone River upstream of Yellowstone Lake, including Two Ocean Pass, is among the most remote in the contiguous United States and lies largely within protected federal wilderness. In July 2019, Yellowstone National Park and Wyoming Game and Fish Department biologists, with assistance from Wyoming Trout Unlimited and the Wyoming Storer Foundation, sampled environmental DNA in the connected waters near Two Ocean Pass. The sampling was conducted to determine if invasive lake trout or other nonnative fish were present, and could thereby colonize the Yellowstone River basin in the past or future and threaten native cutthroat trout of Yellowstone Lake.</p>
Body size modulates the extent of seasonal diet switching by large mammalian herbivores in Yellowstone National Park
<div> <p><span>Large mammalian herbivores vary their diets markedly with changes in resource availability yet the ways that seasonal changes in individual foraging behaviors scale up to reconfigure complex trophic networks are poorly understood. Two years of dietary DNA data enabled us to quantify fine-grained dietary variation within and among populations of five large herbivore species at Yellowstone National Park, revealing remarkably strong and significant correlations between body size and five key indicators of diet seasonality (R<sup>2</sup> = 0.71–0.80). Data from GPS collars implicated seasonal changes in each species' movement- and habitat-use patterns as potential determinants of foraging constraints and specializations that give rise to the strong allometry in diet composition. Bison and elk showed relatively muted seasonal changes compared to smaller species that exhibited stronger switches. Whereas the taxonomic breadth of individual diets contracted for all species in winter, larger species generally consumed a greater functional diversity of plants and thus maintained more unique dietary niches under resource limitations.</span></p> </div>
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>
Text-fig. 11. Indet. family. Mciveraephyllum nebrascense (SCHIMPER) comb. nov. 1. Lectotype of Cornus acuminata NEWBERRY 1868 [non Webber 1852] = Cornus nebrascensis SCHIMPER 1874, p. 54, originally pl. 37, fig. 4 in Newberry 1898. Yellowstone River, Montana, USNM 8937. 2. Specimen from Black Buttes pit 3, Wyoming showing moderately long petiole and rounded tooth sinus. UF 15886-14308. 3. Specimen with more abundant teeth, Ludlow Formation, locality DMNH 563, Slope County, North Dakota, Collection of K. Johnson, DMNH 2206. 4. Detail of tertiary venation, counterpart specimen of that figured in 2. Scale bar = 2 cm. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. 11. Indet. family. Mciveraephyllum nebrascense (SCHIMPER) comb. nov. 1. Lectotype of Cornus acuminata NEWBERRY 1868 [non Webber 1852] = Cornus nebrascensis SCHIMPER 1874, p. 54, originally pl. 37, fig. 4 in Newberry 1898. Yellowstone River, Montana, USNM 8937. 2. Specimen from Black Buttes pit 3, Wyoming showing moderately long petiole and rounded tooth sinus. UF 15886-14308. 3. Specimen with more abundant teeth, Ludlow Formation, locality DMNH 563, Slope County, North Dakota, Collection of K. Johnson, DMNH 2206. 4. Detail of tertiary venation, counterpart specimen of that figured in 2. Scale bar = 2 cm.
Text-fig. 6. Platanaceae 1–3. Platanites raynoldsii (NEWBERRY) comb. nov. 1. Holotype of Platanus raynoldsii NEWBERRY (1868, 1898), Banks of the Yellowstone River, Montana, USNM 7000. 2. Holotype of Platanites canadensis MCIVER et BASINGER (1993), here treated as synonym of P. raynoldsii, showing clearly the position of the lateral leaflets, US 3-66, Ravenscrag Formation, Saskatchewan, Canada. 3. Specimen from Yellowstone Bridge, Miles City, Montana. UF19021-39390. 4. Detail from fig. 2. Scale bars 5 cm. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. 6. Platanaceae 1–3. Platanites raynoldsii (NEWBERRY) comb. nov. 1. Holotype of Platanus raynoldsii NEWBERRY (1868, 1898), Banks of the Yellowstone River, Montana, USNM 7000. 2. Holotype of Platanites canadensis MCIVER et BASINGER (1993), here treated as synonym of P. raynoldsii, showing clearly the position of the lateral leaflets, US 3-66, Ravenscrag Formation, Saskatchewan, Canada. 3. Specimen from Yellowstone Bridge, Miles City, Montana. UF19021-39390. 4. Detail from fig. 2. Scale bars 5 cm.
Tree-Ring Measurement File: Lodgepole Pine Samples Collected at Steamboat Geyser, Yellowstone National Park
<p>Decade format (Tucson format) tree-ring measurement file (0.001 mm) for lodgepole pine core samples collected adjacent to Steamboat Geyser in Yellowstone National Park, Wyoming, USA.</p>
Northern Yellowstone Elk survival and competing risks
<p>Prey vulnerability to predation can vary by life history stage, and prey population stage structure determines the strength a predator species wields on their community. Prey population stage structure can vary over time, yet little is known about how temporal change in prey stage structure influences predator-prey interactions. We used data of wolves hunting adult female elk in Yellowstone National Park to demonstrate that stage-selective wolf predation of old individuals (>11 years old) was more additive than wolf predation of young individuals (2–11 years old). Additive predation of older elk coupled with an aging female elk population increased the strength of wolf predation over time. When vulnerable prey comprise an increasing proportion of a population, their early demise may decrease population growth. Accounting for temporal variation in predation risk across a prey population is therefore critical to understanding the community-level consequences of predator-prey interactions.</p>
Twenty years of Salix height in response to experimental manipulation of browsing and water table, northern range of Yellowstone National Park
<p>This respository contains multiple datasets collected during a 20 year investigation of the responses of willow (<em>Salix </em>spp.) communities in Yellowstone National Park after the restoration of large carnivores. The study sought to understand whether the restoration of wolves to the food web cause a change in the state of willow communities that occured while wolves were absent. Each dataset has a corresonding file of metadata. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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