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1,513 results for “disturbance”
Disturbance legacies and resilience simulation using an individual-based forest landscape model on the Andrews Experimental Forest
Disturbances are key drivers of forest ecosystem dynamics, and forests are well adapted to their natural disturbance regimes. However, as a result of climate change, disturbance frequency is expected to increase in the future in many regions. It is not yet clear how such changes might affect forest ecosystems, and which mechanisms contribute to (current and future) disturbance resilience. We here studied the 6364-ha HJ Andrews Experimental Forest landscape to investigate how patches of remnant old-growth trees (as one important class of biological legacies) affect the resilience of forest ecosystems to disturbance. Using the spatially explicit, individual-based forest landscape model iLand we analyzed the effect of three different levels of remnant patches (0%, 12%, and 24% of the landscape) on 500-year recovery trajectories after a large, high severity wildfire. In addition, we evaluated how three different levels of fire frequency (no fire, a historic fire return interval of 262 years, and a reduced fire return interval of 131 years) modulate the effects of initial legacies. The study investigated effects of legacies on the resilience of forest ecosystem structure (represented by canopy complexity as described by the rumple index), composition (proportion of late-seral species), and functioning (total ecosystem carbon storage). For each scenario of initial legacy and fire return interval 25 replicates were simulated. More information on the simulation methodology as well as the code and executable used for this study can be obtained at http://iLand.boku.ac.at. The dataset is completed and no further analyses are planned at this point. The results are published in Ecological Applications http://dx.doi.org/10.1890/14-0255.1.
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
Yukon River Basin Fire and Permafrost Study: Elevation of soil surface and permafrost table along transects with different fire disturbance regimes (2009-2012)
Two 100 - 200 m transects were established on hilly loess deposits in the Yukon Flats near Boot Lake to monitor annual changes in the permafrost table and thaw settlement under different fire disturbance regimes. One was located in an area that burned around 1925; the other transect was located in a 2009 burn. Both transects were partially affected by a fire in 1967. This dataset includes the elevations of the ground surface, permafrost surface, water table, and surface organic thickness measurements. Note that there was compaction of the organic layer due to human disturbance along the portion of the transect that burned in only 1925. These transects are associated with the burned and unburned silty upland sites detailed in related datasets.
Riparian disturbance restricts connectivity of Appalachian stream salamander populations at the Coweeta Hydrologic Laboratory
Human populations are rapidly expanding and encroaching on previously undisturbed habitats. Stream salamanders in the southern Appalachian Mountains are a diverse and abundant group threatened by rapid exurban development in high-elevation watersheds. Previous research has demonstrated the sensitivity of salamanders to urbanization, but little research exists describing the mechanisms behind population declines and extirpations. Appalachian stream salamanders are adapted to forested streams with dense overstory and little light, yet following urbanization, light gaps associated with land clearing emerge. Light avoidance behaviors may alter movement behaviors of salamanders, fragmenting populations on opposite sides of light gaps. To study the effects on riparian disturbance on salamanders we established 6 experimental sites with canopy gaps ranging from 13m to 85m in stream length and 2 control sites lacking canopy gaps in May of 2010. Animals were collected within these plots, marked, and translocated to the plot on the opposite side of the gap. To establish detection probabilities in the absence of translocation, we established an additional 10m plot in the forest at each site where individuals were captured, marked, and re-released within this area. Recaptured individuals were measured and in some cases re-marked if those individuals had returned to their capture location.
Gopher-disturbed and undisturbed soil Oribatid mite data for Martinelli slope, 1988.
Soil cores were collected from undisturbed and pocket gopher (Thomomys talpoides) disturbed alpine tundra. Cores were split in half longitudinally with one half being used for extraction and identification of Oribatid mites. Disturbed soils included both old and fresh gopher mounds and all study sites were located on the Martinelli slope directly above the 25 permanent plots. The cores were collected in 1988.
Small mammal disturbance data for Martinelli slope, 1989 - 1992.
Permanent plots were established on the Martinelli slope in 1987. The 25 plots are organized in a Latin square design consisting of 5 rows and 5 columns. Plots are numbered according to row and column, beginning with 11 at the northwest corner of the grid. Plot 55 is located at the southeast corner of the grid. Each plot has dimensions of 1.5 m x 2.0 m. The corners of each plot are marked with rebar stakes. Each plot is divided into an evenly spaced grid consisting of 15 x 20 points on each side, for a total of 300 points, with each point situated 10 cm from its nearest neighbors. Small mammal disturbances were classified each year at each point where such disturbances occurred. The point-quadrat method used was similar to that described by Auerbach (1992): The point-quadrat method was used for estimating canopy stratification and plant cover. Aluminum point-quadrat frames were 3m^2 in size, with double-layer filament spaced 10 cm apart to make a 145 x 195 cm grid for a total of 300 sample points per plot. If small mammal disturbances were present at a point, those disturbances were classified as indicated in the comments section.
Effect of foot disturbance to cyanobacteria-dominated biocrusts on microbes and nitrogen in Chihuhuahan grassland and shrubland
Interactions between plants and soil microbes influence plant nutrient transformations, including nitrogen (N) fixation, nutrient mineralization, and resource exchanges through fungal networks. Physical disturbances to soils can disrupt soil microbes and associated processes that support plant and microbial productivity. In low resource drylands, biological soil crusts ("biocrusts") occupy surface soils and house key autotrophic and diazotrophic bacteria, non-vascular plants, or lichens. Interactions among biocrusts, plants, and fungal networks between them are hypothesized to drive carbon and nutrient dynamics; however, comparisons across ecosystems are needed to generalize how soil disturbances alter microbial communities and their contributions to N pools and transformations. To evaluate linkages among plants, fungi, and biocrusts, we disturbed all unvegetated surfaces with human foot trampling twice yearly in dry conditions from 2013-2018 in cyanobacteria-dominated biocrusts in Chihuahuan Desert grassland and shrubland ecosystems. Our study included microbial communities and N pools sampled at different time points in the disturbance treatments at one or both sites. We began our sampling after observations in April 2018 that the chlorophyll a content was at least double in control than disturbed plots in both ecosystems (Chung et al. 2019). Stomping occurred in May, and we collected soil and plant samples in June 2018 for N pools and soil and root fungal abundance. We collected additional soil samples in September 2018 and conducted the 15N tracer experiment to observe rates of N transfer from biocrust to plants before the fall stomp treatment in October. We collected chlorophyll a samples and soils for sequencing bacteria in September of 2019, also before the fall stomp treatment.
Biocrust disturbance experiment soil responses 2016
Biological soil crusts (biocrusts) dominate soil surfaces in drylands, providing services that include soil stabilization and carbon uptake. In this study, we investigated the direct and biocrust-mediated effects of anthropogenic disturbances in two dryland ecosystems. We applied low intensity soil surface disturbance (twice-yearly footfalls) in grassland and shrubland ecosystems in northern Chihuahuan Desert, USA. Results. After five years of disturbance, biocrust photosynthetic capacity (chlorophyll a) declined by 44%. Declines were largest in interspaces between grassland plants. Levels of scytonemin, a biocrust sunscreen pigment, were 38% greater in shrubland than grassland and 44% greater under grass canopy than in interspaces, but decreased only 5% with disturbance. Disturbance reduced soil surface stability 2 times more in the grassland than shrubland. Disturbance effects on other hydrologic and physical properties were indirectly mediated by the photosynthetic capacity of biocrusts. Disturbance indirectly increased infiltration depth and shallow (2-3 cm) soil moisture in the grassland but reduced surface moisture (<1 cm) in the shrubland. Conclusions. Biocrusts were more sensitive to low intensity soil disturbance in a grassland than shrubland ecosystem. While biocrusts mediated the effects of soil disturbance on dryland soil hydrological and physical properties, the nature of their influence differed between ecosystem types.
Figure 1 in Deadwood and saproxylic beetle diversity in naturally disturbed and managed spruce forests in Nova Scotia
Figure 1. Non-metric multidimensional scaling ordination diagrams of forest sites in two-dimensional space defined by (A) beetle assemblage and (B) habitat structures. Symbols signify disturbance history class of forests: 1= CLEARCUT, D= THINNED, ·= FIRE, and Ɨ= WIND. Numbers identify the specific forest. Forest number 25 was excluded as an outlier in (B) due to much higher deadwood volumes than all other sites.
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Dataset of biodiversity of the Seine nursery over 20 years in a highly disturbed environment
<p>Estuaries are crucial ecosystems where human activities deeply affect numerous ecological functions. The Seine estuary, located on the French coast of the eastern English Channel, is a very dynamic environment where this conflict between the two contrasting backgrounds exists. The Seine watershed is highly disturbed by human activities due to significant industrial development and high population density. The estuary is a historic fishing ground for brown shrimp (<em>Crangon crangon</em>) and various species of flatfish, among which sole (<em>Solea solea</em>) and plaice (<em>Pleutronectes platessa</em>). However, it is a nursery area for fish and plays a crucial role in the life cycle of many demersal and benthic fish and invertebrates.</p> <p>Here we present a survey dataset of biodiversity in the nursery of the Seine estuary and eastern bay of Seine collected using a beam trawl throughout three periods from 1995 to 2019. IFREMER (the French Institute for the Exploitation of the Sea) implemented scientific cruises on coastal nursery grounds aimed at describing the fish population and give an insight into the ecosystem functioning in these areas. The NOURSEINE survey presented here came to existence in this context. The surveys happen at the start of autumn to maximize the catchability of juvenile fish. The beam trawl targets mainly benthic and demersal species over a more than 600 square kilometers study area. The dataset includes abundance and densities of 161 species for 634 hauls performed at around 40 stations each year. These data can be used by fishery scientists and ecologists motivated by earlier stage life of commercial species or by the impact of human disturbances, such as harbor developments, on estuarine communities. They can help in understanding how the nursery functions may change through time and potential human disturbances.</p> <p> </p> <p>Dataset.csv: The data represents the density for the different species encounters in the trawl stations across the 14 years where the NOURSEINE campaign took place. The table contains 22435 rows and 22 columns. Each row corresponds to the density of a species or individuals of the same size in a given haul, and this separation comes from the sorting operation. After each haul, the content of the trawl is emptied on deck, and a total or partial sorting is carried out depending on the volume and homogeneity of the capture. All species, both fish and benthic, are sorted, identified, counted and weighted. Fishes of commercial value and all others flatfish are measured. Fish’s otoliths are collected on the main commercial fish species (sole, plaice, flounder, dab, pouting, large whiting and European bass) and their age group determined later on in the laboratory. In 1999, the sampling was incomplete for technical reasons, and only commercial invertebrates were sampled (King scallop and lobster).</p> <p>Sorting the capture can be separated into three different steps (See pdf figure attached):</p> <p> 1. Total capture weighting: when the hauls are emptied on the deck, the whole capture is distributed in several baskets/box in order to weight it.</p> <p> 2. Fish and large taxa sorting: All fish and large taxa of invertebrates easily identified (edible crab, common spider crab, large cephalopods) are sorted, identified, numbered, measured (for fish) and weighted (total weight per taxa). Depending on the size of the capture, a subsample might be necessary, and the operation is only performed on it. In case visual identification is too difficult (for instance due to a large mud proportion), the capture may be washed using a 5mm sieve. The weight ratio between the total capture and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a taxon has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density.</p> <p> 3. Benthic fauna sorting: What is left from the second step is weighted before the sorting operation. All taxa constituting benthic fauna are sorted, identified, numbered and weighted (total weight per taxa). Some taxa may be measured (whelk, scallop). Just like step 2, according to the quantity of benthic fauna, a subsample might be necessary before sorting. All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database.</p> <p>All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database.</p> <p> </p> <p> </p> <p>Description of the columns found in the dataset</p> <ul> <li><strong>Year</strong></li> </ul> <p>The year when the sampling event took place. Ranges between 1995 and 2019.</p> <ul> <li><strong>Month</strong></li> </ul> <p>The month when the sampling event took place. Either 08 (August) or 09 (September).</p> <ul> <li><strong>Day</strong></li> </ul> <p>The day of the month when the sampling event took place. Ranges from 01 to 30.</p> <ul> <li><strong>Boat_Engine_kw</strong></li> </ul> <p>Engine power reflecting the boat used for sampling, given in kilowatt. Ranges between 81 and 552.</p> <ul> <li><strong>Gear_Code</strong></li> </ul> <p>Code describing the fishing gear used during the sampling event. “BT2” corresponds to beam trawl 2 meters and “BT3” is beam trawl 3 meters.</p> <ul> <li><strong>Haul_duration</strong></li> </ul> <p>The number of minutes of the haul operation. For 1250 rows, NA values indicate that this information was not available.</p> <ul> <li><strong>Station_Code</strong></li> </ul> <p>A unique number identifying the sampling event. Ranges between 1 and 1299.</p> <ul> <li><strong>Starting_Longitude_decimal</strong></li> </ul> <p>The longitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Ending_Longitude_decimal</strong></li> </ul> <p>The longitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Starting_Latitude_decimal</strong></li> </ul> <p>The latitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Ending_Latitude_decimal</strong></li> </ul> <p>The latitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Sector</strong></li> </ul> <p>The sector of the sampling area where the sampling event took place. The study area is divided in 12 sectors roughly based on bathymetry and distance to the mouth of the estuary and identified by letters from “A” to “M”.</p> <ul> <li><strong>Starting_Depth</strong></li> </ul> <p>The depth registered when the sampling event started. Given in meters, ranges from 2 to 26. For 13 rows, NA values indicate that this information was not available.</p> <ul> <li><strong>Ending_Depth</strong></li> </ul> <p>The depth registered when the sampling event ended. Given in meters, ranges from 2 to 26. For 313 rows, NA values indicate that this information was not available.</p> <ul> <li><strong>Trawled_Distance_m</strong></li> </ul> <p>The distance covered between the starting position and the ending position. Given in meters, ranges from 332 to 3128.</p> <ul> <li><strong>Trawled_Surface_m2</strong></li> </ul> <p>The surface trawled between the starting position and the ending position. Given in square meters, ranges from 963 to 9070.</p> <ul> <li><strong>Scientific_Name</strong></li> </ul> <p>Scientific name of the individuals identified in the haul. All names have been checked on WORMS (last accessed: 11/12/2019).</p> <ul> <li><strong>Number_Measured</strong></li> </ul> <p>The number of individuals measured during the sampling event. Ranges between 1 and 340.</p> <ul> <li><strong>Size</strong></li> </ul> <p>Size recorded during the measure of the individuals’ size. Given in centimeters for all species, except benthic fauna where millimeters are used. Ranges between 1 and 185.</p> <ul> <li><strong>Age_Group</strong></li> </ul> <p>Code giving the age category of a particular individual belongs to. “G0” means the individuals are less than 1 year old and born the year the sampling event took place. “G1” means the individuals are 1 year old and born the year before the sampling event took place. “G2+” means they are 2 years old</p> <ul> <li><strong>Number_in_Haul</strong></li> </ul> <p>The number of individuals counted or estimated in the entire haul (if individuals have not been numbered). Depending on the size of the capture, a subsample might be necessary. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density.</p> <ul> <li><strong>Weight_in_Haul</strong></li> </ul> <p>The weight of individuals counted or estimated in the entire haul (if individuals have not been numbered). Given in gram. Depending on the size of the capture, a subsample might be necessary. The weight ratio between the total catch and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density. For 7 rows, NA values indicated that this information was not available.</p> <ul> <li><strong>Subsample</strong></li> </ul> <p>Depending on the size of the capture, a subsample might be performed to estimate abundance and weight. This column indicates “Yes” if the capture has been subsample to identified the taxa and “No” if the taxa as identified on the whole catch.</p> <ul> <li><strong>Division</strong></li> </ul> <p>A number used to calculate the density from the “Number_in_Haul” column. It represents the weight ratio between the total capture and the subsample. It ranges between 1 and 512. When it is equal to one, the species was sorted in the whole capture.</p> <ul> <li><strong>Species_Density</strong></li> </ul> <p>The density calculated in individuals per square meters. Species densities are calculated based on the trawled surface but also taking into account if the haul has been partially sorted or not. The formula to calculate the density of individual per surface unit is:</p> <p><em>Density = (Number_in_Haul * Division) / Trawled_Surface_m2</em></p> <p>where <em>Division </em>is a factor used to elevate the abundance if the whole haul was not sorted.</p>
Supplementary Files-10-visual disturbances
<p>Supplementary File to the Cochrane Review “Drugs for preventing postoperative nausea and vomiting in adults after general anaesthesia: a network meta-analysis”.</p> <p>Supplementary Files-10-visual disturbances_p1-13, Raw data-10-visual disturbances</p>
Balancing risks of injury and disturbance to marine mammals when pile driving at offshore windfarms
<p>1. Offshore windfarms require construction procedures that minimise impacts on protected marine mammals. Uncertainty over the efficacy of existing guidelines for mitigating near-field injury when pile-driving recently resulted in the development of alternative measures, which integrated the routine deployment of acoustic deterrent devices (ADD) into engineering installation procedures without prior monitoring by Marine Mammal Observers.</p> <p>2. We conducted research around the installation of jacket foundations at the UK's first deep-water offshore windfarm to address data gaps identified by regulators when consenting this new approach. Specifically, we aimed to a) measure the relationship between noise levels and hammer energy to inform assessments of near-field injury zones, b) assess the efficacy of ADDs to disperse harbour porpoises from these zones.</p> <p>3. Distance from source had the biggest influence on received noise levels but, unexpectedly, received levels at any given distance were highest at low hammer energies. Modelling highlighted that this was because noise from pin pile installations was dominated by the strong negative relationship with pile penetration depth with only a weak positive relationship with hammer energy.</p> <p>4. Acoustic detections of porpoises along a gradient of ADD exposure decreased in the 3-hours following a 15-minute ADD playback, with a 50% probability of response within 21.7 km. The minimum time to the first porpoise detection after playbacks was > 2 hours for sites within 1 km of the playback.</p> <p>5. Our data suggest that the current regulatory focus on maximum hammer energies needs review, and future assessments of noise exposure should also consider foundation type. Despite higher piling noise levels than predicted, responses to ADD playback suggest mitigation was sufficiently conservative. Conversely, strong responses of porpoises to ADDs resulted in far-field disturbance beyond that required to mitigate injury. We recommend that risks to marine mammals can be further minimised by: 1) optimising ADD source signals and/or deployment schedules to minimise broad-scale disturbance; 2) minimising initial hammer energies when received noise levels were highest; 3) extending the initial phase of soft start with minimum hammer energies and low blow rates.Minhyuk Seo</p>
Dataset used in the publication entitled "Multi-Point Method using Effective Demodulation and Decomposition Techniques allowing Identification of Disturbing Loads in Power Grids"
<p>Dataset obtained from experimental research carried out in the prepared laboratory setup and information on the performed numerical simulation studies. Based on the dataset, the proposed new method of identification of sources of voltage fluctuation has been validated in the publication: Kuwałek P., Wiczyński G., Multi-Point Method using Effective Demodulation and Decomposition Techniques allowing Identification of Disturbing Loads in Power Grids. The description of the prepared laboratory setup is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Data from: Mechanical soil disturbance in a pine savanna has multi-year effects on plant species composition
<p>Data used in the manuscript Mechanical soil disturbance in a pine savanna has multi-year effects on plant species composition accepted for publication in Ecosphere. Data included are species lists for all plots and years, percent cover of species in each plot in 2021, and life-history characteristics (life span, dispersal mechanism, seed bank persistence) of all species. See manuscript for site description and data collection methodology. </p>
Fig. 5 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig. 5. Human detections along five, two-kilometre transects, one each in five habitats/subtypes (young dipterocarp forest, flooded dipterocarp forest, mixed deciduous forest, mature dipterocarp forest, and wetlands) per month in Chatthin Wildlife Sanctuary. Effort = number of habitat subtypes * total months sampled per year * 2 km, X-axis= year, Y-axis= human disturbance index (number of human detections per one unit of effort).
Fig. 1 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig. 1. Map of Chatthin Wildlife Sanctuary with land-cover changes; flooded dipterocarp forest (FL), mixed deciduous forest (MD) and young dipterocarp forest (YI) in Chatthin Wildlife Sanctuary between 1999 and 2020 and locations of bird survey points.
Fig 4 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig 4. Seasonal (y-axis) and long-term trends of abundances of six avian guilds in Chatthin Wildlife Sanctuary from 1999 to 2020. Redder = higher abundances, white/paler = lower abundances, and grey = no data.
Feral pig (Sus scrofa) disturbance facilitates establishment of resource-acquisitive species in Hawaiian forest understories
<p>In this study, we quantify the effects of leaf traits and dispersal attributes on species responses to pig soil disturbance at two spatial scales – 0.5 m<sup>2</sup> patches embedded along 20 m transects within sites – across a gradient of pig density in a Hawaiian montane wet forest using Bayesian mixed models. </p> <p>Native and non-native species demonstrated divergent responses, with increasing presence and abundance of non-native species in the understory as soil disturbance within patches and sites increased. Dominant patterns in measured traits tracked the leaf economic spectrum (LES), with non-native species tending toward resource-acquisitive traits. Species with resource-acquisitive traits, regardless of identity, were favored with disturbance and responded positively to light availability in disturbed sites. Models showed species primarily dispersed by wind were more prevalent in disturbed patches and sites than those dispersed by endozoochory, while seed mass had no effect.</p>
Least tern disturbance observations and colony survey data
<p>Coastal birds that rely on sandy beaches for breeding are vulnerable to catastrophic flooding events resulting from tropical cyclones. The effects of storm surge on annual productivity depend on the propensity and success of renesting attempts post-storm. From 2017-2021, I investigated the effects of storm surge on Least Tern (<em>Sternula antillarum</em>) annual productivity, renesting probability, and nest and chick survival after storms on Mississippi's Gulf of Mexico Coast. Tropical cyclones made landfall during peak breeding period in three of these years, resulting in complete overwash of all colonies. Observers monitored daily nest survival, productivity (max fledge count/max nest count), and frequency of disturbance from avian predators at each colony. Total annual productivity (fledge count/ nest count across the study area) summed across colonies ranged from 0.00-0.07 in storm years and 0.29-0.66 in non-storm years. Probability of colony re-occupation declined as a function of storm date and increased with pre-storm nest success. Disturbance rate from avian predators did not increase post-storm compared to pre-storm periods. Nest survival increased with colony size and decreased in both late-season (non-storm) and post-storm time periods. Mean probability of chick survival was 0.20 ± 0.06 SD for peak nesting period in non-storm years, whereas mean predicted chick survival was 0.003 – 0.004 in storm years and in renesting periods in all years. Storm surge resulted in nearly complete loss of Least Tern breeding productivity despite renesting attempts and colony re-occupation after storms. I found no evidence that avian predation increased post-storm as a result of habitat changes; rather, similarly low productivity was observed for late-season breeding attempts in non-storm years. Repeated storm surge events could pose a serious threat to the viability of Mississippi's coastal-nesting Least Tern population, and protection of Least Terns during early and peak nesting seasons is critical for ensuring breeding success.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.