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326 results for “Southern California”
SBC LTER: REEF: Net primary production, growth and standing crop of Macrocystis pyrifera in Southern California
The giant kelp Macrocystis pyrifera forms subtidal forests on shallow reefs in temperate regions of the world. It is one of the fastest-growing multicellular autotrophs on Earth and its high productivity supports diverse marine food webs. In 2008, we published a method for estimating biomass and net primary production (NPP) of giant kelp along with five years of data, to provide a more integrated measure of NPP than those yielded by previous methods. Our method combines monthly field measurements of standing crop and loss rates with a model of kelp biomass dynamics to estimate instantaneous mass-specific growth rates and NPP for each season of each year. We have since improved our approach to account for several previously unresolved sources of biomass loss. These improvements have led to a near doubling of our prior estimates of growth and NPP. At our site with the most persistent stand of giant kelp, NPP averages ~5.2 kg dry mass m-2 y-1 and results from the rapid growth (~3.5% per day) of a relatively small standing biomass (~ 0.4 kg dry mass m-2 on average) that turns over ~ 12 times annually. Here we provide revised estimates of seasonal biomass, growth and NPP for the five years covered by our previous publication (2002-2006), along with an additional data collect since then (2007-present). We also present updated relationships for predicting giant kelp biomass and NPP from much more easily obtained measurements of frond density. These data can be used to understand the mechanisms that drive variation in giant kelp NPP at a wide range of temporal scales.
Invasion dynamics of quagga mussels within a Southern California reservoir and its spatially intermittent watershed
Since its discovery in Lake Mead, Nevada in 2007, the invasive quagga mussel (Dreissena rostriformis bugensis) spread throughout the lower Colorado River drainage and into connected Southern California water systems. In December 2013, quagga mussels were found in Lake Piru, California, a reservoir with no connection to the Colorado River drainage. An initial “boom” period occurred in the first year after colonization. High densities and settlement rates continued for three years while lake water levels were low and relatively stable, despite periodic removals of mussels from lake infrastructure. Mussels were initially restricted to hard substrates but were regularly found on soft sediments within two years of colonization. Storms in 2017 dramatically increased the lake level and deposited substantial sediment, which eliminated mussels on soft sediments and reduced the overall mussel population. Reproduction and juvenile settlement rebounded within 6 months, despite the low population of adult mussels in the lake. Environmental conditions, particularly fill status and water temperature, rather than adult density, appear to be the primary driver of veliger abundance in this system, while recruitment was primarily explained by veliger abundance. Elevated water releases from the reservoir increased the flux of veligers downstream and led to mussel recruitment >15 km downstream. Sustained establishment of quagga mussels downstream has not occurred in the Santa Clara River and seems unlikely due to the unstable habitat conditions. However, periodic downstream colonization increases the likelihood for the infestation to spread and impact agricultural and municipal water systems that receive water from the river.
Kelp metapopulations: Semi-annual time series of giant kelp patch area, biomass and fecundity in southern California, 1996 - 2006
These data describe the patch-scale canopy biomass and population fecundity of giant kelp, Macrocystis pyrifera, in southern California, USA, from 1996¬ to 2007. Biomass of the surface canopy was estimated using diver-calibrated Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus satellite imagery. Fecundity was estimated from canopy biomass pixel data using a seasonally-adjusted relationship between the diver-measured density of giant kelp spore-bearing tissue and the Landsat estimate of canopy biomass density using data collected across five years at the San Clemente Artificial Reef, located offshore of San Clemente, California, USA. Landsat pixel-scale estimates of giant kelp biomass and fecundity were summed across space for each giant kelp patch and averaged across time separately with two semesters each year (January–June and July–December). The location and area of each giant kelp patch are also provided. These data were described in <ulink url="http://dx.doi.org/10.1890/15-0283.1">Castorani, M. C., D. C. Reed, F. Alberto, T. W. Bell, R. D. Simons, K. C. Cavanaugh, D. A. Siegel and P. T. Raimondi. Connectivity structures local populations dynamics: a long-term empirical test in a large metapopulation system. Ecology. DOI: 10.1890/15-0283.1</ulink> These data are part of the NSF collaborative project: The effect of inbreeding on metapopulation dynamics of the giant kelp, Macrocystis pyrifera (funded wholly or part by NSF Awards OCE-1233283, 1233288, 1233839).
Kelp metapopulations: Semi-annual time series of spore dispersal times among giant kelp patches in southern California, 1996 - 2006
These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among patches in southern California, USA, from 1996 to 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations among patches were estimated for 6-month periods (January - June and July - Dececember, 1996 - 2006) using minimum mean transit times connecting source and destination connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS). Minimum transport times between giant kelp patches were assumed to be proportional to minimum transport times between ROMS cells and the alongshore distance between giant kelp patches
SBC LTER: REEF: Macrocystis pyrifera blade area and loss in Southern California
These data describe losses of giant kelp (Macrocystis pyrifera) blade tissue observed in the Santa Barbara Channel (Isla Vista Reef) during the summer of 2012. Data are contained in two tables: 1) a time series of measured change in blade area over time at different depths and locations in the kelp forest, 2) modeled estimates of partial blade losses specific to blades on subsurface fronds and blades in the water column and canopy sections of surface reaching fronds.
Supplementary Material for "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems"
<p>This Zenodo repository contains all data, scripts, and supplementary materials for the manuscript entitled, "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems".</p>
Temperature Measurements of Southern California Deserts 2022.
The following data was recorded at various desert field sites within Southern California. Data was collected between May 2022 and June 2022. 3 different deserts; Carrizo, Cuyama, and Mojave, were tested. Temperature pendants were deployed for 30 days and recorded local temperature at 1 hour intervals.
SBC LTER: Spatial definitions of giant kelp (Macrocystis pyrifera) patches in southern and central California
These data describe the spatial definitions of patches of giant kelp, Macrocystis pyrifera, in central and southern California, USA, using a 27-year time series of giant kelp canopy biomass from Landsat 5 Thematic Mapper satellite imagery (1984-2011). Giant kelp patches were delineated using a spatial synchrony-based method that avoids the consolidation of adjacent, independently fluctuating local populations into "megapatches". The method uses a network theory modularity approach to optimally cluster Landsat pixels into patches based on suitable habitat area (i.e., all areas containing giant kelp, 1984-2011) and the spatial synchrony of canopy biomass. These data were described in Cavanaugh, K. C., D. A. Siegel, P. T. Raimondi, and F. A. Alberto. 2014. Patch definition in metapopulation analysis: a graph theory approach to solve the mega-patch problem. Ecology 95:316-328. doi:10.1890/13-0221.1
Annual and monthly time series of estimated kelp spore dispersal times among ROMS cells in southern California, 1996 – 2006
These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS) in southern California, USA, for an 11-year period from the beginning of 1996 to the end of 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations connecting source and destination ROMS cells were estimated on monthly and annual timescales using minimum mean transit times.
Fig. 5 in Hidden in plain sight, Chaetopterus dewysee sp. nov. (Chaetopteridae, Annelida) - A new species from Southern California
Fig. 5. Maximum likelihood tree of Chaetopterus Cuvier, 1830 spp. COI sequences with the Mesochaetopterus Potts, 1914 clade as outgroup, based on Moore et al. (2017). Only bootstrap supports> 80 are shown. The new species Chaetopterus dewysee sp. nov. and C. variopedatus (Renier, 1804) from the type locality are in bold. Number of sequences included for each terminal is in brackets. Details on sequences that were analyzed can be found in Table 1. Haplotype network for the nine Chaetopterus dewysee sp. nov. sequences is shown next to the tree.
Fig. 4 in Hidden in plain sight, Chaetopterus dewysee sp. nov. (Chaetopteridae, Annelida) - A new species from Southern California
Fig. 4. Chaetopterus dewysee sp. nov. micro-CT surface renderings of the cybertype (SIO-BIC A12034). A. Dorsal view of the whole specimen. B. Lateral view of the whole specimen. C. Frontal view with virtual dissection planes (I–III) showing cross-sections along the body. D–E. Lateral view with a virtual dissection window showing the parapodial musculature of region A. Direct link to cybertype: www.morphdbase.de/?E_Tilic_20200122-S-5.1
Fig. 2 in Hidden in plain sight, Chaetopterus dewysee sp. nov. (Chaetopteridae, Annelida) - A new species from Southern California
Fig. 2. Chaetopterus dewysee sp. nov. A. Habitus of a paratype (SIO-BIC A11653). B–C. Anterior region A, chaetigers are numbered a1–10. D–E. Details of chaetiger a4 cutting chaetae; * marks a developing cutting chaeta. p = palps, ey = eyes, cg = ciliated groove, nt = notopodium.
KaKiOS-16: a probabilistic, non-linear, absolute location catalog of the 1981-2011 Southern California seismicity
<p>This is the KaKiOS-16 earthquake catalog for southern California. We locate the southern California seismicity using the state-of-the-art probabilistic and nonlinear method NonLinLoc. We use only the P wavepicks to avoid introducing the velocity-model and picking-time errors of the S phase, which is harder to detect and thus less constrained. Using a subset of the best locatable earthquakes, we conduct a joint inversion using the VELEST software to obtain a minimum 1D velocity model and station corrections. We use the NonLinLoc method with this 1D velocity model and the inferred model uncertainties to obtain realistic location distributions for each event.<br> </p>
FIGURE 1. A in A new hypogeal species of Oregmopyga Hoy (Hemiptera: Coccoidea: Eriococcidae) from southern California, U. S. A., and a key to species of the genus
FIGURE 1. A, Encasement of Oregmopyga viscosa Kondo sp. nov. showing root fibers and imprinted abdominal segmentation; B, Dorsal view of insect, showing bare dorsum of shiny red color and abdominal segmentation; C, Live specimen inside encasement with venter covered with powdery wax.
Southern California Earthquake Center (SCEC) Community Geodetic Model (CGM)
<p><strong>Overview</strong></p><p>Measuring accurately the relative movement of the surface of the Earth is a critical constraint on the slow and broad tectonic loading and unloading to which faults respond, and is one of the few observations of the solid Earth that may be made directly without inference. High-precision geodetic observations, such as from Global Navigation Satellite Systems (GNSS), which includes the Global Positioning System (GPS), and interferometric synthetic aperture radar (InSAR), allow measurement of fault motions between, during and in the aftermath of earthquakes and other related tectonic phenomena, densely in both space and time.</p><p>The Community Geodetic Model (CGM) provides velocities and time series of observed points on the Earth's surface over Southern California using data from a number of contributing researchers, institutions and analysis centers. The GNSS products provide high temporal resolution (nominally daily measurement points for continuous stations) in three dimensions at specific observation sites and the InSAR products provide high spatial resolution (approximately one point per tens of m on the ground, depending on exact specifications of data and processing). Combined, they provide the ability to study crustal deformation over a wide range of distances and periods.</p><p>The CGM differs from other<a href="https://www.scec.org/research/cxm"> SCEC Community Models</a> in that it is constantly extending with time as new data are acquired daily, so it is not static.</p><p>The CGM version 1 (2016; <a href="https://doi.org/10.5281/zenodo.4926528">doi:10.5281/zenodo.4926528</a>) was a collection of time-independent (velocity-only) geodetic products gathered from published papers. The GNSS velocities were then combined and modeled by a Working Group researching methods and contributing interpolated deformation fields. The main goal of the CGM version 2 is to add time-dependent (time series) products to both the GNSS and InSAR products. For the GNSS, this is done by ingesting survey and (mostly) continuous time series from five analysis centers in the U.S.: the Geodetic Facility for the Advancement of Geoscience (GAGE); the Nevada Geodetic Laboratory (NGL) at the University of Nevada, Reno (UNR); the NASA Jet Propulsion Laboratory (JPL) and Scripps Orbital and Permanent Array Center (SOPAC) contributions to the MEaSUREs ESESES project; and the U.S. Geological Survey (USGS). Like the various contributions to the CGMv1 GNSS velocities, these time series are rigorously adjusted to be self-consistent, before a weighted mean is calculated to produce the consensus products. Much of the InSAR contribution is a consensus from research by the SCEC community within the CGM (InSAR) Working Group, whose individual contributions are listed below and in more detail in the README.txt file in the top directory of the archive. The CGMv2 is therefore a "union" or "superset" of survey and continuous GNSS and InSAR time series.</p><p>Please see<a href="https://www.scec.org/research/cgm"> https://www.scec.org/research/cgm</a> for more information.</p><p><strong>Version: CGMv2.0.0</strong></p><p>This is the second major release of the CGM (version 2.0.0) and is distributed as a zip-file. See below and in the README.txt file for information about the directory structure and contents of the entire zipped archive. Much of the SCEC5 activity was focused on the assembly of GNSS and InSAR time series for measuring temporally variable motions, expanding the CGMv1 with the time dimension. The CGMv2.0.0 is a time-dependent set of products, consisting of time series and velocities of the Earth's surface measured by GNSS and InSAR.</p><p><strong>Directory Structure and Contents</strong></p><p><strong>data/gnss/pos/</strong><br>The CGMv2.0.0 GNSS time series in <a href="https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf">"pos" format</a> (plain text), relative to various reference frames described below. Header lines in each file provide information about the nominal reference coordinates and data columns. Files named "*.wmrss_*" are the continuous stations (<i>w</i>eighted <i>m</i>ean with <i>r</i>e<i>s</i>caled <i>s</i>igma) and files named "*.final_" are the survey sites.</p><p><strong>data/gnss/pos/igb14/</strong> The International GNSS Service's (IGS's) <a href="https://lists.igs.org/pipermail/igsmail/2020/007917.html">revised realization of ITRF2014</a></p><p><strong>data/gnss/pos/nam14/ </strong>North America defined by <a href="https://doi.org/10.1093/gji/ggx136">Altamimi et al.'s (2017)</a> ITRF2014 plate motion model</p><p><strong>data/gnss/pos/pcf14/ </strong>The Pacific defined by <a href="https://doi.org/10.1093/gji/ggx136">Altamimi et al.'s (2017)</a> ITRF2014 plate motion model</p><p><strong>data/gnss/pos/nam17/ </strong>North America defined by <a href="https://doi.org/10.1029/2017JB015257">Kreemer et al. (2018)</a></p><p><strong>data/gnss/vel/</strong><br>The CGMv2.0.0 GNSS velocities in a CSV file similar to <a href="https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf">GAGE's "vel" format</a> (plain text), relative to the same reference frames described above. Header lines in each file provide information about the data columns.</p><p><strong>data/insar/</strong><br>The CGMv2.0.0 InSAR line-of-sight consensus time series and velocities for four ESA Sentinel-1 tracks (ascending tracks 64 and 166, and descending tracks 71 and 173) over Southern California, in an <a href="https://github.com/kmaterna/InSAR_CGM_readers_writers#cgm-insar-hdf5-structure">HDF5 format designed for the CGM</a>. A description of and reader for the HDF5 files may be found <a href="https://github.com/kmaterna/InSAR_CGM_readers_writers">here</a>.</p><p><strong>data/insar/contrib/</strong><br>Individual contributions to the InSAR time series and velocity products, as described below and in more detail in the top-level README.txt file.</p><p><strong>Contributors</strong></p><p>The GNSS time series are a weighted mean, after restoration of global scale if processed using Gipsy (JPL, NGL/UNR and USGS) and self-consistent alignment of reference frame, of the following GNSS analysis centers, whose products are publicly available at the embedded hyperlinks:</p><ul><li>The <a href="https://www.unavco.org/data/gps-gnss/derived-products/derived-products.html">Geodetic Facility for the Advancement of Geoscience (GAGE)</a> (<a href="https://doi.org/10.1002/2016RG000529">Herring et al., 2016</a>)</li><li>The <a href="http://geodesy.unr.edu/">Nevada Geodetic Laboratory</a> at the University of Nevada, Reno (<a href="https://doi.org/10.1029/2018EO104623">Blewitt et al., 2018</a>)</li><li>NASA's <a href="http://garner.ucsd.edu/pub/solutions/gipsy">Jet Propulsion Laboratory contribution</a> to the <a href="http://sopac-csrc.ucsd.edu/index.php/measures-2/">MEaSUREs ESESES Project</a></li><li><a href="http://sopac-csrc.ucsd.edu/">SOPAC</a>'s <a href="http://garner.ucsd.edu/pub/measuresESESES_products/Timeseries/">contribution</a> to the <a href="http://sopac-csrc.ucsd.edu/index.php/measures-2/">MEaSUREs ESESES Project</a></li><li>The <a href="https://earthquake.usgs.gov/monitoring/gps">United States Geological Survey</a> (<a href="https://doi.org/10.1785/0220160204">Murray and Svarc, 2017</a>)</li><li><a href="https://www.scec.org/user/zshen">Zheng-Kang Shen's (UCLA)</a> <a href="http://scec.ess.ucla.edu/~zshen/cgm/">survey time series</a></li></ul><p>Z.-K. Shen processed the raw data from the <a href="https://service.scedc.caltech.edu/gps/">SCEC survey-mode GPS data archive</a> to provide the corresponding time series and velocities. A. Gonzalez Ortega provided processed time series from <a href="https://regnom.cicese.mx/">CICESE's REGNOM network of continuous GNSS stations</a>. M. Floyd and T. Herring designed the download, alignment and combination of the publicly available continuous GNSS archives, listed above, in various reference frames.</p><p>Contributions from individuals and institutions within the SCEC community to the CGM (InSAR) products are:</p><ul><li>K. Wang contributed time series and velocity solutions</li><li>K. Guns and X. Xu contributed time series and velocity solutions</li><li>Z. Liu contributed time series and velocity solutions</li><li>S. Sangha, M. Govorcin and D. Bekaert contributed time series and velocity solutions</li><li>G. Funning contributed time series and velocity solutions</li><li>E. Tymofyeyeva calculated the combination of contributed solutions to generate the consensus product</li><li>K. Materna contributed time series and velocity solutions, and wrote the translation tools for converting to and from HDF5 format, as designed by all InSAR contributors listed immediately above plus M. Floyd</li></ul><p>Three groups (K. Guns and X. Xu; Z. Liu; and S. Sangha, M. Govorcin and D. Bekaert) independently processed interferograms from common raw datasets using different processing approaches.</p><p>E. Tymofyeyeva coordinated and led the InSAR Working Group.</p><p>M. Floyd coordinated and led the wider CGM Working Group.</p><p>All contributed to the design of the HDF5 format in which the InSAR products are distributed.</p>
Figure 2 in The dentition of the extinct megamouth shark, (Lamniformes: Megachasmidae), from southern California, USA, based on geometric morphometrics
Figure 2. Homologous landmark (numbered black or white circles) and semi-homologous landmark (red circles with asterisk [*] connected by red lines) on tooth samples of Megachasma applegatei (A), M. pelagios (B), and Odontaspis ferox (C) for principal component analysis (not to scale). Seven homologous landmarks: 1, the crown apex, 2 and 3, right- and left-most extremities of the crown; 4, apical-most point around the middle of the crown base; 5 and 6, basal extremity of each of the two root lobes; and 7, apical-most point of the basal root concavity.
Figure 3. A in The dentition of the extinct megamouth shark, (Lamniformes: Megachasmidae), from southern California, USA, based on geometric morphometrics
Figure 3. A. Scatter plot diagram showing principal component analysis of 207 teeth of Megachasma applegatei (black plots) compared with all 178 teeth of extant M. pelagios (red plots), and all 78 teeth of extant Odontaspis ferox separated into tooth types using different colors (symphysial teeth = green; anterior teeth = dark blue; intermediate teeth = purple; lateral teeth = brown). B. Scatter plot diagram exclusively of M. applegatei, showing examples of actual specimens (not to scale) represented by certain plots (illustrated teeth: LACM 9883, 150907, 155340, 155348, 155357, 155373, 155393, 155424, 155434, 155456, 155563, 155622, 155630, 155651, 155653, 155694, and 155700). C. Scatter plot diagram exclusively of M. pelagios, showing examples of actual specimens (not to scale: see Fig. 1C, D) represented by certain plots. D. Scatter plot diagram exclusively of O. ferox, showing examples of actual specimens (not to scale: see Fig. 1F) represented by certain plots. Asterisk (*): on axes in B and C = PC1 and PC2 originally labeled inversely by the software (see text for detail); by photograph of teeth in C-D = Upper teeth.
Figure 1. A in The dentition of the extinct megamouth shark, (Lamniformes: Megachasmidae), from southern California, USA, based on geometric morphometrics
Figure 1. A. Generalized consensus tree of extant lamniform families on the basis of molecular-based phylogenetic studies, highlighting Megachasmidae in bold (see Stone and Shimada 2019, fig. 6, and references therein). B. Extant megamouth shark, Megachasma pelagios (after Compagno 1984). C, D. Right upper (C) and right lower (D) teeth of extant M. pelagios (BPBM 22730, 446 cm TL, male) in (from top row to bottom row) lingual, labial, mesial, apical, and basal views, showing strong tendency towards homodonty. E. Extant smalltooth sand tiger, Odontaspis ferox (after Compagno 1984). F. Left upper and left lower dental series of extant O. ferox (BPBM 9335, 297(?) cm TL, male(?)) showing representative 'lamnoid tooth pattern' (A or a = anterior teeth; I or i = intermediate tooth; L or l = lateral tooth; S or s = symphysial tooth). Scale bars: B and E = 50 cm; C, D, F = 5 mm
Figure 4 in The dentition of the extinct megamouth shark, (Lamniformes: Megachasmidae), from southern California, USA, based on geometric morphometrics
Figure 4. Three reconstructed dentitions of Megachasma applegatei under three different assumptions (see text for detail). A. Artificial dentition based on Odontaspis ferox as a model. B. Artificial dentition depicted as intermediate between O. ferox and M. pelagios. C. Artificial dentition based on M. pelagios as a model. Scale bar = 5 mm (note: each scale bar applies to each respective dentition consisting of teeth with digitally adjusted sizes [see text]).
Annual biomass data (2001-2021) for southern California: above- and below-ground, standing dead, and litter
<p>Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2021 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term.</p> <p>The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at <a href="https://manzanita.forestry.oregonstate.edu/ecoservices/">https://manzanita.forestry.oregonstate.edu/ecoservices/</a> and described in Underwood et al. (2022).</p> <p>REFERENCES</p> <p>Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003</p> <p>Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581</p> <p>Schrader-Patton et al. 2022. "Estimating Wildfire Impacts on the Biomass of Southern California's Chaparral Shrublands." Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA.</p> <p>Underwood et al. 2022. "Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool." Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA.</p>
ScienceDex guides
Understand access before you commit
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.