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Biomass estimates for shrub‐dominated ecosystems in southern California have been generated at national and statewide extents. However, existing data tend to underestimate biomass in shrub vegetation types are limited to one point in time, or estimate aboveground live biomass only. In this study, we extended our previously developed estimates of aboveground live biomass (AGLBM) based on the empirical relationship of plot‐based field biomass measurements to Landsat normalized difference vegetation index (NDVI) and multiple environmental factors to include other vegetative pools of biomass. AGLBM estimates were made by extracting plot values from elevation, solar radiation, aspect, slope, soil type, landform, climatic water deficit, evapotranspiration, and precipitation rasters and then using a random forest model to estimate per‐pixel AGLBM across our southern California study area. By substituting year‐specific Landsat NDVI and precipitation data, we created a stack of annual AGLBM raster layers for each year from 2001 to 2021. Using these AGLBM data as a foundation, we developed decision rules to estimate belowground, standing dead, and litter biomass pools. These rules were based on relationships between AGLBM and the biomass of the other vegetative pools derived primarily from peer‐reviewed literature and an existing spatial data set. For shrub vegetation types (our primary focus), rules were based on literature estimates by the postfire regeneration strategy of each species (obligate seeder, facultative seeder, obligate resprouter). Similarly, for nonshrub vegetation types (grasslands, woodlands) we used literature and existing spatial data sets specific to each vegetation type to define rules to estimate the other pools from AGLBM. Using a Python language script that accessed Environmental Systems Research Institute raster geographic information system utilities, we applied decision rules to create raster layers for each of the non‐AGLBM pools for the years 2001–2021. The resulting spatial data archive contains a zipped file for each year; each of these files contains four 32‐bit tiff files for each of the four biomass pools (AGLBM, standing dead, litter, and belowground). The biomass units are grams per square meter (g/m2). We estimated the uncertainty of our biomass data by conducting a Monte Carlo analysis of the inputs used to generate the data. Our Monte Carlo technique used randomly generated values for each of the literature‐based and spatial inputs based on their expected distribution. We conducted 200 Monte Carlo iterations, which produced percentage uncertainty values for each of the biomass pools. Results showed, using 2010 as an example, mean biomass for the study area and percentage uncertainty for each of the pools as follows: AGLBM (905.4 g/m2, 14.4%); standing dead (644.9 g/m2, 1.3%); litter (731.2 g/m2, 1.2%); and belowground (776.2 g/m2, 17.2%). Because our methods are consistently applied across each year, the data produced can be used to inform changes in biomass pools due to disturbance and subsequent recovery. As such, these data provide an important contribution to supporting the management of shrub‐dominated ecosystems for monitoring trends in carbon storage and assessing the impacts of wildfire and management activities, such as fuel management and restoration. There are no copyright restrictions on the data set; please cite this paper and the data package when using these data. [ABSTRACT FROM AUTHOR]

In 1949–1951, ecologist Robert H. Whittaker sampled plant community composition at 470 sites in the Siskiyou Mountains (Oregon and California; also known as Klamath or Klamath‐Siskiyou Mountains). His primary goal was to develop methods to quantify plant community variation across environmental gradients, following on his seminal work challenging communities as discrete entities. He selected the Siskiyous because of their diverse and endemic‐rich flora, which he attributed to geological complexity and an ancient stable climate. He chose sites to span gradients of topography, elevation, geologic substrate, and distance from the coast. He used the frequencies of indicator species in his data to assign sampling locations to positions on the topographic gradient, nested within the elevational and substrate gradients. He originated in this study the concept of diversity partitioning, in which gamma diversity (species richness of a community) equals alpha diversity (species richness in homogeneous sites) times beta diversity (species turnover among sites along gradients). Diversity partitioning subsequently became highly influential and new developments on it continue. Whittaker published his Siskiyou work covering paleohistory, biogeography, floristics, vegetation, gradient analysis, and diversity partitioning in Ecological Monographs in 1960. Discussed in 2 pages of his 60‐page monograph, diversity partitioning accounts for >95% of its current >4300 citations. In 2006, we retrieved Whittaker's Siskiyou data in hard copy from the Cornell University archives and entered them in a database. We used these data for multiple published analyses, including some based on (re)sampling the approximate locations of a subset of his sites. Because of the continued interest in diversity partitioning and in historic data sets, here we present his data, including 359 sampling locations and their descriptors and, for each sample, a list of species with their estimated percent cover (herbs and shrubs) and numbers by diameter at breast height (DBH) category (trees). Site descriptors include the approximate location (road, trail, or stream), elevation, topographic aspect, geologic substrate (serpentine, gabbro, or diorite), and dominant woody vegetation of each location. For 111 sites, including the small number chosen to represent the distance‐to‐coast gradient, we could not locate his data. There are no copyright restrictions and users of these data should cite this data paper in any publications that result from its use. The authors are available for consultations about and collaborations involving the data. [ABSTRACT FROM AUTHOR]

Kelp forests are among the most productive ecosystems on Earth. In combination with their close proximity to the shore, the productivity and biodiversity of these ecosystems generate a wide range of ecosystem services including supporting (e.g., primary production, habitat), regulating (e.g., water flow, coastal erosion), provisioning (e.g., commercial and recreational fisheries), and cultural (e.g., recreational, artisanal) services. For these reasons, kelp forests have long been the target of ecological studies. However, with few exceptions, these studies have been localized and short term (<5 years). In 1999, recognizing the importance of large‐scale, long‐term studies for understanding the structure, functioning, and dynamics of coastal marine ecosystems, and for informing policy, the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) designed and initiated a large‐scale, long‐term monitoring study of kelp forest ecosystems along 1400 km of coast stretching from southern California to southern Oregon, USA. The purpose of the study has been to characterize the spatial and temporal patterns of kelp forest ecosystem structure and evaluate the relative contributions of biological and environmental variables derived from external sources (e.g., sea otter density, Chl‐a concentration, sea surface temperature, wave energy) in explaining observed spatial and temporal patterns. For this purpose, the ecological community (i.e., density, percent cover, or biomass of conspicuous fishes, invertebrates, and macroalgae) and geomorphological attributes (bottom depth, substratum type, and vertical relief) of kelp forest ecosystems have been surveyed annually using SCUBA divers trained in both scientific diving and data collection techniques and the identification of kelp forest species. The study region spans distinct ecological and biogeographic provinces, which enables investigations of how variation in environmental drivers and distinctive species compositions influence community structure, and its response to climate‐related environmental change across a portion of the California Current Large Marine Ecosystem. These data have been used to inform fisheries management, design and evaluate California's state‐wide network of marine protected areas (MPAs), and assess the ecological consequences of climate change (e.g., marine heatwaves). Over time, the spatial and temporal design of the monitoring program was adapted to fill its role in evaluating the ecological responses to the establishment of MPAs. There are no copyright restrictions; please cite this paper when data are used. [ABSTRACT FROM AUTHOR]