Abstract Ecological factors favoring either resprouting or obligate seeding in plants have received considerable attention recently. Three ecological models have been proposed to explain patterns of these two life history types. In this study we test these three models using data from California chaparral. We take an innovative approach to testing these models by not testing community or landscape patterns, but instead, investigating environmental patterns characteristic of pairs of either resprouting or obligate seeding subspecies of Arctostaphylos (Ericaceae), a dominant and diverse shrub genus in chaparral. Four species were investigated that contain both a resprouting and an obligate seeding subspecies. Data were analyzed for % bare ground, elevation, annual precipitation, number of fires, and time between fires and were compared independently for each subspecies pair. Results were consistently supportive of the gap-dependent model suggesting that obligate seeders are favored when post-disturbance gaps are large. Results for other aspects were inconclusive or contrary to expectations for either of the other models.

Populations of the chaparral shrub were sampled in southern California and further north in Monterey and Santa Clara counties and it was discovered that postfire regeneration modes were different. The southern California populations had substantial resprouting with some seedling recruitment. The Monterey populations had no resprouting ability and recovery was entirely by seedlings. However, there is an age effect in that when young these northern California populations fail to recruit seedlings due to lack of a seed bank buildup in the short interval since the last fire. These populations likely will be extirpated. I hypothesize that this obligate seeding mode has been selected for because seed reproduction is more reliable when intervals between fires are very long, longer that resprouting shrubs would survive. Support for this is provided by demonstrating substantially higher lightning fire frequencies in southern California than in the Monterey and Santa Clara area.

Populations of the chaparral shrub were sampled in southern California and further north in Monterey and Santa Clara counties and it was discovered that postfire regeneration modes were different. The southern California populations had substantial resprouting with some seedling recruitment. The Monterey populations had no resprouting ability and recovery was entirely by seedlings. However, there is an age effect in that when young these northern California populations fail to recruit seedlings due to lack of a seed bank buildup in the short interval since the last fire. These populations likely will be extirpated. I hypothesize that this obligate seeding mode has been selected for because seed reproduction is more reliable when intervals between fires are very long, longer that resprouting shrubs would survive. Support for this is provided by demonstrating substantially higher lightning fire frequencies in southern California than in the Monterey and Santa Clara area.

These data summarize postfire resprouts and seedling counts for Ceanothus species in subgenus Ceanothus based on published data that is cited and personal observation by the author and other co-authors of the publication.

These data summarize postfire resprouts and seedling counts for Ceanothus species in subgenus Ceanothus based on published data that is cited and personal observation by the author and other co-authors of the publication.

California seed zone boundaries were first proposed in 1946, revised, and published as a joint report between the U.S. Forest Service and CAL FIRE in 1970. The 85 seed zones are defined by major areas in California having similar climatic, topographic, and soil conditions. These are areas where plant materials can be moved or transferred with minimal risk of being poorly adapted to a new location. Each seed zone is labelled by a three-digit identifier that reflects the layer’s creation. The first digit indicates the physiographic and climatic region. The second digit gives the subregion, which captures the next lower level of environmental changes known to affect growth and adaptability of plants. The last digit denotes the subzone. Subzones are limited to about 50 miles in latitude and further refine the uniformity of environment within each subregion. Where possible, boundaries follow natural or physical features. For more information, see the original publication: https://www.fs.usda.gov/research/treesearch/41438.

This dataset contains rasters of vegetation refugia and habitat exposure variables for the state of California. Two potential future climate scenarios were used: warmer and wetter (CNRM-CM5), and hotter and drier (MIROC-ESM) & 2 emission scenarios: a higher level one that represents our current trajectory (RCP 8.5) and a lower level one that represents a more optimistic scenario (RCP 4.5). The vegetation exposure models used aims to help in assessing potential climatic stress to vegetation communities and this dataset contains the statewide data for use in assessing the potential risk to each of the California Allotments. Current and future vegetation stress was determined by integrating the hydroclimate data with a detailed 2015 map of the spatial patterns of California’s vegetation community types, and examining how climate conditions will change at those locations using 9 hydroclimatic variables (30-year averages) from the Basin Characterization Model. The main habitat exposure outputs contain rasters all of the climate exposure results: 1 historic run: 1981-2010 and 12 future runs: 3 time periods (2010-2039, 2040-2069, 2070-2099) under 2 emission scenarios and 2 climate scenarios as well as reclassified rasters where the outputs were binned into 5 groups. To distinguish refugia areas from high-stress areas in the climate exposure results above, the team classified the climate frequency distribution for each vegetation type, which are labeled as CA refugia combined 45 and 85 for the respective RCP. Finally, the team looked at the spatial patterns of just refugia for the 2 climate models to identify areas where they align, defined as CA refugia concensus.

We evaluated the expected success of habitat recovery in priority areas under 3 different restoration scenarios: passive, planting, and seeding. Passive means no human intervention following a fire disturbance. Under a planting scenario, field technicians methodically plant young sagebrush saplings at the burned site. The seeding scenario involves distributing large amounts of sagebrush seeds throughout the affected area.

We evaluated the expected success of habitat recovery in priority areas under 3 different restoration scenarios: passive, planting, and seeding. Passive means no human intervention following a fire disturbance. Under a planting scenario, field technicians methodically plant young sagebrush saplings at the burned site. The seeding scenario involves distributing large amounts of sagebrush seeds throughout the affected area.

This data release contains a formatted dataset compiled from multiple databases on restoration treatments and environmental conditions from across the sagebrush (Artemisia spp.) biome. With these data, we modeled the influence of environmental conditions and restoration treatments on trends in sagebrush cover using generalized additive models. We then used these models to create maps of projected sagebrush cover 30 years following wildfire (no treatment, and aerial or drill seeding of sagebrush). We also provide maps for the probability of recovery after 30 years without treatment, with aerial seeding of sagebrush, or with drill seeding of sagebrush. Widespread degradation of ecosystem function and biodiversity loss has led to calls for massive investments in ecological restoration across the globe, but limited resources necessitate targeted application of restoration efforts. In western North America, disturbances such as wildfire, drought, and invasive species are increasingly altering the sagebrush biome, degrading habitat for species of conservation concern such as greater sage-grouse (Centrocercus urophasianus). Effective restoration is needed to address these challenges, but understanding the conditions determining when, where, and at what rate sagebrush recovery will occur is a pressing research need across the vast and heterogeneous sagebrush landscape. Files included in this data release: sage_dat_release.csv – compiled and formatted multiple treatment and environmental datasets spanning broad spatio-temporal extents sagebrush_notreat.tif – projected sagebrush cover 30 years following wildfire given local environmental conditions, without treatment sagebrush_notreat_sd.tif – error (summarized across simulations) in projected sagebrush cover 30 years following wildfire given local environmental conditions, without treatment perc_change_sage_aerial_artemisia.tif – projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with aerial seeding Artemisia spp. perc_change_sage_aerial_artemisia_sd.tif – error (summarized across simulations) in projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with aerial seeding Artemisia spp. perc_change_sage_drill_artemisia.tif – projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with drill seeding Artemisia spp. perc_change_sage_drill_artemisia_sd.tif – error (summarized across simulations) in projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with drill seeding Artemisia spp. prob_recovery_notreat.tif – probability of recovery 30 years after wildfire, without treatment prob_recovery_aerial_artemisia.tif – probability of recovery 30 years after wildfire, with aerial seeding Artemisia spp. prob_recovery_drill_artemisia.tif – probability of recovery 30 years after wildfire, with drill seeding Artemisia spp.