Aim: Forest regeneration data provide an early signal of the persistence and migration of tree species, so we investigated whether species shifts due to climate change exhibit a common signal of response or whether changes vary by species.
Location: California Floristic Province, United States; mediterranean biome.
Methods: We related Forest Inventory and Analysis (FIA) data from 2000−07 for 13 tree species to high-resolution climate and geographical data. Using methods from invasion ecology, we derived indices of species-specific regeneration overlap and central tendency change (range-wide global indicators) based on kernel density estimation of presence and absence of regeneration. We then built regeneration surfaces to identify areas of occurrence of high regeneration (regeneration
hotspots, local indicators) in both geographical and climate space for 13 common tree species.
Results: Differences between presence and absence of regeneration in forests varied in magnitude across species, with little evidence that tree regeneration is shifting to higher latitudes and elevations, the expected geographical fingerprint of climate change. We also identified potential topographic mediators of regeneration dynamics. Multiple regeneration hotspots were found for many species, suggesting the influence of non-climatic factors on regeneration. Differences between the presence and absence of regeneration in geographic and climate spaces were not always congruent, suggesting that shifting climate space and range area are not entirely coupled.
Main conclusions: The distributions of regeneration in Californian forests show diverse signals, not always tracking the higher latitudinal–elevation fingerprint of climate change. Local regeneration hotspots are common in our analysis, suggesting spatially varying persistence of forest linked to natural and anthropogenic disturbances. Our results emphasize that projections of tree range shifts in the context of climate change should consider the variation of regeneration drivers
Survival of early life stages is key for population expansion into new locations and for persistence of current populations (Grubb 1977, Harper 1977). Relative to adults, these early life stages are very sensitive to climate fluctuations (Ropert-Coudert et al.2015), which often drive episodic or event-limited regeneration (e.g. pulses) in long-lived plant species (Jackson et al. 2009). Thus, it is difficult to mechanistically associate 30-yr climate norms to dynamic processes involved in species range shifts (e.g. seedling survival). What are the consequences of temporal aggregation for estimating areas of potential establishment? We modeled seedling survival for three widespread tree species in California, USA (Quercus douglasii,Q. kelloggii, Pinus sabiniana) by coupling a large-scale, multi-year common garden experiment to high-resolution downscaled grids of climatic water deficit and air temperature (Flint and Flint 2012, Supplementary material Appendix 1). We projected seedling survival for nine climate change projections in two mountain landscapes spanning wide elevation and moisture gradients. We compared areas with windows of opportunity for seedling survival defined as three consecutive years of seedling survival in our species, a period selected based on studies of tree niche ontogeny (Supplementary material Appendix 1) to areas of 30-yr averaged estimates of seedling survival. We found that temporal aggregation greatly underestimated the potential for species establishment (e.g. seedling survival) under climate change scenarios.
Context Predicting climate-driven species’ range shifts depends substantially on species’ exposure to climate change. Mountain landscapes contain a wide range of topoclimates and soil characteristics that are thought to mediate range shifts and buffer species’ exposure. Quantifying fine-scale patterns of exposure across mountainous terrain is a key step in understanding vulnerability of species to regional climate change.
Objectives We demonstrated a transferable, flexible approach for mapping climate change exposure in a moisture-limited, mountainous California landscape across 4 climate change projections under phase 5 of the Coupled Model Intercomparison Project (CMIP5) for mid-(2040–2069) and end-of-century (2070–2099).Methods We produced a 149-year dataset (1951–2099) of modeled climatic water deficit (CWD), which is strongly associated with plant distributions, at 30-m resolution to map climate change exposure in the Tehachapi Mountains, California, USA. We defined climate change exposure in terms of departure from the 1951–1980 mean and historical range of variability in CWD in individual years and 3-year moving windows.
Results Climate change exposure was generally greatest at high elevations across all future projections, though we encountered moderate topographic buffering on poleward-facing slopes. Historically dry lowlands demonstrated the least exposure to climate change.
Conclusions In moisture-limited, Mediterraneanclimate landscapes, high elevations may experience the greatest exposure to climate change in the 21st century. High elevation species may thus be especially vulnerable to continued climate change as habitats shrink and historically energy-limited locations become increasingly moisture-limited in the future.
Climate change has already affected southern California where regional increases in temperature and vegetation shifts have been observed. While all the CMIP5 temperature projections agree on a substantial level of warming throughout the year, there is fair bit of divergence in the magnitude and seasonality of projected changes in rainfall. While desert plants and animals are generally adapted to extreme conditions, some species may be approaching their physiological threshold. We calculated the climate velocity of both temperature and aridity (PPT/PET) in SE California to illustrate the spatial variability of climate projections and reported on the probable expansion of barren lands reducing current species survivorship. We used a vegetation model to illustrate both temporal and spatial shifts in land cover in response to changes in environmental conditions. Such information is useful to plan land use for renewable energy siting in the region.
Brown, M. 2015. Creating Useful and Usable Climate Tools for Sagebrush Land Management Through Scientist and Manager Collaboration, Oregon State University thesis. http://hdl.handle.net/1957/56343
The sagebrush ecosystem, home to numerous plant and animal species including big sagebrush (Artemisia tridentata) and the endemic greater sage-grouse (Centrocercus urophasianus), has endured fragmentation and degradation of both quantity and quality due to the cumulative and synergistic relationships between an abundance of individual disturbances including grazing, invasive annuals and fire. Climate change may now be an additional threat that poses the greatest risk to these imperiled habitats. Natural resource agencies such as the Bureau of Land Management (BLM) seek to conserve sagelands through land management activities that ensure the survival of sage-grouse and continuity of the sagebrush biome. Web-based climate tools can help convey climate information that may be necessary for long-term land management, but these tools may not agree with the needs of land managers, may be too complex, or may be misinterpreted. To overcome barriers of user compatibility, the participation of both climate scientists and land managers is necessary during tool development. With the collaboration of Oregon and Idaho BLM sagebrush land managers and climate scientists, this study sought to assess land manager needs and define the criteria for useful and useable climate tools. Using an initial online survey, individual phone interviews with land managers, and a follow-up online survey, a series of land management activities and related climate variables were identified, and several web-based climate tools were assessed. Most managers perform vegetation management through a variety of means including seeding and herbicide application. Such activities are affected by the magnitude and timing of precipitation and temperature, as well as other variables, on seasonal and annual timeframes. For planning purposes land managers also need information on long-term 10-20 year climate trends. The act of listening to the needs of land managers uncovered communication barriers, and provided feedback on existing climate tools emphasizing accessibility, dependability and consistency, clear explanation of terminology, effective visualizations, and relevant spatial and temporal scales to the scope of management activities. We also identified a need for basic information and education on the location of existing climate tools and climate impacts, and a need for near-term forecasting tools that could bridge the gap between weather (≤ 6 months) and climate (≥ 30 years) projections.
Terra Magazine, Oregon State University’s research magazine, featured Brown’s research.
The dynamic global vegetation model (DGVM) MC2 was run over the conterminous US at 30arc sec (~800m) to simulate the impacts of nine climate futures generated by 3GCMs (CSIRO, MIROC and CGCM3) using 3 emission scenarios (A2, A1B, B1) in the context of the LandCarbon national carbon sequestration assessment. It first simulated potential vegetation dynamics from coast to coast assuming no human impacts and naturally occurring wildfires. A moderate effect of increased atmospheric CO2 on water use efficiency and growth enhanced carbon sequestration but did not greatly influence woody encroachment. The wildfires maintained prairie-forest ecotones in the Great Plains. With simulated fire suppression, the number and impacts of wildfires was reduced since only catastrophic fires were allowed to escape. This greatly increased the expansion of forests and woodlands across the western US and some of the ecotones disappeared. However, when fires did occur their impacts (both extent and biomass consumed) were very large. We also evaluated the relative influence of human land use including forest and crop harvest by running the DGVM with land use (and fire suppression) and simple land management rules. From 2041 through 2060, carbon stocks (live biomass, soil and dead biomass) of US terrestrial ecosystems varied between 155 and 162 Pg C across the three emission scenarios when potential natural vegetation was simulated. With land use, periodic harvest of croplands and timberlands as well as the prevention of woody expansion across the West reduced carbon stocks to a range of 122-126 Pg C while effective fire suppression reduced fire emissions by about 50%. Despite the simplicity of our approach, the differences between the size of the carbon stocks confirm other reports of the importance of land use on the carbon cycle over climate change.
Climate change adaptation and mitigation require understanding of vegetation response to climate change. Using the MC2 dynamic global vegetation model (DGVM) we simulate vegetation for the Northwest United States using results from 20 different Climate Model Intercomparison Project Phase 5 (CMIP5) models downscaled using the MACA algorithm. Results were generated for representative concentration pathways (RCPs) 4.5 and 8.5 under vegetation modeling scenarios with and without fire suppression for a total of 80 model runs for future projections. For analysis, results were aggregated by three subregions: the Western Northwest (WNW), from the crest of the Cascade Mountains west; Northwest Plains and Plateau (NWPP), the non-mountainous areas east of the Cascade Mountains; and Eastern Northwest Mountains (ENWM), the mountainous areas east of the Cascade Mountains. In the WNW, mean fire interval (MFI) averaged over all climate projections decreases by up to 48%, and potential vegetation shifts from conifer to mixed forest under RCP 4.5 and 8.5 with and without fire suppression. In the NWPP MFI averaged over all climate projections decreases by up to 82% without fire suppression and increases by up to 14% with fire suppression resulting in woodier vegetation cover. In the ENWM, MFI averaged across all climate projections decreases by up to 81%, subalpine communities are lost, but conifer forests continue to dominate the subregion in the future.
Context
Wildfires destroy thousands of buildings every year in the wildland urban interface. However, fire typically only destroys a fraction of the buildings within a given fire perimeter, suggesting more could be done to mitigate risk if we understood how to configure residential landscapes so that both people and buildings could survive fire.
Objectives
Our goal was to understand the relative importance of vegetation, topography and spatial arrangement of buildings on building loss, within the fire’s landscape context.
Methods
We analyzed two fires: one in San Diego, CA and another in Boulder, CO. We analyzed Google Earth historical imagery to digitize buildings exposed to the fires, a geographic information system to measure some of the explanatory variables, and FRAGSTATS to quantify landscape metrics. Using logistic regression we conducted an exhaustive model search to select the best models.
Results
The type of variables that were important varied across communities. We found complex spatial effects and no single model explained building loss everywhere, but topography and the spatial arrangement of buildings explained most of the variability in building losses. Vegetation connectivity was more important than vegetation type.
Conclusions
Location and spatial arrangement of buildings affect which buildings burn in a wildfire, which is important for urban planning, building siting, landscape design of future development, and to target fire prevention, fuel reduction, and homeowner education efforts in existing communities. Landscape context of buildings and communities is an important aspect of building loss, and if taken into consideration, could help communities adapt to fire.
Climate change has significant effects on critical ecosystem functions such as carbon and water cycling. Vegetation and especially forest ecosystems play an important role in the carbon and hydrological cycles.Vegetation models that include detailed belowground processes require accurate soil data to decrease uncertainty and increase realism in their simulations. The MC2 DGVM uses three modules to simulate biogeography, biogeochemistry and fire effects, all three of which use soil data either directly or indirectly. This study includes a correlation analysis of the MC2 model to soil depth by comparing a subset of the model’s carbon and hydrological outputs using soil depth data of different scales and qualities. The results show that the model is very sensitive to soil depth in simulations of carbon and hydrological variables, but competing algorithms make the fire module less sensitive to changes in soil depth. Simulated historic evapotranspiration and net primary productivity show the strongest positive correlations (both have correlation coefficients of 0.82). The strongest negative correlation is streamflow (0.82). Ecosystem carbon, vegetation carbon and forest carbon show the next strongest correlations (0.78, 0.74 and 0.74, respectively). Carbon consumed by forest fires and the part of each grid cell burned show only weak negative correlations (0.24 and 0.0013 respectively). In the model, when the water demand is met (deep soil with good water availability), production increases and fuels build up as more litter gets generated, thus increasing the overall fire risk during upcoming dry periods. However, when soil moisture is low, fuels dry and fire risk increases. In conclusion, it is clear climate change impact models need accurate soil depth data to simulate the resilience or vulnerability of ecosystems to future conditions.
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The potential evapotranspiration (PET) that would occur with unlimited plant access to water is a central driver of simulated plant growth in many ecological models. PET is in!uenced by solar and longwave radiation, temperature, wind speed, and humidity, but it is often modeled as a function of temperature alone. This approach can cause biases in projections of future climate impacts in part because it confounds the effects of warming due to increased greenhouse gases with that which would be caused by increased radiation from the sun. We developed an algorithm for linking PET to extraterrestrial solar radiation (incoming top-of atmosphere solar radiation), as well as temperature and atmospheric water vapor pressure, and incorporated this algorithm into the dynamic global vegetation model MC1. We tested the new algorithm for the Northern Great Plains, USA, whose remaining grasslands are threatened by continuing woody encroachment. Both the new and the standard temperature-dependent MC1 algorithm adequately simulated current PET, as compared to the more rigorous PenPan model of Rotstayn et al. (2006). However, compared to the standard algorithm, the new algorithm projected a much more gradual increase in PET over the 21st century for three contrasting future climates. This difference led to lower simulated drought effects and hence greater woody encroachment with the new algorithm, illustrating the importance of more rigorous calculations of PET in ecological models dealing with climate change.