The impact of climate change uncertainty on California's vegetation and adaptation management

Climate-limited vegetation change in the conterminous United States of America

The effects of climate change on vegetation composition and distribution are evident in different ecosystems around the world. Although some climate-derived alterations on vegetation are expected to result in changes in lifeform fractional cover, disentangling the direct effects of climate change from different non-climate factors, such as land-use change, is challenging. By applying "Liebig's law of the minimum" in a geospatial context, we determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change for the conterminous United States and compared these potential rates to observed change rates for the period 1986 to 2018. We found that 10% of the land area of the conterminous United States appears to have climate limitations on the change in fractional cover, with a high proportion of these sites located in arid and semiarid ecosystems in the Southwest part of the country. The rates of change in lifeform fractional cover for the remaining area of the country are likely limited by non-climate factors such as the disturbance regime, land management, land-use history, soil conditions, and species interactions and adaptations.

The high climate vulnerability of western Mediterranean forests

Understanding the effects of climate change is one of the most challenging goals for biodiversity conservation. The forests of Andalusia, in Southern Spain, are part of an important Mediterranean Basin biodiversity hotspot. However, great changes in climate are expected to occur in this region, and there is an increasing need to assess the vulnerability of its vegetation. We assess the vulnerability of twelve forest types in the region that are included in the European Directive 92/43/EEC as Habitats of Community Interest (HCI). HCI are natural habitat types which are in danger, have a small natural range, or present an outstanding example of a biogeographical regions in the European Union. We assessed vulnerability by analyzing the climate exposure level of each forest type under two global climate models (MRI-CGCM3, which predicts warmer and wetter conditions, and MIROC-ESM which predicts hotter and drier conditions), two emission scenarios (RCP4.5, a representative concentration pathway that predicts stable emissions of CO2, and RCP8.5, that predicts the highest CO2 emissions) by the mid- and end-century time periods. The vulnerability analysis also includes the sensitivity and adaptive capacity of the dominant tree species which compose each forest type. An overall vulnerability score was calculated for each forest type, model, scenario and time period. High-elevation forest types and those with high moisture requirements were more vulnerable to climate change, while forest types dominated by more thermophilic species were less vulnerable and more resilient. The worst climate impacts were predicted in the MIROC-ESM model and RCP8.5 scenario by the end of the century (2070-2100), while the least climatic stress was obtained in the MRI-CGCM3 model and RCP4.5 scenario by the mid-century (2040-2070), which still shows high potential stress for most forest types. By the end of the century, the climate exposure of the entire forest domain will range between 32 % in the least stressful situation (MRI-CGCM3 and RCP4.5), and 98 % in the most climatically stressful situation (MIROC-ESM and RCP8.5). However, the effects of climate change will be perceptible by the mid-century, with most of the HCI forest types suffering climate stress. The "Andalusian oak forest" and the "Corylus wet forest" types were the most vulnerable to climate change, while the "Mediterranean pine forest", the "Olea and Ceratonia forests" and the "oak forests" were the least vulnerable. This assessment identifies the vulnerable forest types to climate change in the south of the Iberian Peninsula, and provides context for natural resource managers in making decisions about how to adapt forests to the impacts of climate change.

Leveraging species richness and ecological condition indices to guide systematic conservation planning

The global crises of biodiversity loss and climate change are interconnected in root cause and solutions. Targeted land conservation has emerged as a leading strategy to protect vulnerable species and buffer climate impacts, however, consistent methods to assess biodiversity and prioritize areas for protection have not yet been established. Recent landscape-scale planning initiatives in California present an opportunity to conserve biodiversity, but to enhance their effectiveness, assessment approaches should move beyond commonly used measures of terrestrial species richness. In this study, we compile publicly available datasets and explore how distinct biodiversity conservation indices - including indicators of terrestrial and aquatic species richness and of biotic and physical ecosystem condition - are represented in watersheds of the northern Sierra Nevada mountain region of California (n = 253). We also evaluate the extent to which the existing protected area network covers watersheds that support high species richness and intact ecosystems. Terrestrial and aquatic species richness showed unique spatial patterns (Spearman R = 0.27), with highest richness of aquatic species in the low-elevation watersheds of the study area and highest richness of terrestrial species in mid- and high-elevation watersheds. Watersheds with the highest ecosystem condition were concentrated in upper-elevations and were poorly correlated with those with the highest species richness (Spearman R = -0.34). We found that 28% of watersheds in the study area are conserved by the existing protected area network. Protected watersheds had higher ecosystem condition (mean rank-normalized score = 0.71) than unprotected areas (0.42), but species richness was generally lower (0.33 in protected versus 0.57 in unprotected watersheds). We illustrate how the complementary measures of species richness and ecosystem condition can be used to guide strategies for landscape-scale ecosystem management, including prioritization of watersheds for targeted protection, restoration, monitoring, and multi-benefit management. Though designed for California, application of these indices to guide conservation planning, design monitoring networks, and implement landscape-scale management interventions provides a model for other regions of the world.

Impacts of climate change on vegetation pattern: Mathematical modeling and data analysis

Climate change has become increasingly severe, threatening ecosystem stability and, in particular, biodiversity. As a typical indicator of ecosystem evolution, vegetation growth is inevitably affected by climate change, and therefore has a great potential to provide valuable information for addressing such ecosystem problems. However, the impacts of climate change on vegetation growth, especially the spatial and temporal distribution of vegetation, are still lacking of comprehensive exposition. To this end, this review systematically reveals the influences of climate change on vegetation dynamics in both time and space by dynamical modeling the interactions of meteorological elements and vegetation growth. Moreover, we characterize the long-term evolution trend of vegetation growth under climate change in some typical regions based on data analysis. This work is expected to lay a necessary foundation for systematically revealing the coupling effect of climate change on the ecosystem.

A multi-data ensemble approach for predicting woodland type distribution: Oak woodland in Britain

Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate-related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross-validated (50%:50% - training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS-weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak-stand locations as a training dataset; the national forest inventory (NFI) "published regional reports" of oak woodland area; and an "NFI map" of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from "NFI survey" sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice.

Trait-based climate vulnerability of native rodents in southwestern Mexico

AIM

Incorporate species' trait information together with climate projections for associated habitat to assess the potential vulnerability of rodent taxa to climate change.

LOCATION

Oaxaca State, Mexico.

METHODS

We used a trait-based approach together with climate exposure models to evaluate the vulnerability of rodent species to projected climate conditions in the study region. Vulnerability was estimated based on three factors: (a) Level of climatic exposure that species are projected to experience across their current statewide range; (b) inherent species-specific sensitivity to stochastic events; and (c) species' capacity to cope with climate change effects. We defined species as inherently sensitive if they had any of the following: restricted geographic distribution in Mexico; narrow altitudinal range; low dispersal ability; or long generation length.

RESULTS

Vulnerability varied depending on the climate change scenario applied. Under the MPI general circulation model and current emissions trends, by 2099, all species evaluated were projected to have some level of threat (vulnerable for at least one factor), with 4 out of 55 species vulnerable for all three factors, 29 for two factors, and 22 for one factor. Six out of ten rodent species endemic to Oaxaca were vulnerable for two or more factors. We found that species with narrow and restricted-range distributions combined with low adaptive capacity were projected to be particularly vulnerable.

MAIN CONCLUSIONS

By including species-specific trait information in climate exposure assessments, researchers can contextualize and enhance their understanding about how climate change is likely to affect individual taxa in an area of interest. As such, studies like this one provide more relevant threat assessment information than exposure analyses alone and serve as a starting point for considering how climatic changes interact with an array of other variables to affect native species across their range.

Effects of 21st-century climate, land use, and disturbances on ecosystem carbon balance in California

Terrestrial ecosystems are an important sink for atmospheric carbon dioxide (CO₂ ), sequestering ~30% of annual anthropogenic emissions and slowing the rise of atmospheric CO₂ . However, the future direction and magnitude of the land sink is highly uncertain. We examined how historical and projected changes in climate, land use, and ecosystem disturbances affect the carbon balance of terrestrial ecosystems in California over the period 2001-2100. We modeled 32 unique scenarios, spanning 4 land use and 2 radiative forcing scenarios as simulated by four global climate models. Between 2001 and 2015, carbon storage in California's terrestrial ecosystems declined by -188.4 Tg C, with a mean annual flux ranging from a source of -89.8 Tg C/year to a sink of 60.1 Tg C/year. The large variability in the magnitude of the state's carbon source/sink was primarily attributable to interannual variability in weather and climate, which affected the rate of carbon uptake in vegetation and the rate of ecosystem respiration. Under nearly all future scenarios, carbon storage in terrestrial ecosystems was projected to decline, with an average loss of -9.4% (-432.3 Tg C) by the year 2100 from current stocks. However, uncertainty in the magnitude of carbon loss was high, with individual scenario projections ranging from -916.2 to 121.2 Tg C and was largely driven by differences in future climate conditions projected by climate models. Moving from a high to a low radiative forcing scenario reduced net ecosystem carbon loss by 21% and when combined with reductions in land-use change (i.e., moving from a high to a low land-use scenario), net carbon losses were reduced by 55% on average. However, reconciling large uncertainties associated with the effect of increasing atmospheric CO₂ is needed to better constrain models used to establish baseline conditions from which ecosystem-based climate mitigation strategies can be evaluated.

Near-future forest vulnerability to drought and fire varies across the western United States

Recent prolonged droughts and catastrophic wildfires in the western United States have raised concerns about the potential for forest mortality to impact forest structure, forest ecosystem services, and the economic vitality of communities in the coming decades. We used the Community Land Model (CLM) to determine forest vulnerability to mortality from drought and fire by the year 2049. We modified CLM to represent 13 major forest types in the western United States and ran simulations at a 4-km grid resolution, driven with climate projections from two general circulation models under one emissions scenario (RCP 8.5). We developed metrics of vulnerability to short-term extreme and prolonged drought based on annual allocation to stem growth and net primary productivity. We calculated fire vulnerability based on changes in simulated future area burned relative to historical area burned. Simulated historical drought vulnerability was medium to high in areas with observations of recent drought-related mortality. Comparisons of observed and simulated historical area burned indicate simulated future fire vulnerability could be underestimated by 3% in the Sierra Nevada and overestimated by 3% in the Rocky Mountains. Projections show that water-limited forests in the Rocky Mountains, Southwest, and Great Basin regions will be the most vulnerable to future drought-related mortality, and vulnerability to future fire will be highest in the Sierra Nevada and portions of the Rocky Mountains. High carbon-density forests in the Pacific coast and western Cascades regions are projected to be the least vulnerable to either drought or fire. Importantly, differences in climate projections lead to only 1% of the domain with conflicting low and high vulnerability to fire and no area with conflicting drought vulnerability. Our drought vulnerability metrics could be incorporated as probabilistic mortality rates in earth system models, enabling more robust estimates of the feedbacks between the land and atmosphere over the 21st century.