On-road emissions of ammonia: An underappreciated source of atmospheric nitrogen deposition.

We provide updated spatial distribution and inventory data for on-road NH3 emissions for the continental United States (U.S.) On-road NH3 emissions were determined from on-road CO2 emissions data and empirical NH3:CO2 vehicle emissions ratios. Emissions of NH3 from on-road sources in urbanized regions are typically 0.1-1.3tkm-2yr-1 while NH3 emissions in agricultural regions generally range from 0.4-5.5tkm-2yr-1, with a few hotspots as high as 5.5-11.2tkm-2yr-1. Counties with higher vehicle NH3 emissions than from agriculture include 40% of the U.S. POPULATION: The amount of wet inorganic N deposition as NH4+ from the National Atmospheric Deposition Program (NADP) network ranged from 37 to 83% with a mean of 58.7%. Only 4% of the NADP sites across the U.S. had <45% of the N deposition as NH4+ based on data from 2014 to 2016, illustrating the near-universal elevated proportions of NH4+ in deposition across the U.S. Case studies of on-road NH3 emissions in relation to N deposition include four urban sites in Oregon and Washington where the average NH4-N:NO3-N ratio in bulk deposition was 2.3. At urban sites in the greater Los Angeles Basin, bulk deposition of NH4-N and NO3-N were equivalent, while NH4-N:NO3-N in throughfall under shrubs ranged from 0.6 to 1.7. The NH4-N:NO3-N ratio at 7-10 sites in the Lake Tahoe Basin averaged 1.4 and 1.6 in bulk deposition and throughfall, and deposition of NH4-N was strongly correlated with summertime NH3 concentrations. On-road emissions of NH3 should not be ignored as an important source of atmospheric NH3, as a major contributor to particulate air pollution, and as a driver of N deposition in urban and urban-affected regions.

A Geographic Mosaic of Climate Change Impacts on Terrestrial Vegetation: Which Areas Are Most at Risk?

Changes in climate projected for the 21st century are expected to trigger widespread and pervasive biotic impacts. Forecasting these changes and their implications for ecosystem services is a major research goal. Much of the research on biotic responses to climate change has focused on either projected shifts in individual species distributions or broad-scale changes in biome distributions. Here, we introduce a novel application of multinomial logistic regression as a powerful approach to model vegetation distributions and potential responses to 21st century climate change. We modeled the distribution of 22 major vegetation types, most defined by a single dominant woody species, across the San Francisco Bay Area. Predictor variables included climate and topographic variables. The novel aspect of our model is the output: a vector of relative probabilities for each vegetation type in each location within the study domain. The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles. We found that sensitivity of vegetation to climate change is highly heterogeneous across the region. Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation. While such sites may provide refugia for mesic and cool-adapted vegetation in the face of a warming climate, the model suggests they will still be highly dynamic and relatively sensitive to climate-driven vegetation transitions. The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia. Projections provide a foundation for conservation planning and land management, and highlight the need for a greater understanding of the mechanisms and time scales of potential climate-driven vegetation transitions.

The theory behind, and the challenges of, conserving nature's stage in a time of rapid change.

Most conservation planning to date has focused on protecting today's biodiversity with the assumption that it will be tomorrow's biodiversity. However, modern climate change has already resulted in distributional shifts of some species and is projected to result in many more shifts in the coming decades. As species redistribute and biotic communities reorganize, conservation plans based on current patterns of biodiversity may fail to adequately protect species in the future. One approach for addressing this issue is to focus on conserving a range of abiotic conditions in the conservation-planning process. By doing so, it may be possible to conserve an abiotically diverse "stage" upon which evolution will play out and support many actors (biodiversity). We reviewed the fundamental underpinnings of the concept of conserving the abiotic stage, starting with the early observations of von Humboldt, who mapped the concordance of abiotic conditions and vegetation, and progressing to the concept of the ecological niche. We discuss challenges posed by issues of spatial and temporal scale, the role of biotic drivers of species distributions, and latitudinal and topographic variation in relationships between climate and landform. For example, abiotic conditions are not static, but change through time-albeit at different and often relatively slow rates. In some places, biotic interactions play a substantial role in structuring patterns of biodiversity, meaning that patterns of biodiversity may be less tightly linked to the abiotic stage. Furthermore, abiotic drivers of biodiversity can change with latitude and topographic position, meaning that the abiotic stage may need to be defined differently in different places. We conclude that protecting a diversity of abiotic conditions will likely best conserve biodiversity into the future in places where abiotic drivers of species distributions are strong relative to biotic drivers, where the diversity of abiotic settings will be conserved through time, and where connectivity allows for movement among areas providing different abiotic conditions.

Estimating female reproductive success of a threatened butterfly: influence of emergence time and hostplant phenology.

We estimated lifetime reproductive success of Euphydryas editha bayensis (Nymphalidae), a federally listed threatened butterfly, based on age-specific fecundity and both adult and offspring survival. Our results indicate that the relative timing of adult emergence and larval hosplant senescence strongly influenced reproductive success of females. For 1992, we estimated that only 8-21% of the eggs laid by females emerging on the 1st day of the 4-week flight season would produce larvae that reach diapause. This figure dropped to 1-5% for females emerging 7 days into the flight season. Within our entire sample, we estimated that 64-88% of the females produced offspring with less than a 2% probability of reaching diapause. These estimates are particularly striking given that they are based on only one source of larval mortality - prediapause starvation due to hostplant senescence. This dependence of reproductive success on the relative timing of female emergence and hostplant senescence may reduce effective population size and render E. editha bayensis especially vulnerable to local extinction events.

Adult emergence phenology in checkerspot butterflies: the effects of macroclimate, topoclimate, and population history.

The prediction of adult emergence times in insect populations can be greatly complicated by microclimatic gradients, especially in circumstances where distributions of juveniles along those gradients vary from year to year. To investigate adult emergence patterns in topographically heterogeneous habitats, we built a model of postdiapause development of the Bay checkerspot butterfly, Euphydryas editha bayensis. The model uses slope-specific insolation as the rate-controlling variable, and accounts for both solar exposure of the habitat and cloud cover. Instar-specific larval mass gains per unit of insolation were determined from mark-recapture experiments. A small correction for daily low temperatures was used to calibrate the model to five years of field data on larval mass. The model predicted mean mass of 90% of larval samples within 4 clear days over a 70-120 day growing season. The magnitude of spatial variation in emergence times across habitat slopes is greater than annual variation in emergence times due to yearly weather conditions. Historical variation (yearly shifts in larval distributions across slopes) is an important determinant of mean population emergence dates. All of these factors need to be considered in understanding adult emergence phenology in this butterfly and in other insects inhabiting heterogeneous thermal environments. Such an understanding can be useful in managing insect populations for both pest control and conservation.