Gregory P. Asner

The velocity of climate change

The ranges of plants and animals are moving in response to recent changes in climate. As temperatures rise, ecosystems with ‘nowhere to go’, such as mountains, are considered to be more threatened. However, species survival may depend as much on keeping pace with moving climates as the climate’s ultimate persistence. Here we present a new index of the velocity of temperature change (km yr-1), derived from spatial gradients (°C km-1) and multimodel ensemble forecasts of rates of temperature increase (°C yr-1) in the twenty-first century. This index represents the instantaneous local velocity along Earth’s surface needed to maintain constant temperatures, and has a global mean of 0.42 km yr-1 (A1B emission scenario). Owing to topographic effects, the velocity of temperature change is lowest in mountainous biomes such as tropical and subtropical coniferous forests (0.08 km yr-1), temperate coniferous forest, and montane grasslands. Velocities are highest in flooded grasslands (1.26 km yr-1), mangroves and deserts. High velocities suggest that the climates of only 8% of global protected areas have residence times exceeding 100 years. Small protected areas exacerbate the problem in Mediterranean-type and temperate coniferous forest biomes. Large protected areas may mitigate the problem in desert biomes. These results indicate management strategies for minimizing biodiversity loss from climate change. Montane landscapes may effectively shelter many species into the next century. Elsewhere, reduced emissions, a much expanded network of protected areas, or efforts to increase species movement may be necessary.

Biophysical and Biochemical Sources of Variability in Canopy Reflectance

Abstract Analyses of various biophysical and biochemical factors affecting plant canopy reflectance have been carried out over the past few decades, yet the relative importance of these factors has not been adequately addressed. A combination of field and modeling techniques were used to quantify the relative contribution of leaf, stem, and litter optical properties (incorporating known variation in foliar biochemical properties) and canopy structural attributes to nadir-viewed vegetation reflectance data. Variability in tissue optical properties was wavelength-dependent. For green foliage, the lowest variation was in the visible (VIS) spectral region and the highest in the near-infrared (NIR). For standing litter material, minimum variation occurred in the VIS/NIR, while the largest differences were observed in the shortwave-IR (SWIR). Woody stem material showed opposite trends, with lowest variation in the SWIR and highest in the NIR. Leaf area index (LAI) and leaf angle distribution (LAD) were the dominant controls on canopy reflectance data with the exception of soil reflectance and vegetation cover in sparse canopies. Leaf optical properties (and thus foliar chemistry) were expressed most directly at the canopy level in the NIR, but LAI and LAD strongly controlled the relationship between leaf and canopy spectral characteristics. Stem material played a small but significant role in determining canopy reflectance in woody plant canopies, especially those with LAI

Land-use choices: balancing human needs and ecosystem function

Conversion of land to grow crops, raise animals, obtain timber, and build cities is one of the foundations of human civilization. While land use provides these essential ecosystem goods, it alters a range of other ecosystem functions, such as the provisioning of freshwater, regulation of climate and biogeochemical cycles, and maintenance of soil fertility. It also alters habitat for biological diversity. Balancing the inherent trade-offs between satisfying immediate human needs and maintaining other ecosystem functions requires quantitative knowledge about ecosystem responses to land use. These responses vary according to the type of land-use change and the ecological setting, and have local, short-term as well as global, longterm effects. Land-use decisions ultimately weigh the need to satisfy human demands and the unintended ecosystem responses based on societal values, but ecological knowledge can provide a basis for assessing the trade-offs.

Dissolved Organic Carbon in Terrestrial Ecosystems: Synthesis and a Model

The movement of dissolved organic carbon (DOC) through soils is an important process for the transport of carbon within ecosystems and the formation of soil organic matter. In some cases, DOC fluxes may also contribute to the carbon balance of terrestrial ecosystems; in most ecosystems, they are an important source of energy, carbon, and nutrient transfers from terrestrial to aquatic ecosystems. Despite their importance for terrestrial and aquatic biogeochemistry, these fluxes are rarely represented in conceptual or numerical models of terrestrial biogeochemistry. In part, this is due to the lack of a comprehensive understanding of the suite of processes that control DOC dynamics in soils. In this article, we synthesize information on the geochemical and biological factors that control DOC fluxes through soils. We focus on conceptual issues and quantitative evaluations of key process rates to present a general numerical model of DOC dynamics. We then test the sensitivity of the model to variation in the controlling parameters to highlight both the significance of DOC fluxes to terrestrial carbon processes and the key uncertainties that require additional experiments and data. Simulation model results indicate the importance of representing both root carbon inputs and soluble carbon fluxes to predict the quantity and distribution of soil carbon in soil layers. For a test case in a temperate forest, DOC contributed 25% of the total soil profile carbon, whereas roots provided the remainder. The analysis also shows that physical factors—most notably, sorption dynamics and hydrology—play the dominant role in regulating DOC losses from terrestrial ecosystems but that interactions between hydrology and microbial–DOC relationships are important in regulating the fluxes of DOC in the litter and surface soil horizons. The model also indicates that DOC fluxes to deeper soil layers can support a large fraction (up to 30%) of microbial activity below 40 cm.

High-resolution forest carbon stocks and emissions in the Amazon

Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.

Using Imaging Spectroscopy to Study Ecosystem Processes and Properties

Abstract Remote sensing data provide essential input for todays climate and ecosystem models. It is generally agreed that many model processes are not accurately depicted by current remotely sensed indices of vegetation and that new observational capabilities are needed at different spatial and spectral scales to reduce uncertainty. Recent advances in materials and optics have allowed the development of smaller, more stable, accurately calibrated imaging spectrometers that can quantify biophysical properties on the basis of the spectral absorbing and scattering characteristics of the land surface. Airborne and spaceborne imaging spectrometers, which measure large numbers (hundreds) of narrow spectral bands, are becoming more widely available from government and commercial sources; thus, it is increasingly feasible to use data from imaging spectroscopy for environmental research. In contrast to multispectral sensors, imaging spectroscopy produces quantitative estimates of biophysical absorptions, which can be used to improve scientific understanding of ecosystem functioning and properties. We present the recent advances in imaging spectroscopy and new capabilities for using it to quantify a range of ecological variables.

Climate and management contributions to recent trends in U.S. agricultural yields.

“Climate and Management Contributions to Recent Trends in U.S. Agricultural Yields” Gu (1) claims that by analyzing a subset of counties, we guaranteed that temperature trends would significantly impact yields. This was not the case. If temperature trends were small, or if nonclimatic contributions to yields were large, then the effect of temperature trends on yields would have been insignificant even if yields were strongly correlated with temperature from year to year. The crucial point is that it is the relative importance of climate and other factors that determine climate’s overall influence on yield trends. In the simulation presented by Gu, he claims that the numbers are not important because they are only for illustrative purposes. In our opinion, the numbers are crucial. For example, we have rerun the simulation presented by Gu, but with Rt having a range of ( 1,1). In this case, after selecting counties with a negative correlation with yield, the squared correlation between yield and temperature trends was only 8%. Varying other parameters had similar effects. Clearly, the actual numbers are important in determining the strength of the relationship between climate and yield trends. Further evidence of this is the fact that precipitation trends were not correlated with yield trends in (2), even though most counties showed positive correlation with yields. Even if we select only counties with a positive correlation with precipitation (rather than temperature), precipitation trends explain less than 5% of yield trends. This does not say that precipitation is not important for yields (clearly, it is), but rather that the observed trends in precipitation did not measurably impact yields. Gu also expresses concern that by focusing on counties with negative correlations, we cannot extrapolate our findings to the entire nation. We agree with this to the extent that these counties fail to represent the entire United States. However, as we noted in (2), a majority of counties in the United States exhibited a negative correlation with temperature. The reason we selected a subset of counties was not to bias our climate effect, but rather to ensure an unbiased estimate of nonclimate contributions. As mentioned in (2), there are distinct regions of corn growth in the United States; combining all regions in a single regression will result in a biased estimate of nonclimate contributions unless the distribution of climate trends is the same in each region (as assumed in Gu’s simulation). The bias introduced by including all regions is clearly seen in the simulation if the probability of St equal to 1 varies for Sy positive and negative. As described in (2), the temperature effect derived from the regression is applicable only to those counties within the subset (that is, Midwest and Southeast). To test potential bias toward management in these regions when computing the nonclimatic gains for the entire United States, we have performed an independent test of temperature contribution to national yield trends based on average national yield and area weighted temperature time series, following (3). First differences of these time series were used to compute the slope of yield response to temperature change; this slope was then multiplied by the temperature trend to estimate the national yield change due to temperature. This analysis estimated a 10% and 20% reduction in corn and soybean yield trends, respectively, when removing the effect of temperature, which is close to the values of 18% and 20% derived in (2). The lower value for corn when using the entire nation likely reflects the suppression of yield gains in Northern states by negative temperature trends. However, the agreement between these two approaches is significant, given the different assumptions necessary in each approach (4–6). Finally, Gu states that we assumed that nonclimate contributions were constant temporally, which is not the case. The parameter m reflects changes in management that were spatially uniform, but the temporal nature of this trend did not impact our analysis. We recognized that spatial covariance of management and temperature trends would impact the apparent effect of temperature; indeed, it is well known that farmers often adjust management in response to climate (7 ). This can be considered one mechanism by which climate trends affect yield trends. Various other mechanisms potentially play an important role in the temperature–yield relationship, including changes in crop development rates, increased water stress, and increased pest damage (8). Although we did not attempt to discuss causal mechanisms in our brief paper, we clearly did not suggest that direct physiological effects were the only link between temperature and yield trends.

Changing drivers of deforestation and new opportunities for conservation.

Over the past 50 years, human agents of deforestation have changed in ways that have potentially important implications for conservation efforts. We characterized these changes through a meta-analysis of case studies of land-cover change in the tropics. From the 1960s to the 1980s, small-scale farmers, with state assistance, deforested large areas of tropical forest in Southeast Asia and Latin America. As globalization and urbanization increased during the 1980s, the agents of deforestation changed in two important parts of the tropical biome, the lowland rainforests in Brazil and Indonesia. Well-capitalized ranchers, farmers, and loggers producing for consumers in distant markets became more prominent in these places and this globalization weakened the historically strong relationship between local population growth and forest cover. At the same time, forests have begun to regrow in some tropical uplands. These changing circumstances, we believe, suggest two new and differing strategies for biodiversity conservation in the tropics, one focused on conserving uplands and the other on promoting environmental stewardship in lowlands and other areas conducive to industrial agriculture.

Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin

The Amazon Basin is one of the worlds most important bioregions, harboring a rich array of plant and animal species and offering a wealth of goods and services to society. For years, ecological science has shown how large-scale forest clearings cause declines in biodiversity and the availability of forest products. Yet some important changes in the rainforests, and in the ecosystem services they provide, have been underappreciated until recently. Emerging research indicates that land use in the Amazon goes far beyond clearing large areas of forest; selective logging and other canopy damage is much more pervasive than once believed. Deforestation causes collateral damage to the surrounding forests – through enhanced drying of the forest floor, increased frequency of fires, and lowered productivity. The loss of healthy forests can degrade key ecosystem services, such as carbon storage in biomass and soils, the regulation of water balance and river flow, the modulation of regional climate patterns, and the ...

A biogeophysical approach for automated SWIR unmixing of soils and vegetation.

Abstract Arid and semiarid ecosystems endure strong spatial and temporal variation of climate and land use that results in uniquely dynamic vegetation phenology, cover, and leaf area characteristics. Previous remote sensing efforts have not fully captured the spatial heterogeneity of vegetation properties required for functional analyses of these ecosystems, or have done so only with manually intensive algorithms of spectral mixture analysis that have limited operational use. These limitations motivated the development of an automated spectral unmixing approach based on a comprehensive analysis of vegetation and soil spectral variability resulting from biogeophysical variation in arid and semiarid regions. A field spectroscopic database of bare soils, green canopies, and litter canopies was compiled for 17 arid and semiarid sites in North and South America, representing a wide array of plant growth forms and species, vegetation conditions, and soil mineralogical-hydrological properties. Spectral reflectance of dominant cover types (green vegetation, litter, and bare soil) varied widely within and between sites, but the reflectance derivatives in the shortwave-infrared (SWIR2: 2,100–2,400 nm) were similar within and separable between each cover type. Using this result, an automated SWIR2 spectral unmixing algorithm was developed that includes a Monte Carlo approach for estimating errors in derived subpixel cover fractions resulting from endmember variability. The algorithm was applied to SWIR2 spectral data collected by the Airborne Visible and Infrared Imaging Spectrometer instrument over the Sevilleta and Jornada Long-Term Ecological Research sites. Subsequent comparisons to field data and geographical information system (GIS) maps were deemed successful. The SWIR2 region of the reflected solar spectrum provides a robust means to estimate the extent of bare soil and vegetation covers in arid and semiarid regions. The computationally efficient method developed here could be extended globally using SWIR2 spectrometer data to be collected from platforms such as the NASA Earth Observing-1 satellite.