Sagebrush Ecosystem Characterization, Monitoring, and Forecasting with Remote Sensing: Quantifying Future Climate and Wildlife Habitat Change

Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances continue to alter this ecosystem, with climate change possibly representing the greatest future disturbance risk. Improved ways to characterize and monitor gradual change in this ecosystem are vital to its future management. A new remote sensing sagebrush characterization approach was developed in Wyoming which integrates three scales of remote sensing to derive four primary continuous field components (bare ground, herbaceousness, litter, and shrub), and four secondary components (sagebrush, big sagebrush, Wyoming sagebrush, and shrub height) using a regression tree. An independent accuracy assessment of results revealed the primary component root mean square error values ranged from 4.90% to 10.16% for 2.4-m QuickBird, 6.01% to 15.54% for 30-m Landsat, and 6.97% to 16.14% for 56-m AWiFS. The change over time of five of these continuous field components (bare ground, herbaceous, litter, sagebrush, and shrub) was measured on the ground and by satellite across six seasons and four years to validate component change capability. Correlation of ground measurements to remote sensing predictions indicated that annual component predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. Correlation of component predictions to DAYMET precipitation revealed QuickBird components had better response to precipitation patterns than Landsat components. Further in-depth analysis of precipitation and component change patterns was completed from 1984 to 2011 for the same five components. A statistically significant correlation model between vegetation components and precipitation was established, and used to forecast vegetation components response in 2050 using IPCC precipitation scenarios. Bare ground increased under future scenarios, with the remaining components all decreasing. When 2050 future component results were applied to sage-grouse habitat models, a loss of about 12% of nesting habitat and 4% of summer habitat were predicted to occur. Results demonstrate the successful ability of sagebrush components to characterize the sagebrush ecosystem, monitor precipitation driven gradual change, support linear models to forecast future component response, and quantify future habitat impacts on sage-grouse

Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance

A new tasselled cap transformation based on Landsat 7 at-satellite reflectance was developed. This transformation is most appropriate for regional applications where atmospheric correction is not feasible. The brightness, greenness and wetness of the derived transformation collectively explained over 97% of the spectral variance of the individual scenes used in this study

Gap analysis: a geographic approach for assessing national biological diversity

The global concern with reduction in biodiversity has generated responses in the United States, such as the Endangered Species Act (ESA). Although the ESA has had some effect, the species-by-species approach presents a problem because it does not consider the broad ecological principles of biodiversity including the need for balance between different species and their combined influence on a given habitat. There is an implicit assumption that national parks, wildlife sanctuaries, and other protected areas provide for conservation needs. However, these areas have not necessarily been delineated on the basis of animal habitat zones or ecologically significant units. Gap Analysis is an evaluation method providing a systematic approach for assessing the protection afforded biodiversity in a given area. It uses geographic information systems to identify gaps in biodiversity protection that may be filled by the establishment of new preserves or changes in land-use practices. Gap Analysis has three primary layers: (1) distribution of vegetation types delineated from satellite imagery, (2) land ownership, and (3) distribution of vegetation types delineated from satellite imagery, habitat preference models. Vegetation classification procedures using satellite image or aerial photograph analysis are linked to wildlife/ habitat databases. Gap analysis includes seral as well as climax vegetation, and classes must be compatible with those used in neighboring states. The examples of these procedures for the Utah Gap Analysis are given with some reference to Gap Analysis in other states. The overall approach provides a logical base for evaluating and protecting national biological diversity

Forecasting climate change impacts on plant populations over large spatial extents

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot

Microstructure Effects for Casimir Forces in Chiral Metamaterials

We examine a recent prediction for the chirality-dependence of the Casimir force in chiral metamaterials by numerical computation of the forces between the exact microstructures, rather than homogeneous approximations. We compute the exact force for a chiral bent-cross pattern, as well as forces for an idealized "omega"-particle medium in the dilute approximation and identify the effects of structural inhomogeneity (i.e. proximity forces and anisotropy). We find that these microstructure effects dominate the force for separations where chirality was predicted to have a strong influence. To get observations of chirality free from microstructure effects, one must go to large separations where the effect of chirality is at most $\sim10^{-4}$ of the total force.Comment: 5 pages, 4 figure

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival