Leveraging High Resolution Classifications and Random Forests for Hindcasting Decades of Mesic Ecosystem Dynamics in the Landsat Time Series

Mesic ecosystems are fundamental to conservation efforts in semi-arid systems, but are threatened by climate change and development. Newer earth observation datasets, including Sentinel-1 and −2, provide opportunities to monitor mesic ecosystems at meaningful spatial scales, but are insufficient for measuring decadal-scale changes. Conversely, the Landsat time series has decades of data, but images are spatially coarse relative to many of the mesic ecosystem areas that sustain dryland systems, resulting in classifications with mixed pixels inadequate for effective monitoring. We developed a workflow that uses 10-m classifications produced from fusion of the Sentinel-1 and −2 time series (2017–2020) to estimate sub-pixel proportions of Landsat time series observations (2004–2020). Using random forest regression models, we quantified water resource proportions (WRP) of surface water, mesic vegetation, and upland land covers within each 30-m Landsat pixel. We incorporated ancillary covariates to account for varying topographic conditions, land cover, and climate. Results indicate that our approach consistently estimates sub-pixel proportions of Landsat pixels more accurately compared to spectral mixture analysis (SMA). The WRP product for surface water had up to 8% less error than SMA as measured by Mean Absolute Error (MAE) and up to 17% less error as measured by Root Mean Squared Error (RMSE). For mesic vegetation, the WRP product outperformed SMA by up to 4% (MAE) and 7% (RMSE). Finally, we demonstrated the ability of our time series to characterize historical water resource availability at a case study site with a well documented restoration history by qualitatively examining the mesic vegetation dynamics time series to identify system responses to restoration efforts. Our approach allows us to hindcast observations of Sentinel products and measure water resource dynamics with greater precision over larger temporal scales. We envision these WRP data to be useful for measuring the impacts of conservation interventions, disturbance recovery, or land use changes that pre-date the Sentinel time series

Forecasting Natural Regeneration of Sagebrush After Wildfires Using Population Models and Spatial Matching

Context Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions. Objectives An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where postwildfire regeneration of sagebrush cover (Artemisia spp.) is likely to occur within the burnt areas across the region (\u3e900,000 km2). Methods First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfres not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy. Results Regional-level models revealed that sagebrush post-fire recovery is slow, estimating \u3e 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfre and within-wildfre spatial heterogeneity improved out-ofsample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover. Conclusions We demonstrate that combining population models and non-parametric spatial matching provides a fexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration

Pulsation modes in rapidly rotating stellar models based on the Self-Consistent Field method

Context: New observational means such as the space missions CoRoT and Kepler and ground-based networks are and will be collecting stellar pulsation data with unprecedented accuracy. A significant fraction of the stars in which pulsations are observed are rotating rapidly. Aims: Our aim is to characterise pulsation modes in rapidly rotating stellar models so as to be able to interpret asteroseismic data from such stars. Methods: The pulsation code developed in Ligni\`eres et al. (2006) and Reese et al. (2006) is applied to stellar models based on the self-consistent field (SCF) method (Jackson et al. 2004, 2005, MacGregor et al. 2007). Results: Pulsation modes in SCF models follow a similar behaviour to those in uniformly rotating polytropic models, provided that the rotation profile is not too differential. Pulsation modes fall into different categories, the three main ones being island, chaotic, and whispering gallery modes, which are rotating counterparts to modes with low, medium, and high l-|m| values, respectively. The frequencies of the island modes follow an asymptotic pattern quite similar to what was found for polytropic models. Extending this asymptotic formula to higher azimuthal orders reveals more subtle behaviour as a function of m and provides a first estimate of the average advection of pulsation modes by rotation. Further calculations based on a variational principle confirm this estimate and provide rotation kernels that could be used in inversion methods. When the rotation profile becomes highly differential, it becomes more and more difficult to find island and whispering gallery modes at low azimuthal orders. At high azimuthal orders, whispering gallery modes, and in some cases island modes, reappear.Comment: 16 pages, 11 figures, accepted for publication in A&

Remotely-Sensing Chemical Diversity and Function of Native Plants Across Sagebrush-Steppe Landscapes

Plant chemical diversity provides ecosystem services by supporting wildlife diversity and offering sources for novel medicines. Current mapping of phytochemicals can be expensive, time-intensive and provides only a snapshot of available diversity. To overcome this, I will use handheld and airborne instruments collecting near infrared spectra and hyperspectral imagery to remotely sense chemical diversity within plants and ecosystems. I hypothesize that greater plant chemical diversity will be correlated with greater habitat use by wildlife and greater bioactivity of plant extracts. This research provides a powerful tool to map chemical diversity, target wildlife conservation and direct the discovery of novel medicines

Contrasting effects of defaunation on aboveground carbon storage across the global tropics

Defaunation is causing declines of large-seeded animal-dispersed trees in tropical forests worldwide, but whether and how these declines will affect carbon storage across this biome is unclear. Here we show, using a pan-tropical dataset, that simulated declines of large-seeded animal-dispersed trees have contrasting effects on aboveground carbon stocks across Earth’s tropical forests. In our simulations, African, Neotropical and South Asian forests which have high proportions of animal-dispersed species consistently show carbon losses (2-12%), but Southeast Asian and Australian forests where there are more abiotically-dispersed species show little to no carbon losses or marginal gains (±1%). These patterns result primarily from changes in wood volume, and are underlain by consistent relationships in our empirical data (~2100 species), wherein, large-seeded animal-dispersed species are larger as adults than small-seeded animal-dispersed species, but are smaller than abiotically-dispersed species. Thus, floristic differences and distinct dispersal mode-seed size-adult size combinations can drive contrasting regional responses to defaunation

Review: Using Physiologically Based Models to Predict Population Responses to Phytochemicals by Wild Vertebrate Herbivores

To understand how foraging decisions impact individual fitness of herbivores, nutritional ecologists must consider the complex in vivo dynamics of nutrient–nutrient interactions and nutrient–toxin interactions associated with foraging. Mathematical modeling has long been used to make foraging predictions (e.g. optimal foraging theory) but has largely been restricted to a single currency (e.g. energy) or using simple indices of nutrition (e.g. fecal nitrogen) without full consideration of physiologically based interactions among numerous co-ingested phytochemicals. Here, we describe a physiologically based model (PBM) that provides a mechanistic link between foraging decisions and demographic consequences. Including physiological mechanisms of absorption, digestion and metabolism of phytochemicals in PBMs allows us to estimate concentrations of ingested and interacting phytochemicals in the body. Estimated phytochemical concentrations more accurately link intake of phytochemicals to changes in individual fitness than measures of intake alone. Further, we illustrate how estimated physiological parameters can be integrated with the geometric framework of nutrition and into integral projection models and agent-based models to predict fitness and population responses of vertebrate herbivores to ingested phytochemicals. The PBMs will improve our ability to understand the foraging decisions of vertebrate herbivores and consequences of those decisions and may help identify key physiological mechanisms that underlie diet-based ecological adaptations

Loss of animal seed dispersal increases extinction risk in a tropical tree species due to pervasive negative density dependence across life stages

Overhunting in tropical forests reduces populations of vertebrate seed dispersers. If reduced seed dispersal has a negative impact on tree population viability, overhunting could lead to altered forest structure and dynamics, including decreased biodiversity. However, empirical data showing decreased animal-dispersed tree abundance in overhunted forests contradict demographic models which predict minimal sensitivity of tree population growth rate to early life stages. One resolution to this discrepancy is that seed dispersal determines spatial aggregation, which could have demographic consequences for all life stages. We tested the impact of dispersal loss on population viability of a tropical tree species, Miliusa horsfieldii, currently dispersed by an intact community of large mammals in a Thai forest. We evaluated the effect of spatial aggregation for all tree life stages, from seeds to adult trees, and constructed simulation models to compare population viability with and without animal-mediated seed dispersal. In simulated populations, disperser loss increased spatial aggregation by fourfold, leading to increased negative density dependence across the life cycle and a 10-fold increase in the probability of extinction. Given that the majority of tree species in tropical forests are animal-dispersed, overhunting will potentially result in forests that are fundamentally different from those existing now