Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series.

Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state-space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat-derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra-annual phenological variation to a model based on Landsat-derived normalized difference vegetation index (NDVI). The best-performing model accounted for inter- and intra-annual noise in spectral reflectance and translated NDVI to canopy height via Landsat-lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state-space models to quantify ecological dynamics from time series of space-borne imagery. State-space models also provide a statistical approach well-suited to fusing high-resolution data, such as airborne lidar, with lower-resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

Grazing enhances belowground carbon allocation, microbial biomass, and soil carbon in a subtropical grassland.

Despite the large contribution of rangeland and pasture to global soil organic carbon (SOC) stocks, there is considerable uncertainty about the impact of large herbivore grazing on SOC, especially for understudied subtropical grazing lands. It is well known that root system inputs are the source of most grassland SOC, but the impact of grazing on partitioning of carbon allocation to root tissue production compared to fine root exudation is unclear. Given that different forms of root C have differing implications for SOC synthesis and decomposition, this represents a significant gap in knowledge. Root exudates should contribute to SOC primarily after microbial assimilation, and thus promote microbial contributions to SOC based on stabilization of microbial necromass, whereas root litter deposition contributes directly as plant-derived SOC following microbial decomposition. Here, we used in situ isotope pulse-chase methodology paired with plant and soil sampling to link plant carbon allocation patterns with SOC pools in replicated long-term grazing exclosures in subtropical pasture in Florida, USA. We quantified allocation of carbon to root tissue and measured root exudation across grazed and ungrazed plots and quantified lignin phenols to assess the relative contribution of microbial vs. plant products to total SOC. We found that grazing exclusion was associated with dramatically less overall belowground allocation, with lower root biomass, fine root exudates, and microbial biomass. Concurrently, grazed pasture contained greater total SOC, and a larger fraction of SOC that originated from plant tissue deposition, suggesting that higher root litter deposition under grazing promotes greater SOC. We conclude that grazing effects on SOC depend on root system biomass, a pattern that may generalize to other C4-dominated grasslands, especially in the subtropics. Improved understanding of ecological factors underlying root system biomass may be the key to forecasting SOC and optimizing grazing management to enhance SOC accumulation.

Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland.

Soil carbon sequestration in agroecosystems could play a key role in climate change mitigation but will require accurate predictions of soil organic carbon (SOC) stocks over spatial scales relevant to land management. Spatial variation in underlying drivers of SOC, such as plant productivity and soil mineralogy, complicates these predictions. Recent advances in the availability of remotely sensed data make it practical to generate multidecadal time series of vegetation indices with high spatial resolution and coverage. However, the utility of such data largely is unknown, only having been tested with shorter (e.g., 1-2 yr) data summaries. Across a 2,000 ha subtropical grassland, we found that a long time series (28 yr) of a vegetation index (Enhanced Vegetation Index; EVI) derived from the Landsat 5 satellite significantly enhanced prediction of spatially varying SOC pools, while a short summary (2 yr) was an ineffective predictor. EVI was the best predictor for surface SOC (0-5 cm depth) and total measured SOC stocks (0-15 cm). The optimum models for SOC in the upper soil layer combined EVI records with elevation and calcium concentration, while deeper SOC was more strongly associated with calcium availability. We demonstrate how data from the open access Landsat archive can predict SOC stocks, a key ecosystem metric, and illustrate the rich variety of analytical approaches that can be applied to long time series of remotely sensed greenness. Overall, our results showed that SOC pools were closely coupled to EVI in this ecosystem, demonstrating that maintenance of higher average green leaf area is correlated with higher SOC. The strong associations of vegetation greenness and calcium concentration with SOC suggest that the ability to sequester additional SOC likely will rely on strategic management of pasture vegetation and soil fertility.

Combining mesocosm and field experiments to predict invasive plant performance: a hierarchical Bayesian approach.

Invasive plant fecundity underlies propagule pressure and ultimately range expansion. Predicting fecundity across large spatial scales, from regions to landscapes, is critical for understanding invasion dynamics and optimizing management. However, to accurately predict fecundity and other demographic processes, improved models that scale individual plant responses to abiotic drivers across heterogeneous environments are needed. Here we combine two experimental data sets to predict fecundity of a widespread and problematic invasive grass over large spatial scales. First, we analyzed seed production as a function of plant biomass in a small-scale mesocosm experiment with manipulated light levels. Then, in a field introduction experiment, we tracked plant performance across 21 common garden sites that differed widely in available light and other factors. We jointly analyzed these data using a Bayesian hierarchical model (BHM) framework to predict fecundity as a function of light in the field. Our analysis reveals that the invasive species is likely to produce sufficient seed to overwhelm establishment resistance, even in deeply shaded environments, and is likely seed-limited across much of its range. Finally, we extend this framework to address the general problem of how to scale up plant demographic processes and analyze the factors that control plant distribution and abundance at large scales.