Topological descriptors for coral reef resilience using a stochastic spatial model

A complex interplay between species governs the evolution of spatial patterns in ecology. An open problem in the biological sciences is characterizing spatio-temporal data and understanding how changes at the local scale affect global dynamics/behavior. We present a toolkit of multiscale methods and use them to analyze coral reef resilience and dynamics.Here, we extend a well-studied temporal mathematical model of coral reef dynamics to include stochastic and spatial interactions and then generate data to study different ecological scenarios. We present descriptors to characterize patterns in heterogeneous spatio-temporal data surpassing spatially averaged measures. We apply these descriptors to simulated coral data and demonstrate the utility of two topological data analysis techniques--persistent homology and zigzag persistence--for characterizing the spatiotemporal evolution of reefs and generating insight into mechanisms of reef resilience. We show that the introduction of local competition between species leads to the appearance of coral clusters in the reef. Furthermore, we use our analyses to distinguish the temporal dynamics that stem from different initial configurations of coral, showing that the neighborhood composition of coral sites determines their long-term survival. Finally, we use zigzag persistence to quantify spatial behavior in the metastable regime as the level of fish grazing on algae varies and determine which spatial configurations protect coral from extinction in different environments

Hydrogeomorphology of the Hyporheic Zone: Stream Solute and Fine Particle Interactions With a Dynamic Streambed

Hyporheic flow in streams has typically been studied separately from geomorphic processes. We investigated interactions between bed mobility and dynamic hyporheic storage of solutes and fine particles in a sand-bed stream before, during, and after a flood. A conservatively transported solute tracer (bromide) and a fine particles tracer (5 μm latex particles), a surrogate for fine particulate organic matter, were co-injected during base flow. The tracers were differentially stored, with fine particles penetrating more shallowly in hyporheic flow and retained more efficiently due to the high rate of particle filtration in bed sediment compared to solute. Tracer injections lasted 3.5 h after which we released a small flood from an upstream dam one hour later. Due to shallower storage in the bed, fine particles were rapidly entrained during the rising limb of the flood hydrograph. Rather than being flushed by the flood, we observed that solutes were stored longer due to expansion of hyporheic flow paths beneath the temporarily enlarged bedforms. Three important timescales determined the fate of solutes and fine particles: (1) flood duration, (2) relaxation time of flood-enlarged bedforms back to base flow dimensions, and (3) resulting adjustments and lag times of hyporheic flow. Recurrent transitions between these timescales explain why we observed a peak accumulation of natural particulate organic matter between 2 and 4 cm deep in the bed, i.e., below the scour layer of mobile bedforms but above the maximum depth of particle filtration in hyporheic flow paths. Thus, physical interactions between bed mobility and hyporheic transport influence how organic matter is stored in the bed and how long it is retained, which affects decomposition rate and metabolism of this southeastern Coastal Plain stream. In summary we found that dynamic interactions between hyporheic flow, bed mobility, and flow variation had strong but differential influences on base flow retention and flood mobilization of solutes and fine particulates. These hydrogeomorphic relationships have implications for microbial respiration of organic matter, carbon and nutrient cycling, and fate of contaminants in streams

Iterative Near-Term Ecological Forecasting: Needs, Opportunities, And Challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward

From savanna to suburb: Effects of 160 years of landscape change on carbon storage in Silicon Valley, California

Landscape changes such as urbanization can dramatically affect the provision of ecosystem services such as carbon storage. However, while cities have been shown to store substantial amounts of carbon in soils and vegetation, we have little information from long-term studies about how contemporary carbon storage in urban areas compares to carbon storage in the natural ecosystems that characterized these landscapes prior to urbanization. We used historical archival sources and land-cover data to quantify and map historical tree carbon storage in the now-urban Santa Clara Valley, California, USA prior to substantial Euro-American modification (ca. 1850) and to analyze change in the amount and distribution of carbon storage over the past ca. 160 years. We estimate that total tree carbon storage in the study area was ~784,000 to 2.2 million Mg (13.6–38.1 Mg C/ha) when the region was characterized by oak savanna and woodland habitats, compared to ~895,000 Mg C (15.5 Mg C/ha) today. This represents a non-significant gain of 14% to a significant loss of 60% depending on scenario. We also demonstrate changes in the spatial distribution of carbon on the landscape, as losses in carbon storage in areas of former oak woodland were partially offset by gains in carbon storage in historical habitat types that historically had few or no trees. This challenges the hypothesis that aboveground carbon storage increases with urbanization in Mediterranean-climate ecosystems due to irrigation and tree planting. Our study demonstrates the utility of using pre-1900s historical sources to reconstruct changes in ecosystem services such as carbon storage over century time scales

Controls on the size distributions of shallow landslides

Rainfall-triggered shallow landslides are destructive hazards and play an important role in landscape processes. A theory explaining the size distributions of such features remains elusive. Prior work connects size distributions to topography, but field-mapped inventories reveal pronounced similarities in the form, mode, and spread of distributions from diverse landscapes. We analyze nearly identical distributions occurring in the Oregon Coast Range and the English Lake District, two regions of strikingly different topography, lithology, and vegetation. Similarity in minimum sizes at these sites is partly explained by theory that accounts for the interplay of mechanical soil strength controls resisting failure. Maximum sizes, however, are not explained by current theory. We develop a generalized framework to account for the entire size distribution by unifying a mechanistic slope stability model with a flexible spatial-statistical description for the variability of hillslope strength. Using hillslope-scale numerical experiments, we find that landslides can occur not only in individual low strength areas but also across multiple smaller patches that coalesce. We show that reproducing observed size distributions requires spatial strength variations to be strongly localized, of large amplitude, and a consequence of multiple interacting factors. Such constraints can act together with the mechanical determinants of landslide initiation to produce size distributions of broadly similar character in widely different landscapes, as found in our examples. We propose that size distributions reflect the systematic scale dependence of the spatially averaged strength. Our results highlight the critical need to constrain the form, amplitude, and wavelength of spatial variability in material strength properties of hillslopes

Appendix F. Sensitivity analysis of model parameters.

Sensitivity analysis of model parameters

Appendix E. Depth/redox potential cases evaluated in model and corresponding regression coefficients for depth effect, f(h–zdat).

Depth/redox potential cases evaluated in model and corresponding regression coefficients for depth effect, f(h–zdat)

Appendix B. PeatAccrete model parameter values and sources.

PeatAccrete model parameter values and sources