Geo-evolutionary feedbacks: integrating rapid evolution and landscape change.

We develop a conceptual framework for geo-evolutionary feedbacks which describes the mutual interplay between landscape change and the evolution of traits of organisms residing on the landscape, with an emphasis on contemporary timeframes. Geo-evolutionary feedbacks can be realized via the direct evolution of geomorphic engineering traits or can be mediated by the evolution of trait variation that affects the population size and distribution of the specific geomorphic engineering organisms involved. Organisms that modify their local environments provide the basis for patch-scale geo-evolutionary feedbacks, whereas spatial self-organization provides a mechanism for geo-evolutionary feedbacks at the landscape scale. Understanding these likely prevalent geo-evolutionary feedbacks, that occur at timescales similar to anthropogenic climate change, will be essential to better predict landscape adaptive capacity and change.

Sediment austerity is the new coastal norm.

Insufficient river-derived sediment for tidal marshes requires creative, local management.

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.

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.

Identifying fluorescent pulp mill effluent in the Gulf of Maine and its watershed.

Using fluorescence spectroscopy and parallel factor analysis (PARAFAC) we characterized and modeled the fluorescence properties of dissolved organic matter (DOM) in samples from the Penobscot River, Androscoggin River, Penobscot Bay, and the Gulf of Maine (GoM). We analyzed excitation-emission matrices (EEMs) using an existing PARAFAC model (Cory and McKnight, 2005) and created a system-specific model with seven components (GoM PARAFAC). The GoM PARAFAC model contained six components similar to those in other PARAFAC models and one unique component with a spectrum similar to a residual found using the Cory and McKnight (2005) model. The unique component was abundant in samples from the Androscoggin River immediately downstream of a pulp mill effluent release site. The detection of a PARAFAC component associated with an anthropogenic source of DOM, such as pulp mill effluent, demonstrates the importance for rigorously analyzing PARAFAC residuals and developing system-specific models.

Directional connectivity in hydrology and ecology.

Quantifying hydrologic and ecological connectivity has contributed to understanding transport and dispersal processes and assessing ecosystem degradation or restoration potential. However, there has been little synthesis across disciplines. The growing field of ecohydrology and recent recognition that loss of hydrologic connectivity is leading to a global decline in biodiversity underscore the need for a unified connectivity concept. One outstanding need is a way to quantify directional connectivity that is consistent, robust to variations in sampling, and transferable across scales or environmental settings. Understanding connectivity in a particular direction (e.g., streamwise, along or across gradient, between sources and sinks, along cardinal directions) provides critical information for predicting contaminant transport, planning conservation corridor design, and understanding how landscapes or hydroscapes respond to directional forces like wind or water flow. Here we synthesize progress on quantifying connectivity and develop a new strategy for evaluating directional connectivity that benefits from use of graph theory in ecology and percolation theory in hydrology. The directional connectivity index (DCI) is a graph-theory based, multiscale metric that is generalizable to a range of different structural and functional connectivity applications. It exhibits minimal sensitivity to image rotation or resolution within a given range and responds intuitively to progressive, unidirectional change. Further, it is linearly related to the integral connectivity scale length--a metric common in hydrology that correlates well with actual fluxes--but is less computationally challenging and more readily comparable across different landscapes. Connectivity-orientation curves (i.e., directional connectivity computed over a range of headings) provide a quantitative, information-dense representation of environmental structure that can be used for comparison or detection of subtle differences in the physical-biological feedbacks driving pattern formation. Case-study application of the DCI to the Everglades in south Florida revealed that loss of directional hydrologic connectivity occurs more rapidly and is a more sensitive indicator of declining ecosystem function than other metrics (e.g., habitat area) used previously. Here and elsewhere, directional connectivity can provide insight into landscape drivers and processes, act as an early-warning indicator of environmental degradation, and serve as a planning tool or performance measure for conservation and restoration efforts.

How vegetation and sediment transport feedbacks drive landscape change in the everglades and wetlands worldwide.

Mechanisms reported to promote landscape self-organization cannot explain vegetation patterning oriented parallel to flow. Recent catastrophic shifts in Everglades landscape pattern and ecological function highlight the need to understand the feedbacks governing these ecosystems. We modeled feedback between vegetation, hydrology, and sediment transport on the basis of a decade of experimentation. Results from more than 100 simulations showed that flows just sufficient to redistribute sediment from sparsely vegetated sloughs to dense ridges were needed for an equilibrium patterned landscape oriented parallel to flow. Surprisingly, although vegetation heterogeneity typically conveys resilience, in wetlands governed by flow/sediment feedbacks it indicates metastability, whereby the landscape is prone to catastrophic shifts. Substantial increases or decreases in flow relative to the equilibrium condition caused an expansion of emergent vegetation and loss of open-water areas that was unlikely to revert upon restoration of the equilibrium hydrology. Understanding these feedbacks is critical in forecasting wetland responses to changing conditions and designing management strategies that optimize ecosystem services, such as carbon sequestration or habitat provision. Our model and new sensitivity analysis techniques address these issues and make it newly apparent that simply returning flow to predrainage conditions in the Everglades may not be sufficient to restore historic landscape patterns and processes.