Martha Nungesser

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.

Recent and historic drivers of landscape change in the everglades ridge, Slough, and Tree Island Mosaic

More than half of the original Everglades extent formed a patterned peat mosaic of elevated ridges, lower and more open sloughs, and tree islands aligned parallel to the dominant flow direction. This ecologically important landscape structure remained in a dynamic equilibrium for millennia prior to rapid degradation over the past century in response to human manipulation of the hydrologic system. Restoration of the patterned landscape structure is one of the primary objectives of the Everglades restoration effort. Recent research has revealed that three main drivers regulated feedbacks that initiated and maintained landscape structure: the spatial and temporal distribution of surface water depths, surface and subsurface flow, and phosphorus supply. Causes of recent degradation include but are not limited to perturbations to these historically important controls; shifts in mineral and sulfate supply may have also contributed to degradation. Restoring predrainage hydrologic conditions will likely preserve remaining landscape pattern structure, provided a sufficient supply of surface water with low nutrient and low total dissolved solids content exists to maintain a rainfall-driven water chemistry. However, because of hysteresis in landscape evolution trajectories, restoration of areas with a fully degraded landscape could require additional human intervention.

Linking metrics of landscape pattern to hydrological process in a lotic wetland

ContextStrong reciprocal interactions exist between landscape patterns and ecological processes. In wetlands, hydrology is the dominant abiotic driver of ecological processes and both controls, and is controlled, by vegetation presence and patterning. We focus on binary patterning in the Everglades ridge-slough landscape, where longitudinally connected flow, principally in sloughs, is integral to landscape function. Patterning controls discharge competence in this low-gradient peatland, with important feedbacks on hydroperiod and thus peat accretion and patch transitions.ObjectivesTo quantitatively predict pattern effects on hydrologic connectivity and thus hydroperiod.MethodsWe evaluated three pattern metrics that vary in their hydrologic specificity. (1) Landscape discharge competence considers elongation and patch-type density that capture geostatistical landscape features. (2) Directional connectivity index (DCI) extracts both flow path and direction based on graph theory. (3) Least flow cost (LFC) is based on a global spatial distance algorithm strongly analogous to landscape water routing, where ridges have higher flow cost than sloughs because of their elevation and vegetation structure. Metrics were evaluated in comparison to hydroperiod estimated using a numerically intensive hydrologic model for synthetic landscapes. Fitted relationships between metrics and hydroperiod for synthetic landscapes were extrapolated to contemporary and historical maps to explore hydroperiod trends in space and time.ResultsBoth LFC and DCI were excellent predictors of hydroperiod and useful for diagnosing how the modern landscape has reorganized in response to modified hydrology.ConclusionsMetric simplicity and performance indicates potential to provide hydrologically explicit, computationally simple, and spatially independent predictions of landscape hydrology, and thus effectively measure of restoration performance.

Introduction - Unprecedented Challenges in Ecological Research: Past and Present

The focus of ecological research has been changing in fundamental ways as the need for humanity to address large-scale environmental perturbations and global crises increasingly places ecologists in the limelight. Ecologists are asked to explain and help mitigate effects from local to global scale issues, such as climate change, wetlands loss, hurricane devastation, deforestation, and land degradation. The traditional focus of ecology as ‘‘the study of the causes of patterns in nature’’ (e.g., Tilman 1987) has shifted to a new era in which ecological science must play a greatly expanded role in improving the human condition by addressing the sustainability and resilience of socio-ecological systems (Millennium Ecosystem Assessment 2003, Palmer et al. 2004). In the twenty-first century, scientists studying ecological science are required not only to understand mechanisms of ecosystem change and develop new ecological theories but also to contribute to a future in which natural and human systems can coexist sustainably on the Earth (Carpenter and Turner 1998, Hassett et al. 2005). This unprecedented challenge demands that ecologists link science to planning, decisionand policy-making, forecasting ecosystem states, and evaluating ecosystem services and natural capital (Carpenter et al. 1998, Clark et al. 2001b). To realize these goals, ecologists must expand temporal and spatial scales of research, develop novel design approaches and analytical tools that meet the demands of this increasingly complex milieu, and provide education and training in using these tools. Ecological research began with observational field studies, then moved to experimentation, at which time the difficulty of isolating and controlling the variables that influence ecosystems became apparent (McIntosh 1985). In response, ecologists tried to reproduce systems on a smaller spatial scale using microcosms and mesocosms, where the influence of variables could be systematically isolated, controlled, and tested (Forbes 1887, Beyers 1963, Hutchinson