Geology topics

Stephanie S. Romanach

Publications and source records attributed to Stephanie S. Romanach.

At least 19 recordsLinked to original sources

USGS2026

Towards a refined definition of “tidal influence” for the coastal wetland sciences

The astronomical tide relates to lunar (and to a lesser extent solar) influences on rising and falling water bodies, and is a key control of many coastal processes. However, tides manifest along coastal margins in different ways to muddle application beyond Sir Isaac Newton’s first descriptions. Here, we argue that tide, or specifically “tidal,” has not been adequately defined for the coastal wetland sciences to facilitate contemporary interdisciplinary distinction for rapidly changing coastlines and future climate adaptation planning. We provide a brief history of the tidal concept, and we describe tides in a diversity of uncommonly recognized marginal coastal environments. Common astronomical tide manifestations are described through hydrographic examples, then expanded to include those tidal environments exposed additionally to fluvial, meteorological (wind), or groundwater influences. Our expanded definition of “tidal” is intended to be a forward-looking construct as environments will inevitably change with sea-level rise, water extraction, and anthropogenic flow alteration. Our definition of wetland tidal influence, which we broaden, includes those “wetlands affected by astronomic water level fluctuations within coastally restricted environments causing frequent or infrequent surface inundation or groundwater tidal variation that affects the biogeochemistry of the soil (e.g., ion exchange capacity, oxygen state, mineralization processes)”. Astronomic water level fluctuations are often amplified or dampened by non-astronomical events, such as wind. Expanding the definition of what is considered tidal by the scientific community is critical for improved understanding of coastal wetland function and the suite of ecosystem services they provide people and society, including climate mitigation opportunity, management, and adaptation.

Wetlands↗

USGS2021

The Everglades vulnerability analysis—Integrating ecological models and addressing uncertainty

The Everglades vulnerability analysis (EVA) is a project led by the U.S. Geological Survey in cooperation with the National Park Service and U.S. Army Corps of Engineers to accomplish one of the science goals of Restoration Coordination & Verification (RECOVER), a multiagency group responsible for providing scientific and technical evaluations and assessments for improving the ability of the Comprehensive Everglades Restoration Plan to restore, preserve, and protect the south Florida ecosystem while providing for the region’s other water-related needs. In 2016, RECOVER acknowledged the need for a tool that could synthesize the decades of Everglades ecosystem science and identify areas vulnerable to changing conditions on the landscape. The EVA tool answers this need through a landscape-scale modeling framework that provides annual responses and relative vulnerability for a suite of indicators of Everglades ecosystem health.

Florida↗

USGS2018

Designing a protected area to safeguard imperiled species from urbanization

Reserve design is a process that can address ecological, social, and political factors to identify parcels of land needed to sustain wildlife populations and other natural resources. Acquisition of parcels for a large terrestrial reserve is difficult because it typically occurs over a long timeframe and thus invokes consideration of future conditions such as climate and urbanization changes. In central Florida, a new protected area, the Everglades Headwaters National Wildlife Refuge, has been authorized by the United States Government. The new refuge will host important threatened and endangered species and habitats, as well as be located to allow for species adaptation from climate change impacts. For this study we combined habitat objectives defined by the U.S. Fish and Wildlife Service and projections from two urbanization models to provide guidance for Everglades Headwaters National Wildlife Refuge design. We used Marxan with Zones to find near-optimal solutions for protecting explicit amounts of five target habitats. We identified parcels for inclusion into the reserve design that the model allocated among two zones representing different methods of protection: fee-simple purchase (up to 20,234 hectares authorized by the United States government), and conservation easement agreements (up to 40,469 hectares authorized). As expected, for all scenarios we found an increase in costs as the proportion of fee-simple purchases was increased, reflecting the lesser cost of easements, but the number of parcels required for protection differed little among scenarios. The two urbanization models showed considerable agreement over which habitat patches were not forecast to be developed, and showed some agreement over which parcels might be developed. The U.S. Fish and Wildlife Service may benefit from focusing on parcels that are selected frequently by our analyses under both urban scenarios because these parcels are more likely to be in areas where urbanization threats and demand for land is reduced. The reserve designs we generated met U.S. Fish and Wildlife Service habitat goals within fee and easement zone restrictions, and we found reserve configurations that fell well below the mandated size limit.

Florida↗

USGS2018

A spatial approach to combatting wildlife crime

Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. Here, we show how geographic profiling – a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology – can be adapted for use in investigations of wildlife crime, using data from an eight-year study in Savé Valley Conservancy, Zimbabwe that in total includes more than 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. Using a subset of these data for which the illegal hunters’ identities are known, we show that the model can successfully identify the illegal hunters’ home villages using the spatial locations of hunting incidences (for example, snares) as input, and show how this can be improved by manipulating the probability surface inside the Conservancy to reflect the fact that – although the illegal hunters mostly live outside the Conservancy, the majority of hunting occurs inside (in criminology, ‘commuter crime’). The results of this analysis – combined with rigorous simulations – show for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns – for example, where landscape heterogeneity means that some parts of the study area are unsuitable (e.g. aquatic areas for terrestrial animals, or vice versa), or where landscape permeability differs (for example, forest bats tending not to fly over open areas). More broadly, these results show how geographic profiling can be used to target anti-poaching interventions more effectively and more efficiently, with important implications for the development of management strategies and conservation plans in a range of conservation scenarios.

Conservation Biology↗

USGS2018

Conservation and restoration of mangroves: Global status, perspectives, and prognosis

Mangrove forests provide critical services around the globe to both human populations and the ecosystems they occupy. However, losses of mangrove habitat of more than 50% have been recorded in some parts of the world, and these losses are largely attributable to human activities. The importance of mangroves and the threats to their persistence have long been recognized, leading to actions taken locally, by national governments, and through international agreements for their protection. In this review, we explore the status of mangrove forests as well as efforts to protect them. We examine threats to the persistence of mangroves, consequences, and potential solutions for effective conservation. We present case studies from disparate regions of the world, showing that the integration of human livelihood needs in a manner that balances conservation goals can present solutions that could lead to long-term sustainability of mangrove forests throughout the world.

Ocean and Coastal Management↗

USGS2018

Managing conflicts in the River of Grass

Chances are, you would not pack up and move to a new home without first researching the neighborhood, reviewing your finances, and maybe investigating schools nearby. Similarly, you would not buy the first car you find on a magazine cover without first reviewing the technical specifications, exploring your options, and perhaps taking a test drive. Even when making simple purchases online, you probably shop around, check out customer reviews, and scan seller feedback ratings. Rarely do we act on impulse alone when faced with decisions. Rather, we typically start with a vision of what we want before gathering the necessary information, weighing our options, prioritizing our values, and evaluating possible tradeoffs. In making choices that range from lighthearted to life-changing, we use this commonsense strategy every day of our lives. Environmental decision-making is no different. For decades, natural resource managers faced with complicated problems have made decisions by breaking them into smaller components that are easier to approach. And like our day-to day decisions, the process is ideally guided by a shared vision of the future. However, it can become complicated when multiple, seemingly disparate views on what the future should hold emerge. As Lewis Carroll once wrote, “If you don’t know where you are going, any road will get you there.” Divining a vision of the future for the Florida Everglades and making supporting decisions is a complex undertaking. But unlike Alice thrust through the looking glass, we have tools and techniques to help us make sense of it all. The application of tools to help decision-making can move us closer to the successful restoration of this imperiled landscape.

Florida↗

USGS2017

Making ecological models adequate

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems’ responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

Ecology Letters↗

USGS2017

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.

Ecological Modelling↗

USGS2017

Structured decision making as a conservation tool for recovery planning of two endangered salamanders

At least one-third of all amphibian species face the threat of extinction, and current amphibian extinction rates are four orders of magnitude greater than background rates. Preventing extirpation often requires both ex situ (i.e., conservation breeding programs) and in situ strategies (i.e., protecting natural habitats). Flatwoods salamanders ( Ambystoma bishopi and A. cingulatum ) are protected under the U.S. Endangered Species Act. The two species have decreased from 476 historical locations to 63 recently extant locations (86.8% loss). We suggest that recovery efforts are needed to increase populations and prevent extinction, but uncertainty regarding optimal actions in both ex situ and in situ realms hinders recovery planning. We used structured decision making (SDM) to address key uncertainties regarding both captive breeding and habitat restoration, and we developed short-, medium-, and long-term goals to achieve recovery objectives. By promoting a transparent, logical approach, SDM has proven vital to recovery plan development for flatwoods salamanders. The SDM approach has clear advantages over other previous approaches to recovery efforts, and we suggest that it should be considered for other complex decisions regarding endangered species.

Journal for Nature Conservation↗

USGS2016

Value-focused framework for defining landscape-scale conservation targets

Conservation of natural resources can be challenging in a rapidly changing world and require collaborative efforts for success. Conservation planning is the process of deciding how to protect, conserve, and enhance or minimize loss of natural and cultural resources. Establishing conservation targets (also called indicators or endpoints), the measurable expressions of desired resource conditions, can help with site-specific up to landscape-scale conservation planning. Using conservation targets and tracking them through time can deliver benefits such as insight into ecosystem health and providing early warnings about undesirable trends. We describe an approach using value-focused thinking to develop statewide conservation targets for Florida. Using such an approach allowed us to first identify stakeholder objectives and then define conservation targets to meet those objectives. Stakeholders were able to see how their shared efforts fit into the broader conservation context, and also anticipate the benefits of multi-agency and -organization collaboration. We developed an iterative process for large-scale conservation planning that included defining a shared framework for the process, defining the conservation targets themselves, as well as developing management and monitoring strategies for evaluation of their effectiveness. The process we describe is applicable to other geographies where multiple parties are seeking to implement collaborative, large-scale biological planning.

Journal for Nature Conservation↗

USGS2016

Using Cape Sable seaside sparrow distribution data for water management decision support

The Cape Sable Seaside Sparrow (Ammodramus maritimus mirabilis; hereafter sparrow) is endemic to south Florida and a key indicator species of marl prairie, the most diverse freshwater community in the Florida Everglades. Marl prairie habitat is shaped by intermediate levels of disturbances such as flooding, drying, and fire, which maintain periphyton production (Gaiser et al. 2011), vegetation composition (Sah et al. 2011), and habitat structure for wildlife (Lockwood et al. 2003). Historically, patches of marl prairie shifted in response to changing climatic conditions,; however, habitat loss and hydrologic alteration have restricted the sparrow’s range and increased their sensitivity to changing hydropatterns. As a result, sparrow numbers have declined as much as 60% range-wide since 1992 (Curnutt et al. 1998, Nott et al. 1998). Currently, the sparrow is restricted to the freshwater prairies of the Everglades National Park (ENP) and Big Cypress Preserve (Lockwood et al. 1997). Because this non-migratory bird is restricted in its range it was among the first species to be listed as endangered by the US Fish and Wildlife Service on March 11, 1967 (Pimm et al. 2000). Now protected by the Endangered Species Act of 1973, the sparrow is listed as an endangered species, and the marl prairies that it resides in are listed as critical habitat. Since its designation as an endangered species, federal agencies have a statutory obligation to not jeopardize the survival of the species or modify its critical habitat. However, there are still uncertainties in how to increase suitable habitat within and surrounding the six existing sparrow subpopulations (Fig. 1) which are vulnerable to environmental stochasticity because of their small population size and restricted range. Since Because maintenance and creation of suitable habitat is seen as the most important pathway to the persistence of sparrow subpopulations (Sustainable Ecosystems Institute 2007), emphasis should be on identifying factors affecting sparrow habitat suitability and expanding the total area of suitable habitat over a gradient of environmental conditions. Our objective is to improve the definition of suitable sparrow habitat based on the relationship between daily sparrow distributions from 1992-present and hydrologic and habitat variables. Further, these models can provide an estimate of habitat quality when linked with estimates of reproductive responses.

Report↗

USGS2016

Considerations for building climate-based species distribution models

Climate plays an important role in the distribution of species. A given species may adjust to new conditions in-place, move to new areas with suitable climates, or go extinct. Scientists and conservation practitioners use mathematical models to predict the effects of future climate change on wildlife and plan for a biodiverse future. This 8-page fact sheet written by David N. Bucklin, Mathieu Basille, Stephanie S. Romañach, Laura A. Brandt, Frank J. Mazzotti, and James I. Watling and published by the Department of Wildlife Ecology and Conservation explains how, with a better understanding of species distribution models, we can predict how species may respond to climate change. The models alone cannot tell us how a certain species will actually respond to changes in climate, but they can inform conservation planning that aims to allow species to both adapt in place and (for those that are able to) move to newly suitable areas. Such planning will likely minimize loss of biodiversity due to climate change.

Report↗

USGS2015

Modeling apple snail population dynamics on the Everglades landscape

Context The Florida Everglades has diminished in size and its existing wetland hydrology has been altered. The endangered snail kite ( Rostrhamus sociabilis ) has nearly abandoned the Everglades, and its prey, the apple snail ( Pomacea paludosa ), has declined. Objective We developed a population model (EverSnail) to understand apple snail response to inter- and intra-annual fluctuations in water depths over the Everglades landscape. EverSnail was developed as a tool to understand how apple snails respond to different hydrologic scenarios. Methods EverSnail is an age- and size-structured, spatially-explicit landscape model of P. paludosa in the Everglades. Landscape-level inputs are water depth and air temperature. We conducted sensitivity analyses by running EverSnail with ± 20 % the baseline value of eight parameters. Results EverSnail was sensitive to changes in survival and water depth associated with reproduction. The EverSnail population varied with changes and/or differences in depth generally consistent with empirical data; site-specific comparisons to field data proved less reliable. A simulated 3-year wet period resulted in a shift in apple snail distribution, but little change in total abundance over the landscape. In contrast, a simulated 3-year succession of relatively dry years resulted in overall lower snail abundances. Conclusions Comparisons of model output to empirical data indicate the need for more data to better understand, and eventually parameterize, several aspects of snail ecology in support of EverSnail. A primary value of EverSnail is its capacity to describe the relative response of snail abundance to alternative hydrologic scenarios considered for Everglades water management and restoration.

Florida↗

USGS2015

Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models

Species distribution models (SDMs) are widely used in basic and applied ecology, making it important to understand sources and magnitudes of uncertainty in SDM performance and predictions. We analyzed SDM performance and partitioned variance among prediction maps for 15 rare vertebrate species in the southeastern USA using all possible combinations of seven potential sources of uncertainty in SDMs: algorithms, climate datasets, model domain, species presences, variable collinearity, CO 2 emissions scenarios, and general circulation models. The choice of modeling algorithm was the greatest source of uncertainty in SDM performance and prediction maps, with some additional variation in performance associated with the comprehensiveness of the species presences used for modeling. Other sources of uncertainty that have received attention in the SDM literature such as variable collinearity and model domain contributed little to differences in SDM performance or predictions in this study. Predictions from different algorithms tended to be more variable at northern range margins for species with more northern distributions, which may complicate conservation planning at the leading edge of species' geographic ranges. The clear message emerging from this work is that researchers should use multiple algorithms for modeling rather than relying on predictions from a single algorithm, invest resources in compiling a comprehensive set of species presences, and explicitly evaluate uncertainty in SDM predictions at leading range margins.

Ecological Modelling↗

USGS2015

Climate changes, shifting ranges

Even a fleeting mention of the Everglades conjures colorful images of alligators, panthers, flamingos, and manatees. Over the centuries, this familiar cast of characters has become synonymous with life in south Florida. But the workings of a changing climate have the potential to significantly alter the menagerie of animals that call this area home. Global projections suggest south Florida wildlife will need to contend with higher temperatures, drier conditions, and rising seas in the years ahead. Recent modeling efforts shed new light on the potential outcomes these changes may have for threatened and endangered species in the area.

Report↗

USGS2015

Dynamic reserve design in the face of climate change and urbanization

Reserve design is a process that must address many ecological, social, and political factors to successfully identify parcels of land in need of protection to sustain wildlife populations and other natural resources. Making land acquisition choices for a large, terrestrial protected area is difficult because it occurs over a long timeframe and may involve consideration future conditions such as climate and urbanization changes. Decision makers need to consider factors including: order of parcel purchasing given budget constraints, future uncertainty, potential future landscape‐scale changes from urbanization and climate. In central Florida, two new refuges and the expansion of a third refuge are in various stages of USFWS planning. The Everglades Headwaters National Wildlife Refuge (EHNWR) has recently been established, is at the top of the Presidential Administration’s priority conservation areas, and is cited by the Secretary of DOI routinely in the context of conservation. The new refuges were strategically located for both for species adaptation from climate change impacts as well as currently being host to a number of important threatened and endangered species and habitats. We plan to combine a structured decision making framework, optimal solution theory, and output from ecological and sociological models (these modeling efforts were previously funded by DOI partners) that incorporate climate change to provide guidance for EHNWR reserve design. Utilizing a SDM approach and optimal solution theory, decision support tools will be developed that will incorporate stakeholder and agency objectives into targeting conservation lands both through fee simple purchase and other incentives such as easements based on ecological and socioeconomic modeling outputs driven by climate change.

Report↗

USGS2015

Defining conservation targets on a landscape-scale

Conservation planning, the process of deciding how to protect, conserve, enhance and(or) minimize loss of natural and cultural resources, is a fundamental process to achieve conservation success in a time of rapid environmental change. Conservation targets, the measurable expressions of desired resource conditions, are an important tool in biological planning to achieve effective outcomes. Conservation targets provide a focus for planning, design, conservation action, and collaborative monitoring of environmental trends to guide landscape-scale conservation to improve the quality and quantity of key ecological and cultural resources. It is essential to have an iterative and inclusive method to define conservation targets that is replicable and allows for the evaluation of the effectiveness of conservation targets over time. In this document, we describe a process that can be implemented to achieve landscape-scale conservation, which includes defining conservation targets. We also describe what has been accomplished to date (September 2015) through this process for the Peninsular Florida Landscape Conservation Cooperative (PFLCC).

Florida↗

USGS2014

Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Journal of Fish and Wildlife Management↗