Geology topics

Leonard G. Pearlstine

Publications and source records attributed to Leonard G. Pearlstine.

14 recordsLinked to original sources

USGS2023

The Everglades vulnerability analysis: Linking ecological models to support ecosystem restoration

Understanding of the Everglades’ ecological vulnerabilities and restoration needs has advanced over the past decade but has not been applied in an integrated manner. To address this need, we developed the Everglades Vulnerability Analysis (EVA), a decision support tool that uses modular Bayesian networks to predict the ecological outcomes of a subset of the ecosystem’s health indicators. This tool takes advantage of the extensive modeling work already done in the Everglades and synthesizes information across indicators of ecosystem health to forecast long-term, landscape-scale changes. In addition, the tool can predict indicator vulnerability through comparison to user-defined ideal system states that can vary in the level of certainty of outcomes. An integrated understanding of the Everglades system is essential for evaluation of trade-offs at local, regional, and system-wide scales. Through EVA, Everglades restoration decision makers can provide effective guidance during restoration planning and implementation processes to mitigate unintended consequences that could result in further damage to the Everglades system.

Florida↗

USGS2022

Managing multiple species with conflicting needs in the Greater Everglades

Given limited funding, natural resources decision making is riddled with tradeoffs, including which species or landscapes to prioritize for management action. Florida’s Everglades wetland is home to numerous indicator species, some of which are endangered. But with a multitude of species comes differing hydrologic requirements to yield appropriate foraging and breeding conditions for each. The Everglades ecosystem is highly managed, with water being moved across the landscape to meet the habitat and reproductive needs of species of concern. Predictive modeling can help water managers understand potential consequences to targeted water conditions. EverForecast is a novel spatially explicit, hydrologic, and ecological operational forecast developed to inform conservation management decisions. Not only does EverForecast provide probable near-term water conditions, but also predicted species responses to those hydrologic conditions. Using examples from two focal regions of the Everglades, we show the magnitude of impacts to a suite of species and an almost 70% decline in suitable conditions for one species when prioritizing water management to meet the needs of another species. Although EverForecast is a relatively new decision support tool, its hydrologic outputs are already commonly used to make water management recommendations because it provides near-term hydrologic forecasts that scientists and managers need for water operations decision making. Because species management decisions have historically been made to target a single species at a time, it may take longer for full utility of EverForecast’s ability to quantify tradeoffs among species to become integrated into decision making.

Florida↗

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↗

USGS2021

Joint species distribution models of Everglades wading birds to inform restoration planning

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

Florida↗

USGS2021

Forecasting ecological responses for wetland restoration planning in Florida's Everglades

The Everglades wetland was once a river of grass, with water flowing slowly through the sawgrass, southward across the landscape. As developers took hold of south Florida, water was sent away from the heart of the Everglades through canals and levees to protect the former wetland for residential and agricultural development. In the 1990s, planning began to restore the Everglades in what is the largest hydrologic restoration undertaking in the world. With billions of taxpayer dollars at stake, restoration planners benefit from forecasting tools to inform restoration planning. To meet this need, scientists developed predictive ecological models and other decision support tools tailored to this dynamic ecosystem as well as to the needs of restoration planning teams. Predictive modeling has been able to take advantage of well-understood relationships between species of interest and hydrologic dynamics in the Everglades. Recent modeling advances include multi-species approaches that consider interactions among species as well as explicit consideration of trade-offs among species from potential water management actions. Scientists are also starting to look at ecosystem-wide vulnerabilities with explicit consideration of future change such as sea level rise. Modeling tools and approaches continue to be refined to meet decision making needs for Everglades restoration. However, more work is needed to consider additional complexities of this dynamic wetland as well as to consider the broader socio-environmental system.

Florida↗

USGS2021

Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach

The Florida Everglades is a vast and iconic wetland ecosystem in the southern United States that has undergone dramatic changes from habitat degradation, development encroachment, and water impoundment. Starting in the past few decades, large restoration projects have been undertaken to restore the landscape, including improving conditions for threatened and imperiled taxa. One focus of restoration has been the marl prairie ecosystem, where the federally endangered Cape Sable Seaside Sparrow ( Ammospiza maritima mirabilis ; CSSS) resides. The CSSS is endemic to the Everglades where populations have been steadily declining, signaling the importance of decision support tools for natural resource managers for evaluating water management and restoration scenarios. Here we developed an ensemble logistic regression, combining a frequentist and Bayesian approach, to model CSSS presence and measure how environmental factors such as hydrometrics, fire occurrence, and vegetation structure impact CSSS habitat suitability. This is the first analysis to quantitatively assess the interdependent relationships between a broad range of environmental factors and CSSS presence across the landscape. Our results show that the probability of CSSS presence was highest in areas with dry conditions, hydroperiods between 80 and 120 days, percentages of canopy cover and woody vegetation less than 10%, and more than six years post-fire where 75% or more of the area was burned. Because the frequentist and Bayesian models had nearly identical spatial outputs with the Bayesian model having slightly higher validation metrics, we used the Bayesian approach as our final model (EverSparrow). The results from our analysis can provide a valuable decision support tool as natural resource managers work to restore the Everglades landscape.

Florida↗

USGS2020

Near-term spatial hydrologic forecasting in Everglades, USA for landscape planning and ecological forecasting

Operational ecological forecasting is an emerging field that leverages ecological models in a new, cross-disciplinary way – using a real-time or nearly real-time climate forecast to project near-term ecosystem states. These applications give decision-makers lead time to anticipate and manage state changes that degrade ecosystem functions or directly impact humans. The Everglades Forecasting model (EverForecast) is an operational forecast model designed specifically for conservation management purposes including water management. It provides up to six-month forecasts of daily projected, spatially continuous stage values across the Everglades. We validated EverForecast quarterly to measured historical values at 207 gages (1 Jan 2000 – 31 Dec 2019). EverForecast hindcasted water stage accurately captured measured stage variation, with a low percentage of measured stage exceeding hindcasted values. Over the whole spatial extent, the mean RMSE is 20.98 cm, the mean MAE is 14.42 cm, and the mean MBE is 0.91 cm.

Florida↗

USGS2019

Fire disturbance influences endangered Cape Sable Seaside Sparrow (Ammopiza maritima mirabilis) relative bird count

Periodicity of fire disturbance is a known driver of ecosystem function and is reported as important in both promoting and maintaining viable breeding habitat for the endangered Cape Sable Seaside Sparrow ( Ammospiza maritima mirabilis ; CSSS). In south Florida, the CSSS serves as a fine-scale indicator of the marl and mixed-marl prairie communities of the Florida Everglades. The CSSS distribution is affected by numerous well-documented physical drivers, including water depth and fire regime. Here, we fit zero-inflated negative binomial generalized linear mixed models and used model selection to determine the relationship between CSSS bird count observations from 1992 to 2014 and the spatially-specific fire return interval on the landscape. CSSS bird count was highest at a 5–8-year fire return interval and increased linearly with the percent of cell burned (400 × 400 m cells). The results of this study can inform management plans designed to maintain existing, and promote new, marl prairie habitat for conservation of the CSSS.

Florida↗

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↗

USGS2015

Application of a coupled vegetation competition and groundwater simulation model to study effects of sea level rise and storm surges on coastal vegetation

Global climate change poses challenges to areas such as low-lying coastal zones, where sea level rise (SLR) and storm-surge overwash events can have long-term effects on vegetation and on soil and groundwater salinities, posing risks of habitat loss critical to native species. An early warning system is urgently needed to predict and prepare for the consequences of these climate-related impacts on both the short-term dynamics of salinity in the soil and groundwater and the long-term effects on vegetation. For this purpose, the U.S. Geological Survey’s spatially explicit model of vegetation community dynamics along coastal salinity gradients (MANHAM) is integrated into the USGS groundwater model (SUTRA) to create a coupled hydrology–salinity–vegetation model, MANTRA. In MANTRA, the uptake of water by plants is modeled as a fluid mass sink term. Groundwater salinity, water saturation and vegetation biomass determine the water available for plant transpiration. Formulations and assumptions used in the coupled model are presented. MANTRA is calibrated with salinity data and vegetation pattern for a coastal area of Florida Everglades vulnerable to storm surges. A possible regime shift at that site is investigated by simulating the vegetation responses to climate variability and disturbances, including SLR and storm surges based on empirical information.

Journal of Marine Science and Engineering↗

USGS2014

Using scenario planning to evaluate the impacts of climate change on wildlife populations and communities in the Florida Everglades

It is uncertain how climate change will impact hydrologic drivers of wildlife population dynamics in freshwater wetlands of the Florida Everglades, or how to accommodate this uncertainty in restoration decisions. Using projections of climate scenarios for the year 2060, we evaluated how several possible futures could affect wildlife populations (wading birds, fish, alligators, native apple snails, amphibians, threatened and invasive species) across the Everglades landscape and inform planning already underway. We used data collected from prior research and monitoring to parameterize our wildlife population models. Hydrologic data were simulated using a spatially explicit, regional-scale model. Our scenario evaluations show that expected changes in temperature, precipitation, and sea level could significantly alter important ecological functions. All of our wildlife indicators were negatively affected by scenarios with less rainfall and more evapotranspiration. Under such scenarios, habitat suitability was substantially reduced for iconic animals such as wading birds and alligators. Conversely, the increased rainfall scenario benefited aquatic prey productivity and apex predators. Cascading impacts on non-native species is speculative, but increasing temperatures could increase the time between cold events that currently limit expansion and abundance of non-native fishes, amphibians, and reptiles with natural ranges in the tropics. This scenario planning framework underscored the benefits of proceeding with Everglades restoration plans that capture and clean more freshwater with the potential to mitigate rainfall loss and postpone impacts of sea level rise.

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↗

USGS2012

Do bioclimate variables improve performance of climate envelope models?

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Ecological Modelling↗

USGS2009

Relationship between body condition of American alligators and water depth in the Everglades, Florida

Feeding opportunities of American alligators (Alligator mississippiensis) in freshwater wetlands in south Florida are closely linked to hydrologic conditions. In the Everglades, seasonally and annually fluctuating surface water levels affect populations of aquatic organisms that alligators consume. Since prey becomes more concentrated when water depth decreases, we hypothesized an inverse relationship between body condition and water depth in the Everglades. On average, condition of adult alligators in the dry season was significantly higher than in the wet season, but this was not the case for juveniles/subadults. The correlation between body condition and measured water depth at capture locations was weak; however, there was a significant negative correlation between the condition and predicted water depth prior to capture for all animals except for spring juveniles/subadults which had a weak positive condition-water depth relationship. Overall, a relatively strong inverse correlation occurred at 10-49 days prior to the capture day, suggesting that current body condition of alligators may depend on feeding opportunities during that period. Fitted regression of body condition on water depth (mean depth of 10 days when condition-water depth correlation was greatest) resulted in a significantly negative slope, except for spring adult females and spring juveniles/subadults for which slopes were not significantly different from zero. Our results imply that water management practices may be critical for alligators in the Everglades since water depth can affect animal condition in a relatively short period of time.

Hydrobiologia↗