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USGS2010

Habitat suitability and conservation of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley of California

Resource managers often have little information regarding the habitat requirements and distribution of rare species. Factor analysis-based habitat suitability models describe the ecological niche of a species and identify locations where these conditions occur on the landscape using existing occurrence data.We used factor analyses to assess the suitability of habitats for Thamnophis gigas (Giant Gartersnake), a rare, threatened species endemic to the Central Valley of California, USA, and to map the locations of habitat suitable for T. gigas in the Sacramento Valley. Factor analyses indicated that the niche of T. gigas is composed of sites near rice agriculture with low stream densities. Sites with high canal densities and near wetlands also appeared suitable, but results for these variables were sensitive to potential sampling bias. In the Sacramento Valley, suitable habitats occur primarily in the central portion of the valley floor. Based upon the results of the factor analyses, recovery planning for T. gigas will require an on-the-ground assessment of the current distribution and abundance of T. gigas, maintaining the few remaining natural wetlands and the practice of rice agriculture in the Sacramento Valley, and studying the effects of agricultural practices and land use changes on populations of T. gigas.

California↗

USGS2024

Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types

Spatial modeling of wildlife diseases can be used to describe patterns of disease risk, understand biological mechanisms of disease occurrence, and for spatial prediction. Risk of wildlife disease occurrence in relation to environmental variables is often modeled and predicted using Markov chain Monte Carlo (MCMC) methods, which are unsuitable for large datasets and those covering large spatial extents. Integrated nested Laplace approximation (INLA) and INLA using the stochastic partial differential equation (INLA-SPDE) approach have become popular alternatives to MCMC for Bayesian inference because of their fast computational time and ability to process large datasets. Studies investigating risk of disease occurrence in wildlife, to our knowledge, have not yet compared Bayesian hierarchical spatial models over large spatial extents using real world data. Using chronic wasting disease (CWD) surveillance data from white-tailed deer ( Odocoileus virginianus ) collected in Pennsylvania, United States, as a case study, we first demonstrate how parameter estimates compare among MCMC, INLA, and INLA-SPDE modeling frameworks. We then model CWD (detected/non-detected) using INLA-SPDE over a much larger spatial extent than has been conducted previously for this disease to determine how surveillance type (e.g., hunter harvest, roadkill, or all surveillance) influences model parameters and predicted risk of CWD occurrence at locations not sampled. Fixed effects considered in the models included deer age and sex, elevation, slope, distance to streams, percent clay, and proportion of two habitat classes (forest and open) known to influence deer movements. We found INLA to produce comparable estimates to MCMC and permit modeling large datasets covering expansive spatial extents much faster and more efficiently than MCMC. We identified potential biases in surveillance types, indicating the value of including all surveillance in models rather than only a single type. Comparing modeling tools available for mapping diseases of wildlife in relation to ecological variables at large spatial extents will guide future modeling efforts for CWD and other wildlife diseases. Understanding spatial patterns of CWD using different surveillance types can help improve understanding of CWD disease outbreaks, assist with control of CWD through geographical targeting, and inform future CWD surveillance efforts.

Ecological Modeling↗

USGS2009

Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions

Climate change in the coming centuries will be characterized by interannual, decadal, and multidecadal fluctuations superimposed on anthropogenic trends. Predicting ecological and biogeographic responses to these changes constitutes an immense challenge for ecologists. Perspectives from climatic and ecological history indicate that responses will be laden with contingencies, resulting from episodic climatic events interacting with demographic and colonization events. This effect is compounded by the dependency of environmental sensitivity upon life-stage for many species. Climate variables often used in empirical niche models may become decoupled from the proximal variables that directly influence individuals and populations. Greater predictive capacity, and more-fundamental ecological and biogeographic understanding, will come from integration of correlational niche modeling with mechanistic niche modeling, dynamic ecological modeling, targeted experiments, and systematic observations of past and present patterns and dynamics.

Proceedings of the National Academy of Sciences↗

USGS2018

Flow-ecology relationships are spatially structured and differ among flow regimes

In streams, hydrology is a predominant driver of ecological structure and function. Providing adequate flows to support aquatic life, or environmental flows, is therefore a top management priority in stream systems. Flow regime classification is a widely accepted approach for establishing environmental flow guidelines. However, it is surprisingly difficult to quantify relationships between hydrology and ecology (flow–ecology relationships) while describing how these relationships vary across classified flow regimes. Developing such relationships is complicated by several sources of spatial bias, such as autocorrelation due to spatial design, flow regime classification and other environmental or ecological sources of spatial bias. We used mixed moving‐average spatial stream network models to develop flow–ecology relationships across classified flow regimes and to assess spatial patterns of these relationships. We compared relationships between fish traits and life‐history strategies with hydrologic metrics across flow regimes and assessed whether spatial autocorrelation influenced these relationships. Trait–hydrology relationships varied between flow regimes and across all streams combined. Some relationships between traits and hydrologic metrics fit predictions based on life‐history theory, while others exhibited unexpected relationships with hydrology. Spatial factors described a large proportion of variability in fish traits and different patterns of spatial autocorrelation were observed in different flow regimes. Synthesis and applications. Further work is needed to understand why flow–ecology relationships vary across classified flow regimes and why these relationships may not fit predictions based on life‐history theories. Managers determining environmental flow standards need to be aware that different hydrologic metrics are often important drivers of fish trait diversity in different flow regimes. Flow–ecology relationships may therefore be confounded by spatial structure inherent in flow regime classification and much existing biological data. Complex patterns of spatial bias should be considered when managing stream systems within an environmental flows framework.

Journal of Applied Ecology↗

USGS2015

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Journal of Animal Ecology↗

USGS2025

Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River

Information on the favorable conditions for invasive species as well as potential constraints to their distribution can be valuable for management efforts. We used a niche modeling approach to analyze the patterns of species distributions along gradients of hypothesized influential environmental variables. Many ecological datasets may have incomplete coverage across the environmental gradients, infrequent sampling under some conditions, insufficient time for an invasive species to occupy all sites, and complex interactions among environmental variables (measured or unmeasured) that may result in species response curves that are difficult to interpret and may be ecologically misleading. To ensure the model and species response curves aligned with ecological niche theory, shape constraints were imposed to guarantee relationships follow a unimodal distribution to reflect the fundamental niche (where a species could occur). We compared a shape-constrained model to an unconstrained model and interpreted the species response curves from the constrained model to better characterize the ecological niche of reed canarygrass in floodplain forests of the Upper Mississippi River, USA. We found the probability of reed canarygrass occurrence decreases with increasing tree canopy cover, tree species richness, distance from forest edge, distance from invaded wet meadows, and island isolation. Probability of reed canarygrass presence exhibited bell-shaped curves in response to hydrology (inundation depth, frequency, and duration) and forest stress metrics indicating an optimum with less favorable conditions on either end of the ecological gradients. This information could be used to prioritize restoration efforts and enhance landcover change research in forested floodplains.

upper Mississippi River↗

USGS2023

Application of habitat association models across regions: Useful explanatory power retained in wetland bird case study

Species often exhibit regionally specific habitat associations, so habitat association models developed in one region might not be accurate or even appropriate for other regions. Three programs to survey wetland-breeding birds covering (respectively) Great Lakes coastal wetlands, inland Great Lakes wetlands, and the Prairie Pothole Region offer an opportunity to test whether regionally specific models of habitat use by wetland-obligate breeding birds are transferrable across regions. We first developed independent, regional population density models for four species of wetland-obligate birds: Pied-billed Grebe ( Podilymbus podiceps ), Virginia Rail ( Rallus limicola ), Sora ( Porzana carolina ), and American Bittern ( Botaurus lentiginosus ). We then used adjusted pseudo- R 2 values to compare the amount of variation explained by each model when applied to data collected in each of the three regions. Although certain habitat characteristics, such as emergent vegetation and wetland area, were consistently important across regions, models for each species differed by region—both in variables selected for inclusion and often in the directionality of relationships for common variables—indicating that habitat associations for these species are regionally specific. When we applied a model developed in one region to data collected in another region, we found that explanatory power was reduced in most (71%) models. Therefore, we suggest that ecological analyses should emphasize regionally specific habitat association models whenever possible. Nonetheless, models created from inland Great Lakes wetland data had higher median explanatory power when applied to other regions, and the amount of explanatory power lost by other transferred models was relatively small. Thus, while regionally specific habitat association models are preferable, in the absence of reliable regional data, habitat association models developed in one region may be applied to another region, but the results need to be cautiously interpreted. Additionally, we found that median explanatory power was higher when local-scale habitat characteristics were included in the models, indicating that regionally specific models should ideally be based on a combination of local- and landscape-scale habitat characteristics. Conservation practitioners can leverage such regionally specific models and associated monitoring data to help prioritize areas for management activities that contribute to regional conservation efforts.

Illinois, Indiana, Michigan, Minnesota, New York, ↗

USGS2013

Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO database over the Greater Platte River Basin, USA

This study assessed and described a relationship between satellite-derived growing season averaged Normalized Difference Vegetation Index (NDVI) and annual productivity for grasslands within the Greater Platte River Basin (GPRB) of the United States. We compared growing season averaged NDVI (GSN) with Soil Survey Geographic (SSURGO) database rangeland productivity and flux tower Gross Primary Productivity (GPP) for grassland areas. The GSN was calculated for each of nine years (2000–2008) using the 7-day composite 250-m eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. Strong correlations exist between the nine-year mean GSN (MGSN) and SSURGO annual productivity for grasslands (R2 = 0.74 for approximately 8000 pixels randomly selected from eight homogeneous regions within the GPRB; R2 = 0.96 for the 14 cluster-averaged points). Results also reveal a strong correlation between GSN and flux tower growing season averaged GPP (R2 = 0.71). Finally, we developed an empirical equation to estimate grassland productivity based on the MGSN. Spatially explicit estimates of grassland productivity over the GPRB were generated, which improved the regional consistency of SSURGO grassland productivity data and can help scientists and land managers to better understand the actual biophysical and ecological characteristics of grassland systems in the GPRB. This final estimated grassland production map can also be used as an input for biogeochemical, ecological, and climate change models.

Ecological Indicators↗

USGS2012

Matrix population models from 20 studies of perennial plant populations

Demographic transition matrices are one of the most commonly applied population models for both basic and applied ecological research. The relatively simple framework of these models and simple, easily interpretable summary statistics they produce have prompted the wide use of these models across an exceptionally broad range of taxa. Here, we provide annual transition matrices and observed stage structures/population sizes for 20 perennial plant species which have been the focal species for long-term demographic monitoring. These data were assembled as part of the "Testing Matrix Models" working group through the National Center for Ecological Analysis and Synthesis (NCEAS). In sum, these data represent 82 populations with >460 total population-years of data. It is our hope that making these data available will help promote and improve our ability to monitor and understand plant population dynamics.

Ecology↗

USGS2023

A synergistic future for AI and ecology

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence—they are critical for both persisting and thriving in an uncertain future.

Proceedings of the National Academy of Sciences↗

USGS2015

Predictive modelling of habitat use by marine predators with respect to the abundance and depth distribution of pelagic prey

Understanding the ecological processes that underpin species distribution patterns is a fundamental goal in spatial ecology. However, developing predictive models of habitat use is challenging for species that forage in marine environments, as both predators and prey are often highly mobile and difficult to monitor. Consequently, few studies have developed resource selection functions for marine predators based directly on the abundance and distribution of their prey. We analysed contemporaneous data on the diving locations of two seabird species, the shallow-diving Peruvian Booby ( Sula variegata ) and deeper diving Guanay Cormorant ( Phalacrocorax bougainvilliorum ), and the abundance and depth distribution of their main prey, Peruvian anchoveta ( Engraulis ringens ). Based on this unique data set, we developed resource selection functions to test the hypothesis that the probability of seabird diving behaviour at a given location is a function of the relative abundance of prey in the upper water column. For both species, we show that the probability of diving behaviour is mostly explained by the distribution of prey at shallow depths. While the probability of diving behaviour increases sharply with prey abundance at relatively low levels of abundance, support for including abundance in addition to the depth distribution of prey is weak, suggesting that prey abundance was not a major factor determining the location of diving behaviour during the study period. The study thus highlights the importance of the depth distribution of prey for two species of seabird with different diving capabilities. The results complement previous research that points towards the importance of oceanographic processes that enhance the accessibility of prey to seabirds. The implications are that locations where prey is predictably found at accessible depths may be more important for surface foragers, such as seabirds, than locations where prey is predictably abundant. Analysis of the relative importance of abundance and accessibility is essential for the design and evaluation of effective management responses to reduced prey availability for seabirds and other top predators in marine systems.

Gulf of Guinea↗

USGS2008

The human footprint in the west: a large-scale analysis of anthropogenic impacts.

Anthropogenic features such as urbanization, roads, and power lines, are increasing in western United States landscapes in response to rapidly growing human populations. However, their spatial effects have not been evaluated. Our goal was to model the human footprint across the western United States. We first delineated the actual area occupied by anthropogenic features, the physical effect area. Next, we developed the human footprint model based on the ecological effect area, the zone influenced by features beyond their physical presence, by combining seven input models: three models quantified top-down anthropogenic influences of synanthropic predators (avian predators, domestic dog and cat presence risk), and four models quantified bottom-up anthropogenic influences on habitat (invasion of exotic plants, human-caused fires, energy extraction, and anthropogenic wildland fragmentation). Using independent bird population data, we found bird abundance of four synanthropic species to correlate positively with human footprint intensity and negatively for three of the six species influenced by habitat fragmentation. We then evaluated the extent of the human footprint in relation to terrestrial (ecoregions) and aquatic systems (major rivers and lakes), regional management and conservation status, physical environment, and temporal changes in human actions. The physical effect area of anthropogenic features covered 13% of the western United States with agricultural land (9.8%) being most dominant. High-intensity human footprint areas (class 8 – 10) overlapped highly productive low-elevation private landholdings and covered 7% of the western United States compared to 48% for low-intensity areas (class 1 – 3), which were confined to low-productivity high-elevation federal landholdings. Areas within 1 km of rivers were more affected by the human footprint compared to lakes. Percentage human population growth was higher in low-intensity human footprint areas. The disproportional regional effects of the human footprint on landscapes in the western United States create a challenge to management of ecosystems and wildlife populations. Using footprint models, managers can plan land use actions, develop restoration scenarios, and identify areas of high conservation value at local landscapes within a regional context. Moreover, human footprint models serve as a tool to stratify landscapes for studies investigating floral and faunal response to human disturbance intensity gradients.

Ecological Applications↗

USGS2013

Ecological prediction with nonlinear multivariate time-frequency functional data models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Lower Missouri River↗

USGS2026

Environmental and demographic mechanisms underlying population dynamics provide relative stability in an island songbird

Understanding demographic and ecological mechanisms underlying population dynamics is a key goal in population ecology and can inform effective conservation and management. Species may be well adapted to persist under normal ranges of environmental conditions, but increasingly novel conditions due to climate change may strain their demographic buffering mechanisms. Small isolated populations on islands are expected to be especially vulnerable to declines caused by novel environmental conditions, but the ecological drivers of population dynamics on islands are often unknown. We used an integrated population model to (1) examine ecological drivers of population growth and (2) assess global population trends of the Island Scrub-Jay ( Aphelocoma insularis ) from 2009 to 2019. Our results suggest that population size increased slightly over this interval despite declines during severe drought. We also found evidence that density dependence, precipitation, and food availability affected fecundity and the survival of nonbreeding individuals. Breeder survival was relatively stable and had a weak effect on population growth as expected for long-lived species. Overall, our results provide an optimistic snapshot for this species by demonstrating resilience to contemporary drought but also emphasize the species' potential vulnerability due to its small population size.

California↗

USGS2019

Decision-making in agent-based modeling: A current review and future prospectus

All basic processes of ecological populations involve decisions; when and where to move, when and what to eat, and whether to fight or flee. Yet decisions and the underlying principles of decision-making have been difficult to integrate into the classical population-level models of ecology. Certainly, there is a long history of modeling individuals' searching behavior, diet selection, or conflict dynamics within social interactions. When all the individuals are given certain simple rules to govern their decision-making processes, the resultant population–level models have yielded important generalizations and theory. But it is also recognized that such models do not represent the way real individuals decide on actions. Factors that influence a decision include the organism's environment with its dynamic rewards and risks, the complex internal state of the organism, and its imperfect knowledge of the environment. In the case of animals, it may also involve complex social factors, and experience and learning, which vary among individuals. The way that all factors are weighed and processed to lead to decisions is a major area of behavioral theory. While classic population-level modeling is limited in its ability to integrate decision-making in its actual complexity, the development of individual- or agent-based models (IBM/ABMs) (we use ABM throughout to designate both “agent-based modeling” and an “agent-based model”) has opened the possibility of describing the way that decisions are made, and their effects, in minute detail. Over the years, these models have increased in size and complexity. Current ABMs can simulate thousands of individuals in realistic environments, and with highly detailed internal physiology, perception and ability to process the perceptions and make decisions based on those and their internal states. The implementation of decision-making in ABMs ranges from fairly simple to highly complex; the process of an individual deciding on an action can occur through the use of logical and simple (if-then) rules to more sophisticated neural networks and genetic algorithms. The purpose of this paper is to give an overview of the ways in which decisions are integrated into a variety of ABMs and to give a prospectus on the future of modeling of decisions in ABMs.

Frontiers in Ecology and Evolution↗

USGS2015

Adaptive invasive species distribution models: A framework for modeling incipient invasions

The utilization of species distribution model(s) (SDM) for approximating, explaining, and predicting changes in species’ geographic locations is increasingly promoted for proactive ecological management. Although frameworks for modeling non-invasive species distributions are relatively well developed, their counterparts for invasive species—which may not be at equilibrium within recipient environments and often exhibit rapid transformations—are lacking. Additionally, adaptive ecological management strategies address the causes and effects of biological invasions and other complex issues in social-ecological systems. We conducted a review of biological invasions, species distribution models, and adaptive practices in ecological management, and developed a framework for adaptive, niche-based, invasive species distribution model (iSDM) development and utilization. This iterative, 10-step framework promotes consistency and transparency in iSDM development, allows for changes in invasive drivers and filters, integrates mechanistic and correlative modeling techniques, balances the avoidance of type 1 and type 2 errors in predictions, encourages the linking of monitoring and management actions, and facilitates incremental improvements in models and management across space, time, and institutional boundaries. These improvements are useful for advancing coordinated invasive species modeling, management and monitoring from local scales to the regional, continental and global scales at which biological invasions occur and harm native ecosystems and economies, as well as for anticipating and responding to biological invasions under continuing global change.

Biological Invasions↗

USGS2006

GIS-based niche modeling for mapping species' habitats

Ecological a??niche modelinga?? using presence-only locality data and large-scale environmental variables provides a powerful tool for identifying and mapping suitable habitat for species over large spatial extents. We describe a niche modeling approach that identifies a minimum (rather than an optimum) set of basic habitat requirements for a species, based on the assumption that constant environmental relationships in a species' distribution (i.e., variables that maintain a consistent value where the species occurs) are most likely to be associated with limiting factors. Environmental variables that take on a wide range of values where a species occurs are less informative because they do not limit a species' distribution, at least over the range of variation sampled. This approach is operationalized by partitioning Mahalanobis D2 (standardized difference between values of a set of environmental variables for any point and mean values for those same variables calculated from all points at which a species was detected) into independent components. The smallest of these components represents the linear combination of variables with minimum variance; increasingly larger components represent larger variances and are increasingly less limiting. We illustrate this approach using the California Gnatcatcher (Polioptila californica Brewster) and provide SAS code to implement it.

Ecology↗

USGS2014

Land-margin ecosystem hydrologic data for the coastal Everglades, Florida, water years 1996-2012

Mangrove forests and salt marshes dominate the landscape of the coastal Everglades (Odum and McIvor, 1990). However, the ecological effects from potential sea-level rise and increased water flows from planned freshwater Everglades restoration on these coastal systems are poorly understood. The National Park Service (NPS) proposed the South Florida Global Climate Change Project (SOFL-GCC) in 1990 to evaluate climate change and the effect from rising sea levels on the coastal Everglades, particularly at the marsh/mangrove interface or ecotone (Soukup and others, 1990). A primary objective of SOFL-GCC project was to monitor and synthesize the hydrodynamics of the coastal Everglades from the upstream freshwater marsh to the downstream estuary mangrove. Two related hypotheses were set forward (Nuttle and Cosby, 1993): 1. There exists hydrologic conditions (tide, local rainfall, and upstream water deliveries), which characterize the location of the marsh/mangrove ecotone along the marine and terrestrial hydrologic gradient; and 2. The marsh/mangrove ecotone is sensitive to fluctuations in sea level and freshwater inflow from inland areas. Hydrologic monitoring of the SOFL-GCC network began in 1995 after startup delays from Hurricane Andrew (August 1992) and organizational transfers from the NPS to the National Biological Survey (October 1993) and the merger with the U.S. Geological Survey (USGS) Biological Research Division in 1996 (Smith, 2004). As the SOFL-GCC project progressed, concern by environmental scientists and land managers over how the diversion of water from Everglades National Park would affect the restoration of the greater Everglades ecosystem. Everglades restoration scenarios were based on hydrodynamic models, none of which included the coastal zone (Fennema and others, 1994). Modeling efforts were expanded to include the Everglades coastal zone (Schaffranek and others, 2001) with SOFL-GCC hydrologic data assisting the ecological modeling needs. In 2002, as a response for a more interdisciplinary science approach to understanding the coastal Everglades ecological system, the SOFL-GCC hydrology project was integrated into the “Dynamics of Land-Margin Ecosystems: Historical Change, Hydrology, Vegetation, Sediment, and Climate” study (Smith and others, 2002). Data from the ongoing study has been useful in providing an empirical hydrologic baseline for the greater Everglades ecosystem restoration science and management needs. The hydrology network consisted of 13 hydrologic gaging stations installed in the southwestern coastal region of Everglades National Park along three transects: Shark River (Shark or SH) transect, Lostmans River (Lostmans or LO) transect, and Chatham River (Chatham or CH) transect (fig. 1). There were five paired surface-water/groundwater gaging stations on the Shark transect (SH1, SH2, SH3, SH4, and SH5) and one stage gaging station (BSC) in the Big Sable Creek; four paired surface-water/groundwater gaging stations on the Lostmans transect (LO1, LO2, LO3, and LO4); and three paired surface-water/groundwater gaging stations on the Chatham transect (CH1, CH2, and CH3). Both surface-water and groundwater levels, salinities, and temperatures were monitored at the paired gaging stations. Rainfall was recorded at marsh and open canopy gaging stations. This report details the study introduction, method, and description of data collected, which are accessible through the final instantaneous hydrologic dataset stored in the USGS South Florida Information Access (SOFIA) South Florida Hydrology Database website, http://sofia.usgs.gov/exchange/sfl_hydro_data/location.html#brdlandmargin .

Florida↗