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USGS2022

Data synthesis for environmental management: A case study of Chesapeake Bay

Synthesizing large, complex data sets to inform resource managers towards effective environmental stewardship is a universal challenge. In Chesapeake Bay, a well-studied and intensively monitored estuary in North America, the challenge of synthesizing data on water quality and land use as factors related to a key habitat, submerged aquatic vegetation, was tackled by a team of scientists and resource managers operating at multiple levels of governance (state, federal). The synthesis effort took place over a two-year period (2016–2018), and the results were communicated widely to a) scientists via peer review publications and conference presentations; b) resource managers via web materials and workshop presentations; and c) the public through newspaper articles, radio interviews, and podcasts. The synthesis effort was initiated by resource managers at the United States Environmental Protection Agencys’ Chesapeake Bay Program and 16 scientist participants were recruited from a diversity of organizations. Multiple short, immersive workshops were conducted regularly to conceptualize the problem, followed by data analysis and interpretation that supported the preparation of the synthetic products that were communicated widely. Reflections on the process indicate that there are a variety of structural and functional requirements, as well as enabling conditions, that need to be considered to achieve successful outcomes from synthesis efforts.

Chesapeake Bay↗

USGS2025

Linking environmental variability to long-term demographic change of an endangered species using integrated population models

Understanding how species populations change with environmental conditions is important for implementing effective habitat management and conservation strategies. Challenges to evaluating population-level responses to environmental conditions arise when data are sparse or not spatiotemporally aligned, especially for at-risk species with small, declining numbers. We synthesized 30 years (1992–2021) of three partially aligned data sets to build a Bayesian integrated population model (IPM) and evaluate demographic and environmental drivers of growth rates for six separately managed ‘subpopulations’ (A–F) of the federally endangered Cape Sable seaside sparrow endemic to the Florida Everglades. We found that juvenile survival peaked at inundation periods (hydroperiods) around 100–220 days and dropped sharply outside those values, while adult survival increased with longer periods of water depth <20 cm, but not with longer periods of water depth >20 cm. Fecundity increased when water depths were more stable, more area was dry, intervals between fires were longer and less area was burned. Changes in population growth rates tended to occur in years that juvenile and adult survival were associated with hydroperiod, especially in the two largest subpopulations B and E. Population growth rates were also associated with hydrologic conditions during the breeding season and fire dynamics through changes in fecundity, most notably in the smaller subpopulations A, C/F and D. Synthesis and applications . Our IPM represents the first long-term population analysis of the Cape Sable seaside sparrow connecting demographic processes to environmental factors. Our results suggest that sustaining periods of shallow water year-round may enhance Cape Sable seaside sparrow survival and population growth. Also, limiting water depth variability and maintaining dry conditions during the breeding season and inhibiting fires in consecutive years may increase fecundity and population growth. Identifying the mechanistic links between environmental and population dynamics could inform how species are expected to respond to management decisions and anticipated ecosystem changes.

Florida↗

USGS1999

Environmental issues associated with fossil fuel resources; an evaluation of research opportunities for the U.S. Geological Survey's Energy Resources Program

Fossil fuel consumption in the United States has nearly tripled within the last 50 years (fig. 1), and is expected to continue increasing. As our knowledge and awareness of environmental consequences related to fossil fuel extraction and use grow, we as a nation, face the challenge of balancing our energy requirements with a desire for environmentally cleaner fuel. To help meet this challenge, the Energy Resources Program (ERP) of the Geologic Division (GD), U.S. Geological Survey (USGS) sponsored three internal workshops and a World Wide Web (WWW) workshop. The primary goal of these workshops was to identify those environmental issues that are related to fossil fuels, are national in scope, and are within the purview of the USGS as an earth-science government agency. Those issues so identified will be prioritized according to ERP's future research plan and according to the extent they provide an environmental framework for ERP's petroleum and coal assessments. The purpose of this paper is to provide the results of the internal and WWW workshops as proposed topics of research for future ERP work, delineate studies to answer the questions related to each proposed research area, and link the studies to the seven science goals of the Geologic Division. The information provided herein will be used in preparing the USGS Geologic Division's FY2000 prospectus and ERP's next five-year strategic plan. Acknowledgements. The authors wish to acknowledge information on ERP environmental projects provided by Les Magoon, Paul Lillis, Keith Kvenvolden, Tom Lorenson, and Bob Rosenbauer. Peter Schweitzer was instrumental in providing the software for the WWW workshop, Cheryl Adkisson for constructing the workshop web pages, and Laura Friedrich for constructing the web pages summarizing the internal workshops. Stan Mroczkowski, Jim Otton, Donna Read, and Dorothy Tanti provided notes of or logistical assistance for the internal workshops. This report reflects the collective input of numerous USGS managers and scientists their participation in the workshops is greatly appreciated.

Open-File Report↗

USGS2025

Public, bottled, and private drinking water: Shared contaminant-mixture exposures and effects challenge

BACKGROUND: Humans are primary drivers of environmental contaminant exposures worldwide, including in drinking-water (DW). In the United States (US), point-of-use DW (POU DW) is supplied via private tapwater (TW, predominantly private wells), public-supply TW, and bottled water (BW). Differences in management, monitoring, and messaging and lack of directly intercomparable exposure data influence the actual and perceived quality and safety of different DW supplies and directly impact consumer decision making. OBJECTIVES: The purpose of this paper is to provide a meta-analysis (quantitative synthesis) of POU DW contaminant mixture exposures and corresponding potential human health effects of private-TW, public-TW, and BW by aggregating exposure results and harmonizing apical health benchmark weighted and bioactivity weighted effects predictions across previous studies by this research group. DISCUSSION: Simultaneous exposures to multiple inorganic and organic contaminants of known or suspected human-health concern are common across all three DW supplies, with substantial variability observed in each and no systematic difference in predicted cumulative risk between supply chains. Differences in contaminant or contaminant class exposures (e.g., trace metals, disinfection byproducts), with important implications for DW quality improvements, were observed and attributed to corresponding differences in regulation and compliance monitoring. CONCLUSION: The results indicate that human-health risks from contaminant exposures are common to and comparable in all three DW supplies, including BW. Importantly, this study’s target analytical coverage, which exceeds that currently feasible for water purveyors or homeowners, nevertheless is a substantial underestimation of the full breadth of contaminant mixtures in the environment and potentially present in DW. Thus, the results emphasize the need for improved understanding of the adverse human-health implications of long-term exposures to low level inorganic /organic contaminant mixtures across all three distribution pipelines and do not support commercial messaging of BW as a systematically safer alternative to public-TW. Regardless of the supply, increased engagement in source-water protection and drinking-water treatment, including consumer point of use treatment, is necessary to reduce risks associated with long-term DW contaminant exposures, especially in vulnerable populations, and to reduce environmental waste and plastics contamination.

Environment International↗

USGS2004

Development and Application of Pyrolysis Gas Chromatography/Mass Spectrometry for the Analysis of Bound Trinitrotoluene Residues in Soil

TNT (trinitrotoluene) is a contaminant of global environmental significance, yet determining its environmental fate has posed longstanding challenges. To date, only differential extraction-based approaches have been able to determine the presence of covalently bound, reduced forms of TNT in field soils. Here, we employed thermal elution, pyrolysis, and gas chromatography/mass spectrometry (GC/MS) to distinguish between covalently bound and noncovalently bound reduced forms of TNT in soil. Model soil organic matter-based matrixes were used to develop an assay in which noncovalently bound (monomeric) aminodinitrotoluene (ADNT) and diaminonitrotoluene (DANT) were desorbed from the matrix and analyzed at a lower temperature than covalently bound forms of these same compounds. A thermal desorption technique, evolved gas analysis, was initially employed to differentiate between covalently bound and added 15N-labeled monomeric compounds. A refined thermal elution procedure, termed "double-shot analysis" (DSA), allowed a sample to be sequentially analyzed in two phases. In phase 1, all of an added 15N-labeled monomeric contaminant was eluted from the sample at relatively low temperature. In phase 2 during high-temperature pyrolysis, the remaining covalently bound contaminants were detected. DSA analysis of soil from the Louisiana Army Ammunition Plant (LAAP; ???5000 ppm TNT) revealed the presence of DANT, ADNT, and TNT. After scrutinizing the DSA data and comparing them to results from solvent-extracted and base/acid-hydrolyzed LAAP soil, we concluded that the TNT was a noncovalently bound "carryover" from phase 1. Thus, the pyrolysis-GC/MS technique successfully defined covalently bound pools of ADNT and DANT in the field soil sample.

Environmental Science & Technology↗

USGS2025

Similar population dynamics before and after a chytridiomycosis outbreak in a tropical riparian amphibian species

Emerging infectious diseases can cause rapid, widespread host mortality, and the lack of demographic data before and after pathogen emergence complicates understanding mechanisms of host persistence. This challenge is further compounded by environmental conditions that influence host behavior, while driving pathogen growth and virulence. These interactions create complex disease outcomes that hinder predictions of when and how hosts endure pathogen outbreaks. Here, we analyzed 10 years of capture-mark-recapture data (2000–2014) spanning wet and dry seasons for male Espadarana prosoblepon in El Copé, Panama, encompassing a period before (2000–2004) and after (2010–2014) a Batrachochytrium dendrobatidis ( Bd ) outbreak using Jolly-Seber models. We found that post- Bd male E. prosoblepon population size (range in mean population size among primary periods = 136–225 individuals) was similar to pre- Bd population size (range in mean population size among primary periods = 201–242 individuals). Pre- Bd , average monthly survival probability in the wet season was 0.93 (95% credible interval [CI] = 0.90–0.96). Post- Bd , uninfected individuals had survival probability higher in the wet season (mean = 0.97; [95% CI = 0.95–0.98]) than the dry season (mean = 0.90 [95% CI = 0.84–0.94]), while survival probability for infected individuals decreased as a function of Bd infection intensity. Pre- Bd , mean monthly per-capita entry probability was 0.07 (95% CI = 0.05–0.10), and post- Bd , mean monthly per-capita entry probability was 0.06 (95% CI = 0.00–0.10). Lastly, infection probability during the wet season was lower (mean = 0.04 [95% CI = 0.03–0.05]) than the dry season (mean = 0.10 [95% CI = 0.05–0.15]), and recovery probability during the wet season was lower (mean = 0.19 [95% CI = 0.11–0.28]) than the dry season (mean = 0.54 [95% CI = 0.20–0.88]). Our findings suggest that survival probabilities of uninfected individuals, as well as per-capita entry probabilities, are similar pre- and post- Bd , leading to a stable and similar sized pre- Bd population. These results contribute to understanding disease dynamics and tropical amphibian ecology.

Ecosphere↗

USGS2024

Balancing the scales: Including under-represented herptile species in a One Health approach

The One Health High-Level Expert Panel’s definition of One Health includes optimizing the health of people, animals (wild and domestic) and ecosystems. For many One Health practitioners, wildlife that can spread zoonoses are the focus, particularly if they can come in contact with people. However, ecosystem health is often best-indicated by less-encountered species, for instance, amphibians and reptiles. This review highlights how these taxa can benefit human health and well-being, including cultural significance, as well as their impact on plant, animal and environmental health. We highlight current challenges to the health of these species and the need to include them in the One Health Joint Action Plan. We conclude with a call to action for inclusion of amphibians and reptiles in a One Health approach.

Research Directions: One Health↗

USGS2020

From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world

Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon‐climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool‐based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool‐based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. Here, we propose a novel conceptual framework to explore controls on belowground carbon cycling: P robabilistic R epresentation of O rganic M atter I nteractions within the S oil E nvironment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.

Global Change Biology↗

USGS2016

Terrestrial nitrogen cycling in Earth system models revisited

Understanding the degree to which nitrogen (N) availability limits land carbon (C) uptake under global environmental change represents an unresolved challenge. First-generation ‘C-only’vegetation models, lacking explicit representations of N cycling,projected a substantial and increasing land C sink under rising atmospheric CO 2 concentrations. This prediction was questioned for not taking into account the potentially limiting effect of N availability, which is necessary for plant growth (Hungate et al.,2003). More recent global models include coupled C and N cycles in land ecosystems (C–N models) and are widely assumed to be more realistic. However, inclusion of more processes has not consistently improved their performance in capturing observed responses of the global C cycle (e.g. Wenzel et al., 2014). With the advent of a new generation of global models, including coupled C, N, and phosphorus (P) cycling, model complexity is sure to increase; but model reliability may not, unless greater attention is paid to the correspondence of model process representations ande mpirical evidence. It was in this context that the ‘Nitrogen Cycle Workshop’ at Dartington Hall, Devon, UK was held on 1–5 February 2016. Organized by I. Colin Prentice and Benjamin D. Stocker (Imperial College London, UK), the workshop was funded by the European Research Council,project ‘Earth system Model Bias Reduction and assessing Abrupt Climate change’ (EMBRACE). We gathered empirical ecologists and ecosystem modellers to identify key uncertainties in terrestrial C–N cycling, and to discuss processes that are missing or poorly represented in current models.

New Phytologist↗

USGS2024

Inoculated biocrust cover and functions diverged over a gradient of soil textures and water availability

Restoring biological crust (biocrust) in disturbed drylands is challenging due to the difficult environmental conditions, such as limited soil moisture, low soil nutrients, and extreme temperatures, that impede growth. Understanding how the key components of biocrust—mosses, lichens, and cyanobacteria—react to different environmental factors informs the optimal timing, locations, and species composition for biocrust reintroduction, thereby increasing the likelihood of establishment. Here, we inoculated soils with a diverse range of biocrust organisms, analogous to seeding an area with diverse vascular plant seeds, and varied environmental conditions to observe how these changes influenced the development and functions of reintroduced biocrust. We found that by manipulating soil texture and time spent wet, we can change the proportional cover of biocrust within a restoration-like setting. Specifically, we found that 4 months after inoculation, finer textured soils that received more water become dominated by moss cover, while coarser textured soils with less water remained dominated by cyanobacteria cover, and the interactions between texture and time spent wet strongly influenced cover. We found biocrust morphological group cover had a small, but detectable, effect on ecosystem functions (soil stability and nitrogenase activity, a proxy for nitrogen fixation), but that environmental conditions had a stronger impact on the functions we measured. Manipulative experiments in controlled environments, like this one, can help elucidate the mechanisms underlying the establishment rate and patterns of biocrusts post-inoculation, and inform implementation of inoculations in the field.

New Mexico↗

USGS2016

“One Health” or three? Publication silos among the One Health disciplines

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation—especially along the veterinary/ecological research interface—remains.

PLoS Biology↗

USGS2017

Distributional changes and range predictions of downy brome (Bromus tectorum) in Rocky Mountain National Park

Downy brome (Bromus tectorum L.), an invasive winter annual grass, may be increasing in extent and abundance at high elevations in the western United States. This would pose a great threat to high-elevation plant communities and resources. However, data to track this species in high-elevation environments are limited. To address changes in the distribution and abundance of downy brome and the factors most associated with its occurrence, we used field sampling and statistical methods, and niche modeling. In 2007, we resampled plots from two vegetation surveys in Rocky Mountain National Park for presence and cover of downy brome. One survey was established in 1993 and had been resampled in 1999. The other survey was established in 1996 and had not been resampled until our study. Although not all comparisons between years demonstrated significant changes in downy brome abundance, its mean cover increased nearly fivefold from 1993 (0.7%) to 2007 (3.6%) in one of the two vegetation surveys (P = 0.06). Although the average cover of downy brome within the second survey appeared to be increasing from 1996 to 2007, this slight change from 0.5% to 1.2% was not statistically significant (P = 0.24). Downy brome was present in 50% more plots in 1999 than in 1993 (P = 0.02) in the first survey. In the second survey, downy brome was present in 30% more plots in 2007 than in 1996 (P = 0.08). Maxent, a species-environmental matching model, was generally able to predict occurrences of downy brome, as new locations were in the ranges predicted by earlier generated models. The model found that distance to roads, elevation, and vegetation community influenced the predictions most. The strong response of downy brome to interannual environmental variability makes detecting change challenging, especially with small sample sizes. However, our results suggest that the area in which downy brome occurs is likely increasing in Rocky Mountain National Park through increased frequency and cover. Field surveys along with predictive modeling will be vital in directing efforts to manage this highly invasive species. ?? Weed Science Society of America 2011.

Invasive Plant Science and Management↗

USGS2021

Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality

This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.

Technical Report↗

USGS2009

Population and habitat restoration - Preamble to section 5

Diadromous fish populations are particularly difficult to understand, model and manage because they traverse multiple habitats that present not only environmental, ecological, reproductive, and physiological challenges, but also frequently convey them across multiple management jurisdictions. Our knowledge of population-level effects is also dependent on the quality and extent of biological, population, and demographic data. For some species, such as Pacific salmon, populations are routinely monitored, life cycles are fairly well understood, and population trends are, in general, well documented. However, for other species such as anguillid eels, our understanding of life history is incomplete, population-level data are meager, and the trends in abundance are less clear.

Book chapter↗

USGS2013

LANDFIRE 2010 - updated data to support wildfire and ecological management

Wildfire is a global phenomenon that affects human populations and ecosystems. Wildfire effects occur at local to global scales impacting many people in different ways (Figure 1). Ecological concerns due to land use, fragmentation, and climate change impact natural resource use, allocation, and conservation. Access to consistent and current environmental data is a constant challenge, yet necessary for understanding the complexities of wildfire and ecological management. Data products and tools from the LANDFIRE Program help decision-makers to clarify problems and identify possible solutions when managing fires and natural resources. LANDFIRE supports the reduction of risk from wildfire to human lives and property, monitoring of fire danger, prediction of fire behavior on active incidents, and assessment of fire severity and impacts on natural systems [1] [2] [3]. LANDFIRE products are unique in that they are nationally consistent and provide the only complete geospatial dataset describing vegetation and wildland fuel information for the entire U.S. As such, LANDFIRE data are useful for many ecological applications [3]. For example, LANDFIRE data were recently integrated into a decision-support system for resource management and conservation decision-making along the Appalachian Trail. LANDFIRE is a joint effort between the U.S. Department of the Interior Office of Wildland Fire, U.S. Department of Agriculture Forest Service Fire & Aviation Management, and The Nature Conservancy. To date, seven versions of LANDFIRE data have been released, with each successive version improving the quality of the data, adding additional features, and/or updating the time period represented by the data. The latest version, LANDFIRE 2010 (LF 2010), released mid-2013, represents circa 2010 landscape conditions and succeeds LANDFIRE 2008 (LF 2008), which represented circa 2008 landscape conditions. LF 2010 used many of the same processes developed for the LF 2008 effort [3]. Ongoing refinement of the LANDFIRE vegetation and fuel data is necessary to improve the quality and usability of the data and to capture landscape disturbance. LANDFIRE relies on Landsat multi-spectral imagery to produce and update vegetation and fuel data. The deep Landsat archive provides data needed for vegetation classification, change analysis, and historical disturbance characterization, for which LANDFIRE has used more than 24,000 image scenes since the program’s inception. In addition, LF 2010 used airborne and spaceborne lidar, and spaceborne synthetic aperture radar (SAR) to map vegetation structure in areas where ground-based field information was lacking, including Alaska and U.S.-affiliated islands in the Caribbean and the Pacific. The mapping of insular areas is new for the 2010 data release; previous versions of LANDFIRE were limited to the conterminous U.S., Alaska, and Hawaii.

Earthzine↗

USGS2015

Estimating concentrations of fine-grained and total suspended sediment from close-range remote sensing imagery

Fluvial sediment, a vital surface water resource, is hazardous in excess. Suspended sediment, the most prevalent source of impairment of river systems, can adversely affect flood control, navigation, fisheries and aquatic ecosystems, recreation, and water supply (e.g., Rasmussen et al., 2009; Qu, 2014). Monitoring programs typically focus on suspended-sediment concentration (SSC) and discharge (SSQ). These time-series data are used to study changes to basin hydrology, geomorphology, and ecology caused by disturbances. The U.S. Geological Survey (USGS) has traditionally used physical sediment sample-based methods (Edwards and Glysson, 1999; Nolan et al., 2005; Gray et al., 2008) to compute SSC and SSQ from continuous streamflow data using a sediment transport-curve (e.g., Walling, 1977) or hydrologic interpretation (Porterfield, 1972). Accuracy of these data is typically constrained by the resources required to collect and analyze intermittent physical samples. Quantifying SSC using continuous instream turbidity is rapidly becoming common practice among sediment monitoring programs. Estimations of SSC and SSQ are modeled from linear regression analysis of concurrent turbidity and physical samples. Sediment-surrogate technologies such as turbidity promise near real-time information, increased accuracy, and reduced cost compared to traditional physical sample-based methods (Walling, 1977; Uhrich and Bragg, 2003; Gray and Gartner, 2009; Rasmussen et al., 2009; Landers et al., 2012; Landers and Sturm, 2013; Uhrich et al., 2014). Statistical comparisons among SSQ computation methods show that turbidity-SSC regression models can have much less uncertainty than streamflow-based sediment transport-curves or hydrologic interpretation (Walling, 1977; Lewis, 1996; Glysson et al., 2001; Lee et al., 2008). However, computation of SSC and SSQ records from continuous instream turbidity data is not without challenges; some of these include environmental fouling, calibration, and data range among sensors. Of greatest interest to many programs is a hysteresis in the relationship between turbidity and SSC, attributed to temporal variation of particle size distribution (Landers and Sturm, 2013; Uhrich et al., 2014). This phenomenon causes increased uncertainty in regression-estimated values of SSC, due to changes in nephelometric reflectance off the varying grain sizes in suspension (Uhrich et al., 2014). Here, we assess the feasibility and application of close-range remote sensing to quantify SSC and particle size distribution of a disturbed, and highly-turbid, river system. We use a consumer-grade digital camera to acquire imagery of the river surface and a depth-integrating sampler to collect concurrent suspended-sediment samples. We then develop two empirical linear regression models to relate image spectral information to concentrations of fine sediment (clay to silt) and total suspended sediment. Before presenting our regression model development, we briefly summarize each data-acquisition method.

Conference Paper↗

USGS2024

Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients

Mangrove is one of the most productive and sensitive ecosystems in the world. Due to the complexity and specificity of mangrove habitat, the development of mangrove is regulated by several factors. Species distribution models (SDMs) are effective tools to identify the potential habitats for establishing and regenerating the ecosystem. Such models usually include exclusively environmental factors. Nevertheless, recent studies have challenged this notion and highlight the importance of including biotic interactions. Both factors are necessary for a mechanistic understanding of the mangrove distribution in order to promote the protection and restoration of mangroves. Thus, we present a novel approach of combining environmental factors and interactions with salt marsh for projecting mangrove distributions at the global level and within latitudinal zones. To test the salt marsh interaction, we fit the MaxEnt model with two predicting sets: (1) environments only and (2) environments + salt marsh interaction index (SII). We found that both sets of models had good predictive ability, although the SII improved model performance slightly. Potential distribution areas of mangrove decrease with latitudes, and are controlled by biotic and abiotic factors. Temperature, precipitation and wind speed are generally critical at both global scale and ecotones along latitudes. SII is important on global scale, with a contribution of 5.9%, ranking 6th, and is particularly critical in the 10–30°S and 20–30°N zone. Interactions with salt marsh, including facilitation and competition, are shown to affect the distribution of mangroves at the zone of coastal ecotone, especially in the latitudinal range from 10° - 30°. The contribution of SII to mangrove distribution increases with latitudes due to the difference in the adaptive capacity of salt marsh plants and mangroves to environments. Totally, this study identified and quantified the effects of salt marsh on mangrove distribution by establishing the SII. The results not only facilitate to establish a more accurate mangrove distribution map, but also improve the efficiency of mangrove restoration by considering the salt marsh interaction in the mangrove management projects. In addition, the method of incorporating biotic interaction into SDMs through establish the biotic interaction index has contributed to the development of SDMs.

Journal of Environmental Management↗

USGS2012

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

Environmental Modelling and Software↗