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Results for “Ecological Modeling”

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At least 847 records · Page 47Linked to original sources
USGS2022

Informing surveillance through the characterization of outbreak potential of chronic wasting disease in white-tailed deer

Understanding the role that an environmental prion reservoir plays in the outbreak dynamics of chronic wasting disease (CWD) in free ranging white-tailed deer ( Odocoileus virginianus ) is critical for the allocation of disease surveillance resources by state and provincial wildlife agencies. We hypothesized that demographic, ecological, and epidemiological configurations naturally attenuate epidemic risk despite the introduction of infectious prions into a susceptible population of deer, but the magnitude of infectious prions in the environmental prion reservoir complicate outbreak expectations. We developed a Susceptible-Latent-Exposed-Infective (SLEI) compartment model to represent the dynamics of CWD epidemics in free-ranging white-tailed deer, then used the basic reproductive ratio ( R 0 "> R0 ) to pinpoint counties under which pathogenic introduction (transport of infectious bodily fluids, tissues, and carcasses through natural or anthropogenic means) naturally produced (or failed to produce) an epidemic. We found that the outlook for an epidemic hinged on transmission rates, the magnitude of environmental contamination, and system type (density-, frequency-, or density/frequency-dependent). CWD can persistently infect individuals living in a contaminated environment even if direct transmission is insufficient to sustain circulation. Theoretical results show that transmission of CWD cannot be exclusively density dependent, and must behave as either a mix between frequency and density dependent, or strictly frequency dependent. While the compartment model is a simplistic representation of reality and did not contain many complicating biological considerations, it was immediately useful in hypothesis generation, motivating the collection of additional data for use in more biologically detailed models, and in the allocation of finite surveillance resources to place emphasis on data collection in areas where an introduction of infectious prions is comparatively more likely to result in an epidemic.

Ecological Modelling↗
USGS2014

A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems

Ecological processes in arid lands are often described by the pulse-reserve paradigm, in which rain events drive biological activity until moisture is depleted, leaving a reserve. This paradigm is frequently applied to processes stimulated by one or a few precipitation events within a growing season. Here we expand the original framework in time and space and include other pulses that interact with rainfall. This new hierarchical pulse-dynamics framework integrates space and time through pulse-driven exchanges, interactions, transitions, and transfers that occur across individual to multiple pulses extending from micro to watershed scales. Climate change will likely alter the size, frequency, and intensity of precipitation pulses in the future, and arid-land ecosystems are known to be highly sensitive to climate variability. Thus, a more comprehensive understanding of arid-land pulse dynamics is needed to determine how these ecosystems will respond to, and be shaped by, increased climate variability.

Annual Review of Ecology, Evolution, and Systemati↗
USGS2015

Bayesian models: A statistical primer for ecologists

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models

Book↗
USGS2019

Pleistocene glacial cycles drove lineage diversification and fusion in the Yosemite toad (Anaxyrus canorus)

Species endemic to alpine environments can evolve via steep ecological selection gradients between lowland and upland environments. Additionally, many alpine environments have faced repeated glacial episodes over the past two million years, fracturing these endemics into isolated populations. In this “glacial pulse” model of alpine diversification, cycles of allopatry and ecologically divergent glacial refugia play a role in generating biodiversity, including novel admixed (“fused”) lineages. We tested for patterns of glacial pulse lineage diversification in the Yosemite toad ( Anaxyrus [ Bufo ] canorus ), an alpine endemic tied to glacially influenced meadow environments. Using double‐digest RADseq on populations densely sampled from a portion of the species range, we identified nine distinct lineages with divergence times ranging from 18 to 724 thousand years ago (ka), coinciding with multiple Sierra Nevada glacial events. Three lineages have admixed origins, and demographic models suggest these fused lineages have persisted throughout past glacial cycles. Directionality indices supported the hypothesis that some lineages recolonized Yosemite from east of the ice sheet, whereas other lineages remained in western refugia. Finally, refugial niche reconstructions suggest that low‐ and high‐elevation lineages have convergently adapted to similar climatic niches. Our results suggest glacial cycles and refugia may be important crucibles of adaptive diversity across deep evolutionary time.

California↗
USGS2014

Reconstructing historical habitat data with predictive models Read More: http://www.esajournals.org/doi/abs/10.1890/13-0327.1

Historical vegetation data are important to ecological studies, as many structuring processes operate at long time scales, from decades to centuries. Capturing the pattern of variability within a system (enough to declare a significant change from past to present) relies on correct assumptions about the temporal scale of the processes involved. Sufficient long-term data are often lacking, and current techniques have their weaknesses. To address this concern, we constructed multistate and artificial neural network models (ANN) to provide fore- and hindcast vegetation communities considered critical foraging habitat for an endangered bird, the Florida Snail Kite ( Rostrhamus sociabilis ). Multistate models were not able to hindcast due to our data not satisfying a detailed balance requirement for time reversibility in Markovian dynamics. Multistate models were useful for forecasting and providing environmental variables for the ANN. Results from our ANN hindcast closely mirrored the population collapse of the Snail Kite population using only environmental data to inform the model. The parallel between the two gives us confidence in the hindcasting results and their use in future demographic models.

Ecological Applications↗
USGS2014

A stage-structured, spatially explicit migration model for Myotis bats: mortality location affects system dynamics

Bats are ecologically and economically important species because they consume insects, transport nutrients, and pollinate flowers. Many species of bats, including those in the Myotis genus, are facing population decline and increased extinction risk. Despite these conservation concerns, few models exist for providing insight into the population dynamics of bats in a spatially explicit context. We developed a model for bats by considering the stage-structured colonial life history of Myotis bats with their annual migration behavior. This model provided insight into network dynamics. We specifically focused on two Myotis species living in the eastern United States: the Indiana bat ( M. sodalis ), which is a Federally listed endangered species, and the little brown bat ( M. lucifugus ), which is under consideration for listing as an endangered species. We found that multiple equilibria exist for the local, migratory subpopulations even though the total population was constant. These equilibria suggest the location and magnitude of stressors such as White-nose Syndrome, meteorological phenomena, or impacts of wind turbines on survival influence system dynamics and risk of population extirpation in difficult to predict ways.

Letters in Biomathematics↗
USGS2023

Spatial modeling of two mosquito vectors of West Nile virus using integrated nested Laplace approximations

The abundance of Culex restuans and Culex pipiens in relation to ecological predictors is poorly understood in regions of the United States where their ranges overlap. It is suspected that these species play different roles in spreading West Nile virus (WNV) in these regions, but few studies have modeled these species separately or accounted for spatial correlation using Bayesian methods. We used mosquito surveillance data collected by the Pennsylvania Department of Environmental Protection from 2002 to 2016 and integrated nested Laplace approximations with the stochastic partial differential equation approach to predict C. restuans and C. pipiens abundance in relation to several ecological predictors. We then made a predictive risk surface of abundance for each species at locations that were not sampled. Explanatory variables in the models included ecological variables previously described to be important predictors of the abundance of these mosquito species. Developed habitat, temperature, and precipitation were important predictor variables for the abundance of C. restuans , whereas developed habitat, snow water equivalent, and normalized difference water index were important predictor variables for the abundance of C. pipiens . The abundance of C. restuans had a negative relationship with developed habitat in contrast to C. pipiens abundance, which had a positive relationship with developed habitat. Julian date was modeled as a temporal trend for both species and showed C. restuans to be more abundant from late April through late June and C. pipiens to be more abundant from July through September. A seasonal crossover was observed between these two species on Julian day 185, 4 July. We observed different spatial patterns of abundance in the predictive risk maps of each of the species. Our results indicate that modeling the abundance of these species spatially and separately in regions where these two mosquito vectors coexist can help gain further insight into understanding the epidemiology of WNV in human and susceptible animal populations.

Pennsylvania↗
USGS2011

Proactive aquatic ecotoxicological assessment of room-temperature ionic liquids

Aquatic environments are being contaminated with a myriad of anthropogenic chemicals, a problem likely to continue due to both unintentional and intentional releases. To protect valuable natural resources, novel chemicals should be shown to be environmentally safe prior to use and potential release into the environment. Such proactive assessment is currently being applied to room-temperature ionic liquids (ILs). Because most ILs are water-soluble, their effects are likely to manifest in aquatic ecosystems. Information on the impacts of ILs on numerous aquatic organisms, focused primarily on acute LC50 and EC50 endpoints, is now available, and trends in toxicity are emerging. Cation structure tends to influence IL toxicity more so than anion structure, and within a cation class, the length of alkyl chain substituents is positively correlated with toxicity. While the effects of ILs on several aquatic organisms have been studied, the challenge for aquatic toxicology is now to predict the effects of ILs in complex natural environments that often include diverse mixtures of organisms, abiotic conditions, and additional stressors. To make robust predictions about ILs will require coupling of ecologically realistic laboratory and field experiments with standard toxicity bioassays and models. Such assessments would likely discourage the development of especially toxic ILs while shifting focus to those that are more environmentally benign. Understanding the broader ecological effects of emerging chemicals, incorporating that information into predictive models, and conveying the conclusions to those who develop, regulate, and use those chemicals, should help avoid future environmental degradation.

Current Organic Chemistry↗
USGS2022

A practical guide to understanding and validating complex models using data simulations

Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood-based approaches (e.g. packages lme4 ) and machine learning methods. These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journal Nature Ecology & Evolution , and we found that the majority of published papers did not report any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods. Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journal Methods Ecology & Evolution . We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement. Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.

Methods in Ecology and Evolution↗
USGS2010

Rejoinder: Sifting through model space

Observational data sets generated by complex processes are common in ecology. Traditionally these have been very challenging to analyze because of the limitations of available statistical tools. This seems to be changing, and these are exciting times to be involved with ecological statistics, not just because of the neo-Bayesian revival but also because of the proliferation of computationally intensive methods in general. It is now possible to fit much richer models to observational data than in the relatively recent past, which in turn has stimulated much interest in how to evaluate and compare such models. In such an immature, vibrant, and rapidly growing field, not everyone is going to agree on the best way to do things. This is reflected in the contrast of opinions offered by the discussants. Each offers a thoughtful and thought-provoking critique of our work that reflects the current thinking in a non-negligible segment of the ecological data analysis community. We want to thank them for their insights.

Ecology↗
USGS2021

Habitat suitability index model improvements

Habitat suitability index (HSI) models were developed for the 2023 Coastal Master Plan to evaluate the potential effects of coastal restoration and protection projects on habitat for key coastal fish, shellfish, and wildlife species. These species included: eastern oyster, brown shrimp, white shrimp, blue crab, crayfish, gulf menhaden, spotted seatrout, largemouth bass, American alligator, gadwall, mottled duck, brown pelican, seaside sparrow, and bald eagle. Most of these species were included in the 2017 Coastal Master Plan analyses, and the HSI models from that effort were refined and improved following the recommendations described in the technical memorandum: 2023 Coastal Master Plan Habitat Suitability Index Model Improvement Recommendations (Sable et al., 2019). In addition to model improvements, HSI models were created for seaside sparrow and bald eagle, both of which are new species for the master plan analyses. For the HSI models that are primarily literature-based, literature reviews were conducted for recent studies that could be used to improve the suitability index (SI) relationships that compose the models. As a result of this review, modifications were made to the salinity-related SIs of the oyster model including: expanding the time period used for salinity effects to spawning; adjusting the range of suitable annual average salinity to be more representative of Louisiana populations; and making oyster’s minimum salinity tolerance temperature dependent. In addition, a new SI was incorporated in the oyster HSI model that accounts for the effects of sediment deposition on oysters. The crayfish HSI model was improved by adjusting the time periods used for the SIs that describe the hydrology required for the crayfish life cycle, and the soil characteristics SI that was part of the 2017 crayfish model was removed because soil conditions do not appear to be limiting for crayfish burrow construction in coastal Louisiana. The other literature-based HSI models from the 2017 Coastal Master Plan, i.e., American alligator, gadwall, mottled duck, and brown pelican, were unchanged, with the exception of a small adjustment made to the suitability of forested wetlands for gadwall. Lastly, a literature-based HSI model was created for seaside sparrow that consists of SIs related to vegetated habitat type, marsh vegetation coverage, and marsh elevation. Statistical-based HSI models were developed for brown shrimp (both small and large juvenile stages), white shrimp (small and large juvenile stages), blue crab (juvenile stage), gulf menhaden (juvenile and adult stages), spotted seatrout (juvenile and adult stages), largemouth bass, and bald eagle. The bald eagle HSI model was developed from a bald eagle nest probability of occurrence model that related nest occurrence from survey data with land cover type. The resulting model showed that combinations of forested wetlands, flotant marsh, and open water habitats were most suitable for nesting bald eagles. The 2023 fish, shrimp, and blue crab HSI models were developed using new approaches for the formulation of the water quality and structural habitat SIs that compose the models. For the 2017 models, the water quality SI was derived using only generalized linear mixed models (GLMMs) to estimate the relationship between salinity, water temperature, and species’ catch. For the 2023 models, however, multiple GLMMs and generalized additive models (GAMMs) were created for each species or life stage. These alternative models were compared and a single model that performed well statistically and was ecologically reasonable was selected for the species’ water quality SI. The structural habitat SI was developed using a meta-analysis of published literature to estimate the relative importance of various estuarine habitats to the fish and shellfish species. The results of this analysis were then used to modify the 2017 structural habitat SI relationship to account for the added habitat value of submerged aquatic vegetation and oyster reefs, which are also important habitats for juvenile fish and shellfish. Similar to the 2017 fish, shrimp, and blue crab models, the water quality and structural habitat SIs were then combined to create the 2023 HSI models. The 2023 Coastal Master Plan HSI models were integrated with the Integrated Compartment Model (and are referred to as ICM-HSIs) and tested using environmental output from the 2017 Coastal Master Plan Future Without Action scenario. The tests showed that, in general, the models produced reasonable representations of species’ habitat distribution. Furthermore, the improvements made to the oyster, crayfish, fish, shrimp, and blue crab HSI models generally yielded more realistic results compared to the 2017 HSI models.

Report↗
USGS2020

Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance

Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush ( Artemisia tridentata ) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass ( Pseudoroegneria spicata ) and Thurber's needlegrass ( Achnatherum thurberianum ) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass ( Bromus tectorum ) and medusahead ( Taeniatherum caput-medusae ), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.

Oregon↗
USGS2024

Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes

Conservation prioritization frameworks are used worldwide to identify species at greatest risk of extinction and to allocate limited resources across regions, species, and populations. Conservation prioritization can be impeded by ecological knowledge gaps and data deficiency, especially in freshwater species inhabiting highly complex aquatic ecosystems. Therefore, we developed a flexible approach that calculates a species' imperilment risk based on the conservation principles of resiliency, redundancy, and representation (i.e., the “three R's”). Our approach organizes data on species traits, distributions, population connectivity, and threats within a Bayesian belief network capable of predicting resiliency and redundancy within representative ecological settings. Empirical data and expert judgment inform the model to provide robust and repeatable risk assessments for rare and data-deficient species. The model calculates resiliency at hierarchical spatial scales from distributional trends and population strength. Redundancy is estimated from the connectivity and quantities of extant populations. Resiliency, redundancy, and species' inherent vulnerability based on species traits collectively estimate extirpation risk within each unique ecological setting. Extirpation risks across ecological settings characterize representation and are aggregated to estimate global imperilment risk. We demonstrate the model's utility with Piebald Madtom ( Noturus gladiator ), a species petitioned for listing under the U.S. Endangered Species Act. Our results revealed that resiliency, redundancy, and extirpation risks can vary spatially across the species' range while identifying populations where additional sampling could disproportionally reduce uncertainty in estimated global imperilment risk. Our approach could standardize and expedite conservation status assessments, identify opportunities for early management intervention of at-risk species and populations, and strategically reduce uncertainty by focusing monitoring and research on priority information gaps.

Ecosphere↗
USGS2023

A comprehensive multi-state conditional occupancy model for evaluating interactions of non-native and native species

A major challenge in ecology is disentangling interactions of non-native, potentially invasive species on native species. Conditional two-species occupancy models examine the effects of dominant species (e.g., non-native) on subordinate species (e.g., native) while considering the possibility that occupancy of one species may affect occupancy and/ or detection of the other. Although conditional two-species models are useful for evaluating the influence of one species on presence of another, it is possible that species interactions are density dependent. Therefore, we developed a novel two-species occupancy model that incorporates multiple abundance states (i.e., absent, present, abundant) of the native species. We showcase the utility of this model with a case study that incorporates random effects and covariates on both occupancy and detection to help disentangle species interactions given varying occupancy and detection in different abundance states. We use snorkel survey data from the Umpqua basin, Oregon, where it is hypothesized that smallmouth bass Micropterus dolomieu , a non-native piscivore, exclude Umpqua chub Oregonichthys kalawatseti , a small endemic minnow. From our two-species multi-state (2SMS) model, we concluded that average occupancy was low for both fishes, and that when non-native bass were present, overall native chub occupancy in the present (0.18 ± 0.05 SD) and abundant (0.19 ± 0.03) states was higher than when non-natives were absent (0.14 ± 0.02/ 0.08 ± 0.02), indicating the non-native was not excluding the native species. By incorporating a species interaction factor, we found a positive association (6.75 ± 5.54 SD) between native chub and non-native bass. The covariates strongly related to occupancy were elevation, algae, and land cover type (urban and shrub). Detection probability for both species (0.21–0.82) was most strongly related to the covariates day of year, water temperature, gravel substrate, and stream order/ magnitude. Incorporation of detection probability and covariates enabled interpretation of interactions between the two species that may have been missed without their inclusion in the modeling process. Our new 2SMS occupancy model can be used by scientists and managers with a broad range of survey and covariate data to disentangle species interactions problems to help them inform management decisions.

Frontiers in Ecology and Evolution↗
USGS2010

Advances in the hydrogeochemistry and microbiology of acid mine waters

The last decade has witnessed a plethora of research related to the hydrogeochemistry and microbiology of acid mine waters and associated tailings and waste-rock waters. Numerous books, reviews, technical papers, and proceedings have been published that examine the complex bio-geochemical process of sulfide mineral oxidation, develop and apply geochemical models to site characterization, and characterize the microbial ecology of these environments. This review summarizes many of these recent works, and provides references for those investigating this field. Comparisons of measured versus calculated Eh and measured versus calculated pH for water samples from several field sites demonstrate the reliability of some current geochemical models for aqueous speciation and mass balances. Geochemical models are not, however, used to predict accurately time-dependent processes but to improve our understanding of these systems and to constrain possible processes that contribute to actual or potential water quality issues. Microbiological studies are demonstrating that there is much we have yet to learn about the types of different microorganisms and their function and ecology in mine-waste environments. A broad diversity of green algae, bacteria, archaea, yeasts, and fungi are encountered in acid mine waters, and a better understanding of their ecology and function may potentially enhance remediation possibilities as well as our understanding of the evolution of life.

International Geology Review↗
USGS2000

Gap analysis: Concepts, methods, and recent results

Rapid progress is being made in the conceptual, technical, and organizational requirements for generating synoptic multi-scale views of the earth's surface and its biological content. Using the spatially comprehensive data that are now available, researchers, land managers, and land-use planners can, for the first time, quantitatively place landscape units - from general categories such as 'Forests' or 'Cold-Deciduous Shrubland Formation' to more categories such as 'Picea glauca-Abies balsamea-Populus spp. Forest Alliance' - in their large-area contexts. The National Gap Analysis Program (GAP) has developed the technical and organizational capabilities necessary for the regular production and analysis of such information. This paper provides a brief overview of concepts and methods as well as some recent results from the GAP projects. Clearly, new frameworks for biogeographic information and organizational cooperation are needed if we are to have any hope of documenting the full range of species occurrences and ecological processes in ways meaningful to their management. The GAP experience provides one model for achieving these new frameworks.

Landscape Ecology↗
USGS2004

Evaluating the effect of salinity on a simulated American Crocodile ( Crocodylus acutus ) population with applications to conservation and Everglades restoration

Everglades restoration will alter the hydrology of South Florida, affecting both water depth and salinity levels in the southern fringes of the Everglades, the habitat of the endangered American crocodile ( Crocodylus acutus ). A key question is what the effects of these hydrologic changes will be on the crocodile population. Reliable predictions of the viability of endangered species under a variety of management scenarios are of vital importance in conservation ecology. Juvenile American crocodiles are thought to be sensitive to high salinity levels, suffering reduced mass, and potentially reduced survivorship and recruitment. This could negatively impact the population recovery. We addressed the management issue of how the crocodile population will respond to alterations in hydrology with a spatially explicit individual-based model. The model is designed to relate water levels, salinities, and dominant vegetation to crocodile distribution, abundance, population growth, individual growth, survival, nesting effort, and nesting success. Our analysis shows that Everglades restoration, through its effects on water flow to estuaries, may benefit crocodile populations if increased freshwater flow reduces the chance that regional salinity levels exceed levels where small individuals lose mass. In addition, we conclude that conservation priority should be placed on reducing anthropogenic sources of mortality on large individuals, such as road mortality. Finally, research should focus on estimates of annual survivorship for large individuals.

Florida↗
USGS2019

Back to the future: Rebuilding the Everglades

Society values landscapes that are engrained in cultural tradition and have a rich connection with human history. As such, there has been a concerted effort to look at the pristine past and develop plans to move the past into the future. However, bringing the past back is constrained by hysteretic changes, irrevocable damages, and anthropogenic trends that do not reflect past conditions. The scale of the Everglades and its importance to water supply and flood control is such that a full recovery, to past, pre-drainage conditions, is not possible. What is possible? The answer is the federally authorized Comprehensive Everglades Restoration Plan (CERP) and the first, most significant implementation of the $12 Billion CERP is the $2 Billion Central Everglades Planning Project (CEPP). CEPP is our “flux-capacitor” in the DeLorean sports car that generates the ability to go back and forth in time, in the movie series “Back to the Future.” The primary hydrological modeling outputs of CEPP came from a version of the Regional Simulation Model (RSM), developed by the South Florida Water Management District. The RSM is the DeLorean vehicle, designed to carry the bags of ecological restoration. Unfortunately, the capacity of this vehicle (i.e., CEPP) is limited, but is it large enough? Will CEPP make a difference? The 20-year RSM simulations (1965 – 1985) without restoration showed nine dry periods when there was no water in the sloughs of Everglades National Park (ENP). When the model was run with CEPP conditions, all of these extreme dry conditions were eliminated. The impact of this was most apparent for fish, especially the size classes that wading birds eat. With our DeLorean (i.e. the RSM) we saw a 60-90% increase in fish density. As one might expect, the birds in our alternative future responded to the fish. The increased volume, flow, and connectivity in the CEPP simulations significantly improved the foraging response of all wading bird species, especially in Water Conservation Area 3 (WCA-3) and ENP. Foraging conditions for an average CEPP year improved by 25-100%. Further downstream, the CEPP simulations showed increased delivery of low nutrient fresh water to the Coastal Everglades and Florida Bay that displaced the relatively P-rich marine water, increased water transparency, and thus decreased algal blooms. However, in a future with accelerating sea levels and estuarine lakes with legacy phosphorus (P), how much more fresh water will be needed to maintain submerged aquatic vegetated habitats? The quest for Everglades Restoration will reach a resource management milestone with the implementation of CEPP. CEPP successfully used a broad suite of hydrological, ecological and societal models to build an acceptable and feasible adaptive management vision of the future. It has been a long and difficult journey, but what we have learned in the process will guide future travelers back in time.

Florida↗