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USGS2015

Spatial capture-recapture models allowing Markovian transience or dispersal

Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.

Population Ecology↗

USGS2019

Identifying and characterizing extrapolation in multivariate response data

Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.

PLoS ONE↗

USGS2014

Book review: Spatial capture-recapture

Understanding how animals use space is a vital aspect of conservation planning and wildlife management. Technological developments (e.g., increased computer power and desktop geographic information system [GIS] applications) are bringing the ability to analyze spatial data sets to the individual biologist. Therefore, it is not surprising that methodologies have been developed to incorporate space into capture-recapture models, which are some of the most fundamental models in the field of wildlife ecology. Spatial Capture-Recapture (hereafter SCR) is a timely and informative contribution that summarizes the history and motivation behind SCR models, in addition to providing details of the methodological framework that allows the reader to develop and customize SCR models to address their own ecological questions. Review info: Spatial Capture-Recapture. By J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, and Beth Gardner, 2014. ISBN: 978-0124059399, 577 pp.

Journal of Wildlife Management↗

USGS2007

Ecological gradients within a Pennsylvanian mire forest

Pennsylvanian coals represent remains of the earliest peat-forming rain forests, but there is no current consensus on forest ecology. Localized studies of fossil forests suggest intermixture of taxa (heterogeneity), while, in contrast, coal ball and palynological analyses imply the existence of pronounced ecological gradients. Here, we report the discovery of a spectacular fossil forest preserved over ???1000 ha on top of the Pennsylvanian (Desmoinesian) Herrin (No. 6) Coal of Illinois, United States. The forest was abruptly drowned when fault movement dropped a segment of coastal mire below sea level. In the largest study of its kind to date, forest composition is statistically analyzed within a well-constrained paleogeographic context. Findings resolve apparent conflicts in models of Pennsylvanian mire ecology by confirming the existence of forest heterogeneity at the local scale, while additionally demonstrating the emergence of ecological gradients at landscape scale. ?? 2007 The Geological Society of America.

Geology↗

USGS2015

Experimental infection of snakes with Ophidiomyces ophiodiicola causes pathological changes that typify snake fungal disease

Snake fungal disease (SFD) is an emerging skin infection of wild snakes in eastern North America. The fungus Ophidiomyces ophiodiicola is frequently associated with the skin lesions that are characteristic of SFD, but a causal relationship between the fungus and the disease has not been established. We experimentally infected captive-bred corn snakes ( Pantherophis guttatus ) in the laboratory with pure cultures of O. ophiodiicola . All snakes in the infected group ( n = 8) developed gross and microscopic lesions identical to those observed in wild snakes with SFD; snakes in the control group ( n = 7) did not develop skin infections. Furthermore, the same strain of O. ophiodiicola used to inoculate snakes was recovered from lesions of all animals in the infected group, but no fungi were isolated from individuals in the control group. Monitoring progression of lesions throughout the experiment captured a range of presentations of SFD that have been described in wild snakes. The host response to the infection included marked recruitment of granulocytes to sites of fungal invasion, increased frequency of molting, and abnormal behaviors, such as anorexia and resting in conspicuous areas of enclosures. While these responses may help snakes to fight infection, they could also impact host fitness and may contribute to mortality in wild snakes with chronic O. ophiodiicola infection. This work provides a basis for understanding the pathogenicity of O. ophiodiicola and the ecology of SFD by using a model system that incorporates a host species that is easy to procure and maintain in the laboratory. IMPORTANCE Skin infections in snakes, referred to as snake fungal disease (SFD), have been reported with increasing frequency in wild snakes in the eastern United States. While most of these infections are associated with the fungus Ophidiomyces ophiodiicola , there has been no conclusive evidence to implicate this fungus as a primary pathogen. Furthermore, it is not understood why the infections affect different host populations differently. Our experiment demonstrates that O. ophiodiicola is the causative agent of SFD and can elicit pathological changes that likely impact fitness of wild snakes. This information, and the laboratory model we describe, will be essential in addressing unresolved questions regarding disease ecology and outcomes of O. ophiodiicola infection and helping to conserve snake populations threatened by the disease. The SFD model of infection also offers utility for exploring larger concepts related to comparative fungal virulence, host response, and host-pathogen evolution.

mBio↗

USGS2013

An integrated multi-criteria scenario evaluation web tool for participatory land-use planning in urbanized areas: The Ecosystem Portfolio Model

Land-use land-cover change is one of the most important and direct drivers of changes in ecosystem functions and services. Given the complexity of the decision-making, there is a need for Internet-based decision support systems with scenario evaluation capabilities to help planners, resource managers and communities visualize, compare and consider trade-offs among the many values at stake in land use planning. This article presents details on an Ecosystem Portfolio Model (EPM) prototype that integrates ecological, socio-economic information and associated values of relevance to decision-makers and stakeholders. The EPM uses a multi-criteria scenario evaluation framework, Geographic Information Systems (GIS) analysis and spatially-explicit land-use/land-cover change-sensitive models to characterize changes in important land-cover related ecosystem values related to ecosystem services and functions, land parcel prices, and community quality-of-life (QoL) metrics. Parameters in the underlying models can be modified through the interface, allowing users in a facilitated group setting to explore simultaneously issues of scientific uncertainty and divergence in the preferences of stakeholders. One application of the South Florida EPM prototype reported in this article shows the modeled changes (which are significant) in aggregate ecological value, landscape patterns and fragmentation, biodiversity potential and ecological restoration potential for current land uses compared to the 2050 land-use scenario. Ongoing refinements to EPM, and future work especially in regard to modifiable sea level rise scenarios are also discussed.

Environmental Modelling and Software↗

USGS2024

Climate change amplifies ongoing declines in sagebrush ecological integrity

Understanding how climate change will contribute to ongoing declines in sagebrush ecological integrity is critical for informing natural resource management, yet complicated by interactions with wildfire and biological invasions. Here, we assessed potential future changes in sagebrush ecological integrity under a range of scenarios using an individual plant-based simulation model, integrated with remotely sensed estimates of current sagebrush ecological integrity. The simulation model allowed us to estimate how climate change, wildfire, and invasive annuals interact to alter the potential abundance of key plant functional types that influence sagebrush ecological integrity: sagebrush, perennial grasses, and annual grasses. Our results suggest that climate driven reductions in sagebrush ecological integrity may occur over broader areas than increases in sagebrush ecological integrity. Declines in sagebrush ecological integrity were most likely in hot and dry regions while increases were more likely in cool and wet regions. Increases in wildfire probability, largely driven by increasing invasive annual grass abundance, resulted in projected declines in sagebrush abundance and sagebrush ecological integrity in some regions, particularly the Great Basin. The most common projected transitions in sagebrush habitat classification were declines from Core Sagebrush Area to Growth Opportunity Area and from Growth Opportunity Area to Other Rangeland Area. Responses varied considerably across projections from different global climate models, highlighting the importance of climate uncertainty. However, our projections tended to be robust in areas that currently have the highest sagebrush ecological integrity. Our results provide a long-term perspective on the vulnerability of sagebrush ecosystems to climate change and may inform geographic prioritization of conservation and restoration investments. These results suggest that ongoing threats, such as the continued invasion by annual grasses and increased wildfire frequency, are likely to be amplified by climate change, and imply that the current imbalance between capacity for conservation to address threats to sagebrush will grow as the climate warms.

Rangeland Ecology and Management↗

USGS2022

Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model

Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture–recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears ( Ursus maritimus ) in a 28,125 km 2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture–recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km 2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multiyear integrated population model using capture–recapture and telemetry data (2008–2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR–movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR–movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.

eastern Chukchi Sea↗

USGS2011

Development of a bioenergetics model for age-0 American shad

Bioenergetics modeling can be used as a tool to investigate the impact of non-native age-0 American shad ( Alosa sapidissima ) on reservoir and estuary food webs. The model can increase our understanding of how these fish influence lower trophic levels as well as predatory fish populations that feed on juvenile salmonids. Bioenergetics modeling can be used to investigate ecological processes, evaluate alternative research hypotheses, provide decision support, and quantitative prediction. Bioenergetics modeling has proven to be extremely useful in fisheries research (Ney et al. 1993,Chips and Wahl 2008, Petersen et al. 2008). If growth and diet parameters are known, the bioenergetics model can be used to quantify the relative amount of zooplankton or insects consumed by age-0 American shad. When linked with spatial and temporal information on fish abundance, model output can guide inferential hypothesis development to demonstrate where the greatest impacts of age-0 American shad might occur. Bioenergetics modeling is particularly useful when research questions involve multiple species and trophic levels (e.g. plankton communities). Bioenergetics models are mass-balance equations where the energy acquired from food is partitioned between maintenance costs, waste products, and growth (Winberg 1956). Specifically, the Wisconsin bioenergetics model (Hanson et al. 1997) is widely used in fisheries science. Researchers have extensively tested, reviewed, and improved on this modeling approach for over 30 years (Petersen et al. 2008). Development of a bioenergetics model for any species requires three key components: 1) determine physiological parameters for the model through laboratory experiments or incorporate data from a closely related species, 2) corroboration of the model with growth and consumption estimates from independent research, and 3) error analysis of model parameters. Wisconsin bioenergetics models have been parameterized for many of the salmonids and predatory fishes encountered in the lower Columbia River (Petersen and Ward 1999). The Wisconsin bioenergetics model has not been developed for American shad, however Limburg (1996) parameterized a simplified bioenergetics growth model for this species. A common application for the Wisconsin bioenergetics model is to estimate the consumption or growth of a fish population under different temperature and feeding scenarios (Ney 1993). One advantage of the bioenergetics approach is that consumption can be estimated without direct field measurements of predation rate (prey·predator -1 · day -1 ; Petersen and Ward 1999). Field estimates of fish consumption are time consuming and costly to determine, and estimates may show wide variance due to environmental and sampling variability. However, the consumption parameters used in a newly developed bioenergetics model must be verified with field and laboratory estimates of consumption (Ney 1993). The objective of this research was to parameterize a Wisconsin bioenergetics model for age-0 American shad using published physiological data on American shad and closely related alosine species. The American shad bioenergetics model will be used as a tool to explore various hypotheses about how age-0 American shad directly and indirectly affect Columbia River salmon through ecological interactions in lower Columbia River food webs. One over-arching focus of the larger research project was to identify potential interactions between age-0 American shad and juvenile salmonids, addressing potential outcomes through bioenergetics modeling scenarios. This report contains two bioenergetics modeling applications to demonstrate how these models can be used to address management questions and direct research effort. The first modeling application uses the American shad bioenergetics model described in this report to explore prey consumption by age-0 American shad (Chapter 1, this report). Dietary data on age-0 American shad and previously published reports on the diet of juvenile fall Chinook salmon (Rondorf et al. 1990, USGS unpublished data) suggested there might be considerable dietary overlap between these species in the lower Columbia River. The U.S. Geological Survey (USGS) was interested in using the American shad bioenergetics model to explore hypotheses concerning dietary overlap between age-0 American shad and emigrating fall Chinook salmon. The second modeling application uses the fall Chinook salmon bioenergetics model (Koehler et al. 2006) to explore the growth potential of juvenile fall Chinook salmon predating on age-0 American shad in the lower Columbia River. This modeling was based dietary information on a small number of age-0 fall Chinook salmon (n = 13) collected in John Day Reservoir in 1994 - 1996 (unpublished USGS data). Analysis of this dietary data found that these salmonids were feeding primarily on age-0 American shad (> 75% by weight).

Oregon↗

USGS2026

Severity and certainty metric for kelp stressors: A tool for research, management, and conservation

Foundation species face multiple threats, and many are in decline worldwide. Kelps are no exception, with declines documented throughout their range. Stressors such as warming, eutrophication, and reduced water quality—often linked to human activities—are well recognized, yet their relative severity and certainty have not been systematically evaluated. Such a framework may aid scientific, regulatory, and conservation organizations in prioritizing actions and decision making. Here we build on a conceptual model of stressors to kelp in the Salish Sea by rating stressor severity and scientific certainty. We convened a panel of regional kelp experts to perform the rating exercise for two canopy taxa, Nereocystis luetkeana and Macrocystis tenuifolia , as well as a grouping for understory species, including both the sporophyte (adult) and gametophyte (juvenile) life stages for these algae. Our process produced a rich dataset which can be separated by stressor, taxa, and life stage allowing for broad applications. The panel identified temperature, low nutrients, and substrate change/benthic sedimentation as the most severe stressors to kelp, with relatively high certainty for most taxa. Certainty scores were relatively high for Nereocystis and Macrocystis sporophytes but were considerably lower for understory species and the gametophyte life stage, for most stressors, highlighting research gaps. Our data set could be applied for management prioritization, setting ecological baselines, and spatial modeling exercises. We encourage scientific, regulatory, and conservation organizations to consider both our data and our process when evaluating their needs. As foundation species continue to face threats, exercises such as this can provide scientists and managers with actionable indices that support strategies to stem degradation.

Ecological Indicators↗

USGS2015

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

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

Ecological Modelling↗

USGS2021

Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine

Dissolved organic carbon (DOC) is an important component of the carbon cycle as a measure of the hydrological transport of carbon between terrestrial carbon pools into soil pools and eventually into streams. As a result, changes in DOC in rivers and streams may indicate alterations in the storage of terrestrial carbon. Exploring the complex interactions between biogeochemical cycling and hydrologic processes, as well as the micro-climate variabilities that impact the rate of DOC fluxes, are challenging because the information is not readily available from in-situ measurements or from empirical models alone. This is particularly true of large-scale watersheds. The Penobscot Watershed is the largest watershed of the Gulf of Maine and the second largest in New England. Its typical soils, with high organic matter and a large forested and wetland landscape, result in higher DOC fluxes than what has been observed previously for most rivers in the northern temperate or boreal zones (Hope et al., 1994; Mulholland, 1997; Aitkenhead and McDowell, 2000). In this study, we emphasized the simulation of streamflow and DOC fluxes from the Penobscot Watershed (and several tributaries within the Penobscot Watershed) using the spatially distributed process-based Regional Hydro-Ecological Simulation System (RHESSys) model. Simulated results were evaluated using field measurements (streamflow, DOC fluxes) and remotely sensed products (Net Primary Production (NPP) and Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS). The average DOC flux for the Penobscot Watershed during 2004-2012 using the RHESSys model was 69 kg C/ha/year. The RHESSys simulated DOC flux is shown to correlate well with observed values, as well as with results previously reported from the empirical Load Estimator (LOADEST) model (71 kg C/ha/year) for 2004-2007 ( Huntington and Aiken, 2013). Our simulated results also show a temporal variation in the amount of DOC flux, indicating that the antecedent DOC concentration from one year can impact the DOC export in following years. Thus, DOC concentration is positively correlated with streamflow and antecedent precipitation, in agreement with previous studies (Ågren et al., 2010; Huntington and Aiken, 2013; Tian et al., 2013 ). The successful application of the rigorous RHESSys model in the Penobscot Watershed makes it a reasonable platform to test future scenarios impacting the hydrology and biogeochemistry within similar large complex watersheds.

Maine↗

USGS2023

Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation

This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the freshwater portions of the ranges for species represented 28 families. Cyprinidae was the family with the most species (87 of 271) modeled for this study, followed by Percidae (34) and Ictaluridae (17). Model predictive performance was evaluated using four metrics: area under the receiver operating characteristic curve, sensitivity, specificity, and True Skill Statistic, which are all from tenfold cross-validation results. The relative importance of the predictor variables in the boosted regression tree models was calculated and ranked for each species. The three strongest natural predictors of fish distributions were network catchment area, the mean annual air temperature of the local catchment, and the maximum elevation of the local catchment, while the three strongest anthropogenic predictors were downstream main stem dam density, distance to downstream main stem dam, and the percentage of pasture/hay land use area within network catchment boundaries. Study results showed 61 fish species were sensitive to climate variables, and 40 fish species were sensitive to anthropogenic stressors. The models developed in this study can be used to derive critical information regarding habitat protection priorities, anthropogenic threats, and potential effects of climate change on habitat suitability, aiding in efforts to conserve fluvial fishes now and into the future.

Conterminous United States↗

USGS2011

Natural and human dimensions of a quasi-wild species: The case of kudzu

The human dimensions of biotic invasion are generally poorly understood, even among the most familiar invasive species. Kudzu ( Pueraria montana (Lour.) Merr.) is a prominent invasive plant and an example of quasi-wild species, which has experienced repeated introduction, cultivation, and escape back to the wild. Here, we review a large body of primary scientific and historic records spanning thousands of years to characterize the complex relationships among kudzu, its natural enemies, and humans, and provide a synthesis and conceptual model relevant to the ecology and management of quasi-wild invasive species. We documented over 350, mostly insect, natural enemy species and their impacts on kudzu in its native East Asian range. These natural enemies play a minor role in limiting kudzu in its native range, rarely generating severe impacts on populations of wild kudzu. We identified a number of significant influences of humans including dispersal, diverse cultural selection, and facilitation through disturbances, which catalyzed the expansion and exuberance of kudzu. On the other hand, harvest by humans appears to be the major control mechanism in its native areas. Humans thus have a complex relationship with kudzu. They have acted as both friend and foe, affecting the distribution and abundance of kudzu in ways that vary across its range and over time. Our conceptual model of kudzu emphasizes the importance of multiple human dimensions in shaping the biogeography of a species and illustrates how kudzu and other quasi-wild species are more likely to be successful invaders.

Biological Invasions↗

USGS2003

Resource allocation in offspring provisioning: An evaluation of the conditions favoring the evolution of matrotrophy

We used analytic and simulation models to determine the ecological conditions favoring evolution of a matrotrophic fish from a lecithotrophic ancestor given a complex set of trade‐offs. Matrotrophy is the nourishment of viviparous embryos by resources provided between fertilization and parturition, while lecithotrophy describes embryo nourishment provided before fertilization. In fishes and reptiles, embryo nourishment encompasses a continuum from solely lecithotrophic to primarily matrotrophic. Matrotrophy has evolved independently from lecithotrophic ancestors many times in many groups. We assumed matrotrophy increased the number of offspring a viviparous female could gestate and evaluated conditions of food availability favoring lecithotrophy or matrotrophy. The matrotrophic strategy was superior when food resources exceeded demand during gestation but at a risk of overproduction and reproductive failure if food intake was limited. Matrotrophic females were leaner during gestation than lecithotrophic females, yielding shorter life spans. Our models suggest that matrotrophic embryo nourishment evolved in environments with high food availability, consistently exceeding energy requirements for maintaining relatively large broods. Embryo abortion with some resorption of invested energy is a necessary preadaptation to the evolution of matrotrophy. Future work should explore trade‐offs of age‐specific mortality and reproductive output for females maintaining different levels of fat storage during gestation.

American Naturalist↗

USGS2019

Sampling and analysis frameworks for inference in ecology

1. Reliable statistical inference is central to ecological research, much of which seeks to estimate population attributes and their interactions. The issue of sampling design and its relationship to inference has become increasingly important due to rapid proliferation of modeling methodology (line transect modeling, capture-recapture, estimation of occurrence, model selection procedures, hierarchical modeling) and new sampling approaches (adaptive sampling, other specialized designs). It is important for ecologists using these advanced methods to be aware of how the linkages between sample selection and data analysis can potentially affect inference. 2. We examine design-based and model-based inference frameworks for ecological data collected randomly, purposively, or opportunistically. We elucidate differences in the probability structures for data arising from these frameworks, clarify the assumptions that underlie them, and demonstrate their differences. 3. Design-based inference builds on a probability structure inherited from randomized data collection, whereas model-based inference relies on an assumed stochastic model of the data. By itself, a design-based approach is of limited value for inferences about causal hypotheses. In contrast, model-based inference is dependent on a conditionality principle that can seldom be shown to be met for an ecological system. We describe the conditions under which one can safely ignore sampling design in model-based analysis, along with inferential implications if these conditions are not met. The special case of opportunistic sampling is discussed. 4. We present a combined framework that takes advantage of both approaches to inference, and provides a robust methodology that can deal with the modeling of sampling problems such as nondetection and misclassification, as well as the exploration of causal hypotheses. The combined framework can be useful for identifying optimal sampling strategies. 5. Each approach to inference has its strengths and weaknesses, and practitioners should be aware of these in order to tailor designs and analyses to specific questions. We use the approaches and their underlying rationales to provide guidelines for choosing designs and estimators for reliable inference.

Methods in Ecology and Evolution↗

USGS2005

Performance of four turbulence closure models implemented using a generic length scale method

A two-equation turbulence model (one equation for turbulence kinetic energy and a second for a generic turbulence length-scale quantity) proposed by Umlauf and Burchard [J. Marine Research 61 (2003) 235] is implemented in a three-dimensional oceanographic model (Regional Oceanographic Modeling System; ROMS v2.0). These two equations, along with several stability functions, can represent many popular turbulence closures, including the k-kl (Mellor-Yamada Level 2.5), k-??, and k-?? schemes. The implementation adds flexibility to the model by providing an unprecedented range of turbulence closure selections in a single 3D oceanographic model and allows comparison and evaluation of turbulence models in an otherwise identical numerical environment. This also allows evaluation of the effect of turbulence models on other processes such as suspended-sediment distribution or ecological processes. Performance of the turbulence models and sediment-transport schemes is investigated with three test cases for (1) steady barotropic flow in a rectangular channel, (2) wind-induced surface mixed-layer deepening in a stratified fluid, and (3) oscillatory stratified pressure-gradient driven flow (estuarine circulation) in a rectangular channel. Results from k-??, k-??, and gen (a new closure proposed by Umlauf and Burchard [J. Marine Research 61 (2003) 235]) are very similar for these cases, but the k-kl closure results depend on a wall-proximity function that must be chosen to suit the flow. Greater variations appear in simulations of suspended-sediment concentrations than in salinity simulations because the transport of suspended-sediment amplifies minor variations in the methods. The amplification is caused by the added physics of a vertical settling rate, bottom stress dependent resuspension, and diffusive transport of sediment in regions of well mixed salt and temperature. Despite the amplified sensitivity of sediment to turbulence models in the estuary test case, the four closures investigated here all generated estuarine turbidity maxima that were similar in their shape, location, and concentrations.

Ocean Modelling↗

USGS2024

Treed Gaussian processes for animal movement modeling

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie-chickens ( Tympanuchus pallidicinctus ) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie-chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.

Ecology and Evolution↗