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At least 523 records · Page 29Linked to original sources
USGS2025

Bridging social and ecological science to create spatially-explicit models of human-caused mortality of carnivores

Research indicates that human-caused mortality (HCM) is a key factor limiting numerous large carnivore populations. However, efforts to represent HCM in spatially explicit models have generally been limited in scope—often relying on proxies, such as road or human density. Yet such efforts fail to distinguish different sources of HCM, which can arise from different antecedent processes. We offer a systems-based conceptual framework for understanding the antecedents of HCMs that is grounded in theory from the social and behavioral sciences. Specifically, we first explain how HCMs are usefully distinguished into four types (e.g., accidental, harvest, illicit, control actions), then discuss how these different types tend to be driven by different sets of psychological and sociopolitical processes. We contend that improvements in understanding the spatial variation in HCMs would rise from more explicit attention to the various antecedent processes that precede each mortality type.

Ambio↗
USGS2013

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Ecology and Society↗
USGS2018

A guide to Bayesian model checking for ecologists

Checking that models adequately represent data is an essential component of applied statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in their research. The appeal of this modeling paradigm is undeniable, as researchers can build and fit models that embody complex ecological processes while simultaneously accounting for observation error. However, ecologists tend to be less focused on checking model assumptions and assessing potential lack of fit when applying Bayesian methods than when applying more traditional modes of inference such as maximum likelihood. There are also multiple ways of assessing the fit of Bayesian models, each of which has strengths and weaknesses. For instance, Bayesian P values are relatively easy to compute, but are well known to be conservative, producing P values biased toward 0.5. Alternatively, lesser known approaches to model checking, such as prior predictive checks, cross‐validation probability integral transforms, and pivot discrepancy measures may produce more accurate characterizations of goodness‐of‐fit but are not as well known to ecologists. In addition, a suite of visual and targeted diagnostics can be used to examine violations of different model assumptions and lack of fit at different levels of the modeling hierarchy, and to check for residual temporal or spatial autocorrelation. In this review, we synthesize existing literature to guide ecologists through the many available options for Bayesian model checking. We illustrate methods and procedures with several ecological case studies including (1) analysis of simulated spatiotemporal count data, (2) N‐mixture models for estimating abundance of sea otters from an aircraft, and (3) hidden Markov modeling to describe attendance patterns of California sea lion mothers on a rookery. We find that commonly used procedures based on posterior predictive P values detect extreme model inadequacy, but often do not detect more subtle cases of lack of fit. Tests based on cross‐validation and pivot discrepancy measures (including the “sampled predictive P value”) appear to be better suited to model checking and to have better overall statistical performance. We conclude that model checking is necessary to ensure that scientific inference is well founded. As an essential component of scientific discovery, it should accompany most Bayesian analyses presented in the literature.

Ecological Monographs↗
USGS2015

Legacy effects of wildfire on stream thermal regimes and rainbow trout ecology: an integrated analysis of observation and individual-based models

Management of aquatic resources in fire-prone areas requires understanding of fish species’ responses to wildfire and of the intermediate- and long-term consequences of these disturbances. We examined Rainbow Trout populations in 9 headwater streams 10 y after a major wildfire: 3 with no history of severe wildfire in the watershed (unburned), 3 in severely burned watersheds (burned), and 3 in severely burned watersheds subjected to immediate events that scoured the stream channel and eliminated streamside vegetation (burned and reorganized). Results of a previous study of this system suggested the primary lasting effects of this wildfire history on headwater stream habitat were differences in canopy cover and solar radiation, which led to higher summer stream temperatures. Nevertheless, trout were present throughout streams in burned watersheds. Older age classes were least abundant in streams draining watersheds with a burned and reorganized history, and individuals >1 y old were most abundant in streams draining watersheds with an unburned history. Burned history corresponded with fast growth, low lipid content, and early maturity of Rainbow Trout. We used an individual-based model of Rainbow Trout growth and demographic patterns to determine if temperature interactions with bioenergetics and competition among individuals could lead to observed phenotypic and ecological differences among populations in the absence of other plausible mechanisms. Modeling suggested that moderate warming associated with wildfire and channel disturbance history leads to faster individual growth, which exacerbates competition for limited food, leading to decreases in population densities. The inferred mechanisms from this modeling exercise suggest the transferability of ecological patterns to a variety of temperature-warming scenarios.

Idaho↗
USGS2003

Fitting population models from field data

The application of population and community ecology to solving real-world problems requires population and community dynamics models that reflect the myriad patterns of interaction among organisms and between the biotic and physical environments. Appropriate models are not hard to construct, but the experimental manipulations needed to evaluate their defining coefficients are often both time consuming and costly, and sometimes environmentally destructive, as well. In this paper we present an empirical approach for finding the coefficients of broadly inclusive models without the need for environmental manipulation, demonstrate the approach with both an animal and a plant example, and suggest possible applications. Software has been developed, and is available from the senior author, with a manual describing both field and analytic procedures.

Ecological Modelling↗
USGS2015

Sea-level rise modeling handbook: Resource guide for coastal land managers, engineers, and scientists

Global sea level is rising and may accelerate with continued fossil fuel consumption from industrial and population growth. In 2012, the U.S. Geological Survey conducted more than 30 training and feedback sessions with Federal, State, and nongovernmental organization (NGO) coastal managers and planners across the northern Gulf of Mexico coast to evaluate user needs, potential benefits, current scientific understanding, and utilization of resource aids and modeling tools focused on sea-level rise. In response to the findings from the sessions, this sea-level rise modeling handbook has been designed as a guide to the science and simulation models for understanding the dynamics and impacts of sea-level rise on coastal ecosystems. The review herein of decision-support tools and predictive models was compiled from the training sessions, from online research, and from publications. The purpose of this guide is to describe and categorize the suite of data, methods, and models and their design, structure, and application for hindcasting and forecasting the potential impacts of sea-level rise in coastal ecosystems. The data and models cover a broad spectrum of disciplines involving different designs and scales of spatial and temporal complexity for predicting environmental change and ecosystem response. These data and models have not heretofore been synthesized, nor have appraisals been made of their utility or limitations. Some models are demonstration tools for non-experts, whereas others require more expert capacity to apply for any given park, refuge, or regional application. A simplified tabular context has been developed to list and contrast a host of decision-support tools and models from the ecological, geological, and hydrological perspectives. Criteria were established to distinguish the source, scale, and quality of information input and geographic datasets; physical and biological constraints and relations; datum characteristics of water and land components; utility options for setting sea-level rise and climate change scenarios; and ease or difficulty of storing, displaying, or interpreting model output. Coastal land managers, engineers, and scientists can benefit from this synthesis of tools and models that have been developed for projecting causes and consequences of sea-level change on the landscape and seascape.

Professional Paper↗
USGS1999

Estimating effects of limiting factors with regression quantiles

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Ecology↗
USGS2003

Quaternary vegetation and climate change in the western United States: Developments, perspectives, and prospects

This chapter explores the strengths and shortcomings of the major sources of data on Quaternary vegetation and climate change and discusses the use of models as a means to explore past and potential future environmental changes. The flora and major vegetation types of the western United States are present for several million years. Ongoing changes in atmospheric chemistry , climate, and human activities may lead to major vegetation changes over the coming decades to centuries. The combination of observations from the paleoenvironmental record, modern ecological studies, and modeling now permit assessments of the magnitude of potential future changes in the context of natural variability. They also provide opportunities for hypothesis testing and identification of the processes driving past changes in vegetation and climate. Understanding the dynamics of paleoenvironmental change can contribute to current conservation and natural resource management efforts and will help conservation and natural resource managers anticipate the potential rate, magnitude, and complexity of future vegetation change.

western United States↗
USGS2022

Food web perspectives and methods for riverine fish conservation

Food web analyses offer useful insights into understanding how species interactions, trophic relationships, and energy flow underpin important demographic parameters of fish populations such as survival, growth, and reproduction. However, the vast amount of food web literature and the diversity of approaches can be a deterrent to fisheries practitioners engaged in on-the-ground research, monitoring, or restoration. Incorporation of food web perspectives into contemporary fisheries management and conservation is especially rare in riverine systems, where approaches often focus more on the influence of physical habitat and water temperature on fish populations. In this review, we first discuss the importance of food webs in the context of several common fisheries management issues, including assessing carrying capacity, evaluating the effects of habitat change, examining species introductions or extinctions, considering bioaccumulation of toxins, and predicting the effects of climate change and other anthropogenic stressors on riverine fishes. We then examine several relevant perspectives: basic food web description, metabolic models, trophic basis of production, mass-abundance network approaches, ecological stoichiometry, and mathematical modeling. Finally, we highlight several existing and emerging methodologies including diet and prey surveys, eDNA, stable isotopes, fatty acids, and community and network analysis. Although our emphasis and most examples are focused on salmonids in riverine environments, the concepts are easily generalizable to other freshwater fish taxa and ecosystems.

Wiley Interdisciplinary Reviews (WIREs): Water↗
USGS2011

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost‐effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees ( Trichechus manatus latirostris ) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non‐independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non‐independent detection of individuals. We simulated correlated behaviour using beta‐binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta‐binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta‐binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness‐of‐fit procedure to evaluate the fit of beta‐binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta‐binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta‐binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness‐of‐fit when analysing ecological data with N‐mixture models. Indeed, both the simulations and the goodness‐of‐fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non‐independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N‐mixture models to ecological data.

Methods in Ecology and Evolution↗
USGS2020

Modelling pinniped abundance and distribution by combining counts at terrestrial sites and in-water sightings

Pinnipeds are commonly monitored using aerial photographic surveys at land- or ice-based sites, where animals come ashore for resting, pupping, molting, and to avoid predators. Although these counts form the basis for monitoring population change over time, they do not provide information regarding where animals occur in the water, which is often of management and conservation interest. In this study, we developed a hierarchical model that links counts of pinnipeds at terrestrial sites to sightings-at-sea and estimates abundance, spatial distribution, and the proportion of time spent on land (attendance probability). The structure of the model also allows for the inclusion of predictors that may explain variation in ecological and observation processes. We applied the model to Steller sea lions ( Eumetopias jubatus ) in Glacier Bay, Alaska using counts of sea lions from aerial photographic surveys and opportunistic in-water sightings from vessel surveys. Glacier Bay provided an ideal test and application of the model because data are available on attendance probability based on long-term monitoring. We found that occurrence in the water was positively related to proximity to terrestrial sites, as would be expected for a species that engages in central-place foraging. The proportion of sea lions in attendance at terrestrial sites and overall abundance estimates were consistent with reports from the literature and monitoring programs. The model we describe has benefit and utility for park managers who wish to better understand the overlap between pinnipeds and visitors, and the framework that we present has potential for application across a variety of study systems and taxa.

Alaska↗
USGS2022

A sagebrush conservation design to proactively restore America’s sagebrush biome

A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time. First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term. A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.

western United States↗
USGS2012

Linking river management to species conservation using dynamic landscape scale models

Efforts to conserve stream and river biota could benefit from tools that allow managers to evaluate landscape-scale changes in species distributions in response to water management decisions. We present a framework and methods for integrating hydrology, geographic context and metapopulation processes to simulate effects of changes in streamflow on fish occupancy dynamics across a landscape of interconnected stream segments. We illustrate this approach using a 482 km 2 catchment in the southeastern US supporting 50 or more stream fish species. A spatially distributed, deterministic and physically based hydrologic model is used to simulate daily streamflow for sub-basins composing the catchment. We use geographic data to characterize stream segments with respect to channel size, confinement, position and connectedness within the stream network. Simulated streamflow dynamics are then applied to model fish metapopulation dynamics in stream segments, using hypothesized effects of streamflow magnitude and variability on population processes, conditioned by channel characteristics. The resulting time series simulate spatially explicit, annual changes in species occurrences or assemblage metrics (e.g. species richness) across the catchment as outcomes of management scenarios. Sensitivity analyses using alternative, plausible links between streamflow components and metapopulation processes, or allowing for alternative modes of fish dispersal, demonstrate large effects of ecological uncertainty on model outcomes and highlight needed research and monitoring. Nonetheless, with uncertainties explicitly acknowledged, dynamic, landscape-scale simulations may prove useful for quantitatively comparing river management alternatives with respect to species conservation.

Georgia↗
USGS2019

Sediment classification and the characterization, identification, and mapping of geologic substrates for the glaciated Gulf of Maine seabed and other terrains, providing a physical framework for ecological research and seabed management

A geologic substrate is a surface (or volume) of sediment or rock where physical, chemical, and biological processes occur, such as the movement and deposition of sediment, the formation of bedforms, and the attachment, burrowing, feeding, reproduction, and sheltering of organisms. Seabed mapping surveys in the Stellwagen Bank region off Boston, Massachusetts, from 1993 to 2004 have led to the development of a methodology for characterizing, identifying, and mapping geologic substrates. The resulting high-resolution interpretive maps (1:25,000) show the distribution of substrates in a glaciated terrain of banks and basins in water depths of 30 to 185 meters. Data sources used to characterize substrates are multibeam sonar bathymetric and backscatter imagery to document seabed topography and patterns of sediment and rock distribution, grain-size analyses of sediment samples to determine substrate composition, and video and photographic imagery of the seabed to aid in the interpretation of multibeam sonar imagery and to provide information on substrate layering and mobility, seabed structures, and sediments and nonsediment materials that cannot be physically sampled. Sediment composition is a major property of many seabed substrates. Sediment grains belong to a continuum of grain-diameter sizes previously classified into grades (for example, fine sand, medium sand) and into aggregates (mud, sand, gravel). The definition of grade and aggregate boundaries in a classification is arbitrary, and a useful classification is limited to as few classes as are needed to effectively organize and apply information. For the purpose of mapping substrates, sediment grades and aggregates were simplified and re-classified into eight composite grades based on grain-size content, mode of transport, and ecological role. Five composite grades are identified using grain-size analysis and three are identified using video and photographic imagery of the seabed. Naturally occurring sediments contain various amounts of the aggregates mud, sand, and gravel. The separation of naturally occurring sediments into sediment classes, based on grain-size analysis, requires that limits be set on the amount of mud, sand, and gravel each class contains. Fifteen previously identified basic sediment classes provided interpretive information on sediment transport by emphasizing gravel content (a low 0.01-weight-percent threshold) and on winnowing processes based on the sand-to-mud ratio. The present study recognizes 20 basic sediment classes that are combinations of aggregates in which the lower limits for recognition of mud and sand are 10 weight percent and of gravel, 25 weight percent. These sediment classes can be made more specific by listing their content of the composite grades fine-grained sand (3 and 4 phi), which is transported in suspension, and coarse-grained sand (0, 1, and 2 phi), which is transported as bedload. Additional sediment classes and nonsediment classes that cannot be sampled are recognized on the basis of visual analysis of seabed video and photographic imagery and include pebble, cobble, and boulder gravel, rock outcrops, and shell beds, among others. Substrates are not classified because their properties are too varied for a classification to be concise and useful. Rather, substrates are characterized and identified by sediment grain-size composition (the sediment class); the distribution, in millimeters, of grain diameters in the sediment; the presence of nonsediments (for example, rock outcrops); substrate mobility based on the presence of sediment ripples; substrate layering (for example, a partial veneer of sand on gravel); and seabed structures. These properties have interpretive value by providing information about sedimentary processes acting on a substrate and about its ecological function. A geologic substrate, when it is associated with one or more species, is an important element of a habitat. This methodology was developed to map a glaciated terrain characterized by geologic substrates that typify a wide range of erosional and depositional sedimentary environments, and it likely will be useful for mapping substrates in other terrains. Substrate maps provide the physical framework required for identifying sediment transport processes, validating sediment transport models, studying the ecology of species and communities, and managing marine resources and seabed usage.

Massachusetts↗
USGS2022

A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity

Poaching is a global driver of wildlife population decline, including inside protected areas (PAs). Reducing poaching requires an understanding of its cryptic drivers and accurately quantifying poaching scales and intensity. There is little quantification of how poaching is affected by law enforcement intensity (e.g., ranger stations) versus economic factors (e.g., unemployment), while simultaneously accounting for imperfect detection. Using extensive data of poaching events (i.e., seizures) and censuses of nine ungulate species across the PAs and unprotected lands of Iran from 2010 to 2018, we developed a single-visit hierarchical (N-mixture) model to accurately estimate annual poaching of Iranian ungulates and to differentiate between social and ecological effects on annual poaching intensity. We found that poaching detectability increased with numbers of ranger stations. A recent surge in poaching (2013–2018) coincides with rising unemployment rate. We estimated that 19,727 ungulates (95% confidence interval 11,178–36,195) were poached across the country during 2010–2018. Poaching intensity was positively related to unemployment rate, road density, and ungulate abundance. Our simulations demonstrated that the Poisson and Negative binomial N-mixture models had adequate performance when the conditions of Sólymos et al. (2012) were satisfied, in particular, when at least one covariate is unique to both the detection and abundance parts of the model. Overall, we suggest that single-visit models offer unique insights into understanding the link between poaching intensity, economic conditions, and law enforcement in large-scale landscapes while accounting for imperfect detection of poaching events.

Biological Conservation↗
USGS2017

A new parameterization for integrated population models to document amphibian reintroductions

Managers are increasingly implementing reintroduction programs as part of a global effort to alleviate amphibian declines. Given uncertainty in factors affecting populations and a need to make recurring decisions to achieve objectives, adaptive management is a useful component of these efforts. A major impediment to the estimation of demographic rates often used to parameterize and refine decision-support models is that life-stage-specific monitoring data are frequently sparse for amphibians. We developed a new parameterization for integrated population models to match the ecology of amphibians and capitalize on relatively inexpensive monitoring data to document amphibian reintroductions. We evaluate the capability of this model by fitting it to Oregon spotted frog ( Rana pretiosa ) monitoring data collected from 2007 to 2014 following their reintroduction within the Klamath Basin, Oregon, USA. The number of egg masses encountered and the estimated adult and metamorph abundances generally increased following reintroduction. We found that survival probability from egg to metamorph ranged from 0.01 in 2008 to 0.09 in 2009 and was not related to minimum spring temperatures, metamorph survival probability ranged from 0.13 in 2010–2011 to 0.86 in 2012–2013 and was positively related to mean monthly temperatures (logit-scale slope = 2.37), adult survival probability was lower for founders (0.40) than individuals recruited after reintroduction (0.56), and the mean number of egg masses per adult female was 0.74. Our study is the first to test hypotheses concerning Oregon spotted frog egg-to-metamorph and metamorph-to-adult transition probabilities in the wild and document their response at multiple life stages following reintroduction. Furthermore, we provide an example to illustrate how the structure of our integrated population model serves as a useful foundation for amphibian decision-support models within adaptive management programs. The integration of multiple, but related, data sets has an advantage of being able to estimate complex ecological relationships across multiple life stages, offering a modeling framework that accommodates uncertainty, enforces parsimony, and ensures all model parameters can be confronted with monitoring data.

Ecological Applications↗
USGS2009

Evaluation of trap capture in a geographically closed population of brown treesnakes on Guam

1. Open population mark-recapture analysis of unbounded populations accommodates some types of closure violations (e.g. emigration, immigration). In contrast, closed population analysis of such populations readily allows estimation of capture heterogeneity and behavioural response, but requires crucial assumptions about closure (e.g. no permanent emigration) that are suspect and rarely tested empirically. 2. In 2003, we erected a double-sided barrier to prevent movement of snakes in or out of a 5-ha semi-forested study site in northern Guam. This geographically closed population of >100 snakes was monitored using a series of transects for visual searches and a 13 ?? 13 trapping array, with the aim of marking all snakes within the site. Forty-five marked snakes were also supplemented into the resident population to quantify the efficacy of our sampling methods. We used the program mark to analyse trap captures (101 occasions), referenced to census data from visual surveys, and quantified heterogeneity, behavioural response, and size bias in trappability. Analytical inclusion of untrapped individuals greatly improved precision in the estimation of some covariate effects. 3. A novel discovery was that trap captures for individual snakes consisted of asynchronous bouts of high capture probability lasting about 7 days (ephemeral behavioural effect). There was modest behavioural response (trap happiness) and significant latent (unexplained) heterogeneity, with small influences on capture success of date, gender, residency status (translocated or not), and body condition. 4. Trapping was shown to be an effective tool for eradicating large brown treesnakes Boiga irregularis (>900 mm snout-vent length, SVL). 5. Synthesis and applications. Mark-recapture modelling is commonly used by ecological managers to estimate populations. However, existing models involve making assumptions about either closure violations or response to capture. Physical closure of our population on a landscape scale allowed us to determine the relative importance of covariates influencing capture probability (body size, trappability periods, and latent heterogeneity). This information was used to develop models in which different segments of the population could be assigned different probabilities of capture, and suggests that modelling of open populations should incorporate easily measured, but potentially overlooked, parameters such as body size or condition. ?? 2008 The Authors.

Journal of Applied Ecology↗
USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗