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At least 1,441 records · Page 80Linked to original sources
USGS2015

Comparing spatial capture–recapture modeling and nest count methods to estimate orangutan densities in the Wehea Forest, East Kalimantan, Indonesia

Accurate information on the density and abundance of animal populations is essential for understanding species' ecology and for conservation planning, but is difficult to obtain. The endangered orangutan ( Pongo spp.) is an example; due to its elusive behavior and low densities, researchers have relied on methods that convert nest counts to orangutan densities and require substantial effort for reliable results. Camera trapping and spatial capture–recapture (SCR) models could provide an alternative but have not been used for primates. We compared density estimates calculated using the two methods for orangutans in the Wehea Forest, East Kalimantan, Indonesia. Camera trapping/SCR modeling produced a density estimate of 0.16 ± 0.09–0.29 indiv/km 2 , and nest counts produced a density estimate of 1.05 ± 0.18–6.01 indiv/km 2 . The large confidence interval of the nest count estimate is probably due to high variance in nest encounter rates, indicating the need for larger sample size and the substantial effort required to produce reliable results using this method. The SCR estimate produced a very low density estimate and had a narrower but still fairly wide confidence interval. This was likely due to unmodeled heterogeneity and small sample size, specifically a low number of individual captures and recaptures. We propose methodological fixes that could address these issues and improve precision. A comparison of the overall costs and benefits of the two methods suggests that camera trapping/SCR modeling can potentially be a useful tool for assessing the densities of orangutans and other elusive primates, and warrant further investigation to determine broad applicability and methodological adjustments needed.

East Kalimantan↗
USGS2013

Bayes and empirical Bayes estimators of abundance and density from spatial capture-recapture data

In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar – and often identical – inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.

PLoS ONE↗
USGS2017

Food abundance, prey morphology, and diet specialization influence individual sea otter tool use

Sea otters are well-known tool users, employing objects such as rocks or shells to break open invertebrate prey. We used a series of generalized linear mixed effect models to examine observational data on prey capture and tool use from 211 tagged individuals from 5 geographically defined study areas throughout the sea otter’s range in California. Our best supported model was able to explain 75% of the variation in the frequency of tool use by individual sea otters with only ecological and demographic variables. In one study area, where sea otter food resources were abundant, all individuals had similar diets focusing on preferred prey items and used tools at low to moderate frequencies (4–38% of prey captures). In the remaining areas, where sea otters were food-limited, individuals specialized on different subsets of the available prey and had a wider range of average tool-use frequency (0–98% of prey captures). The prevalence of difficult-to-access prey in individual diets was a major predictor of tool use and increased the likelihood of using tools on prey that were not difficult to access as well. Age, sex, and feeding habitat also contributed to the probability of tool use but to a smaller extent. We developed a conceptual model illustrating how food abundance, the prevalence of difficult-to-access prey, and individual diet specialization interacted to determine the likelihood that individual sea otters would use tools and considered the model’s relevance to other tool-using species.

Behavioral Ecology↗
USGS2013

Modeling mountain pine beetle disturbance in Glacier National Park using multiple lines of evidence

Temperate forest ecosystems are subject to various disturbances which contribute to ecological legacies that can have profound effects on the structure of the ecosystem. Impacts of disturbance can vary widely in extent, duration and severity over space and time. Given that global climate change is expected to increase rates of forest disturbance, an understanding of these events are critical in the interpretation of contemporary forest patterns and those of the near future. We seek to understand the impact of the 1970s mountain pine beetle outbreak on the landscape of Glacier National Park and investigate any connection between this event and subsequent decades of extensive wildfire. The lack of spatially explicit data on the mountain pine beetle disturbance represents a major data gap and inhibits our ability to test for correlations between outbreak severity and fire severity. To overcome this challenge, we utilized multiple lines of evidence to model forest canopy mortality as a proxy for outbreak severity. We used historical aerial and landscape photos, reports, aerial survey data, a six year collection of Landsat imagery and abiotic data in combination with regression analysis. The use of remotely sensed data is critical in large areas where subsequent disturbance (fire) has erased some of the evidence from the landscape. Results indicate that this method is successful in capturing the spatial heterogeneity of the outbreak in a topographically complex landscape. Furthermore, this study provides an example on the use of existing data to reduce levels of uncertainty associated with an historic disturbance.

Conference Paper↗
USGS2007

Model estimation of land-use effects on water levels of northern Prairie wetlands

Wetlands of the Prairie Pothole Region exist in a matrix of grassland dominated by intensive pastoral and cultivation agriculture. Recent conservation management has emphasized the conversion of cultivated farmland and degraded pastures to intact grassland to improve upland nesting habitat. The consequences of changes in land-use cover that alter watershed processes have not been evaluated relative to their effect on the water budgets and vegetation dynamics of associated wetlands. We simulated the effect of upland agricultural practices on the water budget and vegetation of a semipermanent prairie wetland by modifying a previously published mathematical model (WETSIM). Watershed cover/land-use practices were categorized as unmanaged grassland (native grass, smooth brome), managed grassland (moderately heavily grazed, prescribed burned), cultivated crops (row crop, small grain), and alfalfa hayland. Model simulations showed that differing rates of evapotranspiration and runoff associated with different upland plant-cover categories in the surrounding catchment produced differences in wetland water budgets and linked ecological dynamics. Wetland water levels were highest and vegetation the most dynamic under the managed-grassland simulations, while water levels were the lowest and vegetation the least dynamic under the unmanaged-grassland simulations. The modeling results suggest that unmanaged grassland, often planted for waterfowl nesting, may produce the least favorable wetland conditions for birds, especially in drier regions of the Prairie Pothole Region. These results stand as hypotheses that urgently need to be verified with empirical data.

Iowa, Minnesota, Montana, Nebraska, North Dakota, ↗
USGS2025

Bright spots for advancing ecological understanding and conservation decision-making

A lot can be learned by studying bright spots—defined as unexpected positive outcomes. In fields like public health, education, and oncology, identifying factors behind bright spots reveals previously unknown drivers of success that can be replicated elsewhere. This concept is being applied in conservation but is hampered by variations in definitions of bright spots and confusion with hotspots—sites with high absolute values of a metric. We developed a framework to clearly define and distinguish between hotspots (e.g., a wetland with high plant diversity) and bright spots (e.g., a biodiverse wetland in a housing development), which outperform conservation expectations. The framework is an iterative cycle, consisting of setting expectations for relative comparisons, classifying systems into bright, dark, hot, and cold categories, and digging deeper to reveal hidden mechanisms and opportunities for intervention. We drew on examples from diverse fields to demonstrate how our framework can generate new knowledge, identify potential interventions, and inform management priorities. Defining conservation and management expectations, often through predictive models, is essential to understanding drivers of success and fosters hypotheses about overlooked factors. Our framework can enhance ecological understanding, guide interventions, and help prioritize actions in conservation and natural resource management.

Conservation Biology↗
USGS2010

Mechanics and modeling of flow, sediment transport and morphologic change in riverine lateral separation zones

Lateral separation zones or eddies in rivers are critically important features for sediment storage and for a variety of roles they play in riparian and aquatic ecology. As part of a larger effort to predict the morphology of lateral separation zones in the Colorado River in Grand Canyon for a selection of sediment supply and discharge scenarios, we evaluated the performance of two modeling techniques for predicting flow, sediment transport, and morphodynamics in eddies using field data. In order to understand the relative roles of various exchange mechanisms between the main channel and eddies, we applied two-dimensional unsteady and three-dimensional unsteady models in a reach containing a lateral separation zone. Both models were developed, calibrated, and evaluated using detailed field data comprising acoustic-Doppler velocity measurements, water-surface elevations, sediment concentration by size class, and bathymetry measured during a flood event in the Colorado River. Model results and measurements are used to develop a better understanding of the mechanics of water and sediment exchange between the eddy and the mainstem and other factors that control the morphology of the reach.

Arizona↗
USGS1999

Linking stressors and ecological responses

To characterize risk, it is necessary to quantify the linkages and interactions between chemical, physical and biological stressors and endpoints in the conceptual framework for ecological risk assessment (ERA). This can present challenges in a multiple stressor analysis, and it will not always be possible to develop a quantitative stressor-response profile. This review commences with a conceptual representation of the problem of developing a linkage analysis for multiple stressors and responses. The remainder of the review surveys a variety of mathematical and statistical methods (e.g., ranking methods, matrix models, multivariate dose-response for mixtures, indices, visualization, simulation modeling and decision-oriented methods) for accomplishing the linkage analysis for multiple stressors. Describing the relationships between multiple stressors and ecological effects are critical components of 'effects assessment' in the ecological risk assessment framework.

Book chapter↗
USGS2024

Establishing quantitative benchmarks for soil erosion and ecological monitoring, assessment, and management

Soil erosion can have a multitude of negative impacts on agroecosystems and society and there remains an urgent need for tools to support its management. Quantitative benchmarks based on holistic understanding of erosion processes, ecosystem function, and land use objectives can be used with monitoring data and models to inform assessments and make objective and actionable decisions about erosion management. However, managers currently lack a framework for establishing benchmarks. Here, we present a framework and evaluation of different approaches to establishing quantitative benchmarks for soil erosion and ecological monitoring and assessment that can inform land management decisions. We use monitoring data collected across Chihuahuan Desert ecosystems in the United States and an aeolian sediment transport model to illustrate how benchmarks can be established. Approaches include establishing benchmarks from relationships between soil erosion indicators, reference states and land potential, including state-and-transition models, and desired conditions from existing monitoring data. We discuss the benefits and caveats of the different approaches and show how combining different benchmarking approaches can help users ensure that benchmarks appropriately reflect thresholds for soil erosion and achievable management outcomes. We finish by identifying future research needs to support establishment and application of erosion benchmarks across agroecosystems and recognize the opportunity to extend the benchmarking approaches to management of other agroecosystem processes and services.

Ecological Indicators↗
USGS2015

Structural equation modeling: Building and evaluating causal models

Scientists frequently wish to study hypotheses about causal relationships, rather than just statistical associations. This chapter addresses the question of how scientists might approach this ambitious task. Here we describe structural equation modeling (SEM), a general modeling framework for the study of causal hypotheses. Our goals are to (a) concisely describe the methodology, (b) illustrate its utility for investigating ecological systems, and (c) provide guidance for its application. Throughout our presentation, we rely on a study of the effects of human activities on wetland ecosystems to make our description of methodology more tangible. We begin by presenting the fundamental principles of SEM, including both its distinguishing characteristics and the requirements for modeling hypotheses about causal networks. We then illustrate SEM procedures and offer guidelines for conducting SEM analyses. Our focus in this presentation is on basic modeling objectives and core techniques. Pointers to additional modeling options are also given.

Book chapter↗
USGS2016

Importance of regional variation in conservation planning: A rangewide example of the Greater Sage-Grouse

We developed rangewide population and habitat models for Greater Sage-Grouse ( Centrocercus urophasianus ) that account for regional variation in habitat selection and relative densities of birds for use in conservation planning and risk assessments. We developed a probabilistic model of occupied breeding habitat by statistically linking habitat characteristics within 4 miles of an occupied lek using a nonlinear machine learning technique (Random Forests). Habitat characteristics used were quantified in GIS and represent standard abiotic and biotic variables related to sage-grouse biology. Statistical model fit was high (mean correctly classified = 82.0%, range = 75.4–88.0%) as were cross-validation statistics (mean = 80.9%, range = 75.1–85.8%). We also developed a spatially explicit model to quantify the relative density of breeding birds across each Greater Sage-Grouse management zone. The models demonstrate distinct clustering of relative abundance of sage-grouse populations across all management zones. On average, approximately half of the breeding population is predicted to be within 10% of the occupied range. We also found that 80% of sage-grouse populations were contained in 25–34% of the occupied range within each management zone. Our rangewide population and habitat models account for regional variation in habitat selection and the relative densities of birds, and thus, they can serve as a consistent and common currency to assess how sage-grouse habitat and populations overlap with conservation actions or threats over the entire sage-grouse range. We also quantified differences in functional habitat responses and disturbance thresholds across the Western Association of Fish and Wildlife Agencies (WAFWA) management zones using statistical relationships identified during habitat modeling. Even for a species as specialized as Greater Sage-Grouse, our results show that ecological context matters in both the strength of habitat selection (i.e., functional response curves) and response to disturbance.

Ecosphere↗
USGS2017

Estimating thermal performance curves from repeated field observations

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Ecology↗
USGS2021

From site to system: Approaches for producing system-wide estimates of fish habitat in large rivers

Worldwide, many productive rivers are dam-regulated and rely on flow management strategies that must balance support of ecological processes with human water use. One component of evaluating this balance is to understand ecological consequences of alternative flow management strategies, which has often been accomplished by coupling population dynamics models with models that relate streamflow to habitat availability and quality. Numerous methods assign habitat availability to locations within a river basin: These include fine-scale field-measured values that are extrapolated to other locations within the basin having similar physical characteristics or equation-driven values created by functions of model-predicted values of physical characteristics. The array of options for creating habitat models is evolving rapidly as high-resolution remote-sensing data becomes more accessible and computational capacity improves. Our objective was to identify trade-offs among approaches that assign habitat relationships to large rivers and to create a decision support tool to supplement choices of extent and granularity. Using a selection of case studies that represent a breadth of scales and diverse trade-offs, we demonstrate the need for a transparent process of data evaluation and assessment to determine the appropriate fit for model scope or context that best supports management needs and recognize sources of uncertainty. The structured approach proposed here aims at improving future model development and refine population dynamics models that inform the management of rivers.

River Research and Applications↗
USGS2021

Export of photolabile and photoprimable dissolved organic carbon from the Connecticut River

Dissolved organic carbon (DOC) impacts water quality, the carbon cycle, and the ecology of aquatic systems. Understanding what controls DOC is therefore critical for improving large-scale models and best management practices for aquatic ecosystems. The two main processes of DOC transformation and removal, photochemical and microbial DOC degradation, work in tandem to modify and remineralize DOC within natural waters. Here, we examined both the photo- and microbial remineralization of DOC (photolability and biolability), and the indirect phototransformation of DOC into biolabile DOC (photoprimed biolability) for samples that capture the spatiotemporal and hydrological gradients of the Connecticut River watershed. The majority of DOC exported from this temperate watershed was photolabile and the concentration of photolabile DOC correlated with UV absorbance at 254 nm ( r 2 = 0.86). Phototransformation of DOC also increased biolability, and the total photolabile DOC (sum of photolabile and photoprimed biolabile DOC) showed a stronger correlation with UV absorbance at 254 nm (r 2 = 0.92). We estimate that as much as 49% (SD = 3.3%) and 10% (SD = 1.1%) of annual DOC export from the Connecticut River is directly photolabile and photoprimable, respectively. Thus, 2.82 Gg C year −1 (SD = 0.67 Gg C year −1 ) or 1.13 Mg C km −2 year −1 (SD = 0.27 km −2 year −1 ) of total photolabile DOC escapes photochemical degradation within the river network to be exported from the Connecticut River each year.

Connecticut River↗
USGS2019

Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities

Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.

Remote Sensing of Environment↗
USGS2019

Social equity shapes zone-selection: Balancing aquatic biodiversity conservation and ecosystem services delivery in the transboundary Danube River Basin

Freshwater biodiversity is declining, despite national and international efforts to manage and protect freshwater ecosystems. Ecosystem-based management (EBM) has been proposed as an approach that could more efficiently and adaptively balance ecological and societal needs. However, this raises the question of how social and ecological objectives can be included in an integrated management plan. Here, we present a generic model-coupling framework tailored to address this question for freshwater ecosystems, using three components: biodiversity, ecosystem services (ESS), and a spatial prioritisation that aims to balance the spatial representation of biodiversity and ESS supply and demand. We illustrate this model-coupling approach within the Danube River Basin using the spatially explicit, potential distribution of (i) 85 fish species as a surrogate for biodiversity as modelled using hierarchical Bayesian models, and (ii) four estimated ESS layers produced by the Artificial Intelligence for Ecosystem Services (ARIES) platform (with ESS supply defined as carbon storage and flood regulation, and demand specified as recreation and water use). These are then used for (iii) a joint spatial prioritisation of biodiversity and ESS employing Marxan with Zones, laying out the spatial representation of multiple management zones. Given the transboundary setting of the Danube River Basin, we also run comparative analyses including the country-level purchasing power parity (PPP)-adjusted gross domestic product (GDP) and each country's percent cover of the total basin area as potential cost factors, illustrating a scheme for balancing the share of establishing specific zones among countries. We demonstrate how emphasizing various biodiversity or ESS targets in an EBM model-coupling framework can be used to cost-effectively test various spatially explicit management options across a multi-national case study. We further discuss possible limitations, future developments, and requirements for effectively managing a balance between biodiversity and ESS supply and demand in freshwater ecosystems.

Science of the Total Environment↗
USGS2012

Concurrent speciation in the eastern woodland salamanders (Genus Plethodon):DNA sequences of the complete albumin nuclear and partialmitochondrial 12s genes

Salamanders of the North American plethodontid genus Plethodon are important model organisms in a variety of studies that depend on a phylogenetic framework (e.g., chemical communication, ecological competition, life histories, hybridization, and speciation), and consequently their systematics has been intensively investigated over several decades. Nevertheless, we lack a synthesis of relationships among the species. In the analyses reported here we use new DNA sequence data from the complete nuclear albumin gene (1818 bp) and the 12s mitochondrial gene (355 bp), as well as published data for four other genes (Wiens et al., 2006), up to a total of 6989 bp, to infer relationships. We relate these results to past systematic work based on morphology, allozymes, and DNA sequences. Although basal relationships show a strong consensus across studies, many terminal relationships remain in flux despite substantial sequencing and other molecular and morphological studies. This systematic instability appears to be a consequence of contemporaneous bursts of speciation in the late Miocene and Pliocene, yielding many closely related extant species in each of the four eastern species groups. Therefore we conclude that many relationships are likely to remain poorly resolved in the face of additional sequencing efforts. On the other hand, the current classification of the 45 eastern species into four species groups is supported. The Plethodon cinereus group (10 species) is the sister group to the clade comprising the other three groups, but these latter groups (Plethodon glutinosus [28 species], Plethodon welleri [5 species], and Plethodon wehrlei [2 species]) probably diverged from each other at approximately the same time.

Molecular Phylogenetics and Evolution↗