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At least 595 records · Page 33Linked to original sources
USGS2014

Reply to Efford on ‘Integrating resource selection information with spatial capture-recapture’

1. We proposed ( Methods in Ecology and Evolution , 2013, 4) a model for combining telemetry data with spatial capture–recapture (SCR) data that was vigorously criticized by Efford ( Methods in Ecology and Evolution , 2014, 000, 000). Efford's main claim was that our encounter probability model was incorrect, and therefore our R code and simulation results were wrong. 2. In fact, our encounter probability model is correct under the Poisson point process model that we used as a basis for our integrated model. On the other hand, the basis for Efford's claims clearly rest on the assumption of an alternative model which, while possibly useful, is distinct from that analysed in Royle et al. (Methods in Ecology and Evolution, 2013, 4). 3. A key point of Royle et al. ( Methods in Ecology and Evolution , 2013, 4) was that active resource selection induces heterogeneity in encounter probability which, if unaccounted for, should bias estimates of population size or density. The models of Royle et al. ( Methods in Ecology and Evolution , 2013, 4) and Efford ( Methods in Ecology and Evolution , 2014, 000, 000) merely amount to alternative models of resource selection, and hence varying amounts of heterogeneity in encounter probability.

Methods in Ecology and Evolution↗
USGS2016

The role of habitat filtering in the leaf economics spectrum and plant susceptibility to pathogen infection

1.The Leaf Economics Spectrum (LES) describes global covariation in the traits of plant leaves. The LES is thought to arise from biophysical constraints and habitat filtering (ecological selection against unfit trait combinations along environmental gradients). However, the role of habitat filtering in generating the LES has not been tested experimentally. 2.If the process of habitat filtering plays a role in generating the LES, the LES could weaken in communities that have yet to be filtered by the current environment, for example after abiotic environmental change. LES traits are commonly used to predict community and ecosystem processes, and if the LES weakens in unfiltered communities, LES-based models may no longer apply. 3.In the greenhouse, we experimentally simulated three stages of habitat filtering in response to abiotic change: from unfiltered, to semi-filtered, to completely filtered communities. In each stage, we quantified the strength of the LES and assessed the accuracy of trait-based models of an important ecological process, pathogen infection. 4.The strength of the LES increased with the completeness of habitat filtering, as did the accuracy of trait-based models of plant susceptibility to pathogen infection. 5. Synthesis . Our results suggest that habitat filtering plays a fundamental role in strengthening the trait correlations of the LES, and that trait-based models may be less accurate when communities have not been filtered by the current environment, for example, following rapid environmental change.

Journal of Ecology↗
USGS2008

Spatial patterns of simulated transpiration response to climate variability in a snow dominated mountain ecosystem

Transpiration is an important component of soil water storage and stream-flow and is linked with ecosystem productivity, species distribution, and ecosystem health. In mountain environments, complex topography creates heterogeneity in key controls on transpiration as well as logistical challenges for collecting representative measurements. In these settings, ecosystem models can be used to account for variation in space and time of the dominant controls on transpiration and provide estimates of transpiration patterns and their sensitivity to climate variability and change. The Regional Hydro-Ecological Simulation System (RHESSys) model was used to assess elevational differences in sensitivity of transpiration rates to the spatiotemporal variability of climate variables across the Upper Merced River watershed, Yosemite Valley, California, USA. At the basin scale, predicted annual transpiration was lowest in driest and wettest years, and greatest in moderate precipitation years (R2 = 0.32 and 0.29, based on polynomial regression of maximum snow depth and annual precipitation, respectively). At finer spatial scales, responsiveness of transpiration rates to climate differed along an elevational gradient. Low elevations (1200-1800 m) showed little interannual variation in transpiration due to topographically controlled high soil moistures along the river corridor. Annual conifer stand transpiration at intermediate elevations (1800-2150 m) responded more strongly to precipitation, resulting in a unimodal relationship between transpiration and precipitation where highest transpiration occurred during moderate precipitation levels, regardless of annual air temperatures. Higher elevations (2150-2600 m) maintained this trend, but air temperature sensitivities were greater. At these elevations, snowfall provides enough moisture for growth, and increased temperatures influenced transpiration. Transpiration at the highest elevations (2600-4000 m) showed strong sensitivity to air temperature, little sensitivity to precipitation. Model results suggest elevational differences in vegetation water use and sensitivity to climate were significant and will likely play a key role in controlling responses and vulnerability of Sierra Nevada ecosystems to climate change. Copyright ?? 2008 John Wiley & Sons, Ltd.

Hydrological Processes↗
USGS2015

Predicting Brook Trout occurrence in stream reaches throughout their native range in the eastern United States

The Brook Trout Salvelinus fontinalis is an important species of conservation concern in the eastern USA. We developed a model to predict Brook Trout population status within individual stream reaches throughout the species’ native range in the eastern USA. We utilized hierarchical logistic regression with Bayesian estimation to predict Brook Trout occurrence probability, and we allowed slopes and intercepts to vary among ecological drainage units (EDUs). Model performance was similar for 7,327 training samples and 1,832 validation samples based on the area under the receiver operating curve (∼0.78) and Cohen's kappa statistic (0.44). Predicted water temperature had a strong negative effect on Brook Trout occurrence probability at the stream reach scale and was also negatively associated with the EDU average probability of Brook Trout occurrence (i.e., EDU-specific intercepts). The effect of soil permeability was positive but decreased as EDU mean soil permeability increased. Brook Trout were less likely to occur in stream reaches surrounded by agricultural or developed land cover, and an interaction suggested that agricultural land cover also resulted in an increased sensitivity to water temperature. Our model provides a further understanding of how Brook Trout are shaped by habitat characteristics in the region and yields maps of stream-reach-scale predictions, which together can be used to support ongoing conservation and management efforts. These decision support tools can be used to identify the extent of potentially suitable habitat, estimate historic habitat losses, and prioritize conservation efforts by selecting suitable stream reaches for a given action. Future work could extend the model to account for additional landscape or habitat characteristics, include biotic interactions, or estimate potential Brook Trout responses to climate and land use changes.

Connecticut, Delaware, Maine, Maryland, Massachuse↗
USGS2016

Divergent projections of future land use in the United States arising from different models and scenarios

A variety of land-use and land-cover (LULC) models operating at scales from local to global have been developed in recent years, including a number of models that provide spatially explicit, multi-class LULC projections for the conterminous United States. This diversity of modeling approaches raises the question: how consistent are their projections of future land use? We compared projections from six LULC modeling applications for the United States and assessed quantitative, spatial, and conceptual inconsistencies. Each set of projections provided multiple scenarios covering a period from roughly 2000 to 2050. Given the unique spatial, thematic, and temporal characteristics of each set of projections, individual projections were aggregated to a common set of basic, generalized LULC classes (i.e., cropland, pasture, forest, range, and urban) and summarized at the county level across the conterminous United States. We found very little agreement in projected future LULC trends and patterns among the different models. Variability among scenarios for a given model was generally lower than variability among different models, in terms of both trends in the amounts of basic LULC classes and their projected spatial patterns. Even when different models assessed the same purported scenario, model projections varied substantially. Projections of agricultural trends were often far above the maximum historical amounts, raising concerns about the realism of the projections. Comparisons among models were hindered by major discrepancies in categorical definitions, and suggest a need for standardization of historical LULC data sources. To capture a broader range of uncertainties, ensemble modeling approaches are also recommended. However, the vast inconsistencies among LULC models raise questions about the theoretical and conceptual underpinnings of current modeling approaches. Given the substantial effects that land-use change can have on ecological and societal processes, there is a need for improvement in LULC theory and modeling capabilities to improve acceptance and use of regional- to national-scale LULC projections for the United States and elsewhere.

Ecological Modelling↗
USGS2017

Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance

Context Species-specific models of landscape capability (LC) can inform landscape conservation design. Landscape capability is “the ability of the landscape to provide the environment […] and the local resources […] needed for survival and reproduction […] in sufficient quantity, quality and accessibility to meet the life history requirements of individuals and local populations.” Landscape capability incorporates species’ life histories, ecologies, and distributions to model habitat for current and future landscapes and climates as a proactive strategy for conservation planning. Objectives We tested the ability of a set of LC models to explain variation in point occupancy and abundance for seven bird species representative of spruce-fir, mixed conifer-hardwood, and riparian and wooded wetland macrohabitats. Methods We compiled point count data sets used for biological inventory, species monitoring, and field studies across the northeastern United States to create an independent validation data set. Our validation explicitly accounted for underestimation in validation data using joint distance and time removal sampling. Results Blackpoll warbler ( Setophaga striata ), wood thrush ( Hylocichla mustelina ), and Louisiana ( Parkesia motacilla ) and northern waterthrush ( P. noveboracensis ) models were validated as predicting variation in abundance, although this varied from not biologically meaningful (1%) to strongly meaningful (59%). We verified all seven species models [including ovenbird ( Seiurus aurocapilla ), blackburnian ( Setophaga fusca ) and cerulean warbler ( Setophaga cerulea )], as all were positively related to occupancy data. Conclusions LC models represent a useful tool for conservation planning owing to their predictive ability over a regional extent. As improved remote-sensed data become available, LC layers are updated, which will improve predictions.

Landscape Ecology↗
USGS2021

How do lizard niches conserve, diverge or converge? Further exploration of saurian evolutionary ecology

Environmental conditions on Earth are repeated in non-random patterns that often coincide with species from different regions and time periods having consistent combinations of morphological, physiological and behavioral traits. Observation of repeated trait combinations among species confronting similar environmental conditions suggest that adaptive trait combinations are constrained by functional tradeoffs within or across niche dimensions. In an earlier study, we assembled a high-resolution database of functional traits for 134 lizard species to explore ecological diversification in relation to five fundamental niche dimensions. Here we expand and further examine multivariate relationships in that dataset to assess the relative influence of niche dimensions on the distribution of species in 6-dimensional niche space and how these may deviate from distributions generated from null models. We then analyzed a dataset with lower functional-trait resolution for 1023 lizard species that was compiled from our dataset and a published database, representing most of the extant families and environmental conditions occupied by lizards globally. Ordinations from multivariate analysis were compared with null models to assess how ecological and historical factors have resulted in the conservation, divergence or convergence of lizard niches.

BMC Ecology and Evolution↗
USGS2014

Linking multi-temporal satellite imagery to coastal wetland dynamics and bird distribution

Ecosystems are characterized by dynamic ecological processes, such as flooding and fires, but spatial models are often limited to a single measurement in time. The characterization of direct, fine-scale processes affecting animals is potentially valuable for management applications, but these are difficult to quantify over broad extents. Direct predictors are also expected to improve transferability of models beyond the area of study. Here, we investigated the ability of non-static and multi-temporal habitat characteristics to predict marsh bird distributions, while testing model generality and transferability between two coastal habitats. Distribution models were developed for king rail ( Rallus elegans ), common gallinule ( Gallinula galeata ), least bittern ( Ixobrychus exilis ), and purple gallinule ( Porphyrio martinica ) in fresh and intermediate marsh types in the northern Gulf Coast of Louisiana and Texas, USA. For model development, repeated point count surveys of marsh birds were conducted from 2009 to 2011. Landsat satellite imagery was used to quantify both annual conditions and cumulative, multi-temporal habitat characteristics. We used multivariate adaptive regression splines to quantify bird-habitat relationships for fresh, intermediate, and combined marsh habitats. Multi-temporal habitat characteristics ranked as more important than single-date characteristics, as temporary water was most influential in six of eight models. Predictive power was greater for marsh type-specific models compared to general models and model transferability was poor. Birds in fresh marsh selected for annual habitat characterizations, while birds in intermediate marsh selected for cumulative wetness and heterogeneity. Our findings emphasize that dynamic ecological processes can affect species distribution and species-habitat relationships may differ with dominant landscape characteristics.

Ecological Modelling↗
USGS2017

Simulation of rapid ecological change in Lake Ontario

Lower trophic level processes are integral to proper functioning of large aquatic ecosystems and have been disturbed in Lake Ontario by various stressors including exotic species. The invasion of benthic habitats by dreissenid mussels has led to systemic changes and native faunal declines. Size-dependent physiological rates, spatial differences and connectivity, competition, and differential population dynamics among invertebrate groups contributed to the change and system complexity. We developed a spatially explicit, individual-based mechanistic model of the benthic ecosystem in Lake Ontario, with coupling to the pelagic system, to examine ecosystem dynamics and effects of dreissenid mussel invasion and native fauna losses. Benthic organisms were represented by functional groups; filter-feeders (i.e., dreissenid mussels), surface deposit-feeders (e.g., native amphipod Diporeia spp.), and deposit-feeders (e.g., oligochaetes and other burrowers). The model was stable, represented ecological structure and function effectively, and reproduced observed effects of the mussel invasion. Two hypotheses for causes of Diporeia loss, competition or disease-like mortality, were tested. Simple competition for food did not explain observed declines in native surface deposit-feeders during the filter-feeder invasion. However, the elevated mortality scenario supports a disease-like cause for loss of the native amphipod, with population changes in various lake areas and altered benthic biomass transfers. Stabilization of mussel populations and possible recovery of the native, surface-deposit feeding amphipod were predicted. Although further research is required on forcing functions, model parameters, and natural conditions, the model provides a valuable tool to help managers understand the benthic system and plan for response to future disruptions.

Lake Ontario↗
USGS2013

Plausible combinations: An improved method to evaluate the covariate structure of Cormack-Jolly-Seber mark-recapture models

Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly- Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.

Open Journal Of Ecology↗
USGS2017

Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape-directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage-grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R 2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.

Ecology and Evolution↗
USGS2017

Systems approaches for coastal hazard assessment and resilience

The framework presented herein supports a changing paradigm in the approaches used by coastal researchers, engineers, and social scientists to model the impacts of climate change and sea level rise (SLR) in particular along low-gradient coastal landscapes. Use of a System of Systems (SoS) approach to the coastal dynamics of SLR is encouraged to capture the nonlinear feedbacks and dynamic responses of the bio-geo-physical coastal environment to SLR, while assessing the social, economic, and ecologic impacts. The SoS approach divides the coastal environment into smaller subsystems such as morphology, ecology, and hydrodynamics. Integrated models are used to assess the dynamic responses of subsystems to SLR; these models account for complex interactions and feedbacks among individual systems, which provides a more comprehensive evaluation of the future of the coastal system as a whole. Results from the integrated models can be used to inform economic services valuations, in which economic activity is connected back to bio-geo-physical changes in the environment due to SLR by identifying changes in the coastal subsystems, linking them to the understanding of the economic system and assessing the direct and indirect impacts to the economy. These assessments can be translated from scientific data to application through various stakeholder engagement mechanisms, which provide useful feedback for accountability as well as benchmarks and diagnostic insights for future planning. This allows regional and local coastal managers to create more comprehensive policies to reduce the risks associated with future SLR and enhance coastal resilience.

Book chapter↗
USGS2024

Designing count-based studies in a world of hierarchical models

Advances in hierarchical modeling have improved estimation of ecological parameters from count data, especially those quantifying population abundance, distribution, and dynamics by explicitly accounting for observation processes, particularly incomplete detection. Even hierarchical models that account for incomplete detection, however, cannot compensate for data limitations stemming from poorly planned sampling. Ecologists therefore need guidance for planning count-based studies that follow established sampling theory, collect appropriate data, and apply current modeling approaches to answer their research questions. We synthesize available literature relevant to guiding count-based studies. Considering the central historical and ongoing contributions of avian studies to ecological knowledge, we focus on birds as a case study for this review, but the basic principles apply to all populations whose members are sufficiently observable to be counted. The sequence of our review represents the thought process in which we encourage ecologists to engage 1) the research question(s) and population parameters to measure, 2) sampling design, 3) analytical framework, 4) temporal design, and 5) survey protocol. We also provide 2 hypothetical demonstrations of these study plan components representing different research questions and study systems. Mirroring the structure of hierarchical models, we suggest researchers primarily focus on the ecological processes of interest when designing their approach to sampling, and wait to consider logistical constraints of data collection and observation processes when developing the survey protocol. We offer a broad framework for researchers planning count-based studies, while pointing to relevant literature elaborating on particular tools and concepts.

Journal of Wildlife Management↗
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↗
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↗