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Angela K. Fuller

Publications and source records attributed to Angela K. Fuller.

67 records · Page 4Linked to original sources
USGS2016

Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus ( Odobenus rosmarus divergens ) and fishers ( Pekania pennanti ). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Royal Society Open Science↗
USGS2016

Application of decision science to resilience management in Jamaica Bay

This book highlights the growing interest in management interventions designed to enhance the resilience of the Jamaica Bay socio-ecological system. Effective management, whether the focus is on managing biological processes or human behavior or (most likely) both, requires decision makers to anticipate how the managed system will respond to interventions (i.e., via predictions or projections). In systems characterized by many interacting components and high uncertainty, making probabilistic predictions is often difficult and requires careful thinking not only about system dynamics, but also about how management objectives are specified and the analytic method used to select the preferred action(s). Developing a clear statement of the problem(s) and articulation of management objectives is often best achieved by including input from managers, scientists and other stakeholders affected by the decision through a process of joint problem framing (Marcot and others 2012; Keeney and others 1990). Using a deliberate, coherent and transparent framework for deciding among management alternatives to best meet these objectives then ensures a greater likelihood for successful intervention. Decision science provides the theoretical and practical basis for developing this framework and applying decision analysis methods for making complex decisions under uncertainty and risk.

Book chapter↗
USGS2015

Understanding landowner intentions to create early successional forest habitat in the northeastern United States

Early successional forest habitat (ESH) and associated wildlife species in the northeastern United States are in decline. One way to help create early successional forest conditions is engaging private forest landowners in even-aged forest management because their limited participation may have contributed to declines in ESH for wildlife species of high conservation concern. We applied the reasoned action approach from social psychology to predict intentions of landowners in the 13-county Southern Tier of New York State, USA, to conduct patch-cuts, which is a type of even-aged forest management. We tested the predictive ability of the model using data from a mail survey of landowners conducted from November 2010 to January 2011. Landowner intention to conduct patch-cuts was high (55% of respondents), with attitude being the strongest direct predictor of behavioral intention. Our results suggest that patch-cutting intentions are most likely expressed by landowners who think the behavior is good for their land and wildlife, believe in positive outcomes of land and wildlife management, belong to a game wildlife organization, and have conducted patch-cuts in the past. Strategies to engage more landowners in ESH management will have the highest likelihood of success if outreach efforts focus on influencing behavioral beliefs and subsequently attitudes, possibly working with game wildlife organizations to communicate a unified message for habitat conservation, including the importance of maintaining and creating ESH. Our results demonstrate the importance of social science research to increase the likelihood that conservation targets for declining wildlife species are met. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

New York↗
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↗
USGS2015

Climate-smart management of biodiversity

Determining where biodiversity is likely to be most vulnerable to climate change and methods to reduce that vulnerability are necessary first steps to incorporate climate change into biodiversity management plans. Here, we use a spatial climate change vulnerability assessment to (1) map the potential vulnerability of terrestrial biodiversity to climate change in the northeastern United States and (2) provide guidance on how and where management actions for biodiversity could provide long-term benefits under climate change (i.e., climate-smart management considerations). Our model suggests that biodiversity will be most vulnerable in Delaware, Maryland, and the District of Columbia due to the combination of high climate change velocity, high landscape resistance, and high topoclimate homogeneity. Biodiversity is predicted to be least vulnerable in Vermont, Maine, and New Hampshire because large portions of these states have low landscape resistance, low climate change velocity, and low topoclimate homogeneity. Our spatial climate-smart management considerations suggest that: (1) high topoclimate diversity could moderate the effects of climate change across 50% of the region; (2) decreasing local landscape resistance in conjunction with other management actions could increase the benefit of those actions across 17% of the region; and (3) management actions across 24% of the region could provide long-term benefits by promoting short-term population persistence that provides a source population capable of moving in the future. The guidance and framework we provide here should allow conservation organizations to incorporate our climate-smart management considerations into management plans without drastically changing their approach to biodiversity conservation.

Ecosphere↗
USGS2015

Accounting for multiple climate components when estimating climate change exposure and velocity

The effect of anthropogenic climate change on organisms will likely be related to climate change exposure and velocity at local and regional scales. However, common methods to estimate climate change exposure and velocity ignore important components of climate that are known to affect the ecology and evolution of organisms. We develop a novel index of climate change (climate overlap) that simultaneously estimates changes in the means, variation and correlation between multiple weather variables. Specifically, we estimate the overlap between multivariate normal probability distributions representing historical and current or projected future climates. We provide methods for estimating the statistical significance of climate overlap values and methods to estimate velocity using climate overlap. We show that climates have changed significantly across 80% of the continental United States in the last 32 years and that much of this change is due to changes in the variation and correlation between weather variables (two statistics that are rarely incorporated into climate change studies). We also show that projected future temperatures are predicted to be locally novel (<1·5% overlap) across most of the global land surface and that exposure is likely to be highest in areas with low historical climate variation. Last, we show that accounting for changes in the variation and correlation between multiple weather variables can dramatically affect velocity estimates; mean velocity estimates in the continental United States were between 3·1 and 19·0 km yr −1 when estimated using climate overlap compared to 1·4 km yr −1 when estimated using traditional methods. Our results suggest that accounting for changes in the means, variation and correlation between multiple weather variables can dramatically affect estimates of climate change exposure and velocity. These climate components are known to affect the ecology and evolution of organisms, but are ignored by most measures of climate change. We conclude with a set of future directions and recommend future work to determine which measures of climate change exposure and velocity are most related to biological responses to climate change.

Methods in Ecology and Evolution↗
USGS2015

Likelihood analysis of spatial capture-recapture models for stratified or class structured populations

We develop a likelihood analysis framework for fitting spatial capture-recapture (SCR) models to data collected on class structured or stratified populations. Our interest is motivated by the necessity of accommodating the problem of missing observations of individual class membership. This is particularly problematic in SCR data arising from DNA analysis of scat, hair or other material, which frequently yields individual identity but fails to identify the sex. Moreover, this can represent a large fraction of the data and, given the typically small sample sizes of many capture-recapture studies based on DNA information, utilization of the data with missing sex information is necessary. We develop the class structured likelihood for the case of missing covariate values, and then we address the scaling of the likelihood so that models with and without class structured parameters can be formally compared regardless of missing values. We apply our class structured model to black bear data collected in New York in which sex could be determined for only 62 of 169 uniquely identified individuals. The models containing sex-specificity of both the intercept of the SCR encounter probability model and the distance coefficient, and including a behavioral response are strongly favored by log-likelihood. Estimated population sex ratio is strongly influenced by sex structure in model parameters illustrating the importance of rigorous modeling of sex differences in capture-recapture models.

Ecosphere↗
USGS2014

Modelling non-Euclidean movement and landscape connectivity in highly structured ecological networks

Movement is influenced by landscape structure, configuration and geometry, but measuring distance as perceived by animals poses technical and logistical challenges. Instead, movement is typically measured using Euclidean distance, irrespective of location or landscape structure, or is based on arbitrary cost surfaces. A recently proposed extension of spatial capture-recapture (SCR) models resolves this issue using spatial encounter histories of individuals to calculate least-cost paths (ecological distance: Ecology , 94 , 2013, 287) thereby relaxing the Euclidean assumption. We evaluate the consequences of not accounting for movement heterogeneity when estimating abundance in highly structured landscapes, and demonstrate the value of this approach for estimating biologically realistic space-use patterns and landscape connectivity. We simulated SCR data in a riparian habitat network, using the ecological distance model under a range of scenarios where space-use in and around the landscape was increasingly associated with water (i.e. increasingly less Euclidean). To assess the influence of miscalculating distance on estimates of population size, we compared the results from the ecological and Euclidean distance based models. We then demonstrate that the ecological distance model can be used to estimate home range geometry when space use is not symmetrical. Finally, we provide a method for calculating landscape connectivity based on modelled species-landscape interactions generated from capture-recapture data. Using ecological distance always produced unbiased estimates of abundance. Explicitly modelling the strength of the species-landscape interaction provided a direct measure of landscape connectivity and better characterised true home range geometry. Abundance under the Euclidean distance model was increasingly (negatively) biased as space use was more strongly associated with water and, because home ranges are assumed to be symmetrical, produced poor characterisations of home range geometry and no information about landscape connectivity. The ecological distance SCR model uses spatially indexed capture-recapture data to estimate how activity patterns are influenced by landscape structure. As well as reducing bias in estimates of abundance, this approach provides biologically realistic representations of home range geometry, and direct information about species-landscape interactions. The incorporation of both structural (landscape) and functional (movement) components of connectivity provides a direct measure of species-specific landscape connectivity.

Methods in Ecology and Evolution↗
USGS2014

Can managers compensate for coyote predation of white-tailed deer?

Many studies have documented that coyotes ( Canis latrans ) are the greatest source of natural mortality for white-tailed deer ( Odocoileus virginianus ) neonates (<3 months old). With the range expansion of coyotes eastward in North America, many stakeholders are concerned that coyote predation may be affecting deer populations adversely. We hypothesized that declines in neonate survival, perhaps caused by increasing coyote predation, could be offset by adjusting or eliminating antlerless harvest allocations. We used a stochastic, age-based population simulation model to evaluate combinations of low neonate survival rates, severe winters, and low adult deer survival rates to determine the effectiveness of reduced antlerless harvest at stabilizing deer populations. We found that even in regions with high winter mortality, reduced antlerless harvest rates could stabilize deer populations with recruitment and survival rates reported in the literature. When neonate survival rates were low (25%) and yearling and adult female survival rates were reduced by 10%, elimination of antlerless harvests failed to stabilize populations. Our results suggest increased deer mortality from coyotes can be addressed through reduced hunting harvest of adult female deer in most circumstances throughout eastern North America. However, specific knowledge of adult female survival rates is important for making management decisions in areas where both neonate and adult survival may be affected by predation and other mortality factors.

Journal of Wildlife Management↗
USGS2014

Trap configuration and spacing influences parameter estimates in spatial capture-recapture models

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear ( Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

PLoS ONE↗
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&ndash;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↗
USGS2013

Integrating resource selection information with spatial capture--recapture

1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. 2. In this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models. 3. Our method leads to increases in precision for estimating parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of RSFs and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. 4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture–recapture methods.

Methods in Ecology and Evolution↗