Geology topics

Megan D. Higgs

Publications and source records attributed to Megan D. Higgs.

7 recordsLinked to original sources

USGS2019

Identifying occupancy model inadequacies: Can residuals separately assess detection and presence?

Occupancy models are widely applied to estimate species distributions, but few methods exist for model checking. Thorough model assessments can uncover inadequacies and allow for deeper ecological insight by exploring structure in the observed data not accounted for by a model. We introduce occupancy model residual definitions that utilize the posterior distribution of the partially latent occupancy states. Residual‐based assessments are valuable because they can target specific assumptions and identify ways to improve a model, such as adding spatial correlation or meaningful covariates. Our approach defines separate residuals for occupancy and detection, and we use simulation to examine whether missing structure for modeling detection probabilities can be distinguished from that for occupancy probabilities. In many scenarios, our residual diagnostics were able to successfully separate inadequacies at the different model levels, but we describe other situations when this may not be the case. Applying Moran's I residual diagnostics to assess models for silver‐haired (Lasionycteris noctivagans) and little brown (Myotis lucifugus) bats only provided evidence of residual spatial correlation among detections. Targeting specific model assumptions using carefully chosen residual diagnostics is valuable for any analysis, and we remove previous barriers for occupancy analyses — lack of examples and practical advice.

Ecology↗

USGS2014

A multi-scale assessment of animal aggregation patterns to understand increasing pathogen seroprevalence

Understanding how animal density is related to pathogen transmission is important to develop effective disease control strategies, but requires measuring density at a scale relevant to transmission. However, this is not straightforward or well-studied among large mammals with group sizes that range several orders of magnitude or aggregation patterns that vary across space and time. To address this issue, we examined spatial variation in elk ( Cervus canadensis ) aggregation patterns and brucellosis across 10 regions in the Greater Yellowstone Area where previous studies suggest the disease may be increasing. We hypothesized that rates of increasing brucellosis would be better related to the frequency of large groups than mean group size or population density, but we examined whether other measures of density would also explain rising seroprevalence. To do this, we measured wintering elk density and group size across multiple spatial and temporal scales from monthly aerial surveys. We used Bayesian hierarchical models and 20 years of serologic data to estimate rates of increase in brucellosis within the 10 regions, and to examine the linear relationships between these estimated rates of increase and multiple measures of aggregation. Brucellosis seroprevalence increased over time in eight regions (one region showed an estimated increase from 0.015 in 1991 to 0.26 in 2011), and these rates of increase were positively related to all measures of aggregation. The relationships were weaker when the analysis was restricted to areas where brucellosis was present for at least two years, potentially because aggregation was related to disease-establishment within a population. Our findings suggest that (1) group size did not explain brucellosis increases any better than population density and (2) some elk populations may have high densities with small groups or lower densities with large groups, but brucellosis is likely to increase in either scenario. In this case, any one control method such as reducing population density or group size may not be sufficient to reduce transmission. This study highlights the importance of examining the density-transmission relationship at multiple scales and across populations before broadly applying disease control strategies.

Wyoming↗

USGS2014

Re-evaluation of Yellowstone grizzly bear population dynamics not supported by empirical data: response to Doak & Cutler

Doak and Cutler critiqued methods used by the Interagency Grizzly Bear Study Team (IGBST) to estimate grizzly bear population size and trend in the Greater Yellowstone Ecosystem. Here, we focus on the premise, implementation, and interpretation of simulations they used to support their arguments. They argued that population increases documented by IGBST based on females with cubs-of-the-year were an artifact of increased search effort. However, we demonstrate their simulations were neither reflective of the true observation process nor did their results provide statistical support for their conclusion. They further argued that survival and reproductive senescence should be incorporated into population projections, but we demonstrate their choice of extreme mortality risk beyond age 20 and incompatible baseline fecundity led to erroneous conclusions. The conclusions of Doak and Cutler are unsubstantiated when placed within the context of a thorough understanding of the data, study system, and previous research findings and publications.

Conservation Letters↗

USGS2013

Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year

Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.

Journal of Agricultural, Biological, and Environme↗

USGS2013

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (<1 km 2 ) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km 2 ) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

Wyoming↗

USGS2013

Microsatellites indicate minimal barriers to mule deer Odocoileus hemionus dispersal across Montana, USA

To better understand the future spread of chronic wasting disease, we conducted a genetic assessment of mule deer Odocoileus hemionus population structure across the state of Montana, USA. Individual based analyses were used to test for population structure in the absence of a priori designations of population membership across the sampling area. Samples from the states of Wyoming, Colorado and Utah were also included in the analysis to provide a geographic context to the levels of population structure observed within Montana. Results showed that mule deer across our entire study region were characterized by weak isolation by distance and a lack of spatial autocorrelation at distances > 10 km. We found evidence for contemporary male bias in dispersal, with female mule deer exhibiting higher mean individual pairwise genetic distance than males. We tested for potential homogenizing effects of past translocations within Montana, but were unable to detect a genetic signature of these events. Our results indicate high levels of connectivity among mule deer populations in Montana and suggest few, if any, detectable barriers to mule deer gene flow or chronic wasting disease transmission.

Wildlife Biology↗

USGS2013

Response of Yellowstone grizzly bears to changes in food resources: A synthesis. Final report to the Interagency Grizzly Bear Committee and Yellowstone Ecosystem Subcommittee

The Yellowstone grizzly bear ( Ursus arctos ) was listed as a threatened species in 1975 (Federal Register 40 FR:31734-31736). Since listing, recovery efforts have focused on increasing population size, improving habitat security, managing bear mortalities, and reducing bear-human conflicts. The Interagency Grizzly Bear Committee (IGBC; partnership of federal and state agencies responsible for grizzly bear recovery in the lower 48 states) and its Yellowstone Ecosystem Subcommitte (YES; federal, state, county, and tribal partners charged with recovery of grizzly bears in the Greater Yelowston Ecosystem [GYE]) tasked the Interagency Grizzly Bear Study Team to provide information and further research relevant to three concerns arising from the 9th Circuit Court of Appeals November 2011 decision: 1) the ability of grizzly bears as omnivores to find alternative foods to whitebark pine seeds; 2) literature to support their conclusions; and 3) the non-intuitive biological reality that impacts can occur to individuals without causing the overall population to decline. Specifically, the IGBC and YES requested a comprehensive synthesis of the current state of knowledge regarding whitebark pinbe decline and individual and population-level responses of grizzly bears to changing food resources in the GYE. This research was particularly relevant to grizzly bear conservation given changes in the population trajectory observed during the last decade.

Idaho, Montana, Wyoming↗