Geology topics

Sharon R. Melin

Publications and source records attributed to Sharon R. Melin.

3 recordsLinked to original sources

USGS2025

Evaluating mark–resight survey design performance using simulation: A case study of endangered Steller sea lions

Effective monitoring is fundamental to estimating wildlife population parameters with a level of accuracy and precision that is adequate to inform management decisions. However, managers must balance trade-offs between the costs of monitoring and the resulting data quality to identify cost-effective monitoring survey designs. As such, evaluating the expected performance of monitoring surveys relative to monitoring objectives prior to survey implementation is critical. In this study, we present a simulation framework for examining the accuracy and precision of age-specific survival estimates and the probability of detecting a change in survival within the context of mark–resight monitoring programs. We consider 90 survey designs that vary across marked cohort size, marking frequency, study duration, and resight probability (i.e., detection of marked individuals). We apply this approach to the design of a monitoring program for Steller sea lions ( Eumetopias jubatus ), which is complicated by heterogeneity in rookery accessibility, population sizes, and abundance trends across the species' range. To identify cost-effective survey designs in the absence of actual survey costs, we evaluated performance with respect to a relative-costs schema. Our results highlight survey designs that reliably meet pre-defined precision targets, with precision and accuracy strongly affected by marked cohort size, marking frequency, and study duration. We found that historical mark–resight survey effort for Steller sea lions has been sufficient to reliably achieve precision targets for younger age class survival probabilities only for rookeries where abundance has been stable or increasing. In contrast, the probability of achieving survival estimates with target levels of precision at rookeries where abundance has been declining is low (<25%) due to smaller marked cohort sizes, less frequent marking at remote sites, and fewer years of available data. Our results indicate that the precision of survival estimates for subpopulations of conservation concern can be improved by longer-term monitoring, although the constraints of monitoring small populations may limit the ability of biologists to detect changes in population dynamics on management-relevant time horizons. Our survey design evaluation framework can be applied in a variety of contexts to assist natural resource managers in developing cost-effective monitoring programs.

Alaska, California, Oregon↗

USGS2018

A guide to Bayesian model checking for ecologists

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

Ecological Monographs↗

USGS2015

Effects of age, colony, and sex on mercury concentrations in California sea lions

We measured total mercury (THg) concentrations in California sea lions ( Zalophus californianus ) and examined how concentrations varied with age class, colony, and sex. Because Hg exposure is primarily via diet, we used nitrogen ( δ 15 N) and carbon ( δ 13 C) stable isotopes to determine if intraspecific differences in THg concentrations could be explained by feeding ecology. Blood and hair were collected from 21 adult females and 57 juveniles from three colonies in central and southern California (San Nicolas, San Miguel, and Año Nuevo Islands). Total Hg concentrations ranged from 0.01 to 0.31 μg g −1 wet weight (ww) in blood and 0.74 to 21.00 μg g −1 dry weight (dw) in hair. Adult females had greater mean THg concentrations than juveniles in blood (0.15 vs. 0.03 μg −1 ww) and hair (10.10 vs. 3.25 μg −1 dw). Age class differences in THg concentrations did not appear to be driven by trophic level or habitat type because there were no differences in δ 15 N or δ 13 C values between adults and juveniles. Total Hg concentrations in adult females were 54 % (blood) and 24 % (hair) greater in females from San Miguel than females from San Nicolas Island, which may have been because sea lions from the two islands foraged in different areas. For juveniles, we detected some differences in THg concentrations with colony and sex, although these were likely due to sampling effects and not ecological differences. Overall, THg concentrations in California sea lions were within the range documented for other marine mammals and were generally below toxicity benchmarks for fish-eating wildlife.

Archives of Environmental Contamination and Toxico↗