Geology topics

Shelby Kuck

Publications and source records attributed to Shelby Kuck.

2 recordsLinked to original sources

USGS2026

Statistical facilitation in environmental science: Integrating results from complementary statistical analyses can improve ecological interpretations

Professionals working in biological conservation seek to understand, manage, and restore populations of native organisms using many techniques. A common approach for this discipline is using long-term data collections to inform decision making. However, several quantitative issues complicate statistical analysis of monitoring datasets and can reduce the utility of results for conservation decision making. Integrating results from multiple analyses applied to the same dataset (i.e., approaching the same biological problem using different techniques) is one way to address concerns related to field data that violate statistical assumptions. This process allows data analysts, researchers, and managers to assemble insights based on the weight of evidence. Here we tested whether three different statistical techniques [(1) multiple logistic regression on original data, (2) multiple logistic regression on standardized data (i.e., mean of 0 and standard deviation of 1), and (3) random forest analysis] identified a similar hierarchy for selecting natural and anthropogenic habitat regressors. Our examination of how environmental variables affected Plains Minnow ( Hybognathus placitus ), a state-threatened fish, is relevant to other taxa and locations. We gained useful information from redundancies (i.e., agreements across analyses). New directions also emerged by addressing ambiguities (i.e., disagreements among results across analyses). When multiple analyses were integrated into one ecological story, a clearer interpretation emerged. Viewing different statistical tests as facilitators that provide mutual advantages can advance the understanding and application of statistical analyses applied to non-experimental field datasets.

Kansas↗

USGS2026

A weight-of-evidence system for integrating monitoring and environmental indicators to guide biological conservation and resource management of native taxa

Many state and federal environmental management agencies are charged with using data-driven evidence to conserve and restore threatened and endangered species that are adversely impacted by humans. Yet, statistical analyses of available environmental indicator data do not always promote unambiguous, ecologically-meaningful, evidence-based decisions for managing native biota. Here we modified our multi-step quantitative adaptive management framework to identify natural and anthropogenic environmental indicators that maintain or threaten native species. In a proof-of-concept example, we quantified how three native stream fish taxa responded to nine environmental indicators based on three criteria (i.e., statistical significance, ecological relevance, and agreement with expectations). The resulting Weight of Evidence (WoE) integration identified five groups of indicators, clarified ambiguous trends, and identified promising future directions that would not have emerged from examining a single analysis or a single taxon alone. Our evaluation and categorization of environmental indicators established the biological foundation on which multi-disciplinary sustainability rests. Our research contribution demonstrates how following a structured integration framework can lead to novel insights that benefit conservation across organisms and systems even when employing relatively simple analytical techniques. Our results address the following often ignored questions: How do we systematically link together different analytical components of the same larger problems? How do we ecologically integrate similar and dissimilar results across analyses? How can we identify generalities by comparing multiple taxa?

Kansas↗