Geology topics

Michael Dumelle

Publications and source records attributed to Michael Dumelle.

3 recordsLinked to original sources

USGS2025

Missing data in ecology: Syntheses, clarifications, and considerations

In ecology and related sciences, missing data are common and occur in a variety of different contexts. When missing data are not handled properly, subsequent statistical estimates tend to be biased, inefficient, and lack proper confidence interval coverage. Missing data are often grouped into three categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). We review each category and compare their benefits and drawbacks. We review several approaches to handling missing data including complete case analysis, imputation, inverse probability weighting, and data augmentation. We clarify what types of variables should accompany imputation methods and how those variables are influenced by the analysis methods. Additionally, we discuss missing data that lack a formal basis for measurement and hence are fundamentally different from MCAR, MAR, and MNAR missing data. Throughout, we introduce concepts and numeric examples using both simulated data and data from the United States Environmental Protection Agency's 2016 National Wetland Condition Assessment. We conclude by providing five considerations for ecologists and other scientists handling missing data.

Ecological Monographs↗

USGS2025

Diverging fish biodiversity trends in cold and warm rivers and streams

Worldwide, freshwater systems contain more than 18,000 fish species 1,2,3 , which are critical to the functioning of these ecosystems 4 and are vital cultural and economic resources to humans 5,6,7 ; despite this value, fish biodiversity is at risk globally 8,9 . In the USA, leading threats to fish communities in rivers and streams include climate change and invasive fish introductions and game fish stocking by humans 10,11,12,13,14 . Here we harmonized US federal biomonitoring datasets with 389 species spanning 27 years (1993–2019) and 2,992 sites to analyse trends in fish biodiversity. In cold streams (past summer stream temperatures below 15.4 °C), fish abundance and richness declined by 53.4% and 32% over 27 years, respectively, and uniqueness increased. Periodic (large-bodied, late-maturing) fishes increased, and opportunists (small-bodied, short generation time, ‘r-selected’) decreased, possibly due to proliferation of native or introduced game fishes. In warm streams (stream temperatures greater than 23.8 °C), fish abundance and richness increased by 70.5% and 15.6% over 27 years, respectively, and communities homogenized. Small opportunistic fishes replaced large periodic fishes. Intermediate streams (stream temperatures 15.4–23.8 °C), representing the average stream, had minimal changes in fish biodiversity through time. Interactions between warming and introduced fish were associated with increased rates of degradation to local fish biodiversity. Given the magnitude of these changes in a relatively short time span, there is an urgent need to curb degradation of fish biodiversity caused by fish introductions and warming water temperatures.

continental United States↗

USGS2025

Modeling lamprey distribution using flow, geomorphology, and elevation in a terminal lake system

Objective Lampreys are an ecologically important group of fishes. Several species are imperiled and lack key distribution and habitat data. The terminal Goose Lake Basin, U.S.A. is home to two such species, the Goose Lake Lamprey, Entosphenus sp. (formally undescribed), and the Pit-Klamath Brook Lamprey, E. lethophagus . Species distribution models (SDMs) are useful for identifying key habitats; however, SDMs are subject to accuracy impairments caused by scale mismatches and spatial autocorrelation—both exacerbated by the hierarchical structure of dendritic stream networks. Our goal was to relate lamprey presence–absence to ecological drivers and predict the distribution of lampreys across the Goose Lake Basin. Methods Using a dataset pooling approach, we integrated count and presence–absence data from five surveys and relevant habitat variables from publicly available, geospatial datasets to build logistic regression models. To account for potential mismatches of scale, we compared three sample grains for slope and sinuosity (i.e., stream segment lengths: 250, 500, and 1,000 m), and two scales of elevation (site and watershed). We accounted for spatial autocorrelation by incorporating network-based and Euclidean spatial dependencies using a spatial stream network (SSN) modeling approach. Using the best-fit spatial and non-spatial models, we predicted basin-wide lamprey distribution. Result Flow, sinuosity at our largest sample grain (1,000 m), and watershed-scale elevation were positively associated with lamprey presence, whereas slope was negatively associated. The non-spatial model predicted lamprey presence among sinuous, low-gradient streams, whereas the spatial model, which identified Euclidean and flow-connected spatial relationships, predicted contiguous patches with a high probability of occurrence near areas with previously observed presences. Conclusions Our study revealed ecological relationships and produced an accurate basinwide SDM. Prediction and inference improved after accounting for spatial relationships across multiple scales. Developing accurate and efficient modeling strategies that incorporate the hierarchical structure inherent to stream ecosystems aids in the management and conservation of native fishes such as lampreys.

Transactions of the American Fisheries Society↗