Geology topics

Rebecca J. Smith

Publications and source records attributed to Rebecca J. Smith.

3 recordsLinked to original sources

USGS2024

Genetic structure of restored Brook Trout populations in the Southern Appalachian Mountains indicates successful reintroductions

Wildlife reintroduction is an important conservation tool for threatened species, yet identifying appropriate source populations poses a challenge. In particular, the possibility of outbreeding depression is cited as a constraint limiting the range of candidate source populations for translocation. When multiple source lineages are mixed during reintroduction, genetic monitoring is necessary to evaluate whether sources contribute equally to subsequent generations and whether they are interbreeding as expected. Moreover, statistical analysis of genetic data should account for complex life histories that might affect the timescale of admixture and genetic drift. Here, we use samples collected over a 23-year period and a stochastic age-structured model to analyze the genetic mixing process in reintroduced Brook Trout ( Salvelinus fontinalis ) populations in the Southern Appalachians. Each restored population was seeded with two to three source populations. Previous research inferred reproductive isolation between source populations leading to a proposal of splitting the species into multiple taxa. In contrast, we found patterns of ancestry that were consistent with random mating and no advantage for one source lineage over any other. Brook Trout from different source streams are mixing as expected in the restoration sites. This result does not support the hypothesis that Brook Trout in the Southern Appalachian Mountains includes several distinct species. Mixing different sources from the same watershed seems to be an effective way to increase genetic diversity of reintroduced populations while minimizing risk to source populations.

North Carolina, Tennessee↗

USGS2020

The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire

Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.

Scientific Data↗

USGS2019

Giving ecological meaning to satellite-derived fire severity metrics across North American forests

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R 2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.

Remote Sensing↗