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Carl A. Roland

Publications and source records attributed to Carl A. Roland.

4 recordsLinked to original sources

Seasonal drivers of density in a subarctic population of northern red-backed voles

Northern red-backed voles ( Clethrionomys rutilus ) are an important species in the boreal forest ecosystem, both as herbivores and as a key food source for many mammalian and avian predators. They exhibit dramatic inter- and intra-annual population fluctuations, for which causes are not entirely known. We monitored northern red-backed vole densities in Denali National Park and Preserve through time with the goal of examining how environmental factors influenced density over time. Using a 30-year record of mark-recapture data, we used spatially explicit capture-recapture methods to estimate autumn and early summer densities each year. We assessed cyclic patterns in density, variation in amplitude, and any periodicity of population fluctuations using post hoc linear modeling. We found that the vole population appeared to be cyclic with a 2–4 year period, although the pattern varied somewhat among sampling sites. Our results indicated an association between white spruce ( Picea glauca ) seed production and vole density, implying white spruce seeds were either an important source of food during winter seasons, or that the environmental triggers that promote high seed fall were also associated with increased vole density. We also found a negative effect of an autumn harshness index, indicating winter conditions play a role in vole density in the following season. Finally, we found evidence of a negative density-dependent relationship between autumn and early summer. Together, these findings suggest a system in which density dependence and cyclic relationships are irregular but highly influential, with environmental effects capable of enhancing or moderating their impact. Continued monitoring of voles, alongside more thorough assessments of environmental conditions, may provide additional insight into the complex population dynamics of this species.

Alaska

Searching for refuge: A framework for identifying site factors conferring resistance to climate-driven vegetation change

Climate change is occurring at accelerated rates in high latitude regions such as Alaska, causing alterations in woody plant growth and associated ecosystem patterns and processes. Our aim is to assess the magnitude and speed that climate-induced changes in woody plant distribution and volume may be reduced and/or slowed by relatively static landscape features like physical characteristics (e.g. depth to gravel, mineral cover percent and slope degree) and/or edaphic properties (e.g. soil organic matter, soil pH and site wetness rating) that resist climate-vegetation responses

Alaska

Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long-term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster-specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site-level cluster labels associated with assemblage-level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.

Alaska

Geary’s contiguity ratio (Geary’s c)

Geary's contiguity ratio (i.e., Geary's c ) is used to measure spatial autocorrelation in data with discrete spatial support. Calculation of Geary's c depends on the observed spatial data and a set of dyadic weights that are generally defined based on the proximity of observations to each other, but can be generalized to accommodate geographic distances among observations and the lengths of shared boundaries for the regions from which the data arise. Geary's c can be used to test hypotheses about spatial autocorrelation under both parametric and nonparametric assumptions. Also, components of Geary's c can be used to assess how autocorrelation may vary throughout the spatial domain. We demonstrate the application of Geary's c to assess spatial structure in vegetation data arising from a survey in Alaska, USA, using two different approaches for defining proximity.

Book chapter