Using population ecology to inform the conservation of Nevada’s rare plants
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Authors
Ellis, Sage
Issue Date
2024
Type
Thesis
Language
en_US
Keywords
Conservation , Plant Population Demography , Quantitative Ecology , Rare Plants
Alternative Title
Abstract
Population ecology is a fundamental tenet of conservation biology. Estimating population size is a necessary first step in assessing species vulnerability, and to the subsequent examination of population viability, prediction of extinction risks, and evaluation of conservation objectives. Population censuses and demographic monitoring are important tools in illuminating overall population trajectories a species face, as well as reveal the mechanisms behind those trends. Plant species are facing a global biodiversity crisis, with almost two in five species of vascular plants at risk for extinction. Rare plants make up a nontrivial portion of that global diversity, but are disproportionately threatened by global change. Therefore, it is critical to effectively estimate population size as well as understand the drivers of rare plant populations in order to protect biodiversity globally. However, because species are often aggregated across landscapes, precisely estimating total population size can be surprisingly challenging. Incorporating this spatial heterogeneity into estimates of population size is necessary to increase confidence in population estimates. Methods in design-based approaches have attempted this by altering sampling designs to account for heterogeneity, however model-based approaches could also be useful to estimate population size of heterogeneously distributed species but have so far not been examined. In chapter 1, I tested the ability of a model-based approach to accurately estimate population size. I simulated several heterogeneous landscapes with various levels of autocorrelation and then sampled those landscapes with differing scenarios of sampling effort. I then used a Gaussian process model in a Bayesian framework to make predictions of population density in unsampled areas. I found that I was successfully able to recover total population size of landscapes that had high to mid-levels of autocorrelation regardless of sampling effort, but decreased sampling effort lead to wider uncertainty around population estimates. These results highlight the promise for model-based approaches utility in estimating population size of sessile species with heterogenous distributions. Future research should combine design and model-based approaches to increase precision of population size estimates.
Understanding the drivers of population trends is important in conservation of rare species, as well as in the ability to anticipate threats in how already vulnerable species may respond to global change. Both disturbance and climate have been shown to affect the population dynamics of plants, but the interaction between the two has been minimally explored in the context of rare alpine plants. In chapter 2, I quantify how ski resort impacts and climate effect the population growth and demographics of a rare alpine endemic, Draba asterophora, across a long-term study conducted from 2010-2024. Although populations in ski areas have experienced past declines as a direct result of ski resort activities, I found that population trends of Draba asterophora in ski areas were not different from those in less developed areas over our study period. On the other hand, the best supported climate model showed negative effects of winter warming and late snowmelt, and positive effects of summer precipitation on population trends, with no interactions with ski area impacts. We observed that these populations are often driven by high survival, with low recruitment. Lastly, we found that despite population growth rates recovering following periods of decline circa 2015-2020, population sizes never fully recovered by the end of the study period. Our results highlight the risk that rare alpine plants face, where populations may be unlikely to recover from increasingly common unfavorable climatic periods.