University of California, Santa Cruz

About: University of California, Santa Cruz is a education organization based out in Santa Cruz, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 15541 authors who have published 44120 publications receiving 2759983 citations. The organization is also known as: UCSC & UC, Santa Cruz.

Niches, models, and climate change: Assessing the assumptions and uncertainties

Abstract: As the rate and magnitude of climate change accelerate, understanding the consequences becomes increasingly important. Species distribution models (SDMs) based on current ecological niche constraints are used to project future species distributions. These models contain assumptions that add to the uncertainty in model projections stemming from the structure of the models, the algorithms used to translate niche associations into distributional probabilities, the quality and quantity of data, and mismatches between the scales of modeling and data. We illustrate the application of SDMs using two climate models and two distributional algorithms, together with information on distributional shifts in vegetation types, to project fine-scale future distributions of 60 California landbird species. Most species are projected to decrease in distribution by 2070. Changes in total species richness vary over the state, with large losses of species in some “hotspots” of vulnerability. Differences in distributional shifts among species will change species co-occurrences, creating spatial variation in similarities between current and future assemblages. We use these analyses to consider how assumptions can be addressed and uncertainties reduced. SDMs can provide a useful way to incorporate future conditions into conservation and management practices and decisions, but the uncertainties of model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. Doing this will require that the sources and magnitudes of uncertainty are documented, and that conservationists and resource managers be willing to act despite the uncertainties. The alternative, of ignoring the future, is not an option.

Are tropical fungal endophytes hyperdiverse

Abstract: Fungal endophytes are ubiquitous fungi that inhabit healthy plant tissues without causing disease. Endophytes have been found in every plant species examined to date and may be important, but often overlooked, components of fungal biodiversity. In two sites in a lowland, moist tropical forest of central Panama, we quantified endophyte colonization patterns, richness, host preference, and spatial variation in healthy leaves of two co-occurring, understory tree species [Heisteria concinna (Olacaceae) and Ouratea lucens (Ochnaceae)]. From 83 leaves, all of which were colonized by endophytes, we isolated 418 endophyte morphospecies (estimated 347 genetically distinct taxa), most of which were represented by only a single isolate (59%). Among morphospecies encountered in more than one leaf (nonsingletons), we found evidence of host preference and spatial heterogeneity using both morphospecies frequencies and presence/absence records. Based on these data, we postulate that tropical endophytes themselves may be hyperdiverse and suggest that extrapolative estimates that exclude them will markedly underestimate fungal species diversity.

An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit Models

Abstract: The Blinder-Oaxaca decomposition technique is widely used to identify and quantify the separate contributions of group differences in measurable characteristics, such as education, experience, marital status, and geographical differences to racial and gender gaps in outcomes. The technique cannot be used directly, however, if the outcome is binary and the coefficients are from a logit or probit model. I describe a relatively simple method of performing a decomposition that uses estimates from a logit or probit model. Expanding on the original application of the technique in Fairlie [3], I provide a more thorough discussion of how to apply the technique, an analysis of the sensitivity of the decomposition estimates to different parameters, and the calculation of standard errors. I also compare the estimates to Blinder-Oaxaca decomposition estimates and discuss an example of when the Blinder-Oaxaca technique may be problematic.