Frequency-Dependent Selection Predicts Patterns of Radiations and Biodiversity

Most empirical studies support a decline in speciation rates through time, although evidence for constant speciation rates also exists. Declining rates have been explained by invoking pre-existing niches, whereas constant rates have been attributed to non-adaptive processes such as sexual selection and mutation. Trends in speciation rate and the processes underlying it remain unclear, representing a critical information gap in understanding patterns of global diversity. Here we show that the temporal trend in the speciation rate can also be explained by frequency-dependent selection. We construct a frequency-dependent and DNA sequence-based model of speciation. We compare our model to empirical diversity patterns observed for cichlid fish and Darwin's finches, two classic systems for which speciation rates and richness data exist. Negative frequency-dependent selection predicts well both the declining speciation rate found in cichlid fish and explains their species richness. For groups like the Darwin's finches, in which speciation rates are constant and diversity is lower, speciation rate is better explained by a model without frequency-dependent selection. Our analysis shows that differences in diversity may be driven by incipient species abundance with frequency-dependent selection. Our results demonstrate that genetic-distance-based speciation and frequency-dependent selection are sufficient to explain the high diversity observed in natural systems and, importantly, predict decay through time in speciation rate in the absence of pre-existing niches.</p

Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales

Nature provides a wide range of benefits to people. There is increasing consensus about the importance of incorporating these ecosystem services into resource management decisions, but quantifying the levels and values of these services has proven difficult. We use a spatially explicit modeling tool, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), to predict changes in ecosystem services, biodiversity conservation, and commodity production levels. We apply InVEST to stakeholder-defined scenarios of land-use/land-cover change in the Willamette Basin, Oregon. We found that scenarios that received high scores for a variety of ecosystem services also had high scores for biodiversity, suggesting there is little tradeoff between biodiversity conservation and ecosystem services. Scenarios involving more development had higher commodity production values, but lower levels of biodiversity conservation and ecosystem services. However, including payments for carbon sequestration alleviates this tradeoff. Quantifying ecosystem services in a spatially explicit manner, and analyzing tradeoffs between them, can help to make natural resource decisions more effective, efficient, and defensible. © The Ecological Society of America

Frequency-dependent selection predicts patterns of radiations and biodiversity

Most empirical studies support a decline in speciation rates through time, although evidence for constant speciation rates also exists. Declining rates have been explained by invoking niche-filling processes, whereas constant rates have been attributed to non-adaptive processes such as sexual selection, mutation, and dispersal. Trends in speciation rate and the processes underlying it remain unclear, representing a critical information gap in understanding patterns of global diversity. Here we show that the speciation rate is driven by frequency dependent selection. We used a frequency-dependent and DNA sequence-based model of populations and genetic-distance-based speciation, in the absence of adaptation to ecological niches. We tested the frequency-dependent selection mechanism using cichlid fish and Darwin&#x27;s finches, two classic model systems for which speciation rates and richness data exist. Using negative frequency dependent selection, our model both predicts the declining speciation rate found in cichlid fish and explains their species richness. For groups like the Darwin&#x27;s finches, in which speciation rates are constant and diversity is lower, the speciation rate is better explained by a model without frequency-dependent selection. Our analysis shows that differences in diversity are driven by larger incipient species abundance (and consequent lower extinction rates) with frequency-dependent selection. These results demonstrate that mutations, genetic-distance-based speciation, sexual and frequency-dependent selection are sufficient not only for promoting rapid proliferation of new species, but also for maintaining the high diversity observed in natural systems

Tracking climate impacts on the migratory monarch butterfly

Understanding the impacts of climate on migratory species is complicated by the fact that these species travel through several climates that may be changing in diverse ways throughout their complete migratory cycle. Most studies are not designed to tease out the direct and indirect effects of climate at various stages along the migration route. We assess the impacts of spring and summer climate conditions on breeding monarch butterflies, a species that completes its annual migration cycle over several generations. No single, broad-scale climate metric can explain summer breeding phenology or the substantial year-to-year fluctuations observed in population abundances. As such, we built a Poisson regression model to help explain annual arrival times and abundances in the Midwestern United States. We incorporated the climate conditions experienced both during a spring migration/breeding phase in Texas as well as during subsequent arrival and breeding during the main recruitment period in Ohio. Using data from a state-wide butterfly monitoring network in Ohio, our results suggest that climate acts in conflicting ways during the spring and summer seasons. High spring precipitation in Texas is associated with the largest annual population growth in Ohio and the earliest arrival to the summer breeding ground, as are intermediate spring temperatures in Texas. On the other hand, the timing of monarch arrivals to the summer breeding grounds is not affected by climate conditions within Ohio. Once in Ohio for summer breeding, precipitation has minimal impacts on overall abundances, whereas warmer summer temperatures are generally associated with the highest expected abundances, yet this effect is mitigated by the average seasonal temperature of each location in that the warmest sites receive no benefit of above average summer temperatures. Our results highlight the complex relationship between climate and performance for a migrating species and suggest that attempts to understand how monarchs will be affected by future climate conditions will be challenging.This article is from Global Change Biology 18 (2012): 3039–3049, doi:10.1111/j.1365-2486.2012.02751.x.</p

Appendix B. Photo of a field nest box containing a Bombus vosnesenskii study colony.

Photo of a field nest box containing a Bombus vosnesenskii study colony

Appendix C. Flower abundance in different regions of the landscape in spring and summer.

Flower abundance in different regions of the landscape in spring and summer

An Assessment of Methods and Remote-Sensing Derived Covariates for Regional Predictions of 1 km Daily Maximum Air Temperature

The monitoring and prediction of biodiversity and environmental changes is constrained by the availability of accurate and spatially contiguous climatic variables at fine temporal and spatial grains. In this study, we evaluate best practices for generating gridded, one-kilometer resolution, daily maximum air temperature surfaces in a regional context, the state of Oregon, USA. Covariates used in the interpolation include remote sensing derived elevation, aspect, canopy height, percent forest cover and MODIS Land Surface Temperature (LST). Because of missing values, we aggregated daily LST values as long term (2000–2010) monthly climatologies to leverage its spatial detail in the interpolation. We predicted temperature with three methods—Universal Kriging, Geographically Weighted Regression (GWR) and Generalized Additive Models (GAM)—and assessed predictions using meteorological stations over 365 days in 2010. We find that GAM is least sensitive to overtraining (overfitting) and results in lowest errors in term of distance to closest training stations. Mean elevation, LST, and distance to ocean are flagged most frequently as significant covariates among all daily predictions. Results indicate that GAM with latitude, longitude and elevation is the top model but that LST has potential in providing additional fine-grained spatial structure related to land cover effects. The study also highlights the need for more rigorous methods and data to evaluate the spatial structure and fine grained accuracy of predicted surfaces

Using Science in Habitat Conservation Plans

94 pages, 1 article*Using Science in Habitat Conservation Plans* (Kareiva, Peter; Andelman, Sandra; Doak, Daniel; Elderd, Bret; Groom, Martha; Hoekstra, Jonathan; Hood, Laura; James, Frances; Lamoreux, John; LeBuhn, Gretchen; McCulloch, Charles; Regetz, James; Savage, Lisa; Ruckelshaus, Mary; Skelly, David; Wilbur, Henry; Zamudio, Kelly; NCEAS HCP Working Group) 94 page