The twin threshold model: risk-intermediate foraging by rufous hummingbirds, Selasphorus rufus

I developed two versions of the twin threshold model (TTM) to assess risk-sensitive foraging decisions by rufous hummingbirds. The model incorporates energy thresholds for both starvation and reproduction and assesses how three reward distributions with a common mean but different levels of variance interact with these critical thresholds to determine fitness. Fitness, a combination of survival and reproduction, is influenced by both the amount of variance in the distributions and the relative position of the common mean between the thresholds. The model predicts that risk-intermediate foraging is often the optimal policy, and that risk aversion is favoured as the common mean of the distributions approaches the starvation threshold, whereas risk preference is favoured as the common mean approaches the reproduction threshold. Tests with free-living hummingbirds supported these predictions. Hummingbirds were presented with three distributions of nectar rewards that had a common mean but Nil, Moderate or High levels of variance. Birds preferred intermediate levels of variance (Moderate) when presented with all three rewards simultaneously, and became more risk-averse as the mean of the distributions was decreased but more risk-prone as the mean was increased. Birds preferred Nil when it was paired with Moderate or with High, but preferred Moderate in the presence of Nil and High together. This reversal of preference is a violation of regularity, conventionally interpreted as irrational choice behaviour. I provide an alternative version of the TTM demonstrating that violations of regularity can occur when relative instead of absolute evaluation mechanisms are used.     

Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

1. The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.
2. We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.
3. We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.
4. This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.