The Hunger Games as the Key to Happily Ever After?

The world's human population is reaching record longevities. Consequently, our societies are experiencing the impacts of prolonged longevity, such as increased retirement age. A major hypothesized influence on aging patterns is resource limitation, formalized under calorie restriction (CR) theory. This theory predicts extended organismal longevity due to reduced calorie intake without malnutrition. However, several challenges face current CR research and, although several attempts have been made to overcome these challenges, there is still a lack of holistic understanding of how CR shapes organismal vitality. Here, we conduct a literature review of 224 CR peer-reviewed publications to summarize the state-of-the-art in the field. Using this summary, we highlight the challenges of CR research in our understanding of its impacts on longevity. We demonstrate that experimental research is biased toward short-lived species (98.2% of studies examine species with <5 years of mean life expectancy) and lacks realism in key areas, such as stochastic environments or interactions with other environmental drivers (eg, temperature). We argue that only by considering a range of short- and long-lived species and taking more realistic approaches, can CR impacts on longevity be examined and validated in natural settings. We conclude by proposing experimental designs and study species that will allow the discipline to gain much-needed understanding of how restricting caloric intake affects long-lived species in realistic settings. Through incorporating more experimental realism, we anticipate crucial insights that will ultimately shape the myriad of sociobioeconomic impacts of senescence in humans and other species across the Tree of Life.

Short-range multispectral imaging is an inexpensive, fast, and accurate approach to estimate biodiversity in a temperate calcareous grassland.

Image sensing technologies are rapidly increasing the cost-effectiveness of biodiversity monitoring efforts. Species differences in the reflectance of electromagnetic radiation can be used as a surrogate estimate plant biodiversity using multispectral image data. However, these efforts are often hampered by logistical difficulties in broad-scale implementation. Here, we investigate the utility of multispectral imaging technology from commercially available unmanned aerial vehicles (UAVs, or drones) in estimating biodiversity metrics at a fine spatial resolution (0.1-0.5 cm pixel resolution) in a temperate calcareous grassland in Oxfordshire, UK. We calculate a suite of moments (coefficient of variation, standard deviation, skewness, and kurtosis) for the distribution of radiance from multispectral images at five wavelength bands (Blue 450 ± 16 nm; Green 560 ± 16 nm; Red 650 ± 16 nm; Red Edge 730 ± 16 nm; Near Infrared 840 ± 16 nm) and test their effectiveness at estimating ground-truthed biodiversity metrics from in situ botanical surveys for 37-1 × 1 m quadrats. We find positive associations between the average coefficient of variation in spectral radiance and both the Shannon-Weiner and Simpson's biodiversity indices. Furthermore, the average coefficient of variation in spectral radiance is consistent and highly repeatable across sampling days and recording heights. Positive associations with biodiversity indices hold irrespective of the image recording height (2-8 m), but we report reductions in estimates of spectral diversity with increases to UAV recording height. UAV imaging reduced sampling time by a factor of 16 relative to in situ botanical surveys. We demonstrate the utility of multispectral radiance moments as an indicator of biodiversity in this temperate calcareous grassland at a fine spatial resolution using a widely available UAV monitoring system with a coarse spectral resolution. The use of UAV technology with multispectral sensors has far-reaching potential to provide cost-effective and high-resolution monitoring of biodiversity.