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.

Image sensing technologies are rapidly increasing the cost-effectiveness of biodiversity monitoring efforts. Species differences in the reflectance of electromagnetic radiation have recently been highlighted as a promising target to estimate plant biodiversity using multispectral image data. However, these efforts are currently hampered by logistical difficulties in broad-scale implementation and their use in characterizing biodiversity at different spatial scales. Here, we investigate the utility of multispectral imaging technology from commercially available unmanned aerial vehicles (UAVs, or drones) in estimating biodiversity metrics at short-range (<10 m image recording height) in a temperate calcareous grassland ecosystem in Oxfordshire, UK. We calculate a suite of moments (coefficient of variation, standard deviation, skew, kurtosis) for the distribution of reflectance 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 m × 1 m quadrats. We find positive associations between the average coefficient of variation in spectral reflectance and both the Shannon-Weiner and Simpsons biodiversity indices. Furthermore, we find that the average coefficient of variation in spectral reflectance 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 16-fold relative to in-situ botanical surveys. We demonstrate the utility of multispectral reflectance moments as an indicator of grassland biodiversity metrics at high spatial resolution using a widely available UAV monitoring system at 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 in complex environments.

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 hypothesised influence on ageing patterns is resource limitation, formalised 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 summarise the state-of-the-art in the field. Using this summary, we highlight challenges of CR research in our understanding of its impacts on longevity. We demonstrate that experimental research is biased towards short-lived species (98.2% of studies examine species with &lt;5 years of mean life expectancy) and lacks realism in key areas, such as stochastic environments or interactions with other environmental drivers (e.g., 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 socio-bio-economic impacts of senescence in humans and other species across the Tree of Life.

The world&rsquo;s human population is reaching record longevities. Consequently, our societies are experiencing the impacts of prolonged longevity, such as increased retirement age. A major hypothesised influence on ageing patterns is resource limitation, formalised under calorie restriction theory. This theory predicts extended organismal longevity due to reduced calorie intake without malnutrition. However, several challenges face current calorie restriction (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 222 CR peer-reviewed publications to summarise the state-of-the-art in the field. We use this summary to highlight challenges of CR research in our understanding of its impacts on longevity. Our review demonstrates that experimental research in this field is biased towards short-lived species (98.2% of studies examine species with &lt;5 years of mean life expectancy) and lacks realism in key areas, such as stochastic environments or interactions with other environmental drivers such as temperature. We argue that only by considering a range of short- and long-lived species and by taking more realistic approaches can the impacts of CR 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 a 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 socio-bio-economic impacts of senescence in humans and other species across the Tree of Life.