Southern right whales show no behavioral response to low noise levels from a nearby unmanned aerial vehicle

Unmanned aerial vehicles (UAVs) are increasingly used for wildlife research and monitoring, but little information exists on their potential effect on marine mammals. We assessed the effects of a UAV on the behavior of southern right whales (Eubalaena australis) in Australia. Focal follows of ten right whale mother-calf pairs were conducted using a theodolite. Control data were recorded for 30 min, and then a DJI Inspire 1 Pro was flown above the whales for 10 min at 5 m altitude. Potential changes to horizontal behavior (swim speed and turning angle) and surfacing pattern (interbreath intervals) were investigated by comparing mother-calf behavior before and during UAV approaches. Changes in respiration rate were used to quantify energetic effects. We also explored acoustic cue perceptibility of the UAV at 5, 10, and 30 m altitude, by measuring the received UAV underwater noise level on whales equipped with acoustic tags (DTAGs). The received noise levels were 86.0 ± 3.9 dB re 1 μPa, while the measured ambient noise was 80.7 ± 7.3 dB re 1 μPa in the same frequency band (100–1,500 Hz). No behavioral response to the UAV was observed. This provides support for UAVs as a noninvasive tool to study baleen whale behavior and ecophysiology.     

Canopy tree mortality depends on the proportion of crown exposed to sunlight,but this effect varies with species' wood density

Understanding what drives changes in tree mortality as well as the covariates influencing trees' response is a research priority to predict forest responses to global change. Here, we combined drone photogrammetry and ground-based data to assess the influence of crown exposure to light (relative to total crown area), growth deviations (relative to conspecifics), tree size, and species' wood density (as a surrogate for light-demanding and shade-tolerant life-history strategies) on the mortality of 984 canopy trees in an Amazon terra firme forest. Trees with lower wood density were less prone to die when their proportion of crown was more exposed to sunlight, but this relationship with relative crown exposure weakened and slightly reversed as wood density increased. Trees growing less than their species average had higher mortality, especially when the species' wood density decreased. The role of wood density in determining the survival of canopy trees under varying light conditions indicates differential responses of light-demanding versus shade-tolerant species. Our results highlight the importance of accounting for life-history strategies, via plant functional types, in vegetation dynamic models aiming to predict forest demography under a rapidly changing climate. Abstract in Spanish is available with online material.     

Drones,automatic counting tools,and artificial neural networks in wildlife population censusing

1. The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non‐breeding periods was investigated.
2. In 96% of 343 cases, drone counting was successful. 18.8% of non‐breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone.
3. The automatic counting of birds was best done with ImageJ/Fiji microbiology software – the average counting rate was 100 birds in 64 s.
4. Machine learning using neural network algorithms proved to be an effective and quick way of counting birds – 100 birds in 7 s. However, the preparation of images and machine learning time is time‐consuming, so this method is recommended only for large data sets and large bird assemblages.
5. The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behavior of the target animals.

     

Oxygen consumption and flight muscle activity during heating in workers and drones of Apis mellifera

Summary Instantaneous oxygen consumption, muscle potential frequency, thoracic and ambient temperature were simultaneously measured during heating in individual workers and drones of honey bees. Relationships between these parameters and effects of thoracic temperature on power input and temperature elevation were studied. Oxygen consumption increased above basal levels only when flight muscles became active. Increasing muscle potential frequencies correlated with elevated oxygen consumption and raised thoracic temperature. The difference between thoracic and ambient temperature and oxygen consumption were linearly related. Oxygen consumption per muscle potential (l O₂ · g _–1 ^thorax · MP^–1) was two-fold higher in drones than in workers. However, oxygen consumption for heating the thorax (l O₂ · g _–1 ^thorax · (T_th-T_a) · °C^–1) was nearly the same in workers and drones. Thoracic temperature affected the amount of oxygen consumed per muscle potential (R₁₀=1.5). Achieved temperature elevation per 100 MP was more temperature sensitive in drones (R₁₀=6–10) than in workers (R₁₀=3.6). Q₁₀ values for oxygen consumption were 3 in workers and 4.5–6 in drones. Muscle potential frequency decreased with a Q₁₀=1.8 in workers and 2.7 in drones. Heating behaviour of workers and drones was different. Drones generated heat less continuously than workers, and showed greater interindividual variability in predilection to heat. However, the maximal difference between ambient and thoracic temperature observed was 22 °C in drones and 14 °C in workers, indicating greater potential for drones.Abbreviations DL dorsal-longitudinal muscle - DV dorsoventral muscle - MP muscle potential - T _a ambient temperature - T _th thoracic temperature     

Biodemographic analysis of male honey bee mortality

Biodemographic studies of insects have significantly enhanced our understanding of the biology of aging. Eusocial insects have evolved to form different groups of colony members that are specialized for particular tasks and highly dependent on each other. These different groups (castes and sexes) also differ strongly in their life expectancy but relatively little is known about their mortality dynamics. In this study we present data on the age-specific flight activity and mortality of male honey bees from two different genetic lines that are exclusively dedicated to reproduction. We show that males initiating flight at a young age experience more flight events during their lifetime. No (negative) relation between the age at flight initiation and lifespan exists, as might be predicted on the basis of the antagonistic pleiotropy theory of aging. Furthermore, we fit our data to different aging models and conclude that overall a slight deceleration of the age-dependent mortality increase at advanced ages occurs. However, mortality risk increases according to the Gompertz-Makeham model when only days with flight activity (active days) are taken into account. Our interpretation of the latter is that two mortality components act on honey bee males during flight: increasing, age-dependent deaths (possibly from wear-and-tear), and age-independent deaths (possibly due to predation). The overall mortality curve is caused by the interaction of the distribution of age at foraging initiation and the mortality function during the active (flight) lifespan.     

Effects of various breeding strategies on diploid drone frequency and quantitative traits in a honey bee population

When selecting in a finite population of honeybees there is a conflict between gain in a quantitative trait and increasing homozygosity, and therefore the frequency of inviable diploid drones. The consequences when using different mating, import, and selection strategies on diploid drone frequency and genetic gain, was explored with Monte Carlo computer simulations.Within a closed population breeding structure, mass selection gave the highest genetic gain in the quantitative trait, but also the largest increase in percentage diploid drones and queens with unacceptably-low brood viability. Mass selection combined with truncation selection against queens having more than 15% diploid drones gave a comparable genetic gain and was the best strategy of the ones studied to avoid diploid drones. Within-family selection (one replacement per sib group) gave the least genetic gain, and a frequency of diploid drones comparable to random (no) selection. It was intermediate between mass selection and mass selection combined with viability selection concerning the frequency of diploid drones.Insemination with pooled and homogenized semen originating from all breeder queens (30), as compared to natural mating with 12 randomly-selected drones, had little effect on the genetic gain and on the overall frequency of diploid drones (10 to 15% by generation 20).The effect of opening the closed breeding population for the import of external queens every generation, by exchanging breeder queens of lowest performance with a corresponding number of new queens (5, 10and 15 out of 30), was also investigated. Under mass selection (natural mating as well as artificial insemination) the frequency of diploid drones and the proportion of queens discarded were reduced because of low brood viability. However, artificial insemination was superior to natural mating considering the latter criterion. If the imported queens were at the same genetic level for the quantitative trait under selection as the whole breeding population at that generation, or 10% better, the genetic gain was respectively slightly reduced and approximately maintained. If the imported queens were of inferior quality (equal to the initial population) the import of queens slowed genetic progress considerably.     

Evaluating new technology for biodiversity monitoring: Are drone surveys biased?

Drones and machine learning‐based automated detection methods are being used by ecologists to conduct wildlife surveys with increasing frequency. When traditional survey methods have been evaluated, a range of factors have been found to influence detection probabilities, including individual differences among conspecific animals, which can thus introduce biases into survey counts. There has been no such evaluation of drone‐based surveys using automated detection in a natural setting. This is important to establish since any biases in counts made using these methods will need to be accounted for, to provide accurate data and improve decision‐making for threatened species. In this study, a rare opportunity to survey a ground‐truthed, individually marked population of 48 koalas in their natural habitat allowed for direct comparison of the factors impacting detection probability in both ground observation and drone surveys with manual and automated detection. We found that sex and host tree preferences impacted detection in ground surveys and in manual analysis of drone imagery with female koalas likely to be under‐represented, and koalas higher in taller trees detected less frequently when present. Tree species composition of a forest stand also impacted on detections. In contrast, none of these factors impacted on automated detection. This suggests that the combination of drone‐captured imagery and machine learning does not suffer from the same biases that affect conventional ground surveys. This provides further evidence that drones and machine learning are promising tools for gathering reliable detection data to better inform the management of threatened populations.     

The potential for automating assisted natural regeneration of tropical forest ecosystems

Assisted (or accelerated) natural regeneration (ANR) will play an important role in meeting the UN target to restore forest to 350 million hectares of degraded land, by 2030. However, since most accessible land is already used for agriculture, most of the sites, available for ANR, are far from roads and/or on difficult terrain, where implementing ANR with human labour is not practical. Therefore, this paper explores the potential of emerging technologies, such as low‐cost UAVs (drones) and new imaging devices, to automate ANR tasks, including site monitoring (to assess site potential for natural regeneration, plan interventions and assess progress), maintenance of natural regeneration (particularly weeding) and species enrichment through aerial seeding. The usefulness of existing technologies is reviewed and future innovations needed, to provide practicable support for ANR, are discussed. Intensive collaboration, among technologists and forest ecologists, will be essential to ensure that technological innovations are based firmly on sound restoration science.     

Three QTL in the honey bee Apis mellifera L. suppress reproduction of the parasitic mite Varroa destructor

Varroa destructor is a highly virulent ectoparasitic mite of the honey bee Apis mellifera and a major cause of colony losses for global apiculture. Typically, chemical treatment is essential to control the parasite population in the honey bee colony. Nevertheless a few honey bee populations survive mite infestation without any treatment. We used one such Varroa mite tolerant honey bee lineage from the island of Gotland, Sweden, to identify quantitative trait loci (QTL) controlling reduced mite reproduction. We crossed a queen from this tolerant population with drones from susceptible colonies to rear hybrid queens. Two hybrid queens were used to produce a mapping population of haploid drones. We discriminated drone pupae with and without mite reproduction, and screened the genome for potential QTL using a total of 216 heterozygous microsatellite markers in a bulk segregant analysis. Subsequently, we fine mapped three candidate target regions on chromosomes 4, 7, and 9. Although the individual effect of these three QTL was found to be relatively small, the set of all three had significant impact on suppression of V. destructor reproduction by epistasis. Although it is in principle possible to use these loci for marker-assisted selection, the strong epistatic effects between the three loci complicate selective breeding programs with the Gotland Varroa tolerant honey bee stock.