Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review

Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review The use of small Unmanned Aircraft Systems (UAS; also known as ªdronesº) for profes- sional and personal-leisure use is increasing enormously. UAS operate at low altitudes OPENACCESS (<500 m) and in any terrain, thus they are susceptible to interact with local fauna, generating a new type of anthropogenic disturbance that has not been systematically evaluated. To Citation: Mulero-PaÂzmaÂny M, Jenni-Eiermann S, Strebel N, Sattler T, Negro JJ, Tablado Z (2017) address this gap, we performed a review of the existent literature about animals' responses Unmanned aircraft systems as a new source of to UAS flights and conducted a pooled analysis of the data to determine the probability and disturbance for wildlife: A systematic review. PLoS intensity of the disturbance, and to identify the factors influencing animals' reactions towards ONE 12(6): e0178448. https://doi.org/10.1371/ the small aircraft. We found that wildlife reactions depended on both the UAS attributes journal.pone.0178448 (flight pattern, engine type and size of aircraft) and the characteristics of animals themselves Editor: Antoni Margalida, University of Lleida, (type of animal, life-history stage and level of aggregation). Target-oriented flight patterns, SPAIN larger UAS sizes, and fuel-powered (noisier) engines evoked the strongest reactions in wild- Received: November 28, 2016 life. Animals during the non-breeding period and in large groups were more likely to show Accepted: May 12, 2017 behavioral reactions to UAS, and birds are more prone to react than other taxa. We discuss Published: June 21, 2017 the implications of these results in the context of wildlife disturbance and suggest guidelines   Copyright:© 2017 Mulero-Pazmany et al. This is for conservationists, users and manufacturers to minimize the impact of UAS. In addition, an open access article distributed under the terms we propose that the legal framework needs to be adapted so that appropriate actions can be of the Creative Commons Attribution License, undertaken when wildlife is negatively affected by these emergent practices. which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are available on figshare at https://doi.org/10.6084/m9.figshare. Introduction Recently, Unmanned Aircraft Systems (UAS, also known as remote piloted aircraft systems Funding: Part of this study was conducted within the Projects Aeromab, (Andalusia Government, ªRPASº or colloquially as ªdronesº) have experienced a remarkable development. Although Project for Excellence, 2007, P07-RNM-03246) originally UAS were mainly used for military purposes, the technological advances and the and Planet (European Commission 7th FP Grant miniaturization of components have facilitated the emergence of a mass production industry, Agreement 257649) granted to JJN, Doñana which has led to decreasing prices and consequently to a proliferation of the use of UAS in Biological Station, CSIC. The funders had no role in civilian applications. Small UAS (Maximum Take-Off Weight [MTOW] of 250 gÐ25 kg) are study design, data collection and analysis, decision to publish, or preparation of the manuscript. nowadays used for a variety of professional tasks, such as remote sensing, photography, PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 1 / 14 Drones' impact on wildlife Competing interests: The authors have declared precision agriculture, infrastructures inspection, mining, forestry management, and surveil- that no competing interests exist. lance, among others [1,2]. Small UAS can provide high spatial and temporal resolution data, which has favored their incorporation to environmental biology in different research and management topics, such as plant and animal ecology [3±6], wildlife monitoring [7,8], anti-poaching [9] and infrastruc- tures impact assessment [10]. There is also a recent increase of operators using UAS to scare off wild birds that cause economic losses in agricultural areas and fisheries, pose risks in air- ports, damage monuments and interfere in waste management [11±13]. Additionally, UAS are used in ecotourism, hunting, fishing, livestock management and falconry training [14±19]. Small UAS have also become a popular new hobby for the general public for leisure flying and filming, which has resulted in an exponential growth of sales (about 2.5 million units during 2016 in the USA alone;[20]). The expansion on the professional and personal use of small UAS that are operated over any terrain, frequently in natural areas, and at low altitudes above ground level (AGL < 500 m), makes the systems susceptible to interact with the local fauna, constituting a new potential source of anthropogenic disturbance. There is abundant literature about the effects of disturbance on wildlife. Animal responses to on-foot human presence [21], terrestrial vehicles [22±27] and aerial platforms such as manned aircraft, helicopters and gliders [28±30] may vary from punctual behavioral or physio- logical reactions to reductions in fitness (e.g. mortality by collision with the vehicles or stress- related decrease in productivity) or changes in spatial use (e.g. avoidance of certain areas; [21]) that may fragment or compromise the viability of populations. The studies indicate that the factors determining the probability and intensity of animal responses depend on the one hand on the characteristics of the disturbing agent (e.g. size, noise emitted, speed, distance, angle of approach), which affect the perception of the risk by the animal [21,31]. The larger and noisier the approaching agent is, the stronger the anti-predator responses will be [32]. Animals have also been shown to react at larger distances when the approach is faster and more directional towards them [33,34, but see also 35]. On the other hand, animal responses also depend on the characteristics and context of the animal exposed to the disturbance (e.g. species, age, level of aggregation, life history stage, habitat, season), which explain the susceptibility of the animal to react [21,31]. Different types of animals show different reactions to disturbance. Some species are more prone to conduct flight and fight responses, while others seek for protection by form- ing large aggregations, hiding in vegetation or using crypticity strategies, and do not show observable responses despite being physiologically stressed [36±38]. Similarly, reproductive status can also condition the animals' response, since gravid females might have reduced mov- ing capabilities, and individuals involved in parental care may prioritize protection of their off- spring to their own survival [39,40]. Due to the recent expansion of UAS use, assessment of their impact is currently restricted to isolated [41,42] or descriptive studies [6,43,44] and lacks a broad scientific base. The aim of our study is to identify the factors influencing the probability and magnitude of wildlife reac- tions to UAS. We reviewed the literature searching for information about animal responses to small UAS and then performed a combined analysis of the resulting database. According to the existing studies about disturbance, we hypothesized that the response of wildlife to UAS would depend on both the properties (aircraft size and engine) and flight pattern of the air- craft, and the attributes and context of the animals being concerned (animal type, life-history stage and level of aggregation). Finally, we discuss the implications of our findings in the con- text of human-disturbance ecology and species conservation and provide recommendations to guarantee safe operation while minimizing disturbance, which might be of interest to conser- vationists and managers of protected areas, as well as to UAS manufacturers, users, and regula- tors who require solid baseline information for or against UAS use in specific contexts. PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 2 / 14 Drones' impact on wildlife Material and methods st We performed a comprehensive literature search up to December 31 , 2015. One person per- formed the searches in English and Spanish and another person did the search in German with the same criteria. On a consensual way, we gathered information from scientific articles, dissertations, and reports mentioning the use of small UAS in animal monitoring and ecology related topics. In the search we applied the keywords: `unmanned aircraft/aerial system'; `remote piloted aircraft system'; `UAS'; `UAV'; `drone'; `RPAS'; `radio control aircraft'; `model aircraft' and variations of these terms, combined with: `wildlife'; `survey'; `inventory'; `impact'; `monitoring' and similar ones. Searches were made mainly using databases (Google Scholar, Scopus and Web of Science) but also complemented through reference harvesting, citation tracking, abstracts in conference programs, and author search (see, for example [45,46]). Stud- ies conducted using radio-controlled aircraft (RC hereinafter) were also included in this review because they have the same physical characteristics as UAS, and therefore may be equivalent in terms of impact on wildlife. In contrast, we excluded information referring to large UAS (MTOW>25 kg) because their operation takes place at high altitudes, thus their impact may be comparable to the effects produced by manned aircraft. Similarly, we excluded references to UAS with a MTOW below 250 g, since these are normally operated within short ranges (a few meters) around the pilots and often indoors, all of which makes them less susceptible to inter- fere significantly with wildlife. The search provided a total of 82 records, from which we retained 54 publications that indicated actual UAS or RC flights performed over wildlife in fieldwork. From these, we only used the ones explicitly indicating whether small UAS did or did not affect wildlife (i.e. 36 publications). The resulting database was completed with our own unpublished data from 17 UAS field campaigns (250 flights) conducted from 2011 to 2014 (for a PRISMA Flowchart see S1 Checklist and S1 Text, for unpublished campaigns see S2 Text, for a complete list of studies see S3 Text). All the field procedures were reviewed and accepted by the authority yielding the permit of use (Doñana National Park authorities, per- mits for Aeromab P07-RNM-03246 and Planet GA 257649 projects, Junta de Andalucõ Âa). Data on semi-captive animals, nestlings, roosting birds, or nocturnal UAS flights were omitted from subsequent analyses due to the low number of records and the lack of comparability of responses. The physiological responses and the long-term consequences of the flights on ani- mals could not be further explored due to insufficient evidence. From retained studies we collected all available information on animals' reaction to UAS. To investigate the determinants of these reactions, we also recorded any characteristics of the flight, aircraft, or the animals concerned, which could be influencing animal responses accord- ing to previous literature (see for example [21,42]). Unfortunately, many of these variables could not be included in further analyses due to incomplete information (e.g. habitat type) or correlation with other variables (e.g. aircraft shape is correlated with flight pattern and aircraft size). In order to standarize the animal behaviors described in the different studies, we classified the reaction caused by the UAS into the following categories: ªnoneº (i.e. study mentioned no noticeable behavioral change), ªalert reactionº (i.e. at least one individual showed increased attention or alert towards the UAS during the study), or ªactive reactionº (i.e. at least one indi- vidual responded actively towards the UAS, either fleeing or attacking). UAS-dependent parameters that we were able to record from the publications (or from technical data-sheets of the UAS manufacturer) were the characteristics of the flight, the size of the aircraft (i.e. largest measure of the vehicle in any dimension), and the engine type as a proxy of noise (i.e. ªfuelº being noisier than ªelectricº). The flight was characterized by the PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 3 / 14 Drones' impact on wildlife flight pattern followed in the study and by the spatial relation of the aircraft to the animals when the reaction, or lack of it, was observed. The flight pattern was summarized in three categories: ªtarget-orientedº, for those that approached the animal either directly or in consecutive vertical-horizontal approaching steps, ªlawn-mowerº, entailing a flight with systematic turns back and forth at a constant AGL to scan an area, and ªhobbyº, which included the acrobatic or irregular flight patterns (e.g. leisure flights) typically performed by RC planes. The spatial relation of the aircraft to the animals was measured either by the UAS altitude above ground level AGL (in meters) or by the distance (in meters) to the UAS/RC, depending on what was provided on each study. When a given reac- tion was mentioned in a study (i.e. ªalert responseº or ªactive responseº of at least one individ- ual), we recorded the maximum AGL or distance, depending on availability, at which the reaction was observed as the threshold at which the effect started. When the AGL or distance at which the reactions occurred was not given, but they only provided the range of values at which the UAS was flown, we retained consistently the lowest/shortest value to avoid overesti- mation of the impact. When no reaction (ªnoneº) was observed, we took the lowest/shortest value provided in order to represent how close fauna was approached without causing any disturbance. As for the characteristics of the animals subjected to UAS flights, we recorded the species (i.e. lowest taxonomic level available), which was later classified in five animal types according to the expected differences in response to UAS: ªflightless birdsº (i.e. penguins), ªlarge birdsº (flying birds with average body size longer than 70 cm and heavier than 1 kg), ªsmall birdsº (flying birds smaller than the previous category), ªterrestrial mammalsº (mammals being on terrestrial habitats during the flights: carnivores, pinnipeds, primates and ungulates), and ªunderwater speciesº (i.e. vertebrate species permanently underwater, such as fish or ceta- ceans) (for a complete list of species and studies see S3 Text). We also gathered additional information on the life history stage and level of aggregation of the animals. Life history stage was defined as ªbreedingº if the species were known to be within the period of gestation or parental care in the moment in which flights were performed, and ªnon-breedingº for cases in which species were outside their gestation or parenting season. The level of aggregation was classified as ªsolitaryº individuals, ªsmall groupº (2 to 10 individuals), ªmedium groupº (11 to 30 individuals), and ªlarge groupº (more than 30 individuals). Animal characteristics not directly provided in the publication were approximated from specialized sources, such as handbooks or field guides (e.g. the Handbooks of the Birds of the World [47]). Statistical analysis In order to determine which of the factors recorded are affecting animal responses to UAS, we selected a sequential analysis approach that allowed us to integrate information from all stud- ies, which varied importantly in the type of information given, and to cope with correlated explanatory variables (e.g. AGL and flight pattern). In a first step we pooled all data to examine which factors determined the impact of UAS. For this, we fitted two different logistic mixed models with reaction as the response variable. In the first model, we only distinguished between reaction (ª1º, when ªalertº or ªactiveº response was observed) and absence of reaction (ª0º, ªnoneº). In the second model we focused on the intensity of the reaction. For that, we only considered studies where a reaction was observed. The dependent variable was set to ª0º when the response was weak (ªalertº) and ª1º when the reaction was strong (ªactiveº reaction including attack and fleeing). In both models, we tested for the effect of flight pattern, engine type, and animal type as explanatory variables. Further variables could not be included due to either correlation issues or missing values. PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 4 / 14 Drones' impact on wildlife In a second step we focused only on the cases showing active responses (mostly fleeing) and used mixed models to examine at which AGL or distance from the UAS these responses occurred. Note that the AGL and distances used here are conservative and represent the AGL or distances at which some animals start to actively react, but not necessarily the ones at which all observed animals react. We analyzed the data for each flight pattern separately and tested the effect of variables that we could not investigate in the first step. Note that most data in this second-step analyses are referred to birds, except for seven cases of terrestrial mam- mals for the ªtarget-orientedº and one for the ªlawn-mowerº pattern. To analyze the data on ªtarget-orientedº and ªlawn-mowerº patterns respectively, we used the log-transformed AGL as the response variable, which followed a normal distribution. As explanatory variables, we tested for the effect of life history stage and the size of the UAS. Given that sample size was larger for the ªlawn-mowerº case, we could also include the engine type. For the ªhobbyº pat- tern data, usually distance to animals was provided instead of AGL. Thus, we used the log- transformed distance as dependent variable, also following a normal distribution, and were able to test for the effect of life history stage and the level of aggregation as explanatory variables. In all five mixed models we included three random factors to account for the autocorrela- tion among data within the same study (reference), same species (species), and same family (family). All models were performed with the software R version 2.15.1 [48] using the ªlmerº and ªglmerº functions of the package lme4, for normal and binomial distributions respectively [49]. We used the Bayesian framework to obtain the credible intervals (CrI) of the estimated effects. For this, we simulated a random sample (N = 5000) from the joint posterior distribu- tion of the model parameters using the function ªsimº of the package arm [50]. The 2.5% and 97.5% quantiles from this sample were then used as the lower and upper limits of the 95% CrI and the 5.0% and 95.0% quantiles as the extremes of the 90% CrI. An effect was considered to reach standard significance levels when its 95% CrI did not contain zero and near-significance levels when 90% CrI did not contain zero (S1 and S2 Tables). Results Overall, we were able to include in the analyses data from 36 published studies and from 17 unpublished field campaigns (S3 Text) that reported information about wildlife reactions towards UAS flights. The first-step analyses (i.e. pooled data) showed that the probability of UAS producing a reaction on wildlife was higher in studies conducting ªtarget-orientedº than ªlawn-mowerº flight patterns (standard significance levels were not reached but the 90% CrI did not include zero; Fig 1a; S1 Table). The effect of the ªhobbyº pattern, however, was not clearly different from any of the other two patterns. A similar but less strong trend was found when we compared the intensity of the response (ªalertº vs. ªactiveº escape or attack), with ªtarget-orientedº flight patterns being more likely to cause an active response than the ªlawn- mowerº flights (Fig 1d; S1 Table). UAS with fuel engines also increase (according to the 90% CrI) the likelihood to produce animal reactions than electric systems (Fig 1b; S1 Table). There are also significant differences (i.e. 95% CrI not including zero) among species regarding the response to UAS (Fig 1c and 1f; S1 Table). The less responsive species were the ones living underwater, for which we only found one record mentioning a reaction, followed by terrestrial mammals. The groups that showed the highest sensitivity to UAS were birds, and within birds, the overall probability of responding tended to be higher in flightless and large flying birds than in smaller flying birds, while the intensity of the response was higher in both flying-bird groups than in the flightless ones. However these within-bird trends did not reach significance levels (according neither to the 95% CrI nor the 90% CrI). PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 5 / 14 Drones' impact on wildlife Fig 1. Mean probability of wildlife reaction with 95% CrI (S1 Table) according to UAS flight pattern (a, d), UAS engine type (b, e), and type of animals concerned (c, f). (Model 1: 0 = no reaction vs. 1 = reaction [either alert or active; N = 167]; Model 2: 0 = alert vs. 1 = active response; N = 106). https://doi.org/10.1371/journal.pone.0178448.g001 In the second-step analyses, examining active animal responses, we found that animal-spe- cific characteristics, such as life-history stage and level of aggregation, were important to deter- mine how close individuals can be approached without causing them to respond actively (usually fleeing). For all three types of flight patterns, individuals outside the gestation or parental-care periods responded at significant (or near-significant) larger AGL (i.e. vertical distance) or distances from the UAS than animals in those breeding stages (Fig 2a, 2c and 2f; S2 Table). Individuals also fled at significantly longer distances when they were in larger groups than when they were in small groups or solitary (Fig 2g; S2 Table). The results also demonstrated an influence of the size of UAS, with larger (within the studied range) aircraft causing active reactions at higher AGL than smaller ones (Fig 2b and 2d; S2 Table). Discussion Our findings support our hypothesis that animal reactions to UAS are conditioned by factors related to both UAS attributes and mode of operation, and also to the characteristics of the concerned animals. The flight pattern of the UAS has an important effect on wildlife responses. Target-oriented flights, which are conducted towards the focal animals and usually approach them at shorter AGL and distances, mainly for photography, nest inspections, and recently for ªanimal controlº (e.g. [11]), produce more reactions than the other flying patterns, leading to higher disturbance. On the other extreme, lawn-mower flights, generally conducted for map- ping, surveillance and wildlife census, performed at higher altitudes and following regular tra- jectories are less likely to affect the animals. This result agrees with studies on manned aircraft showing that directness of aircraft approach influence animal responses [29,51,52] and could be related to anti-predator behavior, since animals perceive higher risks when the threat is on a trajectory towards them [53]. We detected that fuel engines (e.g. [54]), generally noisier than electric ones, produce more animal reactions, which would agree with previous authors that indicate the importance of noise as a source of disturbance for wildlife [55]. Besides, it has been suggested that animal reactions are not only influenced by the noise level itself but also by changes on noise intensity. In the case of a UAS these changes in intensity may be associated with aircraft on-flight engine variations due to speed or trajectory changes or wind alterations [41,56]. On the other hand, silent UAS platforms or UAS operated at high altitudes (e.g. over 100 m AGL) where noise is PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 6 / 14 Drones' impact on wildlife Fig 2. Factors influencing the AGL (m) with 95% CrI (S2 Table) at which animal respond actively during target-oriented flights (Model 3; N = 12; a) life-history stage and b) UAS size), and during lawn- mower pattern flights (Model 4; N = 27; c) life-history stage, d) UAS size, and e) engine type). Distance (m) with 95% CrI (S2 Table) at which animals show an active response during hobby (Radio-controlled vehicles) flights (Model 5; N = 21; f) life-history stage and g) level of aggregation). https://doi.org/10.1371/journal.pone.0178448.g002 attenuated, may be hard to hear by wildlife and therefore less distressing [57±59]. The size of the UAS also affects animal reactions, with larger platforms producing responses at larger AGL than small ones, probably because the size of the threat increases perceived risk [53] and the probability of detecting it. As hypothesized, we found substantial differences in occurrence of response to UAS among different animal types. Birds are the most sensitive to UAS, with flightless birds and large birds being more likely to show reactions than smaller ones, probably due to a higher range of detec- tion for larger size species [60]. Terrestrial mammals are overall less reactive to UAS than birds. We found that fully aquatic animals are the least affected animal type, in line with a recent review [43], which could be explained by the water layer providing some isolation from aerial stimuli. The differences detected between animal types in their response to UAS can be also related to their anti-predatory strategies, with species that are naturally threatened by aerial predators (e.g. birds) reacting more than other animal types, such as large terrestrial mammals and fully aquatic species. We found that animal-specific characteristics, such as life-history stage and level of aggrega- tion, have an influence on the animals' response. We found that breeding animals (gravid or providing parental care, in our case mostly breeding birds) are on average less inclined to flee than non-breeders, probably due to a reluctance to abandon the progeny (i.e. nest) or to move- ment difficulties for gravid females [21,31]. In some cases, however, reproducting individuals can also react aggressively to UAS [29,61], probably because of an increase in territoriality PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 7 / 14 Drones' impact on wildlife during this period or defense of the progeny. As for the level of aggregation, our results showed that animal group size increases active reactions towards UAS, in line with observations towards manned aircrafts [42,56] and suggested for UAS [42]. We acknowledge that there might be other important factors affecting animal responses to UAS that we could not explore quantitatively in this study due to incomplete information or correlation with other variables. For example, Vas et al. [42] and Ru È mmler et al. [62] indicate that UAS vertical approaches had a higher impact than horizontal ones, and [42] indicate that it could be because birds associate the approach with a predator attack, while Ru È mmler et al. [62] suggest that the noise emission in this direction may be higher. The shape of the UAS could also have an influence on its disturbance, as predator-like shapes seem to produce more reactions in birds [63], but more standardized experiments should be performed to fully assess the effect of this variable. The time of the day might also play a role, since at night or dusk ani- mals tend to react with less intensity [64]. From the field of manned-aircraft there is evidence that habitat characteristics and animal behavior before the flight affect their reactions. That is, open habitats favor fleeing responses compared to closed habitats [29] and animals involved in active behaviors are more prone to react to disturbances than passive ones [65]. Currently we have insufficient information on the physiological and long-term conse- quences of UAS disturbances. However, there are some indications that UAS might cause non-visible effects. For example, Ditmer et al. [41] found increases in physiological stress but not behavioral changes in animals subjected to close-distance UAS approaches, as has been also shown in on-foot disturbances [38]. Long-term studies on the use of UAS have not yet been performed; however, according to human disturbance literature (see [21] for a compre- hensive review), the physiological or behavioral stress potentially caused by disturbance may lead to higher energy expenditures, decreases in reproduction and survival, and space-use changes, which might compromise the average fitness or even viability of certain populations. In agreement to this, studies conducted on RC fields indicate that abundant flights may lead to territory abandonment and decreased productivity in sensitive bird species [56,66±68] or to habituation in less sensitive ones [56,69]. We agree on the need of further research (with stan- dardized well-thought study designs) to increase our knowledge on the effect of other factors and on the potential consequences for individuals and populations. However, we believe that the generalized patterns shown here, despite the variability in their design and purpose of the reviewed studies, are a good first step towards a better understanding of the effects of using UAS and the ways to mitigate their impact. Conclusions and management implications UAS constitute a potential new source of anthropogenic disturbance, and the extent to which they elicit wildlife reactions depends on UAS and animal related factors. Smaller UAS, electric engines, and lawn-mower flight patterns generally evoke no disturbance at all or only a short disturbance, the latter comparable to those induced by natural predators. More intense reac- tions are observed in bird species, larger group sizes, and animals in non-breeding stage. AGL or distances at which animals were observed to flee during UAS flights are smaller (usually below 500 m) than for manned aircraft (105 m-15 km [28,70]) and for car approaches (e.g. for ungulates, 50 mÐ2.8 km [71]), and are comparable to those reported for on-foot approaches (e.g. for birds: 0±455 m in [72,73] respectively). UAS also produce less noise and have a smaller size than manned aircraft or cars (see S4 Text for car vs. UAS noise comparison), when flying at average engine power. Hence, the careful use of UAS might be a valuable alternative for methods such as bio-logging, on-foot, and manned aircraft censuses used in biological studies. In contrast, when UAS perform direct and too close approaches to animals or sensitive PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 8 / 14 Drones' impact on wildlife structures such as nests, they can evoke more disturbances to wildlife. Therefore, we recom- mend that UAS flights are avoided unless they constitute the least invasive option for necessary wildlife studies, and discouraged if they are performed just for leisure purposes such as flying or filming. As UAS hobby uses are on the rise, some authorities have established regulatory restrictions to prevent undesirable consequences for fauna based on the precautionary princi- ple. Along this line, the use of UAS has been banned in the United States National Parks [74], in reserves for waterbirds and migratory birds in Switzerland [75] and in London green spaces [76], and some drone pilots have received fines for flying in protected areas [77,78]. We recommend using UAS only in projects where flights are justified and adopting the fol- lowing guidelines (provided with more detail in S5 Text and that can be complemented with the ones suggested by [44]) that are made on the basis of the quantitative analysis we presented here, the qualitative information extracted from the literature reviewed and own experience on UAS flights over wildlife,: 1) Use reliable UAS operated by experienced pilots; 2) Favor low-noise or small UAS against noisier or larger ones; 3) Mount the ground control station 100±300 m away from the study area; 4) Conduct missions as short as possible; 5) Fly at the highest altitude possible; 6) Avoid maneuvers above the animals; 7) Favor lawn-mower flight patterns; 8) Minimize flights over sensitive species or during breeding period; 9) Avoid UAS silhouettes that resemble predator shapes; 10) Avoid close-distance direct approaches and favor indirect ones; 11) Monitor target animals before, during, and after the flight; 12) For nest inspections, fly at times in which eggs/chicks are out of risk; 13) If the flights are around aggressive raptor's territories, perform them at day times when the temperature is low and birds are less prone to fly. These recommendations should be updated when more information is available because, as revealed here, there are many factors that condition the impact of UAS missions. There is an urgent need for providing the appropriate information to UAS users and authorities to mini- mize the impact that these systems can produce on wildlife. This can be done with dissemina- tion campaigns, offering guidelines with the products and cooperating with manufacturers. It would also be advisable to adapt the legal frame so that appropriate actions can be undertaken when wildlife is negatively affected by these emergent practices, specifying drone use on the animal welfare legislation, or including references to wildlife impact on the framework for UAS in the civil aviation regulations. Supporting information S1 Text. PRISMA 2009 checklist. (DOC) S2 Text. Methodology for UAS field campaigns (unpublished data). (DOCX) S3 Text. List of studies/cases used in the analyses. (DOCX) S4 Text. UAS and car sound comparison. (DOCX) S5 Text. Guidelines to minimize UAS impact on wildlife. (DOCX) S1 Table. Results of the GLMMs exploring the factors affecting the probability of observ- ing a reaction in wildlife exposed to UAS flights (Model 1) and the determinants of the PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 9 / 14 Drones' impact on wildlife probability of observing an active vs. a passive response to the UAS (Model 2). (DOCX) S2 Table. Results of the GLMMs investigating the factors determining the AGL (Model 3 and 4) or distance to the fauna (Model 5) at which UAS were when an active response (mostly escape) was observed. (DOCX) S1 Checklist. PRISMA flowchart. (PDF) Acknowledgments Part of this study was conducted within the Projects Aeromab, (Andalusia Government, Proj- ect for Excellence, 2007, P07-RNM-03246) and Planet (European Commission 7th FP Grant   Agreement 257649) granted to JJN, Doñana Biological Station, CSIC. M. Mulero-Pazmany and J. J. Negro thank T. Redondo (DBS-CSIC) for helping on the UAS/car noise data analyses.   We thank L. Jenni, M. Schaad, D. Hayden, H. Schmid and M. Gonzalez Suarez for their com- ments on previous versions of the manuscript. We also thank M.C. Ru È mmler for providing valuable data that we used in this review. Author Contributions Conceptualization: MMP ZT. Data curation: MMP NS ZT. Formal analysis: ZT MMP. Funding acquisition: MMP SJE JJN. Investigation: MMP NS ZT. Methodology: ZT MMP. Project administration: SJE JJN MMP ZT. Resources: JJN. Software: ZT. Supervision: JJN SJE TS. Validation: ZT MMP. Visualization: MMP ZT. Writing ± original draft: MMP SJE NS TS JJN ZT. Writing ± review & editing: MMP SJE NS TS JJN ZT. References 1. Cox TH, Nagy CJ, Skoog MA, Somers IA. Civil UAV capability assessment. Houston, TX, US; 2004. 2. Guillot C. Commercial Drones [Internet]. 2016. http://businessresearcher.sagepub.com/sbr-1775- 98070-2712846/20160118/commercial-drones 3. Getzin S, Wiegand K, Schoning I. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol Evol. 2012; 3: 397±404. https://doi.org/10.1111/j.2041- 210X.2011.00158.x PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 10 / 14 Drones' impact on wildlife 4. Anderson K, Gaston KJ. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ. 2013; 11: 138±146. https://doi.org/10.1890/120150 5. Mulero-Pa  zma  ny M, Barasona JA, Acevedo P, Vicente J, Negro JJ. Unmanned Aircraft Systems com- plement biologging in spatial ecology studies. Ecol Evol. 2015; 5: 4808±4818. https://doi.org/10.1002/ ece3.1744 PMID: 26640661 6. Christie KS, Gilbert SL, Brown CL, Hatfield M, Biology A, Fairbanks A, et al. Unmanned aerial systems in wildlife research: current and future applications of a transformative technology. Front Ecol Environ. 2016; https://doi.org/10.1002/fee.1281 7. Hodgson JC, Baylis SM, Mott R, Herrod A, Clarke RH. Precision wildlife monitoring using unmanned aerial vehicles. Sci Rep. Nature Publishing Group; 2016; 6: 22574. https://doi.org/10.1038/srep22574 PMID: 26986721 8. Vermeulen C, Lejeune P, Lisein J, Sawadogo P, Bouche  P. Unmanned aerial survey of elephants. PLoS One. Public Library of Science; 2013; 8: e54700. https://doi.org/10.1371/journal.pone.0054700 PMID: 23405088 9. Mulero-Pa  zma  ny M, Stolper R, Van Essen LD, Negro JJ, Sassen T. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS One. 2014; 9: 1±10. https://doi.org/10.1371/journal. pone.0083873 PMID: 24416177 10. Mulero-Pa  zma  ny M, Negro JJ, Ferrer M. A low cost way for assessing bird risk hazards in power lines: Fixed-wing small unmanned aircraft systems. J Unmanned Veh Syst. 2014; 2: 5±15. https://doi.org/10. 1139/juvs-2013-0012 11. Clear flight solutions. Robirds falcon drone robotic birds of prey. In: Clear Flight Solutions [Internet]. 2014 [cited 1 Jul 2015] pp. 7±9. http://www.clearflightsolutions.com/ 12. Bird-X. BirdXPeller Remote control drone. In: Bird-X [Internet]. 2015 [cited 6 Aug 2015] pp. 1±2. http:// www.bird-x.com/remote-control-drone-pages-64.php 13. Dayly mail. A-flockalypse Now: Canadians order drone strikes on nuisance geese by equipping remote- controlled copters with speakers to scare off the wild birds. 2015; 1±42. http://www.dailymail.co.uk/ news/article-2399941/Canadians-order-DRONE-strikes-nuisance-geese-away-Petrie-Island.html 14. King LM. Will drones revolutionise ecotourism? J Ecotourism. Routledge; 2014; 13: 85±92. https://doi. org/10.1080/14724049.2014.948448 15. Robofalconry. The Robara. In: Robofalconry [Internet]. 2015 pp. 1±4. http://www.rofalconry.com/page. php?id=4 16. Whittingham D. Falconry Drone Test Flight. In: Youtube [Internet]. 2014 [cited 6 Aug 2015] pp. 16±18. https://www.youtube.com/watch?v=dRGxdOqcPQc 17. Marlin B. Drone Hog Hunting. In: Youtube [Internet]. 2015 [cited 6 Aug 2015] pp. 15±17. https://www. youtube.com/watch?v=UXy3NHijl5M 18. RYOT. Catching A Fish With A Drone !! In: Youtube [Internet]. 2013 [cited 1 Jul 2015]. https://www. youtube.com/watch?v=2hqppfBJmng 19. Garling C. Drone, drone on the range. In: Modern farmer [Internet]. 2011 [cited 15 Jul 2015] pp. 10±13. http://modernfarmer.com/2013/07/drones-drones-on-the-range/ 20. APO-100 FAA. FAA Aerospace forecast. Fiscal years 2016±2036. 2016. 21. Tablado Z, Jenni L. Determinants of uncertainty in wildlife responses to human disturbance. Biol Rev. 2015; n/a±n/a. https://doi.org/10.1111/brv.12224 PMID: 26467755 22. Wolf ID, Croft DB. Minimizing disturbance to wildlife by tourists approaching on foot or in a car: A study of kangaroos in the Australian rangelands. Appl Anim Behav Sci. Elsevier B.V.; 2010; 126: 75±84. https://doi.org/10.1016/j.applanim.2010.06.001 23. Jaeger JAG, Bowman J, Brennan J, Fahrig L, Bert D, Bouchard J, et al. Predicting when animal popula- tions are at risk from roads: an interactive model of road avoidance behavior. Ecol Modell. 2005; 185: 329±348. https://doi.org/10.1016/j.ecolmodel.2004.12.015 24. D'Amico M, Pe  riquet S, Roma  n J, Revilla E. Road avoidance responses determine the impact of hetero- geneous road-networks at regional scale. J Appl Ecol. 2015; https://doi.org/10.1111/1365-2664.12572 25. Cole EK, Pope MD, Anthony RG. Effects of road management on movement and survival of Roosevelt Elk. J Wildl Manage. 1997; 61: 1115±1126. Available: http://www.jstor.org/stable/3802109?seq= 1#page_scan_tab_contents 26. Vos ACC, Chardon JP. Effects of habitat fragmentation and road density on the distribution pattern of the moor frog Rana arvalis. J Appl Ecol. 1998; 35: 44±56. 27. Carr LW, Fahrig L. Effect of road traffic on two amphibian species of differing vagility. Conserv Biol. 2001; 15: 1071±1078. https://doi.org/10.1046/j.1523-1739.2001.0150041071.x PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 11 / 14 Drones' impact on wildlife 28. Efroymson RA, Suter GW. Ecological risk assessment framework for low-altitude aircraft overflights: II. Estimating effects on wildlife. Risk Anal. 2001; 21: 263±74. PMID: 11414536 29. Komenda-Zehnder S, Bruderer B. Einfluss des Flugverkehrs auf die Avifauna. Literaturstudie. Schrif- tenreihe Umwelt. Natur und Lanschaft. Bern, Switzerland; 2002. 30. Bruderer B, Komenda-Zehnder S. Einfluss des Flugverkehrs auf die Avifauna. Schriftenr Umwelt. 2005; 376: 1±100. 31. Stankowich T, Blumstein DT. Fear in animals: a meta-analysis and review of risk assessment. Proc Biol Sci. 2005; 272: 2627±2634. https://doi.org/10.1098/rspb.2005.3251 PMID: 16321785 32. Karp DS, Root TL. Sound the stressor: How Hoatzins (Opisthocomus hoazin) react to ecotourist con- versation. Biodivers Conserv. 2009; 18: 3733±3742. https://doi.org/10.1007/s10531-009-9675-6 33. Cooper WE. Factors affecting risk and cost of escape by the broad-headed skink (Eumeces laticeps): predator speed, directness of approach, and female presence. Herpetologica. 1997; 53: 464±474. 34. Bateman PW, Fleming PA. Who are you looking at? Hadeda ibises use direction of gaze, head orienta- tion and approach speed in their risk assessment of a potential predator. J Zool. 2011; 285: 316±323. https://doi.org/10.1111/j.1469-7998.2011.00846.x 35. DeVault TL, Blackwell BF, Seamans TW, Lima SL, Fernandez-Juricic E. Speed kills: ineffective avian escape responses to oncoming vehicles. Proc R Soc B Biol Sci. 2015; 282: 20142188. https://doi.org/ 10.1098/rspb.2014.2188 PMID: 25567648 36. Bracha D. Freeze, flight, fight, fright, faint: adaptationist perspectives on the acute stress response spectrum. CNS Spectr. 2004; 9: 679±685. PMID: 15337864 37. Espmark Y, Langvatn R. Development and habituation of cardiac and behavioral responses in young red deer calves (Cervus elaphus) exposed to alarm stimuli. J Mammal. 1985; 66: 702±711. 38. Steen JB, Gabrielsen GW, Kanwisher JW. Physiological aspects of freezing behaviour in willow ptarmi- gan hens. Acta Physiol Scand. 1988; 299±304. https://doi.org/10.1111/j.1748-1716.1988.tb08493.x PMID: 3227948 39. Albrecht T, Klvaňa P. Nest crypsis, reproductive value of a clutch and escape decisions in incubating female mallards anas platyrhynchos. Ethology. 2004; 110: 603±613. https://doi.org/10.1111/j.1439- 0310.2004.00992.x 40. Cartledge VA, Jones SM. Does adrenal responsiveness vary with sex and reproductive status in Eger- nia whitii, a viviparous skink? Gen Comp Endocrinol. 2007; 150: 132±139. https://doi.org/10.1016/j. ygcen.2006.07.021 PMID: 16979639 41. Ditmer MA, Vincent JB, Werden LK, Tanner JC, Laske TG, Iaizzo PA, et al. Bears show a physiological but limited behavioral response to Unmanned Aerial Vehicles. Curr Biol. Elsevier Ltd; 2015; 25: 2278± 2283. http://dx.doi.org/10.1016/j.cub.2015.07.024 Figure 42. Vas E, Lescro È el A, Duriez O, Boguszewski G, Gre  millet D. Approaching birds with drones : first experi- ments and ethical guidelines. Biol Lett. 2015; 11: 20140754. https://doi.org/10.1098/rsbl.2014.0754 PMID: 25652220 43. Smith C, Sykora-Bodie S, Bloodworth B, Pack S, Spradlin T, LeBoeuf N. Assessment of known impacts of unmanned aerial systems (UAS) on marine mammals: data gaps and recommendations for researchers in the United States. Jounal Unmanned Veh Syst. 2015;in press. 44. Hodgson JC, Koh LP. Best practice for minimising unmanned aerial vehicle disturbance to wildlife in bio- logical field research. Curr Biol. Elsevier; 2016; 26: R404±R405. https://doi.org/10.1016/j.cub.2016.04. 001 PMID: 27218843 45. Barroso J, Gollop CJ, Sandelowski M, Meynell J, Pearce PF, Collins LJ. The challenges of searching for and retrieving qualitative studies. West J Nurs Res. 2003; 25: 153±178. https://doi.org/10.1177/ 0193945902250034 PMID: 12666641 46. Simcock P. One of society's most vulnerable groups? A systematically conducted literature review exploring the vulnerability of deafblind people. Health Soc Care Community. 2016; 47. del Hoyo J, Elliot A, Sargatal J, Christie DA. Handbook of the birds of the world. Lynx, editor. Barcelona, Spain; 48. Development Core Team. R. 2013. 49. Bates D, Maechler M, Bolker B, Walker S. lme4: Linear mixed-effects models using S4 classes. R pack- age version 0.999999±0. 2012. 50. Gelman A, Hill J. Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge Uni- versity Press, editor. New York, USA; 2006. 51. Frid A. Dall's sheep responses to overflights by helicopter and fixed-wing aircraft. Biol Conserv. 2003; 110: 387±399. https://doi.org/10.1016/S0006-3207(02)00236-7 PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 12 / 14 Drones' impact on wildlife 52. Lima SL, Blackwell BF, Devault TL, Ferna  ndez-Juricic E. Animal reactions to oncoming vehicles: A con- ceptual review. Biol Rev. 2015; 90: 60±76. https://doi.org/10.1111/brv.12093 PMID: 24661508 53. Stankowich T, Blumstein DT. Fear in animals: a meta-analysis and review of risk assessment. Proc R Soc Biol Sci. 2005; 272: 2627±34. https://doi.org/10.1098/rspb.2005.3251 PMID: 16321785 54. Hodgson A, Kelly N, Peel D. Unmanned Aerial Vehicles (UAVs) for surveying marine fauna: a dugong case study. Fenton B, editor. PLoS One. 2013; 8: e79556. https://doi.org/10.1371/journal.pone. 0079556 PMID: 24223967 55. Shannon G, McKenna MF, Angeloni LM, Crooks KR, Fristrup KM, Brown E, et al. A synthesis of two decades of research documenting the effects of noise on wildlife. Biol Rev. 2015; https://doi.org/10. 1111/brv.12207 PMID: 26118691 56. Kempf N, Hu È ppop O. What effect do airplanes have on birds?±a summary and update of ªWie wirken Flugzeuge auf Vo È gel?, Naturschutz und Lanschaftsplanung, 30±1 (17±28). 1998º [Internet]. 1998. https://www.google.ch/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved= 0CB8QFjAAahUKEwiMtb79uvvGAhXFDiwKHQ2NCaw&url=http://www.fai.org/component/ phocadownload/category/1107-air-sports-and-wildlife?download=2924:lan-3-1-aircr 57. Jones IV GP. The feasibility of using Small Unmanned Aerial Vehicles for wildlife research. University of Florida. 2003. 58. Jones G, Pearlstine L, Percival H. An assessment of small unmanned aerial vehicles for wildlife research. Wildl Soc Bull. 2006; 34: 750±758. 59. Cunningham KW. Augmenting steller sea lion surveys in the Western Aleutians with Unmanned Air- craft.±Project number 1120. Fairbanks AK; 2014. 60. Kiltie RA. Scaling of visual acuity with body size in mammals and birds. Funct Ecol. 2000; 14: 226±234. https://doi.org/10.1046/j.1365-2435.2000.00404.x 61. Weissensteiner MH, Poelstra JW, Wolf JBW. Low-budget ready-to-fly unmanned aerial vehicles: an effective tool for evaluating the nesting status of canopy-breeding bird species. J Avian Biol. 2015; 46: 425±430. https://doi.org/10.1111/jav.00619 62. Ru È mmler M-C, Mustafa O, Maercker J, Peter H-U, Esefeld J. Measuring the influence of unmanned aerial vehicles on Ade  lie penguins. Polar Biol. 2016; 39: 1329±1334. https://doi.org/10.1007/s00300- 015-1838-1 63. McEvoy JF, Hall GP, McDonald PG. Evaluation of unmanned aerial vehicle shape, flight path and cam- era type for waterfowl surveys: disturbance effects and species recognition. PeerJ. 2016; 4: e1831. https://doi.org/10.7717/peerj.1831 PMID: 27020132 64. Bivings AE. Advantages and limitations of Radio-Controller aircraft in bird dispersal. Bird Strike Commit- tee Europe 19. Madrid, Spain; 1988. pp. 481±487. 65. Tracey JP, Fleming PJSS. Behavioural responses of feral goats (Capra hircus) to helicopters. Appl Anim Behav Sci. 2007; 108: 114±128. https://doi.org/10.1016/j.applanim.2006.10.009 66. Von Opitz H. Brutvorkommen, Gefa È hrdung und Schutz des Grossen Brachvogels. Beih Vero È ffentlichun- gen Natur-und Landschaftspfl Baden-Wu È rttemberg. 1975; 7: 65±67. 67. Putzer D. Wirkung und Wichtung menschlicher Anwesenheit und Sto È rung am Beispiel bestandsbedroh- ter, an Feuchtgebiete gebundener Vogelarten. Lanschatspfl u Naturschutz. 1989; 29: 169±194. 68. Boscher M. Auswirkungen von Modellflug und Straûenverkehr auf die Raumnutzung beim Grossen Brachvogel (Numenius arquata). Z Okologie u Naturschutz. 1993; 2: 11±18. 69. Strebel N, Roth T, Weber D. Die Existenz des Modellflugplatzes in der Selzacherwiti beeinflusst die Ver- teilung der Feldlerchen Alauda arvensis wa È hrend der Brutzeit nicht. Der Ornithol Beobachter. 2016; 113: 53±60. 70. Delaney D, Grubb T, Beier P, Pater LL, Reiser MH. Effects of helicopter noise on Mexican spotted owls. J Wildl Manage. 1999; 63: 60±76. 71. Alves C, Bager A. Review of the factors undelying the mechanisms and effects of roads on vertebrates. Oecologia Aust. 2013; 17: 208±221. 72. Yorio P, Boersma PD. The effects of human disturbance on Magellanic Penguin Spheniscus magellani- cus behaviour and breeding success. Bird Conserv Int. 1992; 2: 161±173. https://doi.org/10.1017/ S0959270900002410 73. Triplet P, Mequin N, Sueur F. Prendre en compte la distance d'envol n'est pas suffisant pour assurer la quieude des oiseaux en milieu littoral. Alauda. 2007; 75(3): 237±242. 74. US National Parks. Drone harasses bighorn sheep at Zion National Park. In: Park Alerts [Internet]. 2014 [cited 10 Jun 2015]. http://www.nps.gov/zion/learn/news/droneharassesbhs.htm 75. Swiss Federal Council. Verordnung u È ber die Wasser- und Zugvogelreservate von internationaler und nationaler Bedeutung (WZVV). 2015. PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 13 / 14 Drones' impact on wildlife 76. de Peyer R. Drones are banned from Royal Parks amid ªfears over impact on wildlife and visitor safetyº. London evening standard. London, UK; 2015: 1±4. http://www.standard.co.uk/news/london/drones- banned-from-royal-parks-amid-fears-over-impact-on-wildlife-and-visitor-safety-10095538.html 77. Clancy A. Two drone pilots ticketed for ªOrca Protection Violations.º In: sUAS News [Internet]. 2015 [cited 1 Sep 2015] pp. 8±10. http://www.suasnews.com/2015/08/38088/two-drone-pilots-ticketed-for- orca-protection-violations/?utm_source=sUAS+News+Daily&utm_campaign=4acda86862-RSS_ EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_b3c0776dde-4acda86862-295623153&mc_ cid=4acda86862&mc_eid=f849 78. ETN Travel News. Tourists in Kenya warned not to fly drones. In: SUAS News [Internet]. 2015 [cited 17 Aug 2015] pp. 1±5. http://www.uasvision.com/2015/01/20/tourists-in-kenya-warned-not-to-fly-drones/? utm_term=0_799756aeb7-5ab6a8ef1b-267637753&utm_campaign=5ab6a8ef1b-RSS_EMAIL_ CAMPAIGN&utm_medium=email&utm_source=Newsletter PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 14 / 14 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png PLoS ONE Pubmed Central

Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review

Loading next page...
 
/lp/pubmed-central/unmanned-aircraft-systems-as-a-new-source-of-disturbance-for-wildlife-q04g6X7uV0

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Pubmed Central
Copyright
© 2017 Mulero-Pázmány et al
ISSN
1932-6203
eISSN
1932-6203
DOI
10.1371/journal.pone.0178448
Publisher site
See Article on Publisher Site

Abstract

The use of small Unmanned Aircraft Systems (UAS; also known as ªdronesº) for profes- sional and personal-leisure use is increasing enormously. UAS operate at low altitudes OPENACCESS (<500 m) and in any terrain, thus they are susceptible to interact with local fauna, generating a new type of anthropogenic disturbance that has not been systematically evaluated. To Citation: Mulero-PaÂzmaÂny M, Jenni-Eiermann S, Strebel N, Sattler T, Negro JJ, Tablado Z (2017) address this gap, we performed a review of the existent literature about animals' responses Unmanned aircraft systems as a new source of to UAS flights and conducted a pooled analysis of the data to determine the probability and disturbance for wildlife: A systematic review. PLoS intensity of the disturbance, and to identify the factors influencing animals' reactions towards ONE 12(6): e0178448. https://doi.org/10.1371/ the small aircraft. We found that wildlife reactions depended on both the UAS attributes journal.pone.0178448 (flight pattern, engine type and size of aircraft) and the characteristics of animals themselves Editor: Antoni Margalida, University of Lleida, (type of animal, life-history stage and level of aggregation). Target-oriented flight patterns, SPAIN larger UAS sizes, and fuel-powered (noisier) engines evoked the strongest reactions in wild- Received: November 28, 2016 life. Animals during the non-breeding period and in large groups were more likely to show Accepted: May 12, 2017 behavioral reactions to UAS, and birds are more prone to react than other taxa. We discuss Published: June 21, 2017 the implications of these results in the context of wildlife disturbance and suggest guidelines   Copyright:© 2017 Mulero-Pazmany et al. This is for conservationists, users and manufacturers to minimize the impact of UAS. In addition, an open access article distributed under the terms we propose that the legal framework needs to be adapted so that appropriate actions can be of the Creative Commons Attribution License, undertaken when wildlife is negatively affected by these emergent practices. which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are available on figshare at https://doi.org/10.6084/m9.figshare. Introduction Recently, Unmanned Aircraft Systems (UAS, also known as remote piloted aircraft systems Funding: Part of this study was conducted within the Projects Aeromab, (Andalusia Government, ªRPASº or colloquially as ªdronesº) have experienced a remarkable development. Although Project for Excellence, 2007, P07-RNM-03246) originally UAS were mainly used for military purposes, the technological advances and the and Planet (European Commission 7th FP Grant miniaturization of components have facilitated the emergence of a mass production industry, Agreement 257649) granted to JJN, Doñana which has led to decreasing prices and consequently to a proliferation of the use of UAS in Biological Station, CSIC. The funders had no role in civilian applications. Small UAS (Maximum Take-Off Weight [MTOW] of 250 gÐ25 kg) are study design, data collection and analysis, decision to publish, or preparation of the manuscript. nowadays used for a variety of professional tasks, such as remote sensing, photography, PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 1 / 14 Drones' impact on wildlife Competing interests: The authors have declared precision agriculture, infrastructures inspection, mining, forestry management, and surveil- that no competing interests exist. lance, among others [1,2]. Small UAS can provide high spatial and temporal resolution data, which has favored their incorporation to environmental biology in different research and management topics, such as plant and animal ecology [3±6], wildlife monitoring [7,8], anti-poaching [9] and infrastruc- tures impact assessment [10]. There is also a recent increase of operators using UAS to scare off wild birds that cause economic losses in agricultural areas and fisheries, pose risks in air- ports, damage monuments and interfere in waste management [11±13]. Additionally, UAS are used in ecotourism, hunting, fishing, livestock management and falconry training [14±19]. Small UAS have also become a popular new hobby for the general public for leisure flying and filming, which has resulted in an exponential growth of sales (about 2.5 million units during 2016 in the USA alone;[20]). The expansion on the professional and personal use of small UAS that are operated over any terrain, frequently in natural areas, and at low altitudes above ground level (AGL < 500 m), makes the systems susceptible to interact with the local fauna, constituting a new potential source of anthropogenic disturbance. There is abundant literature about the effects of disturbance on wildlife. Animal responses to on-foot human presence [21], terrestrial vehicles [22±27] and aerial platforms such as manned aircraft, helicopters and gliders [28±30] may vary from punctual behavioral or physio- logical reactions to reductions in fitness (e.g. mortality by collision with the vehicles or stress- related decrease in productivity) or changes in spatial use (e.g. avoidance of certain areas; [21]) that may fragment or compromise the viability of populations. The studies indicate that the factors determining the probability and intensity of animal responses depend on the one hand on the characteristics of the disturbing agent (e.g. size, noise emitted, speed, distance, angle of approach), which affect the perception of the risk by the animal [21,31]. The larger and noisier the approaching agent is, the stronger the anti-predator responses will be [32]. Animals have also been shown to react at larger distances when the approach is faster and more directional towards them [33,34, but see also 35]. On the other hand, animal responses also depend on the characteristics and context of the animal exposed to the disturbance (e.g. species, age, level of aggregation, life history stage, habitat, season), which explain the susceptibility of the animal to react [21,31]. Different types of animals show different reactions to disturbance. Some species are more prone to conduct flight and fight responses, while others seek for protection by form- ing large aggregations, hiding in vegetation or using crypticity strategies, and do not show observable responses despite being physiologically stressed [36±38]. Similarly, reproductive status can also condition the animals' response, since gravid females might have reduced mov- ing capabilities, and individuals involved in parental care may prioritize protection of their off- spring to their own survival [39,40]. Due to the recent expansion of UAS use, assessment of their impact is currently restricted to isolated [41,42] or descriptive studies [6,43,44] and lacks a broad scientific base. The aim of our study is to identify the factors influencing the probability and magnitude of wildlife reac- tions to UAS. We reviewed the literature searching for information about animal responses to small UAS and then performed a combined analysis of the resulting database. According to the existing studies about disturbance, we hypothesized that the response of wildlife to UAS would depend on both the properties (aircraft size and engine) and flight pattern of the air- craft, and the attributes and context of the animals being concerned (animal type, life-history stage and level of aggregation). Finally, we discuss the implications of our findings in the con- text of human-disturbance ecology and species conservation and provide recommendations to guarantee safe operation while minimizing disturbance, which might be of interest to conser- vationists and managers of protected areas, as well as to UAS manufacturers, users, and regula- tors who require solid baseline information for or against UAS use in specific contexts. PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 2 / 14 Drones' impact on wildlife Material and methods st We performed a comprehensive literature search up to December 31 , 2015. One person per- formed the searches in English and Spanish and another person did the search in German with the same criteria. On a consensual way, we gathered information from scientific articles, dissertations, and reports mentioning the use of small UAS in animal monitoring and ecology related topics. In the search we applied the keywords: `unmanned aircraft/aerial system'; `remote piloted aircraft system'; `UAS'; `UAV'; `drone'; `RPAS'; `radio control aircraft'; `model aircraft' and variations of these terms, combined with: `wildlife'; `survey'; `inventory'; `impact'; `monitoring' and similar ones. Searches were made mainly using databases (Google Scholar, Scopus and Web of Science) but also complemented through reference harvesting, citation tracking, abstracts in conference programs, and author search (see, for example [45,46]). Stud- ies conducted using radio-controlled aircraft (RC hereinafter) were also included in this review because they have the same physical characteristics as UAS, and therefore may be equivalent in terms of impact on wildlife. In contrast, we excluded information referring to large UAS (MTOW>25 kg) because their operation takes place at high altitudes, thus their impact may be comparable to the effects produced by manned aircraft. Similarly, we excluded references to UAS with a MTOW below 250 g, since these are normally operated within short ranges (a few meters) around the pilots and often indoors, all of which makes them less susceptible to inter- fere significantly with wildlife. The search provided a total of 82 records, from which we retained 54 publications that indicated actual UAS or RC flights performed over wildlife in fieldwork. From these, we only used the ones explicitly indicating whether small UAS did or did not affect wildlife (i.e. 36 publications). The resulting database was completed with our own unpublished data from 17 UAS field campaigns (250 flights) conducted from 2011 to 2014 (for a PRISMA Flowchart see S1 Checklist and S1 Text, for unpublished campaigns see S2 Text, for a complete list of studies see S3 Text). All the field procedures were reviewed and accepted by the authority yielding the permit of use (Doñana National Park authorities, per- mits for Aeromab P07-RNM-03246 and Planet GA 257649 projects, Junta de Andalucõ Âa). Data on semi-captive animals, nestlings, roosting birds, or nocturnal UAS flights were omitted from subsequent analyses due to the low number of records and the lack of comparability of responses. The physiological responses and the long-term consequences of the flights on ani- mals could not be further explored due to insufficient evidence. From retained studies we collected all available information on animals' reaction to UAS. To investigate the determinants of these reactions, we also recorded any characteristics of the flight, aircraft, or the animals concerned, which could be influencing animal responses accord- ing to previous literature (see for example [21,42]). Unfortunately, many of these variables could not be included in further analyses due to incomplete information (e.g. habitat type) or correlation with other variables (e.g. aircraft shape is correlated with flight pattern and aircraft size). In order to standarize the animal behaviors described in the different studies, we classified the reaction caused by the UAS into the following categories: ªnoneº (i.e. study mentioned no noticeable behavioral change), ªalert reactionº (i.e. at least one individual showed increased attention or alert towards the UAS during the study), or ªactive reactionº (i.e. at least one indi- vidual responded actively towards the UAS, either fleeing or attacking). UAS-dependent parameters that we were able to record from the publications (or from technical data-sheets of the UAS manufacturer) were the characteristics of the flight, the size of the aircraft (i.e. largest measure of the vehicle in any dimension), and the engine type as a proxy of noise (i.e. ªfuelº being noisier than ªelectricº). The flight was characterized by the PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 3 / 14 Drones' impact on wildlife flight pattern followed in the study and by the spatial relation of the aircraft to the animals when the reaction, or lack of it, was observed. The flight pattern was summarized in three categories: ªtarget-orientedº, for those that approached the animal either directly or in consecutive vertical-horizontal approaching steps, ªlawn-mowerº, entailing a flight with systematic turns back and forth at a constant AGL to scan an area, and ªhobbyº, which included the acrobatic or irregular flight patterns (e.g. leisure flights) typically performed by RC planes. The spatial relation of the aircraft to the animals was measured either by the UAS altitude above ground level AGL (in meters) or by the distance (in meters) to the UAS/RC, depending on what was provided on each study. When a given reac- tion was mentioned in a study (i.e. ªalert responseº or ªactive responseº of at least one individ- ual), we recorded the maximum AGL or distance, depending on availability, at which the reaction was observed as the threshold at which the effect started. When the AGL or distance at which the reactions occurred was not given, but they only provided the range of values at which the UAS was flown, we retained consistently the lowest/shortest value to avoid overesti- mation of the impact. When no reaction (ªnoneº) was observed, we took the lowest/shortest value provided in order to represent how close fauna was approached without causing any disturbance. As for the characteristics of the animals subjected to UAS flights, we recorded the species (i.e. lowest taxonomic level available), which was later classified in five animal types according to the expected differences in response to UAS: ªflightless birdsº (i.e. penguins), ªlarge birdsº (flying birds with average body size longer than 70 cm and heavier than 1 kg), ªsmall birdsº (flying birds smaller than the previous category), ªterrestrial mammalsº (mammals being on terrestrial habitats during the flights: carnivores, pinnipeds, primates and ungulates), and ªunderwater speciesº (i.e. vertebrate species permanently underwater, such as fish or ceta- ceans) (for a complete list of species and studies see S3 Text). We also gathered additional information on the life history stage and level of aggregation of the animals. Life history stage was defined as ªbreedingº if the species were known to be within the period of gestation or parental care in the moment in which flights were performed, and ªnon-breedingº for cases in which species were outside their gestation or parenting season. The level of aggregation was classified as ªsolitaryº individuals, ªsmall groupº (2 to 10 individuals), ªmedium groupº (11 to 30 individuals), and ªlarge groupº (more than 30 individuals). Animal characteristics not directly provided in the publication were approximated from specialized sources, such as handbooks or field guides (e.g. the Handbooks of the Birds of the World [47]). Statistical analysis In order to determine which of the factors recorded are affecting animal responses to UAS, we selected a sequential analysis approach that allowed us to integrate information from all stud- ies, which varied importantly in the type of information given, and to cope with correlated explanatory variables (e.g. AGL and flight pattern). In a first step we pooled all data to examine which factors determined the impact of UAS. For this, we fitted two different logistic mixed models with reaction as the response variable. In the first model, we only distinguished between reaction (ª1º, when ªalertº or ªactiveº response was observed) and absence of reaction (ª0º, ªnoneº). In the second model we focused on the intensity of the reaction. For that, we only considered studies where a reaction was observed. The dependent variable was set to ª0º when the response was weak (ªalertº) and ª1º when the reaction was strong (ªactiveº reaction including attack and fleeing). In both models, we tested for the effect of flight pattern, engine type, and animal type as explanatory variables. Further variables could not be included due to either correlation issues or missing values. PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 4 / 14 Drones' impact on wildlife In a second step we focused only on the cases showing active responses (mostly fleeing) and used mixed models to examine at which AGL or distance from the UAS these responses occurred. Note that the AGL and distances used here are conservative and represent the AGL or distances at which some animals start to actively react, but not necessarily the ones at which all observed animals react. We analyzed the data for each flight pattern separately and tested the effect of variables that we could not investigate in the first step. Note that most data in this second-step analyses are referred to birds, except for seven cases of terrestrial mam- mals for the ªtarget-orientedº and one for the ªlawn-mowerº pattern. To analyze the data on ªtarget-orientedº and ªlawn-mowerº patterns respectively, we used the log-transformed AGL as the response variable, which followed a normal distribution. As explanatory variables, we tested for the effect of life history stage and the size of the UAS. Given that sample size was larger for the ªlawn-mowerº case, we could also include the engine type. For the ªhobbyº pat- tern data, usually distance to animals was provided instead of AGL. Thus, we used the log- transformed distance as dependent variable, also following a normal distribution, and were able to test for the effect of life history stage and the level of aggregation as explanatory variables. In all five mixed models we included three random factors to account for the autocorrela- tion among data within the same study (reference), same species (species), and same family (family). All models were performed with the software R version 2.15.1 [48] using the ªlmerº and ªglmerº functions of the package lme4, for normal and binomial distributions respectively [49]. We used the Bayesian framework to obtain the credible intervals (CrI) of the estimated effects. For this, we simulated a random sample (N = 5000) from the joint posterior distribu- tion of the model parameters using the function ªsimº of the package arm [50]. The 2.5% and 97.5% quantiles from this sample were then used as the lower and upper limits of the 95% CrI and the 5.0% and 95.0% quantiles as the extremes of the 90% CrI. An effect was considered to reach standard significance levels when its 95% CrI did not contain zero and near-significance levels when 90% CrI did not contain zero (S1 and S2 Tables). Results Overall, we were able to include in the analyses data from 36 published studies and from 17 unpublished field campaigns (S3 Text) that reported information about wildlife reactions towards UAS flights. The first-step analyses (i.e. pooled data) showed that the probability of UAS producing a reaction on wildlife was higher in studies conducting ªtarget-orientedº than ªlawn-mowerº flight patterns (standard significance levels were not reached but the 90% CrI did not include zero; Fig 1a; S1 Table). The effect of the ªhobbyº pattern, however, was not clearly different from any of the other two patterns. A similar but less strong trend was found when we compared the intensity of the response (ªalertº vs. ªactiveº escape or attack), with ªtarget-orientedº flight patterns being more likely to cause an active response than the ªlawn- mowerº flights (Fig 1d; S1 Table). UAS with fuel engines also increase (according to the 90% CrI) the likelihood to produce animal reactions than electric systems (Fig 1b; S1 Table). There are also significant differences (i.e. 95% CrI not including zero) among species regarding the response to UAS (Fig 1c and 1f; S1 Table). The less responsive species were the ones living underwater, for which we only found one record mentioning a reaction, followed by terrestrial mammals. The groups that showed the highest sensitivity to UAS were birds, and within birds, the overall probability of responding tended to be higher in flightless and large flying birds than in smaller flying birds, while the intensity of the response was higher in both flying-bird groups than in the flightless ones. However these within-bird trends did not reach significance levels (according neither to the 95% CrI nor the 90% CrI). PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 5 / 14 Drones' impact on wildlife Fig 1. Mean probability of wildlife reaction with 95% CrI (S1 Table) according to UAS flight pattern (a, d), UAS engine type (b, e), and type of animals concerned (c, f). (Model 1: 0 = no reaction vs. 1 = reaction [either alert or active; N = 167]; Model 2: 0 = alert vs. 1 = active response; N = 106). https://doi.org/10.1371/journal.pone.0178448.g001 In the second-step analyses, examining active animal responses, we found that animal-spe- cific characteristics, such as life-history stage and level of aggregation, were important to deter- mine how close individuals can be approached without causing them to respond actively (usually fleeing). For all three types of flight patterns, individuals outside the gestation or parental-care periods responded at significant (or near-significant) larger AGL (i.e. vertical distance) or distances from the UAS than animals in those breeding stages (Fig 2a, 2c and 2f; S2 Table). Individuals also fled at significantly longer distances when they were in larger groups than when they were in small groups or solitary (Fig 2g; S2 Table). The results also demonstrated an influence of the size of UAS, with larger (within the studied range) aircraft causing active reactions at higher AGL than smaller ones (Fig 2b and 2d; S2 Table). Discussion Our findings support our hypothesis that animal reactions to UAS are conditioned by factors related to both UAS attributes and mode of operation, and also to the characteristics of the concerned animals. The flight pattern of the UAS has an important effect on wildlife responses. Target-oriented flights, which are conducted towards the focal animals and usually approach them at shorter AGL and distances, mainly for photography, nest inspections, and recently for ªanimal controlº (e.g. [11]), produce more reactions than the other flying patterns, leading to higher disturbance. On the other extreme, lawn-mower flights, generally conducted for map- ping, surveillance and wildlife census, performed at higher altitudes and following regular tra- jectories are less likely to affect the animals. This result agrees with studies on manned aircraft showing that directness of aircraft approach influence animal responses [29,51,52] and could be related to anti-predator behavior, since animals perceive higher risks when the threat is on a trajectory towards them [53]. We detected that fuel engines (e.g. [54]), generally noisier than electric ones, produce more animal reactions, which would agree with previous authors that indicate the importance of noise as a source of disturbance for wildlife [55]. Besides, it has been suggested that animal reactions are not only influenced by the noise level itself but also by changes on noise intensity. In the case of a UAS these changes in intensity may be associated with aircraft on-flight engine variations due to speed or trajectory changes or wind alterations [41,56]. On the other hand, silent UAS platforms or UAS operated at high altitudes (e.g. over 100 m AGL) where noise is PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 6 / 14 Drones' impact on wildlife Fig 2. Factors influencing the AGL (m) with 95% CrI (S2 Table) at which animal respond actively during target-oriented flights (Model 3; N = 12; a) life-history stage and b) UAS size), and during lawn- mower pattern flights (Model 4; N = 27; c) life-history stage, d) UAS size, and e) engine type). Distance (m) with 95% CrI (S2 Table) at which animals show an active response during hobby (Radio-controlled vehicles) flights (Model 5; N = 21; f) life-history stage and g) level of aggregation). https://doi.org/10.1371/journal.pone.0178448.g002 attenuated, may be hard to hear by wildlife and therefore less distressing [57±59]. The size of the UAS also affects animal reactions, with larger platforms producing responses at larger AGL than small ones, probably because the size of the threat increases perceived risk [53] and the probability of detecting it. As hypothesized, we found substantial differences in occurrence of response to UAS among different animal types. Birds are the most sensitive to UAS, with flightless birds and large birds being more likely to show reactions than smaller ones, probably due to a higher range of detec- tion for larger size species [60]. Terrestrial mammals are overall less reactive to UAS than birds. We found that fully aquatic animals are the least affected animal type, in line with a recent review [43], which could be explained by the water layer providing some isolation from aerial stimuli. The differences detected between animal types in their response to UAS can be also related to their anti-predatory strategies, with species that are naturally threatened by aerial predators (e.g. birds) reacting more than other animal types, such as large terrestrial mammals and fully aquatic species. We found that animal-specific characteristics, such as life-history stage and level of aggrega- tion, have an influence on the animals' response. We found that breeding animals (gravid or providing parental care, in our case mostly breeding birds) are on average less inclined to flee than non-breeders, probably due to a reluctance to abandon the progeny (i.e. nest) or to move- ment difficulties for gravid females [21,31]. In some cases, however, reproducting individuals can also react aggressively to UAS [29,61], probably because of an increase in territoriality PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 7 / 14 Drones' impact on wildlife during this period or defense of the progeny. As for the level of aggregation, our results showed that animal group size increases active reactions towards UAS, in line with observations towards manned aircrafts [42,56] and suggested for UAS [42]. We acknowledge that there might be other important factors affecting animal responses to UAS that we could not explore quantitatively in this study due to incomplete information or correlation with other variables. For example, Vas et al. [42] and Ru È mmler et al. [62] indicate that UAS vertical approaches had a higher impact than horizontal ones, and [42] indicate that it could be because birds associate the approach with a predator attack, while Ru È mmler et al. [62] suggest that the noise emission in this direction may be higher. The shape of the UAS could also have an influence on its disturbance, as predator-like shapes seem to produce more reactions in birds [63], but more standardized experiments should be performed to fully assess the effect of this variable. The time of the day might also play a role, since at night or dusk ani- mals tend to react with less intensity [64]. From the field of manned-aircraft there is evidence that habitat characteristics and animal behavior before the flight affect their reactions. That is, open habitats favor fleeing responses compared to closed habitats [29] and animals involved in active behaviors are more prone to react to disturbances than passive ones [65]. Currently we have insufficient information on the physiological and long-term conse- quences of UAS disturbances. However, there are some indications that UAS might cause non-visible effects. For example, Ditmer et al. [41] found increases in physiological stress but not behavioral changes in animals subjected to close-distance UAS approaches, as has been also shown in on-foot disturbances [38]. Long-term studies on the use of UAS have not yet been performed; however, according to human disturbance literature (see [21] for a compre- hensive review), the physiological or behavioral stress potentially caused by disturbance may lead to higher energy expenditures, decreases in reproduction and survival, and space-use changes, which might compromise the average fitness or even viability of certain populations. In agreement to this, studies conducted on RC fields indicate that abundant flights may lead to territory abandonment and decreased productivity in sensitive bird species [56,66±68] or to habituation in less sensitive ones [56,69]. We agree on the need of further research (with stan- dardized well-thought study designs) to increase our knowledge on the effect of other factors and on the potential consequences for individuals and populations. However, we believe that the generalized patterns shown here, despite the variability in their design and purpose of the reviewed studies, are a good first step towards a better understanding of the effects of using UAS and the ways to mitigate their impact. Conclusions and management implications UAS constitute a potential new source of anthropogenic disturbance, and the extent to which they elicit wildlife reactions depends on UAS and animal related factors. Smaller UAS, electric engines, and lawn-mower flight patterns generally evoke no disturbance at all or only a short disturbance, the latter comparable to those induced by natural predators. More intense reac- tions are observed in bird species, larger group sizes, and animals in non-breeding stage. AGL or distances at which animals were observed to flee during UAS flights are smaller (usually below 500 m) than for manned aircraft (105 m-15 km [28,70]) and for car approaches (e.g. for ungulates, 50 mÐ2.8 km [71]), and are comparable to those reported for on-foot approaches (e.g. for birds: 0±455 m in [72,73] respectively). UAS also produce less noise and have a smaller size than manned aircraft or cars (see S4 Text for car vs. UAS noise comparison), when flying at average engine power. Hence, the careful use of UAS might be a valuable alternative for methods such as bio-logging, on-foot, and manned aircraft censuses used in biological studies. In contrast, when UAS perform direct and too close approaches to animals or sensitive PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 8 / 14 Drones' impact on wildlife structures such as nests, they can evoke more disturbances to wildlife. Therefore, we recom- mend that UAS flights are avoided unless they constitute the least invasive option for necessary wildlife studies, and discouraged if they are performed just for leisure purposes such as flying or filming. As UAS hobby uses are on the rise, some authorities have established regulatory restrictions to prevent undesirable consequences for fauna based on the precautionary princi- ple. Along this line, the use of UAS has been banned in the United States National Parks [74], in reserves for waterbirds and migratory birds in Switzerland [75] and in London green spaces [76], and some drone pilots have received fines for flying in protected areas [77,78]. We recommend using UAS only in projects where flights are justified and adopting the fol- lowing guidelines (provided with more detail in S5 Text and that can be complemented with the ones suggested by [44]) that are made on the basis of the quantitative analysis we presented here, the qualitative information extracted from the literature reviewed and own experience on UAS flights over wildlife,: 1) Use reliable UAS operated by experienced pilots; 2) Favor low-noise or small UAS against noisier or larger ones; 3) Mount the ground control station 100±300 m away from the study area; 4) Conduct missions as short as possible; 5) Fly at the highest altitude possible; 6) Avoid maneuvers above the animals; 7) Favor lawn-mower flight patterns; 8) Minimize flights over sensitive species or during breeding period; 9) Avoid UAS silhouettes that resemble predator shapes; 10) Avoid close-distance direct approaches and favor indirect ones; 11) Monitor target animals before, during, and after the flight; 12) For nest inspections, fly at times in which eggs/chicks are out of risk; 13) If the flights are around aggressive raptor's territories, perform them at day times when the temperature is low and birds are less prone to fly. These recommendations should be updated when more information is available because, as revealed here, there are many factors that condition the impact of UAS missions. There is an urgent need for providing the appropriate information to UAS users and authorities to mini- mize the impact that these systems can produce on wildlife. This can be done with dissemina- tion campaigns, offering guidelines with the products and cooperating with manufacturers. It would also be advisable to adapt the legal frame so that appropriate actions can be undertaken when wildlife is negatively affected by these emergent practices, specifying drone use on the animal welfare legislation, or including references to wildlife impact on the framework for UAS in the civil aviation regulations. Supporting information S1 Text. PRISMA 2009 checklist. (DOC) S2 Text. Methodology for UAS field campaigns (unpublished data). (DOCX) S3 Text. List of studies/cases used in the analyses. (DOCX) S4 Text. UAS and car sound comparison. (DOCX) S5 Text. Guidelines to minimize UAS impact on wildlife. (DOCX) S1 Table. Results of the GLMMs exploring the factors affecting the probability of observ- ing a reaction in wildlife exposed to UAS flights (Model 1) and the determinants of the PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 9 / 14 Drones' impact on wildlife probability of observing an active vs. a passive response to the UAS (Model 2). (DOCX) S2 Table. Results of the GLMMs investigating the factors determining the AGL (Model 3 and 4) or distance to the fauna (Model 5) at which UAS were when an active response (mostly escape) was observed. (DOCX) S1 Checklist. PRISMA flowchart. (PDF) Acknowledgments Part of this study was conducted within the Projects Aeromab, (Andalusia Government, Proj- ect for Excellence, 2007, P07-RNM-03246) and Planet (European Commission 7th FP Grant   Agreement 257649) granted to JJN, Doñana Biological Station, CSIC. M. Mulero-Pazmany and J. J. Negro thank T. Redondo (DBS-CSIC) for helping on the UAS/car noise data analyses.   We thank L. Jenni, M. Schaad, D. Hayden, H. Schmid and M. Gonzalez Suarez for their com- ments on previous versions of the manuscript. We also thank M.C. Ru È mmler for providing valuable data that we used in this review. Author Contributions Conceptualization: MMP ZT. Data curation: MMP NS ZT. Formal analysis: ZT MMP. Funding acquisition: MMP SJE JJN. Investigation: MMP NS ZT. Methodology: ZT MMP. Project administration: SJE JJN MMP ZT. Resources: JJN. Software: ZT. Supervision: JJN SJE TS. Validation: ZT MMP. Visualization: MMP ZT. Writing ± original draft: MMP SJE NS TS JJN ZT. Writing ± review & editing: MMP SJE NS TS JJN ZT. References 1. Cox TH, Nagy CJ, Skoog MA, Somers IA. Civil UAV capability assessment. Houston, TX, US; 2004. 2. Guillot C. Commercial Drones [Internet]. 2016. http://businessresearcher.sagepub.com/sbr-1775- 98070-2712846/20160118/commercial-drones 3. Getzin S, Wiegand K, Schoning I. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol Evol. 2012; 3: 397±404. https://doi.org/10.1111/j.2041- 210X.2011.00158.x PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 10 / 14 Drones' impact on wildlife 4. Anderson K, Gaston KJ. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ. 2013; 11: 138±146. https://doi.org/10.1890/120150 5. Mulero-Pa  zma  ny M, Barasona JA, Acevedo P, Vicente J, Negro JJ. Unmanned Aircraft Systems com- plement biologging in spatial ecology studies. Ecol Evol. 2015; 5: 4808±4818. https://doi.org/10.1002/ ece3.1744 PMID: 26640661 6. Christie KS, Gilbert SL, Brown CL, Hatfield M, Biology A, Fairbanks A, et al. Unmanned aerial systems in wildlife research: current and future applications of a transformative technology. Front Ecol Environ. 2016; https://doi.org/10.1002/fee.1281 7. Hodgson JC, Baylis SM, Mott R, Herrod A, Clarke RH. Precision wildlife monitoring using unmanned aerial vehicles. Sci Rep. Nature Publishing Group; 2016; 6: 22574. https://doi.org/10.1038/srep22574 PMID: 26986721 8. Vermeulen C, Lejeune P, Lisein J, Sawadogo P, Bouche  P. Unmanned aerial survey of elephants. PLoS One. Public Library of Science; 2013; 8: e54700. https://doi.org/10.1371/journal.pone.0054700 PMID: 23405088 9. Mulero-Pa  zma  ny M, Stolper R, Van Essen LD, Negro JJ, Sassen T. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS One. 2014; 9: 1±10. https://doi.org/10.1371/journal. pone.0083873 PMID: 24416177 10. Mulero-Pa  zma  ny M, Negro JJ, Ferrer M. A low cost way for assessing bird risk hazards in power lines: Fixed-wing small unmanned aircraft systems. J Unmanned Veh Syst. 2014; 2: 5±15. https://doi.org/10. 1139/juvs-2013-0012 11. Clear flight solutions. Robirds falcon drone robotic birds of prey. In: Clear Flight Solutions [Internet]. 2014 [cited 1 Jul 2015] pp. 7±9. http://www.clearflightsolutions.com/ 12. Bird-X. BirdXPeller Remote control drone. In: Bird-X [Internet]. 2015 [cited 6 Aug 2015] pp. 1±2. http:// www.bird-x.com/remote-control-drone-pages-64.php 13. Dayly mail. A-flockalypse Now: Canadians order drone strikes on nuisance geese by equipping remote- controlled copters with speakers to scare off the wild birds. 2015; 1±42. http://www.dailymail.co.uk/ news/article-2399941/Canadians-order-DRONE-strikes-nuisance-geese-away-Petrie-Island.html 14. King LM. Will drones revolutionise ecotourism? J Ecotourism. Routledge; 2014; 13: 85±92. https://doi. org/10.1080/14724049.2014.948448 15. Robofalconry. The Robara. In: Robofalconry [Internet]. 2015 pp. 1±4. http://www.rofalconry.com/page. php?id=4 16. Whittingham D. Falconry Drone Test Flight. In: Youtube [Internet]. 2014 [cited 6 Aug 2015] pp. 16±18. https://www.youtube.com/watch?v=dRGxdOqcPQc 17. Marlin B. Drone Hog Hunting. In: Youtube [Internet]. 2015 [cited 6 Aug 2015] pp. 15±17. https://www. youtube.com/watch?v=UXy3NHijl5M 18. RYOT. Catching A Fish With A Drone !! In: Youtube [Internet]. 2013 [cited 1 Jul 2015]. https://www. youtube.com/watch?v=2hqppfBJmng 19. Garling C. Drone, drone on the range. In: Modern farmer [Internet]. 2011 [cited 15 Jul 2015] pp. 10±13. http://modernfarmer.com/2013/07/drones-drones-on-the-range/ 20. APO-100 FAA. FAA Aerospace forecast. Fiscal years 2016±2036. 2016. 21. Tablado Z, Jenni L. Determinants of uncertainty in wildlife responses to human disturbance. Biol Rev. 2015; n/a±n/a. https://doi.org/10.1111/brv.12224 PMID: 26467755 22. Wolf ID, Croft DB. Minimizing disturbance to wildlife by tourists approaching on foot or in a car: A study of kangaroos in the Australian rangelands. Appl Anim Behav Sci. Elsevier B.V.; 2010; 126: 75±84. https://doi.org/10.1016/j.applanim.2010.06.001 23. Jaeger JAG, Bowman J, Brennan J, Fahrig L, Bert D, Bouchard J, et al. Predicting when animal popula- tions are at risk from roads: an interactive model of road avoidance behavior. Ecol Modell. 2005; 185: 329±348. https://doi.org/10.1016/j.ecolmodel.2004.12.015 24. D'Amico M, Pe  riquet S, Roma  n J, Revilla E. Road avoidance responses determine the impact of hetero- geneous road-networks at regional scale. J Appl Ecol. 2015; https://doi.org/10.1111/1365-2664.12572 25. Cole EK, Pope MD, Anthony RG. Effects of road management on movement and survival of Roosevelt Elk. J Wildl Manage. 1997; 61: 1115±1126. Available: http://www.jstor.org/stable/3802109?seq= 1#page_scan_tab_contents 26. Vos ACC, Chardon JP. Effects of habitat fragmentation and road density on the distribution pattern of the moor frog Rana arvalis. J Appl Ecol. 1998; 35: 44±56. 27. Carr LW, Fahrig L. Effect of road traffic on two amphibian species of differing vagility. Conserv Biol. 2001; 15: 1071±1078. https://doi.org/10.1046/j.1523-1739.2001.0150041071.x PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 11 / 14 Drones' impact on wildlife 28. Efroymson RA, Suter GW. Ecological risk assessment framework for low-altitude aircraft overflights: II. Estimating effects on wildlife. Risk Anal. 2001; 21: 263±74. PMID: 11414536 29. Komenda-Zehnder S, Bruderer B. Einfluss des Flugverkehrs auf die Avifauna. Literaturstudie. Schrif- tenreihe Umwelt. Natur und Lanschaft. Bern, Switzerland; 2002. 30. Bruderer B, Komenda-Zehnder S. Einfluss des Flugverkehrs auf die Avifauna. Schriftenr Umwelt. 2005; 376: 1±100. 31. Stankowich T, Blumstein DT. Fear in animals: a meta-analysis and review of risk assessment. Proc Biol Sci. 2005; 272: 2627±2634. https://doi.org/10.1098/rspb.2005.3251 PMID: 16321785 32. Karp DS, Root TL. Sound the stressor: How Hoatzins (Opisthocomus hoazin) react to ecotourist con- versation. Biodivers Conserv. 2009; 18: 3733±3742. https://doi.org/10.1007/s10531-009-9675-6 33. Cooper WE. Factors affecting risk and cost of escape by the broad-headed skink (Eumeces laticeps): predator speed, directness of approach, and female presence. Herpetologica. 1997; 53: 464±474. 34. Bateman PW, Fleming PA. Who are you looking at? Hadeda ibises use direction of gaze, head orienta- tion and approach speed in their risk assessment of a potential predator. J Zool. 2011; 285: 316±323. https://doi.org/10.1111/j.1469-7998.2011.00846.x 35. DeVault TL, Blackwell BF, Seamans TW, Lima SL, Fernandez-Juricic E. Speed kills: ineffective avian escape responses to oncoming vehicles. Proc R Soc B Biol Sci. 2015; 282: 20142188. https://doi.org/ 10.1098/rspb.2014.2188 PMID: 25567648 36. Bracha D. Freeze, flight, fight, fright, faint: adaptationist perspectives on the acute stress response spectrum. CNS Spectr. 2004; 9: 679±685. PMID: 15337864 37. Espmark Y, Langvatn R. Development and habituation of cardiac and behavioral responses in young red deer calves (Cervus elaphus) exposed to alarm stimuli. J Mammal. 1985; 66: 702±711. 38. Steen JB, Gabrielsen GW, Kanwisher JW. Physiological aspects of freezing behaviour in willow ptarmi- gan hens. Acta Physiol Scand. 1988; 299±304. https://doi.org/10.1111/j.1748-1716.1988.tb08493.x PMID: 3227948 39. Albrecht T, Klvaňa P. Nest crypsis, reproductive value of a clutch and escape decisions in incubating female mallards anas platyrhynchos. Ethology. 2004; 110: 603±613. https://doi.org/10.1111/j.1439- 0310.2004.00992.x 40. Cartledge VA, Jones SM. Does adrenal responsiveness vary with sex and reproductive status in Eger- nia whitii, a viviparous skink? Gen Comp Endocrinol. 2007; 150: 132±139. https://doi.org/10.1016/j. ygcen.2006.07.021 PMID: 16979639 41. Ditmer MA, Vincent JB, Werden LK, Tanner JC, Laske TG, Iaizzo PA, et al. Bears show a physiological but limited behavioral response to Unmanned Aerial Vehicles. Curr Biol. Elsevier Ltd; 2015; 25: 2278± 2283. http://dx.doi.org/10.1016/j.cub.2015.07.024 Figure 42. Vas E, Lescro È el A, Duriez O, Boguszewski G, Gre  millet D. Approaching birds with drones : first experi- ments and ethical guidelines. Biol Lett. 2015; 11: 20140754. https://doi.org/10.1098/rsbl.2014.0754 PMID: 25652220 43. Smith C, Sykora-Bodie S, Bloodworth B, Pack S, Spradlin T, LeBoeuf N. Assessment of known impacts of unmanned aerial systems (UAS) on marine mammals: data gaps and recommendations for researchers in the United States. Jounal Unmanned Veh Syst. 2015;in press. 44. Hodgson JC, Koh LP. Best practice for minimising unmanned aerial vehicle disturbance to wildlife in bio- logical field research. Curr Biol. Elsevier; 2016; 26: R404±R405. https://doi.org/10.1016/j.cub.2016.04. 001 PMID: 27218843 45. Barroso J, Gollop CJ, Sandelowski M, Meynell J, Pearce PF, Collins LJ. The challenges of searching for and retrieving qualitative studies. West J Nurs Res. 2003; 25: 153±178. https://doi.org/10.1177/ 0193945902250034 PMID: 12666641 46. Simcock P. One of society's most vulnerable groups? A systematically conducted literature review exploring the vulnerability of deafblind people. Health Soc Care Community. 2016; 47. del Hoyo J, Elliot A, Sargatal J, Christie DA. Handbook of the birds of the world. Lynx, editor. Barcelona, Spain; 48. Development Core Team. R. 2013. 49. Bates D, Maechler M, Bolker B, Walker S. lme4: Linear mixed-effects models using S4 classes. R pack- age version 0.999999±0. 2012. 50. Gelman A, Hill J. Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge Uni- versity Press, editor. New York, USA; 2006. 51. Frid A. Dall's sheep responses to overflights by helicopter and fixed-wing aircraft. Biol Conserv. 2003; 110: 387±399. https://doi.org/10.1016/S0006-3207(02)00236-7 PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 12 / 14 Drones' impact on wildlife 52. Lima SL, Blackwell BF, Devault TL, Ferna  ndez-Juricic E. Animal reactions to oncoming vehicles: A con- ceptual review. Biol Rev. 2015; 90: 60±76. https://doi.org/10.1111/brv.12093 PMID: 24661508 53. Stankowich T, Blumstein DT. Fear in animals: a meta-analysis and review of risk assessment. Proc R Soc Biol Sci. 2005; 272: 2627±34. https://doi.org/10.1098/rspb.2005.3251 PMID: 16321785 54. Hodgson A, Kelly N, Peel D. Unmanned Aerial Vehicles (UAVs) for surveying marine fauna: a dugong case study. Fenton B, editor. PLoS One. 2013; 8: e79556. https://doi.org/10.1371/journal.pone. 0079556 PMID: 24223967 55. Shannon G, McKenna MF, Angeloni LM, Crooks KR, Fristrup KM, Brown E, et al. A synthesis of two decades of research documenting the effects of noise on wildlife. Biol Rev. 2015; https://doi.org/10. 1111/brv.12207 PMID: 26118691 56. Kempf N, Hu È ppop O. What effect do airplanes have on birds?±a summary and update of ªWie wirken Flugzeuge auf Vo È gel?, Naturschutz und Lanschaftsplanung, 30±1 (17±28). 1998º [Internet]. 1998. https://www.google.ch/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved= 0CB8QFjAAahUKEwiMtb79uvvGAhXFDiwKHQ2NCaw&url=http://www.fai.org/component/ phocadownload/category/1107-air-sports-and-wildlife?download=2924:lan-3-1-aircr 57. Jones IV GP. The feasibility of using Small Unmanned Aerial Vehicles for wildlife research. University of Florida. 2003. 58. Jones G, Pearlstine L, Percival H. An assessment of small unmanned aerial vehicles for wildlife research. Wildl Soc Bull. 2006; 34: 750±758. 59. Cunningham KW. Augmenting steller sea lion surveys in the Western Aleutians with Unmanned Air- craft.±Project number 1120. Fairbanks AK; 2014. 60. Kiltie RA. Scaling of visual acuity with body size in mammals and birds. Funct Ecol. 2000; 14: 226±234. https://doi.org/10.1046/j.1365-2435.2000.00404.x 61. Weissensteiner MH, Poelstra JW, Wolf JBW. Low-budget ready-to-fly unmanned aerial vehicles: an effective tool for evaluating the nesting status of canopy-breeding bird species. J Avian Biol. 2015; 46: 425±430. https://doi.org/10.1111/jav.00619 62. Ru È mmler M-C, Mustafa O, Maercker J, Peter H-U, Esefeld J. Measuring the influence of unmanned aerial vehicles on Ade  lie penguins. Polar Biol. 2016; 39: 1329±1334. https://doi.org/10.1007/s00300- 015-1838-1 63. McEvoy JF, Hall GP, McDonald PG. Evaluation of unmanned aerial vehicle shape, flight path and cam- era type for waterfowl surveys: disturbance effects and species recognition. PeerJ. 2016; 4: e1831. https://doi.org/10.7717/peerj.1831 PMID: 27020132 64. Bivings AE. Advantages and limitations of Radio-Controller aircraft in bird dispersal. Bird Strike Commit- tee Europe 19. Madrid, Spain; 1988. pp. 481±487. 65. Tracey JP, Fleming PJSS. Behavioural responses of feral goats (Capra hircus) to helicopters. Appl Anim Behav Sci. 2007; 108: 114±128. https://doi.org/10.1016/j.applanim.2006.10.009 66. Von Opitz H. Brutvorkommen, Gefa È hrdung und Schutz des Grossen Brachvogels. Beih Vero È ffentlichun- gen Natur-und Landschaftspfl Baden-Wu È rttemberg. 1975; 7: 65±67. 67. Putzer D. Wirkung und Wichtung menschlicher Anwesenheit und Sto È rung am Beispiel bestandsbedroh- ter, an Feuchtgebiete gebundener Vogelarten. Lanschatspfl u Naturschutz. 1989; 29: 169±194. 68. Boscher M. Auswirkungen von Modellflug und Straûenverkehr auf die Raumnutzung beim Grossen Brachvogel (Numenius arquata). Z Okologie u Naturschutz. 1993; 2: 11±18. 69. Strebel N, Roth T, Weber D. Die Existenz des Modellflugplatzes in der Selzacherwiti beeinflusst die Ver- teilung der Feldlerchen Alauda arvensis wa È hrend der Brutzeit nicht. Der Ornithol Beobachter. 2016; 113: 53±60. 70. Delaney D, Grubb T, Beier P, Pater LL, Reiser MH. Effects of helicopter noise on Mexican spotted owls. J Wildl Manage. 1999; 63: 60±76. 71. Alves C, Bager A. Review of the factors undelying the mechanisms and effects of roads on vertebrates. Oecologia Aust. 2013; 17: 208±221. 72. Yorio P, Boersma PD. The effects of human disturbance on Magellanic Penguin Spheniscus magellani- cus behaviour and breeding success. Bird Conserv Int. 1992; 2: 161±173. https://doi.org/10.1017/ S0959270900002410 73. Triplet P, Mequin N, Sueur F. Prendre en compte la distance d'envol n'est pas suffisant pour assurer la quieude des oiseaux en milieu littoral. Alauda. 2007; 75(3): 237±242. 74. US National Parks. Drone harasses bighorn sheep at Zion National Park. In: Park Alerts [Internet]. 2014 [cited 10 Jun 2015]. http://www.nps.gov/zion/learn/news/droneharassesbhs.htm 75. Swiss Federal Council. Verordnung u È ber die Wasser- und Zugvogelreservate von internationaler und nationaler Bedeutung (WZVV). 2015. PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 13 / 14 Drones' impact on wildlife 76. de Peyer R. Drones are banned from Royal Parks amid ªfears over impact on wildlife and visitor safetyº. London evening standard. London, UK; 2015: 1±4. http://www.standard.co.uk/news/london/drones- banned-from-royal-parks-amid-fears-over-impact-on-wildlife-and-visitor-safety-10095538.html 77. Clancy A. Two drone pilots ticketed for ªOrca Protection Violations.º In: sUAS News [Internet]. 2015 [cited 1 Sep 2015] pp. 8±10. http://www.suasnews.com/2015/08/38088/two-drone-pilots-ticketed-for- orca-protection-violations/?utm_source=sUAS+News+Daily&utm_campaign=4acda86862-RSS_ EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_b3c0776dde-4acda86862-295623153&mc_ cid=4acda86862&mc_eid=f849 78. ETN Travel News. Tourists in Kenya warned not to fly drones. In: SUAS News [Internet]. 2015 [cited 17 Aug 2015] pp. 1±5. http://www.uasvision.com/2015/01/20/tourists-in-kenya-warned-not-to-fly-drones/? utm_term=0_799756aeb7-5ab6a8ef1b-267637753&utm_campaign=5ab6a8ef1b-RSS_EMAIL_ CAMPAIGN&utm_medium=email&utm_source=Newsletter PLOS ONE | https://doi.org/10.1371/journal.pone.0178448 June 21, 2017 14 / 14

Journal

PLoS ONEPubmed Central

Published: Jun 21, 2017

References