Ecosystems worldwide are currently experiencing a dramatic species extinction process, which has been largely attributed to human activities (Harrop 2011, Ceballos et al. 2015). Recognizing the critical situation, several international conventions have been implemented with the aim to halt the biodiversity crisis and support conservation measures (e.g. Convention on Biological Diversity, Bonn Convention on the Conservation of Migratory Species of Wild Animals, Bern Convention on the Conservation of European Wildlife and Natural Habitats, Convention on International Trade in Endangered Species of Wild Fauna and Flora). These conservation efforts depend on accurate data on species distribution, population size and impact of global changes, and it is therefore necessary to continuously monitor populations of various plant and animal species.
In the noble pursuit of knowledge that is important for preserving wildlife populations, scientists can unfortunately inflict distress on animals, because wildlife biodiversity monitoring has traditionally employed some invasive or even destructive techniques (Vucetich and Nelson 2007, Minteer et al. 2014b, Field et al. 2019). Examples of research activities that might influence animal welfare are chasing, capturing, blood and tissue sampling, marking, attachment of data loggers and lethal sampling (Donnelly et al. 1994, Sutherland et al. 2004, Wilson and McMahon 2006, Walker et al. 2010). The impact on animal welfare is a problem not only for the affected animal but also for the reliability of study results (Powell and Proulx 2003, Cattet 2013, Jewell 2013). It has been shown that pain negatively affects data quality through behavioural, physiological and neurobiological changes (Jirkof 2017, Sneddon 2017). Because sound information about wildlife is of uttermost importance for sound management decisions, it is crucial that the research procedures affect animal welfare as little as possible.
An important milestone in thinking about animal welfare in research was achieved 60 years ago, when Russell and Burch proposed the 3Rs principles (replace, reduce, refine; Russell and Burch 1959). These principles encourage scientists to replace the use of animals with alternative methods whenever possible, to reduce the number of animals in experiments to the absolute minimum, and to refine or limit the pain and distress that animals are exposed to. The 3Rs have since become an integral part of legislation and guidelines on animal experiments in many countries (Sneddon et al. 2017) and the research community itself has encouraged the development of guidelines to improve the use of animals for scientific purposes (Kilkenny et al. 2010, Buchanan et al. 2015, Mellor 2016). While the 3Rs principles were originally proposed for laboratory animals, they can be – and should be – applied also in wildlife research. One example is the use of non-invasive research methods, i.e. methods that do not affect the physical integrity of the animal (Lefort et al. 2019). Some non-invasive methods do not even require capturing and handling. Even though efforts have been made to support the process of incorporating the 3Rs into wildlife research (NORECOPA 2008, Lindsjö et al. 2016, Field et al. 2019, Sloman et al. 2019), there have been recently published several articles that indicate that wildlife biologists may be struggling with implementing these principles (Costello et al. 2016, Waugh and Monamy 2016, Russo et al. 2017, Lindsjö et al. 2019, Zemanova 2019).
In order to encourage the implementation of the 3Rs principles in wildlife research, I carried out a review of the literature on the potential impact of research methods on animal welfare, and provide here specific examples where the 3Rs principles have been successfully applied.
Methods
I derived my synthesis based on published journal articles and books. Specifically, I searched for relevant literature on the Web of Science and Google Scholar until March 2019 and also used references cited in the papers I found. For publications on impact of research methods on animal welfare, my search strings consisted of: Topic=(“welfare” OR “impact” OR “effect” OR “detrimental” OR “lethal” OR “survival”) AND “wildlife” OR “ecology” AND (“method” OR “technique”). To identify publications describing available non-invasive techniques and their implementation, I used the search terms: Topic=(“non-invasive” OR “nonlethal” OR “non-destructive” OR “alternative” OR “replacement” OR “reduction” OR “refinement” OR “improved”) AND “wildlife” OR “ecology” AND (“method” OR “technique”). I excluded publications in which content was not relevant to this synthesis.
Potential impact of commonly used methods in wildlife research on animal welfare
Capturing, trapping and experiments in captivity
In contrast with laboratory animals, wildlife animals are not used to interaction with humans, so any capture or handling can be very stressful (Wilson and McMahon 2006). Increased cortisol levels have been reported for example in captured Weddell seals Leptonychotes weddellii (Harcourt et al. 2010). Cattet et al. (2008) showed that grizzly bears Ursus arctos that have been repeatedly captured significantly differed in their body condition compared with bears that have been captured only once. Nest survival in captured seabirds, yellow-billed loon Gavia adamsii and pacific loon G. pacifica, was 30% lower than in non-captured adults (Uher-Koch et al. 2015). Stress of capture can even lead to capture myopathy, a metabolic muscle disease that often results in death (Nuvoli et al. 2014, Green-Barber et al. 2018).
Capturing can also change the animal's behaviour, which then significantly affects data collected in behavioural and recapture studies. For instance, polar bears Ursus maritimus in the study by Rode et al. (2014) displayed reduction in activity and movement rates 3.5 days post-capture. Linhart et al. (2012) showed that willow warblers Phylloscopus trochilus could recall the capture event by mist netting even a year later and learn to avoid mist nests.
Apart from stress and impact on behaviour, capturing can also result in physical damages. These damages can range from skin abrasions to broken limbs (Phillips et al. 1996, Fleming et al. 1998, Grisham et al. 2015). Trapped animals are also vulnerable to predation (Hilario et al. 2017).
Many experiments on behavior and cognition in animals take place in captive settings. While these conditions allow for logistical control, they often lead to harms for the captive animals (Marino and Frohoff 2011). The damaging effects of captivity have been well documented. Animals can exhibit stereotyped behavior (Wechsler 1991, Callard et al. 2000, Shyne 2006, Jett et al. 2017, Poirier and Bateson 2017, Williams et al. 2018a), and suffer from increased stress (Bordeleau et al. 2018, Ferreira et al. 2018), which can eventually result in higher incidence of diseases and mortality (Terio et al. 2004, Mitchell et al. 2018).
Marking
Research on wildlife often requires marking of animals to obtain data on behaviour, survival, reproduction or home range size. Virtually all marking methods require capture, which is stressful to wild animals, and many methods also involve tissue damage. Common marking techniques include for instance hot- or freeze-branding, mutilations, tags and bands, and the use of radio-transmitters.
Branding
Hot-branding and freeze-branding have been used for marking cattle Bos taurus and horses Equus caballus for centuries (Macpherson and Penner 1967), and have been modified for marking pinnipeds. Not surprisingly, hot-branding is a painful procedure, reflected in the behavioural changes of branded Stellar sea lions Eumetopias jubatus (Walker et al. 2010). Public concerns about animal welfare have resulted in lawsuits and withdrawal of research permits for sea lion research involving hot-branding (Dalton 2005). Moreover, the development of skin tumours following freeze- or hot-branding has been observed in cattle (Yeruham et al. 1996), raising caution for wildlife branding.
Tags and bands
Another common method of marking animals is with tags or bands. Tags can be made from a variety of materials – most commonly metal or plastic – and are usually augmented by alphanumeric codes for individual or group recognition. Larger animals often require immobilization before marking and attaching tags, and the procedure of tag attachment can be painful (Cramer 2017, MacRae et al. 2018). In small animals, for instance, fish, tagging can affect the survival rate (Burdick 2011, Hoye et al. 2015).
Tags can be applied to many different parts of the body depending on the anatomy of the animal, most often to wings (Trefry et al. 2013), fins (Sonne et al. 2012) or flippers (Hazekamp et al. 2010). The study by Robinson and Jones (2014) revealed that tagged seabirds, crested auklets Aethia cristatella, showed reduced return rates and provisioning behaviour. Tags can increase the cost of swimming due to drag in grey seals Halichoerus grypus (Hazekamp et al. 2010), and tagged Magellanic penguins Spheniscus magellanicus have been observed to experience foraging difficulties during low food abundance periods (Wilson et al. 2015). Moreover, tags damaged flippers of Adélie penguins Pygoscelis adeliae (Jackson and Wilson 2002), and modified diving behaviour and decreased survival in the first year after banding in little penguins Eudyptula minor (Fallow et al. 2009).
Radio-transmitters
Radiotelemetry has been key to track the movement of animals. This method uses the transmission of radio signals to locate a radio-transmitter that has been attached to an animal. Radio-transmitters can be glued to the skin, designed as a GPS collar or a harness, or surgically implanted. To track mule deer Odocoileus hemionus, elk Cervus elaphus nelsoni and moose Alces alces females, even vaginal implant transmitters are being used (Bishop et al. 2007, Barbknecht et al. 2009, Thompson et al. 2018).
Several issues have been identified with radio-transmitters. For instance, Dixon et al. (2016) found evidence of decreased survival rate associated with harness-mounted satellite transmitters on falcons Falco cherrug. In passerine birds, both entanglement with vegetation or body parts and non-entanglement injuries have been observed (Hill and Elphick 2011).
Another issue is the method of attachment and weight of the instrument. If the radio-transmitter is attached by glue, this can lead to lesions and abrasions on the skin (Field et al. 2012). The study by Rasiulis et al. (2014) showed that heavy collars decreased survival rate in caribou Rangifer tarandus, and by Brooks et al. (2008) that grazing behavior of Burchell's zebras Equus burchelli antiquorum is affected by collar weight.
Particularly problematic is the use of implanted transmitters as this involves additional trauma to the animal. The recent study by Arnemo et al. (2018) showed that transmitters implanted into the abdominal cavities of brown bears Ursus arctos performed poorly and were not biocompatible, in several cases causing the animal's death. Several cases of mortality caused by implanted radio-transmitters have been reported also in European lynx Lynx lynx (Lechenne et al. 2012), Harlequin ducks Histrionicus histrionicus (Mulcahy and Esler 1999) and American badgers Taxidea taxus (Quinn et al. 2010).
Blood and tissue sampling
Genetic tools have become indispensable for biodiversity assessment and monitoring (Stetz et al. 2011). Genetics is important to assess abundance, occupancy, hybridization, genetic diversity, population structure and effective population size (Stetz et al. 2011, Carroll et al. 2018). Common methods used for DNA collection are blood and non-lethal tissue sampling, such as toe-clipping or fin-clipping. Blood is also commonly used for assessing levels of potential detrimental elements, such as heavy metals, and in physiology studies to assess hormonal levels (Bryan et al. 2007, Berglund 2018). Blood sampling could be however difficult in small animals, such as zebrafish Danio rerio (Zang et al. 2013), and has been even linked to lower survival rates during the first year after sampling in Amarican cliff swallows Petrochelidon pyrrhonota (Brown and Brown 2009). Fin-clipping has been shown to be painful for fish, common carp Cyprinus carpio and Atlantic salmon Salmo salar, and may affect their survival (Hansen 1988, Roques et al. 2010).
Lethal sampling
The use of lethal means for tissue sampling and collection of voucher specimens has a long tradition in wildlife research. Besides the obvious harm to the individual animal, removing a key member of the group in species with complex social formations can result in impaired well-being of the remaining individuals (Shannon et al. 2013). Moreover, lethal methods are unfortunately often used even in cases when this is not necessary, such as in gathering data on abundance, DNA sampling or dietary analysis (Vucetich and Nelson 2007, Hammerschlag and Sulikowski 2011, Costello et al. 2016, Russo et al. 2017, Zemanova 2019).
Application of the 3Rs into wildlife research
There is a significant difference between research on laboratory animals and on wildlife in that the former is used as models for humans, for example, in testing toxicity or effectiveness of new drugs. Wildlife research, on the other hand, focuses on the study animal itself, in order to understand its biology, behavior and health. Moreover, wildlife encompasses a very broad range of species with different ecological and physiological traits, which makes generalizations of guidelines challenging. Nevertheless, the 3Rs principles can be applied to wildlife research in several ways.
Replacement may not be always possible, because the animals are the objects of the study. However, individual identification with natural marking, use of camera traps, or non-invasive sampling can provide data without the necessity of handling an animal (Fig. 1, Table 1).
Figure 1.
Implementation of the 3Rs principles (replacement, reduction, refinement) in wildlife research. Overlapping methods for replacement and refinement depend on whether the animal has to be captured or not. Please see Table 1–3 for more details.
Reduction (Fig. 1, Table 2) can be achieved, for example, through efficient experimental design and planning, calculating the minimum sample size, avoiding repetition through meta-analyses of previously published studies, sharing data and resources (NC3Rs 2018). Individual animals can also be used for multiple purposes – for instance by combining capture–mark–recapture and genotyping studies (Lampert et al. 2003). Another strategy for reduction is the implementation of in silico methods, which could be used for species distribution, population modelling in response to climate change or disease spread predictions (Smith and Cheeseman 2002, Zemanova et al. 2018).
Non-invasive methods can be considered as replacement, but also a part of reduction and refinement strategies (Lindsjo et al. 2016), depending on how the methods are used (Fig. 1, Table 1–3). Refinement (Table 3) includes, for example, the use anesthesia, tranquilization and light-weight radio-transmitters (Harcourt et al. 2010, McGuire et al. 2014). For minimum injuries and capture of non-target species, it has been recommended to use call playback and taxidermy decoys (Veltheim et al. 2015), and to use traps that have been shown to cause no or minimal injuries, for instance, replacing foothold traps with box traps (Kolbe et al. 2003, Bergvall et al. 2017).
Alternatives to capturing and trapping
Many data that used to require trapping can nowadays be achieved by different means. For instance, the presence/ absence data can be collected using camera traps or drones, and DNA can be sourced from hair traps or faeces.
Camera traps
Camera traps can be applied to estimate species richness, habitat occupancy, population density or behavior, with little effort by the researcher (Di Cerbo and Biancardi 2013). The absence of a researcher is particularly beneficial in the study of wild primates, where habituation to human presence could be detrimental due to threat of hunting (Bezerra et al. 2014). Camera traps can be even a more efficient method of detection than other methods, such as hair traps, cage traps or scat count surveys (Monterroso et al. 2014, Welbourne et al. 2015, Day et al. 2016). This efficiency improves when using a lure or bait (Boulerice and Van Fleet 2016, McLean et al. 2017). Modern camera traps can record also videos that can be used in behavioural studies (Lobo et al. 2013, Flagel et al. 2016).
Camera traps have been used for detection of many species, including small terrestrial and arboreal mammals such as red and eastern grey squirrels, Sciurus vulgaris and S. carolinensis (Di Cerbo and Biancardi 2013), foxes Vulpes velox (Stratman and Apker 2014), feral cats and European wildcats, Felis catus and F. silvestris (Anile et al. 2014, Stokeld et al. 2015), dogs Canis familiaris (Rasambainarivo et al. 2017), Hermann's tortoises Testudo hermanni (Ballouard et al. 2016), northern flying squirrels Glaucomys sabrinus (Boulerice and Van Fleet 2016), North American river otters Lontra canadensis (Day et al. 2016) and grey wolves Canis lupus (Sver et al. 2016). To estimate density, individuals are marked or identified by natural markings (Jordan et al. 2011, Thornton and Pekins 2015).
Table 1.
Examples of studies implementing the 3Rs principle of Replacement. See Fig. 1 and the main text for more detail.
Drones
One recent technological advance applied in wildlife monitoring has been the unmanned aerial vehicles, also known as drones. Drones are particularly useful in approaching sensitive wildlife in inaccessible areas. Some studies revealed that drone-derived data are more accurate than data from ground-counting methods (Ezat et al. 2018, Hodgson et al. 2018). Moreover, drone technology can be cheaper than radio-collars (Mulero-Pazmany et al. 2015). Drones have been successfully implemented in monitoring populations of polar bears Ursus maritimus (Barnas et al. 2018b), saltwater crocodiles Crocodylus porosus (Bevan et al. 2018, Ezat et al. 2018), or snow geese Anser caerulescens (Barnas et al. 2018a). Drones can be also used for collecting exhaled breath condensate of humpback whales Megaptera novaeangliae for microbiome analysis (Apprill et al. 2017).
Alternatives to invasive marking
Dyes
For short-term studies, paint can be used to mark individual animals, as has been demonstrated in studies on lizards, Anolis cristatellus, A. gundlachi, A. krugi and Sceloporus undulates (Johnson 2005) or rainbow trout Oncorhynchus mykiss (Frenkel et al. 2002). Birds can be marked with dyes placed on eggs or nests (Cramer 2017).
Natural markings
For identification of individual animals, natural markings can be used, such as unique patterns and scars, fungal patches or pelage markings (Vincent et al. 2001, Maniscalco et al. 2006, Li et al. 2009). Identification based on natural markings has been successfully implemented in studies on e.g. fish (Arzoumanian et al. 2005, Meekan et al. 2006, Auger-Methe et al. 2011, Martin-Smith 2011, Correia et al. 2014, Monteiro et al. 2014, Gonzalez-Ramos et al. 2017), Indo-Pacific bottlenose dolphins Tursiops aduncus (Gomez-Salazar et al. 2011, Bichell et al. 2018), sperm whales Physeter macrocephalus (Alessi et al. 2014), Asian black bears Ursus thibetanus (Higashide et al. 2012), polar bears Ursus maritimus (Anderson et al. 2007), Australian sea lions Neophoca cinerea (Osterrieder et al. 2015), cougars Puma concolor (Alexander and Gese 2018), tigers Panthera tigris (Karanth et al. 2006), cheetahs Acinonyx jubatus (Kelly 2001), giant pandas Ailuropoda melanoleuca (Zheng et al. 2016), salamanders, Eurycea tonkawae, Ambystoma opacum and Salamandrina perspicillata (Gamble et al. 2008, Bendik et al. 2013, Romiti et al. 2017), crustaceans Rhynchocinetes typus and Chionoecetes opilio (Gallardo-Escarate et al. 2007, Gosselin et al. 2007), manatees Trichechus manatus latirostris (Langtimm et al. 2004), Majorcan midwife toads Alytes muletensis (Pinya and Perez-Mellado 2009), common European vipers Vipera berus (Bauwens et al. 2018), green sea turtles Chelonia mydas (Gatto et al. 2018), wunderpus octopuses Wunderpus photogenicus (Huffard et al. 2008), little brown bats Myotis lucifugus (Amelon et al. 2017), jewelled geckos Naultinus gemmeus (Knox et al. 2013), newts Ichthyosaura alpestris and Lissotriton vulgaris (Mettouris et al. 2016), and even beetles Lucanus cervus, Rosalia alpina and Rhynchophorus ferrugineus (Caci et al. 2013, Romiti et al. 2017, Diaz-Calafat et al. 2018).
Table 2.
Examples of studies implementing the 3Rs principle of Reduction. See Fig. 1 and the main text for more detail.
Identification by footprints
Some mammal species can leave signs that are sufficiently distinctive for identification purposes. Footprints have been used as a tracking method for millennia (Pimm et al. 2015), and current specialized software allows for individual, sex and age group classification with more than 90% accuracy (Jewell et al. 2016). Shape and size of footprints was used to identify individual white rhinos Ceratotherium simum (Alibhai et al. 2008, Law et al. 2013), fishers Martes pennanti (Herzog et al. 2007), giant pandas Ailuropoda melanoleuca (Li et al. 2018), tigers Panthera tigris (Gu et al. 2014) or South American tapirs Tapirus terrestris (Moreira et al. 2018).
Table 3.
Examples of studies implementing the 3Rs principle of Refinement. See Fig. 1 and the main text for more detail.
Vocal individuality
Instead of marking, individual animals of certain species can be distinguished by their vocalization features (Terry et al. 2005). This method has been successfully applied not only in birds, such as the great grey owl Strix nebulosa (Rognan et al. 2009), but also in marmots Marmota olympus and Richardson's ground squirrels Spermophilus richardsonii (Pollard et al. 2010).
Alternatives to invasive blood and tissue sampling
Ecotoxicology
Improving our knowledge of the potential impacts of chemical pollutants on wildlife is an important aspect of biological conservation. Unfortunately, traditional methods of obtaining samples in ecotoxicology are invasive (Jasinska et al. 2015, Wilkie et al. 2018, Boisvert et al. 2019, Xing et al. 2019, da Costa Araujo et al. 2020), and research on non-destructive methods is severely lacking (Chaousis et al. 2018).
Nevertheless, non-invasive methods have already been applied in several ecotoxicology studies. For instance, heavy metals can be determined from hair samples, which was done in wood mice Apodemus sylvaticus (Tete et al. 2014), brown rats Rattus norvegicus (McLean et al. 2009), bats Artibeus spp., Myotis bechsteinii, Myotis daubentonii, Myotis myotis and Pipistrellus pipistrellus (Flache et al. 2015, Becker et al. 2018) or European hedgehogs Erinaceus europaeus (Vermeulen et al. 2009). Heavy metals can be also detected in faeces (Afonso et al. 2016, Berglund 2018). Mingo et al. (2017) were able to detect pesticide exposure in common wall lizards Podarcis muralis measured through buccal swabs. The in silico modelling of toxicity pathways has been recently applied to constructing adverse outcomes in wildlife (Madden et al. 2014).
Physiology
Chronic stress can have potentially deleterious effects (please see above for more details). Stress can be quantified by measuring the level of glucocorticoids, a class of steroid hormones (Millspaugh and Washburn 2004). Glucocorticoid levels used to be typically assessed from blood (Hood et al. 1998, Mathies et al. 2001), but this often requires the capture of the animal, which could influence the results. An additional drawback of blood samples is that they may not represent long-term hormone levels (Millspaugh and Washburn 2004).
A non-invasive alternative is the use of faecal samples. Studies in which faecal glucocorticoids were assessed were conducted in elks Cervus elaphus (Millspaugh et al. 2001), mourning doves Zenaida macroura (Washburn et al. 2003), greater sage grouse Centrocercus urophasianus (Jankowski et al. 2009), African bush elephants Loxodonta africana (Munshi-South et al. 2008, Ahlering et al. 2013), Columbian ground squirrels Urocitellus columbianus (Bosson et al. 2009), common degus Octodon degus (Soto-Gamboa et al. 2009), giant pandas Ailuropoda melanoleuca (Yu et al. 2011), aardwolves Proteles cristata (Ganswindt et al. 2012), eastern chipmunks Tamias striatus (Montiglio et al. 2012), coyotes Canis latrans (Schell et al. 2013), crab-eating foxes Cerdocyoun thous (Paz et al. 2015), marmots Marmota flaviventris (Wey et al. 2015), woylies Bettongia penicillata (Hing et al. 2017), pikas Ochotona princeps (Wilkening et al. 2016), North Atlantic right whales Eubalaena glacialis (Hunt et al. 2006) or primates (Behringer and Deschner 2017). In frogs, dermal swabs (Santymire et al. 2018) or urine samples (Narayan et al. 2010, Narayan 2013) collected by gentle massage of the lower abdomen can be used for the analysis.
Due to their small molecular weight and lipid solubility, glucocorticoids pass quickly from blood serum to saliva, where it can be directly measured (Romano et al. 2010). Stress assessment from saliva has been implemented in Indian rhinoceros Rhinoceros unicornis (Gomez et al. 2004), and rhesus macaques Macaca mulatta (Higham et al. 2010).
One of the latest methods of non-invasive sampling is body odour collection. A wide range of volatile and semi-volatile organic compounds create chemical profiles, which can be used in studies on chemical signatures of health as well as kinship, diet and reproduction (Nair et al. 2018, Weiss et al. 2018a, b).
DNA sampling
Non-invasive genetic sampling has a great potential in wildlife biology, with a variety of applications (Waits and Paetkau 2005). Advancements in forensics, medical research and ancient DNA techniques generate new methods that can be relatively easily applied to improve data production and analysis of non-invasive genetic samples also in wildlife research (Beja-Pereira et al. 2009).
Faecal DNA-based sampling to identify individuals and estimate the population size was implemented in e.g. Asian elephants Elephas maximus (Gray et al. 2014), mountain gorillas Gorilla beringei beringei (Roy et al. 2014), Indian rhinoceros Rhinoceros unicornis (Das et al. 2015), Cabrera's voles Microtus cabrerae (Proença-Ferreira et al. 2019), African golden wolves Canis anthus (Karssene et al. 2018), kit foxes Vulpes macrotis mutica (Wilbert et al. 2015) or birds Apteryx spp., Otis tarda, Tetrao urogallus (Idaghdour et al. 2003, Perez et al. 2011, Rosner et al. 2014, Ramon-Laca et al. 2018, Vallant et al. 2018). Faecal DNA has been also used for identifying insects like Bombus spp. and Ceutorhynchus assimilis (Fumanal et al. 2005, Scriven et al. 2013) and spiders Pardosa spp. (Sint et al. 2015). To direct the survey efforts detection dogs may be used for locating faecal samples (Arandjelovic et al. 2015, Wilbert et al. 2015).
Another method of non-invasive genetic sampling is hair trapping. Hair can be collected either by catching an animal and plugging the hair, with baited methods, or passively through natural rubs or travel route snares. Baited methods of hair collection can be divided into four main types: 1) hair corrals with barbed wire encircling a bait, 2) rub stations, which are structures saturated with scent to induce rubbing, 3) trees wrapped with barbed wire or 4) boxes or tubes containing attractants and fitted with hair snaring devices (Kendall and McKelvey 2012). Baited hair collection has been often applied in large carnivores (Canis latrans, C. lupus, Puma concolor) (Ausband et al. 2011, Sawaya et al. 2011). Another method was introduced by Keeley and Keeley (2012), who developed a modified blowgun dart with sticky ends to collect hair from variegated squirrels Sciurus variegatoides without penetrating their skin.
An increasingly common non-invasive genetic sampling technique used primarily in frogs is buccal swabbing (Broquet et al. 2007, Angelone and Holderegger 2009, Gallardo et al. 2012). This method can be however applied also to other types of animals, for instance, birds (Leuconotopicus borealis) (Vilstrup et al. 2018), lizards (Coronella austriaca, Lacerta agilis, Podarcis muralis) (Beebee 2008, Schulte et al. 2011) and fish (Clinostomus elongates, Percina copelandi) (Reid et al. 2012). However, buccal swabbing may not be safe for certain reptiles, such as tortoises (due to their head retraction escape response) and snakes. In these species, cloacal swabbing can be used instead (Mucci et al. 2014, Ford et al. 2017).
Alternatively, skin and mucus swabbing can be implemented. Skin swabbing drastically limits handling in comparison to buccal swabbing, and it is particularly useful for vulnerable and small animals, which was shown in alpine newts Ichthyosaura alpestris (Prunier et al. 2012), fire salamanders Salamandra salamandra (Pichlmuller et al. 2013) and Sierra Nevada yellow-legged frogs Rana sierrae (Poorten et al. 2017). Wing swabbing has been successfully used for DNA collection in bats (Myotis evotis, M. septentrionalis, M. yumanensis, M. lucifugus) (Player et al. 2017). Mucus swabbing has been used to collect DNA in cephalopods (Enteroctopus dofleini, Sepia officinalis) (Hollenbeck et al. 2017, Sykes et al. 2017), land snails (Arianta arbustorum) (Armbruster et al. 2005) and slugs (Arion spp., Geomalacus maculosus) (Morinha et al. 2014), intertidal snails (Nucella spp.) (Kawai et al. 2004), polyplacophoran molluscs (Ischnochiton spp.) (Palmer et al. 2008), freshwater pearl mussels Margaritifera margaritifera (Karlsson et al. 2013) and fish (Manta birostris, Oreochromis niloticus) (Kashiwagi et al. 2015, Taslima et al. 2016).
In birds, eggshells (Strausberger and Ashley 2001, Egloff et al. 2009, Kjelland and Kraemer 2012, Maia et al. 2017) or feathers (Rudnick et al. 2007, Kjelland and Kraemer 2012, Olah et al. 2016) can be used as a source of DNA. Feathers can be collected opportunistically or through a feather-trap (Maurer et al. 2010).
Saliva is also a great source of DNA that can be collected non-invasively by using e.g. baits and porous material (Vargas et al. 2009, Lobo et al. 2015). Additionally, DNA samples can be obtained from mineral lick (Schoenecker et al. 2015), rests of prey (Harms et al. 2015, Wheat et al. 2016) or damaged crop (Saito et al. 2008).
Other potential sources of DNA include scent marks (Malherbe et al. 2009), snow footprints (Dalen et al. 2007), urine (Nagai et al. 2014, Nakamura et al. 2017), insect exuviae (Kranzfelder et al. 2016, Nguyen et al. 2017), spider webs (Xu et al. 2015, Blake et al. 2016), antlers (Hoffmann and Griebeler 2013, Kim et al. 2015) or shed skin (Swanson et al. 2006, Horreo et al. 2015).
As organisms move through the environment, they also leave some DNA traces behind. This environmental DNA (eDNA) can be used for detection of targeted organisms, and it is particularly useful for detection of invasive (Collins et al. 2013, Hunter et al. 2015) or rare species (Jerde et al. 2011). Most eDNA applications have targeted aquatic environments, for instance, in studies on harbor porpoises Phocoena phocoena (Foote et al. 2012), mussels (Unionidae) (Cho et al. 2016), hellbenders Cryptobranchus alleganiensis (Olson et al. 2012), fish (Cyprinus carpio, Oncorhynchus mykiss) (Eichmiller et al. 2016, Fernandez et al. 2018) or platypus Ornithorhynchus anatinus (Lugg et al. 2018). However, eDNA techniques have been used also in deer Capreolus capreolus (Nichols et al. 2012) or wild boar Sus scrofa studies (Williams et al. 2018b), using saliva from twigs or water from drinking reservoirs as the DNA source.
While not completely non-invasive method, blood-sucking insects have been used as a ‘gentle’ stress-free method of DNA collection in several mammalian species (Voigt et al. 2005, Calvignac-Spencer et al. 2013, Habicher et al. 2013, Lee et al. 2015, Rodgers et al. 2017), so-called invertebrate-derived DNA (iDNA). The potential of using terrestrial leeches (Haemadipsa spp.) for the same purpose has been also assessed (Schnell et al. 2015).
Alternatives to lethal sampling
Alternatives to collecting voucher specimen
One of the best methods as an alternative to voucher collection is a series of high-quality photographs, which can be even used to describe a new species (Athreya 2006, Minteer et al. 2014a), especially in combination with other lines of evidence (e.g. DNA from skin or buccal samples and recording a species' mating call).
Dietary composition
The recent application of next generation sequencing and enrichment methods to trophic ecology can enable rapid resolution to questions about diets of practically any animal from their faeces (O'Rorke et al. 2012, Pompanon et al. 2012). Faecal genotyping as a method to examine dietary composition was used in e.g. coyotes Canis latrans (Prugh et al. 2008), European pine martens Martes martes (O'Meara et al. 2014), seals (Arctocephalus forsteri, Phoca vitulina) (Emami-Khoyi et al. 2016, Hui et al. 2017), fish (Barbus barbus, Chondrostoma toxostoma toxostoma, Chondrostoma nasus nasus) (Corse et al. 2010), snakes (Coronella austriaca) (Brown et al. 2014), snails (Achatinella spp.) (O'Rorke et al. 2015, Price et al. 2017), fruit flies Drosophila melanogaster (Fink et al. 2013) or spiders (Pardosa spp.) (Sint et al. 2015).
Concluding remarks
Studies on wildlife are regularly conducted with the assumption that they have an insignificant impact on the studied animals (Jewell 2013) or that the impact is outweighed by any potential benefits to the population or species (Vucetich and Nelson 2007, Parris et al. 2010). Such assumptions however raise concerns for animal welfare, a topic that has been increasingly discussed among public, ethical committees, journal publishers and funding agencies (McMahon et al. 2012, Zemanova 2017).
In this review, I outlined the potential implications of commonly used invasive research methods for wildlife welfare. Some of the research practices can, however, have delayed consequences and monitoring of animals for any adverse impact should be required (Putman 1995). It is also important to note that in many cases, animal welfare implications of research methods are simply not known. In this case it is imperative to exercise the precautionary principle (Crozier and Schulte-Hostedde 2015).
In the past, a high level of invasiveness was necessary to obtain reliable data for understanding and designing management measures for wildlife. However, research methods have to be adjusted as our technical advancement and our understanding of species ability to feel pain grows (Costello et al. 2016, Waugh and Monamy 2016), and wildlife researchers need to limit the harm to the animals in order to ensure ethical acceptability of their work (Crettaz von Roten 2009, Lund et al. 2012). Even though non-invasive methods may not be yet suitable for all types of wildlife research, we should strive to implement them whenever possible. As I showed in this review, many researchers have already succeeded to do so.
Building upon the 3Rs principles (Fig. 1, Table 1–3), Curzer et al. (2013) proposed another R: refusal. Refused should be studies with badly conceived research plans, studies with no prospect of contributing significant knowledge, or studies in which the harm to the animal clearly exceeds any benefit of new knowledge. Some research practices might then have to be rejected simply on ethical grounds (Bekoff 2002).
In conclusion, the 3Rs principles are just as relevant to wildlife research as they are to laboratory animal studies. The current wildlife research needs to shift from using invasive and lethal methods to prioritizing non-invasive alternatives. Study and management of wildlife are necessary, but in doing so, we bear responsibility for ensuring that welfare of the studied animals is compromised as little as possible through our work.
Acknowledgements
I would like to thank Markus Wild for his generous support.
Funding – This project received funding from the Animalfree Research foundation and Dr. Joachim de Giacomi Fund of the Swiss Academy of Sciences.
Author contributions – The author confirms being the sole contributor of this work.
References
1.
Afonso,
E. et al. 2016. Is the lesser horseshoe bat (
Rhinolophus hipposideros) exposed to causes that may have contributed to its decline? A non-invasive approach. – Global Ecol. Conserv. 8: 123–137.
Google Scholar
2.
Ahlering,
M. A. et al. 2013. Conservation outside protected areas and the effect of human-dominated landscapes on stress hormones in savannah elephants. – Conserv. Biol. 27: 569–575.
Google Scholar
3.
Alessi,
J. et al. 2014. Photo-identification of sperm whales in the north-western Mediterranean Sea: an assessment of natural markings. – Aquat. Conserv. 24: 11–22.
Google Scholar
4.
Alexander,
P. D. and
Gese,
E. M. 2018. Identifying individual cougars (
Puma concolor) in remote camera images – implications for population estimates. – Wildl. Res. 45: 274–281.
Google Scholar
5.
Alibhai,
S. et al. 2008. Identifying white rhino (
Ceratotherium simum) by a footprint identification technique, at the individual and species levels. – Endanger. Species Res. 4: 205–218.
Google Scholar
6.
Amelon,
S. K. et al. 2017. Bat wing biometrics: using collagen-elastin bundles in bat wings as a unique individual identifier. – J. Mammal. 98: 744–751.
Google Scholar
7.
Anderson,
C. J. R. et al. 2007. Can whisker spot patterns be used to identify individual polar bears? – J. Zool. 273: 333–339.
Google Scholar
8.
Angelone,
S. and
Holderegger,
R. 2009. Population genetics suggests effectiveness of habitat connectivity measures for the European tree frog in Switzerland. – J. Appl. Ecol. 46: 879–887.
Google Scholar
9.
Anile,
S. et al. 2014. Wildcat population density on the Etna volcano, Italy: a comparison of density estimation methods. – J. Zool. 293: 252–261.
Google Scholar
10.
Apprill,
A. et al. 2017. Extensive core microbiome in drone-captured whale blow supports a framework for health monitoring. – mSystems 2: e00119–17.
Google Scholar
11.
Arandjelovic,
M. et al. 2015. Detection dog efficacy for collecting faecal samples from the critically endangered Cross River gorilla (
Gorilla gorilla diehli) for genetic censusing. – R. Soc. Open Sci. 2: 140423.
Google Scholar
12.
Armbruster,
G. F. J. et al. 2005. Foot mucus and periostracum fraction as non-destructive source of DNA in the land snail
Arianta arbustorum, and the development of new microsatellite loci. – Conserv. Genet. 6: 313–316.
Google Scholar
13.
Arnemo,
J. M. et al. 2018. Long-term safety of intraperitoneal radio transmitter implants in brown bears (
Ursus arctos). – Front. Vet. Sci. 5: 252.
Google Scholar
14.
Arzoumanian,
Z. et al. 2005. An astronomical pattern-matching algorithm for computer-aided identification of whale sharks
Rhincodon typus. – J. Appl. Ecol. 42: 999–1011.
Google Scholar
15.
Athreya,
R. 2006. A new species of
Liocichla (Aves: Timaliidae) from Eaglenest Wildlife Sanctuary. – Indian Birds 2: 82–94.
Google Scholar
16.
Atuo,
F. A. et al. 2019. Resource selection by GPS-tagged California spotted owls in mixed-ownership forests. – For. Ecol. Manage. 433: 295–304.
Google Scholar
17.
Auger-Methe,
M. et al. 2011. Computer-assisted photo-identification of narwhals. – Arctic 64: 342–352.
Google Scholar
18.
Ausband,
D. E. et al. 2011. Hair of the dog: obtaining samples from coyotes and wolves noninvasively. – Wildl. Soc. Bull. 35: 105–111.
Google Scholar
19.
Ballouard,
J. M. et al. 2016. Artificial water ponds and camera trapping of tortoises, and other vertebrates, in a dry Mediterranean landscape. – Wildl. Res. 43: 533–543.
Google Scholar
20.
Barbknecht,
A. E. et al. 2009. Effectiveness of vaginal-implant transmitters for locating elk parturition sites. – J. Wildl. Manage. 73: 144–148.
Google Scholar
21.
Barnas,
A. et al. 2018a. Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys. – Ecol. Evol. 8: 1328–1338.
Google Scholar
22.
Barnas,
A. F. et al. 2018b. A pilot(less) study on the use of an unmanned aircraft system for studying polar bears (
Ursus maritimus). – Polar Biol. 41: 1055–1062.
Google Scholar
23.
Bauch,
C. et al. 2013. ‘Bug-eggs’ for common swifts and other small birds: minimally-invasive and stress-free blood sampling during incubation. – J. Ornithol. 154: 581–585.
Google Scholar
24.
Bauwens,
D. et al. 2018. Genotyping validates photo-identification by the head scale pattern in a large population of the European adder (
Vipera berus). – Ecol. Evol. 8: 2985–2992.
Google Scholar
25.
Becker,
D. J. et al. 2018. Mercury bioaccumulation in bats reflects dietary connectivity to aquatic food webs. – Environ. Pollut. 233: 1076–1085.
Google Scholar
26.
Beebee,
T. J. C. 2008. Buccal swabbing as a source of DNA from squamate reptiles. – Conserv. Genet. 9: 1087–1088.
Google Scholar
27.
Behringer,
V. and
Deschner,
T. 2017. Non-invasive monitoring of physiological markers in primates. – Horm. Behav. 91: 3–18.
Google Scholar
28.
Beja-Pereira,
A. et al. 2009. Advancing ecological understandings through technological transformations in noninvasive genetics. – Mol. Ecol. Res. 9: 1279–1301.
Google Scholar
29.
Bekoff,
M. 2002. The importance of ethics in conservation biology: let's be ethicists, not ostriches. – Endanger. Species Update 19: 23–26.
Google Scholar
30.
Bendik,
N. F. et al. 2013. Computer-assisted photo identification outperforms visible implant elastomers in an endangered salamander,
Eurycea tonkawae. – PLoS One 8: e59424.
Google Scholar
31.
Berglund,
A. M. M. 2018. Evaluating blood and excrement as bioindicators for metal accumulation in birds. – Environ. Pollut. 233: 1198–1206.
Google Scholar
32.
Bergvall,
U. A. et al. 2017. The use of box-traps for wild roe deer: behaviour, injuries and recaptures. – Eur. J. Wildl. Res. 63: 67.
Google Scholar
33.
Bevan,
E. et al. 2018. Measuring behavioral responses of sea turtles, saltwater crocodiles and crested terns to drone disturbance to define ethical operating thresholds. – PLoS One 13: e0194460.
Google Scholar
34.
Bezerra,
B. M. et al. 2014. Camera trap observations of nonhabituated critically endangered wild blonde capuchins,
Sapajus flavius (formerly
Cebus flavius). – Int. J. Primatol. 35: 895–907.
Google Scholar
35.
Bichell,
L. M. V. et al. 2018. The reliability of pigment pattern-based identification of wild bottlenose dolphins. – Mar. Mamm. Sci. 34: 113–124.
Google Scholar
36.
Bishop,
C. J. et al. 2007. Using vaginal implant transmitters to aid in capture of mule deer neonates. – J. Wildl. Manage. 71: 945–954.
Google Scholar
37.
Blake,
M. et al. 2016. DNA extraction from spider webs. – Conserv. Gen. Res. 8: 219–221.
Google Scholar
38.
Bloch,
N. and
Irschick,
D. J. 2005. Toe-clipping dramatically reduces clinging performance in a pad-bearing lizard (
Anolis carolinensis). – J. Herpetol. 39: 288–293.
Google Scholar
39.
Boisvert,
G. et al. 2019. Bioaccumulation and biomagnification of perfluoroalkyl acids and precursors in East Greenland polar bears and their ringed seal prey. – Environ. Pollut. 252: 1335–1343.
Google Scholar
40.
Bordeleau,
X. et al. 2018. Consequences of captive breeding: fitness implications for wild-origin, hatchery-spawned Atlantic salmon kelts upon their return to the wild. – Biol. Conserv. 225: 144–153.
Google Scholar
41.
Bosson,
C. O. et al. 2009. Assessment of the stress response in Columbian ground squirrels: laboratory and field validation of an enzyme immunoassay for fecal cortisol metabolites. – Physiol. Biochem. Zool. 82: 291–301.
Google Scholar
42.
Boulerice,
J. T. and
Van Fleet,
L. A. 2016. A novel technique for detecting northern flying squirrels. – Wildl. Soc. Bull. 40: 786–791.
Google Scholar
43.
Brooks,
C. et al. 2008. Effects of global positioning system collar weight on zebra behavior and location error. – J. Wildl. Manage. 72: 527–534.
Google Scholar
44.
Broquet,
T. et al. 2007. Buccal swabs allow efficient and reliable microsatellite genotyping in amphibians. – Conserv. Genet. 8: 509–511.
Google Scholar
45.
Brown,
M. B. and
Brown,
C. R. 2009. Blood sampling reduces annual survival in cliff swallows (
Petrochelidon pyrrhonota). – Auk 126: 853–861.
Google Scholar
46.
Brown,
D. S. et al. 2014. Molecular analysis of the diets of snakes: changes in prey exploitation during development of the rare smooth snake
Coronella austriaca. – Mol. Ecol. 23: 3734–3743.
Google Scholar
47.
Bryan,
C. E. et al. 2007. Establishing baseline levels of trace elements in blood and skin of bottlenose dolphins in Sarasota Bay, Florida: implications for non-invasive monitoring. – Sci. Total Environ. 388: 325–342.
Google Scholar
48.
Bryan,
H. M. et al. 2014. Stress and reproductive hormones reflect inter-specific social and nutritional conditions mediated by resource availability in a bear-salmon system. – Conserv. Physiol. 2: cou010.
Google Scholar
49.
Buchanan,
K. et al. 2015. Guidelines for the treatment of animals in behavioural research and teaching. – Anim. Behav. 99: I–IX.
Google Scholar
50.
Burdick,
S. M. 2011. Tag loss and short-term mortality associated with passive integrated transponder tagging of juvenile lost river suckers. – N. Am. J. Fish Manage. 31: 1088–1092.
Google Scholar
51.
Caci,
G. et al. 2013. Spotting the right spot: computer-aided individual identification of the threatened cerambycid beetle
Rosalia alpina. – J. Insect Conserv. 17: 787–795.
Google Scholar
52.
Callard,
M. D. et al. 2000. Repetitive backflipping behaviour in captive roof rats (
Rattus rattus) and the effects of cage enrichment. – Anim. Welf. 9: 139–152.
Google Scholar
53.
Calvignac-Spencer,
S. et al. 2013. Carrion fly-derived DNA as a tool for comprehensive and cost–effective assessment of mammalian biodiversity. – Mol. Ecol. 22: 915–924.
Google Scholar
54.
Carroll,
E. L. et al. 2018. Genetic and genomic monitoring with minimally invasive sampling methods. – Evol. Appl. 11: 1094–1119.
Google Scholar
55.
Cattet,
M. R. L. 2013. Falling through the cracks: shortcomings in the collaboration between biologists and veterinarians and their consequences for wildlife. – ILAR J. 54: 33–40.
Google Scholar
56.
Cattet,
M. et al. 2008. An evaluation of long-term capture effects in ursids: implications for wildlife welfare and research. – J. Mammal. 89: 973–990.
Google Scholar
57.
Ceballos,
G. et al. 2015. Accelerated modern human-induced species losses: entering the sixth mass extinction. – Sci. Adv. 1: e1400253.
Google Scholar
58.
Collins,
R. A. et al. 2013. Something in the water: biosecurity monitoring of ornamental fish imports using environmental DNA. – Biol. Invas. 15: 1209–1215.
Google Scholar
59.
Correia,
M. et al. 2014. The use of a non-invasive tool for capture–recapture studies on a seahorse
Hippocampus guttulatus population. – J. Fish Biol. 84: 872–884.
Google Scholar
60.
Corse,
E. et al. 2010. A PCR-based method for diet analysis in freshwater organisms using 18S rDNA barcoding on faeces. – Mol. Ecol. Res. 10: 96–108.
Google Scholar
61.
Costello,
M. J. et al. 2016. Field work ethics in biological research. – Biol. Conserv. 203: 268–271.
Google Scholar
62.
Cramer,
M. J. 2017. Considerations for use of vertebrates in field studies. – In:
Suckow,
M. A. and
Stewart,
K. L. (eds), Principles of animal research for graduate and undergraduate students. Academic Press, pp. 199–223.
Google Scholar
63.
Crettaz von Roten,
F. 2009. European attitudes towards animal research: overview and consequences for science. – Sci. Technol. Soc. 14: 349–364.
Google Scholar
64.
Crozier,
G. K. D. and
Schulte-Hostedde,
A. I. 2015. Towards improving the ethics of ecological research. – Sci. Eng. Ethics 21: 577–594.
Google Scholar
65.
Curzer,
H. J. et al. 2013. The ethics of wildlife research: a nine R theory. – ILAR J. 54: 52–57.
Google Scholar
66.
da Costa Araujo,
A. P. et al. 2020. How much are microplastics harmful to the health of amphibians? A study with pristine polyethylene microplastics and
Physalaemus cuvieri. – J. Hazard Mater. 382: 121066.
Google Scholar
67.
Dalen,
L. et al. 2007. Recovery of DNA from footprints in the snow. – Can. Field-Nat. 121: 321–324.
Google Scholar
68.
Dalton,
R. 2005. Animal-rights group sues over ‘disturbing’ work on sea lions. – Nature 436: 315–315.
Google Scholar
69.
Das,
P. K. et al. 2015. Population genetic assessment of extant populations of greater one-horned rhinoceros (
Rhinoceros unicornis) in India. – Eur. J. Wildl. Res. 61: 841–851.
Google Scholar
70.
Day,
C. C. et al. 2016. Comparing direct and indirect methods to estimate detection rates and site use of a cryptic semi-aquatic carnivore. – Ecol. Indic. 66: 230–234.
Google Scholar
71.
de Abreu,
M. S. et al. 2014. Diazepam and fluoxetine decrease the stress response in zebrafish. – PLoS One 9: e103232.
Google Scholar
72.
Di Cerbo,
A. R. and
Biancardi,
C. M. 2013. Monitoring small and arboreal mammals by camera traps: effectiveness and applications. – Acta Theriol. 58: 279–283.
Google Scholar
73.
Diaz-Calafat,
J. et al. 2018. Individual unique colour patterns of the pronotum of
Rhynchophorus ferrugineus (Coleoptera: Curculionidae) allow for photographic identification methods (PIM). – J. Asia-Pac. Entomol. 21: 519–526.
Google Scholar
74.
Dixon,
A. et al. 2016. Evidence for deleterious effects of harness-mounted satellite transmitters on Saker falcons
Falco cherrug. – Bird Study 63: 96–106.
Google Scholar
75.
Donnelly,
M. A. et al. 1994. Techniques for marking amphibians. – Smithsonian Inst. Press.
Google Scholar
76.
Egloff,
C. et al. 2009. A nondestructive method for obtaining maternal DNA from avian eggshells and its application to embryonic viability determination in herring gulls (
Larus argentatus). – Mol. Ecol. Res. 9: 19–27.
Google Scholar
77.
Eichmiller,
J. J. et al. 2016. Optimizing techniques to capture and extract environmental DNA for detection and quantification of fish. – Mol. Ecol. Res. 16: 56–68.
Google Scholar
78.
Emami-Khoyi,
A. et al. 2016. Identifying prey items from New Zealand fur seal (
Arctocephalus forsteri) faeces using massive parallel sequencing. – Conserv. Gen. Res. 8: 343–352.
Google Scholar
79.
Ezat,
M. A. et al. 2018. Use of an unmanned aerial vehicle (drone) to survey Nile crocodile populations: a case study at Lake Nyamithi, Ndumo game reserve, South Africa. – Biol. Conserv. 223: 76–81.
Google Scholar
80.
Fairhurst,
G. D. et al. 2011. Does environmental enrichment reduce stress? An integrated measure of corticosterone from feathers provides a novel perspective. – PLoS One 6: e17663.
Google Scholar
81.
Fallow,
P. M. et al. 2009. Flipper bands modify the short-term diving behavior of little penguins. – J. Wildl. Manage. 73: 1348–1354.
Google Scholar
82.
Fernandez,
S. et al. 2018. Environmental DNA for freshwater fish monitoring: insights for conservation within a protected area. – Peerj 6: e4486.
Google Scholar
83.
Ferreira,
V. H. B. et al. 2018. Hormonal correlates of behavioural profiles and coping strategies in captive capuchin monkeys (
Sapajus libidinosus). – Appl. Anim. Behav. Sci. 207: 108–115.
Google Scholar
84.
Field,
I. C. et al. 2012. Refining instrument attachment on phocid seals. – Mar. Mamm. Sci. 28: E325–E332.
Google Scholar
85.
Field,
K. A. et al. 2019. Publication reform to safeguard wildlife from researcher harm. – PLoS Biol. 17: e3000193.
Google Scholar
86.
Fink,
C. et al. 2013. Noninvasive analysis of microbiome dynamics in the fruit fly
Drosophila melanogaster. – Appl. Environ. Microbiol. 79: 6984–6988.
Google Scholar
87.
Flagel,
D. G. et al. 2016. Natural and experimental tests of trophic cascades: gray wolves and white-tailed deer in a Great Lakes forest. – Oecologia 180: 1183–1194.
Google Scholar
88.
Flache,
L. et al. 2015. Hair samples as monitoring units for assessing metal exposure of bats: a new tool for risk assessment. – Mamm. Biol. 80: 178–181.
Google Scholar
89.
Fleming,
P. J. S. et al. 1998. The performance of wild-canid traps in Australia: efficiency, selectivity and trap-related injuries. – Wildl. Res. 25: 327–338.
Google Scholar
90.
Foote,
A. D. et al. 2012. Investigating the potential use of environmental DNA (eDNA) for genetic monitoring of marine mammals. – PLoS One 7: e41781.
Google Scholar
91.
Ford,
B. et al. 2017. Evaluating the efficacy of non-invasive genetic sampling of the Northern Pacific rattlesnake with implications for other venomous squamates. – Conserv. Gen. Res. 9: 13–15.
Google Scholar
92.
Frenkel,
V. et al. 2002. Noninvasive, mass marking of fish by immersion in calcein: evaluation of fish size and ultrasound exposure on mark endurance. – Aquaculture 214: 169–183.
Google Scholar
93.
Fumanal,
B. et al. 2005. High through-put characterization of insect morphocryptic entities by a non-invasive method using direct-PCR of fecal DNA. – J. Biotechnol. 119: 15–19.
Google Scholar
94.
Gallardo-Escarate,
C. et al. 2007. Individual identification of decapod crustaceans I: color patterns in rock shrimp (
Rhynchocinetes typus). – J. Crustacean Biol. 27: 393–398.
Google Scholar
95.
Gallardo,
C. E. et al. 2012. Validation of a cheap and simple nondestructive method for obtaining AFLPs and DNA sequences (mitochondrial and nuclear) in amphibians. – Mol. Ecol. Res. 12: 1090–1096.
Google Scholar
96.
Gamble,
L. et al. 2008. Multi-scale features for identifying individuals in large biological databases: an application of pattern recognition technology to the marbled salamander
Ambystoma opacum. – J. Appl. Ecol. 45: 170–180.
Google Scholar
97.
Ganswindt,
A. et al. 2012. Validation of noninvasive monitoring of adrenocortical endocrine activity in ground-feeding aardwolves (
Proteles cristata): exemplifying the influence of consumption of inorganic material for fecal steroid analysis. – Physiol. Biochem. Zool. 85: 194–199.
Google Scholar
98.
Gatto,
C. R. et al. 2018. A novel method for photo-identification of sea turtles using scale patterns on the front flippers. – J. Exp. Mar. Biol. Ecol. 506: 18–24.
Google Scholar
99.
Ginnan,
N. A. et al. 2014. Toe clipping does not affect the survival of leopard frogs (
Rana pipiens). – Copeia 2014: 650–653.
Google Scholar
100.
Girard,
I. et al. 2002. Effects of sampling effort based on GPS telemetry on home-range size estimations. – J. Wildl. Manage. 66: 1290–1300.
Google Scholar
101.
Gomez-Salazar,
C. et al. 2011. Photo-identification: a reliable and noninvasive tool for studying pink river dolphins (
Inia geoffrensis). – Aquat. Mamm. 37: 472–485.
Google Scholar
102.
Gomez,
A. et al. 2004. Use of salivary steroid analyses to assess ovariancyclesinanIndianrhinocerosattheNationalZoological Park. – Zoo. Biol. 23: 501–512.
Google Scholar
103.
Gonzalez-Ramos,
M. S. et al. 2017. Validation of photo-identification as a mark–recapture method in the spotted eagle ray
Aetobatus narinari. – J. Fish Biol. 90: 1021–1030.
Google Scholar
104.
Gosselin,
T. et al. 2007. Individual identification of decapod crustaceans II: natural and genetic markers in snow crab (
Chionoecetes opilio). – J. Crustacean Biol. 27: 399–403.
Google Scholar
105.
Gould,
M. J. et al. 2018. Density of American black bears in New Mexico. – J. Wildl. Manage. 82: 775–788.
Google Scholar
106.
Grafe,
T. U. et al. 2011. Putting toe clipping into perspective: a viable method for marking anurans. – J. Herpetol. 45: 28–35.
Google Scholar
107.
Gray,
T. N. E. et al. 2014. Population size estimation of an Asian elephant population in eastern Cambodia through non-invasive mark–recapture sampling. – Conserv. Genet. 15: 803–810.
Google Scholar
108.
Green-Barber,
J. M. et al. 2018. A suspected case of myopathy in a free-ranging eastern grey kangaroo (
Macropus giganteus). – Aust. Mammal. 40: 122–126.
Google Scholar
109.
Grisham,
B. A. et al. 2015. Evaluation of capture techniques on lesser prairie-chicken trap injury and survival. – J. Fish Wildl. Manage. 6: 318–326.
Google Scholar
110.
Gu,
J. et al. 2014. Sex determination of amur tigers (
Panthera tigris altaica) from footprints in snow. – Wildl. Soc. Bull. 38: 495–502.
Google Scholar
111.
Guldemond,
R. and
Van Aarde,
R. 2008. A meta-analysis of the impact of African elephants on savanna vegetation. – J. Wildl. Manage. 72: 892–899.
Google Scholar
112.
Habicher,
A. et al. 2013. Tsetse flies as tools for minimally invasive blood sampling. – Wildl. Soc. Bull. 37: 423–427.
Google Scholar
113.
Hale,
V. L. et al. 2017. Radio transmitter implantation and movement in the wild timber rattlesnake (
Crotalus horridus). – J. Wildl. Dis. 53: 591–595.
Google Scholar
114.
Hammerschlag,
N. and
Sulikowski,
J. 2011. Killing for conservation: the need for alternatives to lethal sampling of apex predatory sharks. – Endanger. Species Res. 14: 135–140.
Google Scholar
115.
Hansen,
L. P. 1988. Effects of carlin tagging and fin clipping on survival of Atlantic salmon (
Salmo salar) released as smolts. – Aquaculture 70: 391–394.
Google Scholar
116.
Harcourt,
R. G. et al. 2010. Effects of capture stress on free-ranging, reproductively active male Weddell seals. – J. Comp. Physiol. 196: 147–154.
Google Scholar
117.
Harms,
V. et al. 2015. Experimental evaluation of genetic predator identification from saliva traces on wildlife kills. – J. Mammal. 96: 138–143.
Google Scholar
118.
Harrop,
S. 2011. Climate change, conservation and the place for wild animal welfare in international law. – J. Environ. Law 23: 441–462.
Google Scholar
119.
Hazekamp,
A. A. H. et al. 2010. Flow simulation along a seal: the impact of an external device. – Eur. J. Wildl. Res. 56: 131–140.
Google Scholar
120.
Herzog,
C. J. et al. 2007. Using patterns in track-plate footprints to identify individual fishers. – J. Wildl. Manage. 71: 955–963.
Google Scholar
121.
Higashide,
D. et al. 2012. Are chest marks unique to Asiatic black bear individuals? – J. Zool. 288: 199–206.
Google Scholar
122.
Higham,
J. P. et al. 2010. Measuring salivary analytes from free-ranging monkeys. – Physiol. Behav. 101: 601–607.
Google Scholar
123.
Hilario,
R. R. et al. 2017. Predation of birds in mist nets by Callitrichids (primates): how to prevent similar events. – Stud. Neotrop. Fauna Environ. 52: 168–172.
Google Scholar
124.
Hill,
J. M. and
Elphick,
C. S. 2011. Are grassland passerines especially susceptible to negative transmitter impacts? – Wildl. Soc. Bull. 35: 362–367.
Google Scholar
125.
Hing,
S. et al. 2017. Identifying factors that influence stress physiology of the woylie, a critically endangered marsupial. – J. Zool. 302: 49–56.
Google Scholar
126.
Hodgson,
J. C. et al. 2018. Drones count wildlife more accurately and precisely than humans. – Methods Ecol. Evol. 9: 1160–1167.
Google Scholar
127.
Hoffmann,
G. S. and
Griebeler,
E. M. 2013. An improved high yield method to obtain microsatellite genotypes from red deer antlers up to 200 years old. – Mol. Ecol. Res. 13: 440–446.
Google Scholar
128.
Hollenbeck,
N. et al. 2017. Use of swabs for sampling epithelial cells for molecular genetics analyses in
Enteroctopus. – Am. Malacol. Bull. 35: 145–157.
Google Scholar
129.
Hood,
L. C. et al. 1998. The adrenocortical response to stress in incubating Magellanic penguins (
Spheniscus magellanicus). – Auk 115: 76–84.
Google Scholar
130.
Horreo,
J. L. et al. 2015. Skin sheds as a useful DNA source for lizard conservation. – Phyllomedusa 14: 73–77.
Google Scholar
131.
Hoy,
S. R. et al. 2016. Genetic markers validate using the natural phenotypic characteristics of shed feathers to identify individual northern goshawks
Accipiter gentilis. – J. Avian Biol. 47: 443–447.
Google Scholar
132.
Hoye,
S. D. et al. 2015. Covariates of release mortality and tag loss in large-scale tuna tagging experiments. – Fish. Res. 163: 106–118.
Google Scholar
133.
Huffard,
C. L. et al. 2008. Individually unique body color patterns in octopus (
Wunderpus photogenicus) allow for photoidentification. – PLoS One 3: e3732.
Google Scholar
134.
Hui,
T. C. Y. et al. 2017. Dietary analysis of harbour seals (
Phoca vitulina) from faecal samples and overlap with fisheries in Erimo, Japan. – Mar. Ecol. Evol. Perspect. 38: e12431.
Google Scholar
135.
Hunt,
K. E. et al. 2006. Analysis of fecal glucocorticoids in the North Atlantic right whale (
Eubalaena glacialis). – Gen. Comp. Endocrinol. 148: 260–272.
Google Scholar
136.
Hunter,
M. E. et al. 2015. Environmental DNA (eDNA) sampling improves occurrence and detection estimates of invasive Burmese pythons. – PLoS One 10: e0121655.
Google Scholar
137.
Chamberlain,
D. E. et al. 2009. Avian productivity in urban landscapes: a review and meta-analysis. – Ibis 151: 1–18.
Google Scholar
138.
Chaousis,
S. et al. 2018. Charting a path towards non-destructive biomarkers in threatened wildlife: a systematic quantitative literature review. – Environ. Pollut. 234: 59–70.
Google Scholar
139.
Cho,
A. et al. 2016. Development of species-specific primers with potential for amplifying eDNA from imperilled freshwater unionid mussels. – Genome 59: 1141–1149.
Google Scholar
140.
Idaghdour,
Y. et al. 2003. Faeces as a source of DNA for molecular studies in a threatened population of great bustards. – Conserv. Genet. 4: 789–792.
Google Scholar
141.
Jackson,
S. and
Wilson,
R. P. 2002. The potential costs of flipper-bands to penguins. – Funct. Ecol. 16: 141–148.
Google Scholar
142.
Jankowski,
M. D. et al. 2009. The adrenocortical response of greater sage grouse (
Centrocercus urophasianus) to capture, ACTH injection and confinement, as measured in fecal samples. – Physiol. Biochem. Zool. 82: 190–201.
Google Scholar
143.
Jasinska,
E. J. et al. 2015. Assessment of biomarkers for contaminants of emerging concern on aquatic organisms downstream of a municipal wastewater discharge. – Sci. Total. Environ. 530–531: 140–153.
Google Scholar
144.
Jerde,
C. L. et al. 2011. ‘Sight-unseen’ detection of rare aquatic species using environmental DNA. – Conserv. Lett. 4: 150–157.
Google Scholar
145.
Jett,
J. et al. 2017. Tooth damage in captive orcas (
Orcinus orca). – Arch. Oral Biol. 84: 151–160.
Google Scholar
146.
Jewell,
Z. 2013. Effect of monitoring technique on quality of conservation science. – Conserv. Biol. 27: 501–508.
Google Scholar
147.
Jewell,
Z. C. et al. 2016. Spotting cheetahs: identifying individuals by their footprints. – Jove-J. Vis. Exp. 111: e54034.
Google Scholar
148.
Jirkof,
P. 2017. Side effects of pain and analgesia in animal experimentation. – Lab Anim. 46: 123–128.
Google Scholar
149.
Johnson,
M. A. 2005. A new method of temporarily marking lizards. – Herpetol. Rev. 36: 277–279.
Google Scholar
150.
Jordan,
M. J. et al. 2011. Camera trapping estimates of density and survival of fishers
Martes pennanti. – Wildl. Biol. 17: 266–276.
Google Scholar
151.
Jung,
K. and
Threlfall,
C. G. 2018. Trait-dependent tolerance of bats to urbanization: a global meta-analysis. – Proc. R. Soc. B 285: 20181222.
Google Scholar
152.
Karanth,
K. U. et al. 2006. Assessing tiger population dynamics using photographic capture–recapture sampling. – Ecology 87: 2925–2937.
Google Scholar
153.
Karlsson,
S. et al. 2013. Four methods of nondestructive DNA sampling from freshwater pearl mussels
Margaritifera margaritifera L. (Bivalvia: Unionoida). – Freshwater Sci. 32: 525–530.
Google Scholar
154.
Karssene,
Y. et al. 2018. Noninvasive genetic assessment provides evidence of extensive gene flow and possible high movement ability in the African golden wolf. – Mamm. Biol. 92: 94–101.
Google Scholar
155.
Kashiwagi,
T. et al. 2015. Evaluating manta ray mucus as an alternative DNA source for population genetics study: underwater-sampling, dry-storage and PCR success. – PeerJ 3: e1188.
Google Scholar
156.
Kawai,
K. et al. 2004. A non-invasive technique for obtaining DNA from marine intertidal snails. – J. Mar. Biol. Assoc. UK 84: 773–774.
Google Scholar
157.
Keeley,
B. W. and
Keeley,
A. T. H. 2012. Using a specialized blowgun dart to obtain genetic samples from mammals. – Wildl. Soc. Bull. 36: 185–188.
Google Scholar
158.
Kelly,
M. J. 2001. Computer-aided photograph matching in studies using individual identification: an example from Serengeti cheetahs. – J. Mammal. 82: 440–449.
Google Scholar
159.
Kendall,
K. C. and
McKelvey,
K. 2012. Hair collection. – In:
Long,
R. A. (ed.), Noninvasive survey methods for carnivores. Island Press.
Google Scholar
160.
Kilkenny,
C. et al. 2010. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. – PLoS Biol. 8: e1000412.
Google Scholar
161.
Kim,
Y. H. et al. 2015. Development of a PCR-based assay to differentiate
Cervus elaphus sibiricus from
Cervus antlers. – J. Korean Soc. Appl. Biol. Chem. 58: 61–66.
Google Scholar
162.
Kjelland,
M. E. and
Kraemer,
D. 2012. Feathers and post-hatch eggshells: sources of fibroblast cells for conserving genetic diversity. – Avian Biol. Res. 5: 123–130.
Google Scholar
163.
Knox,
C. D. et al. 2013. Accurate identification of individual geckos (
Naultinus gemmeus) through dorsal pattern differentiation. – N. Z. J. Ecol. 37: 60–66.
Google Scholar
164.
Kolbe,
J. A. et al. 2003. An effective box trap for capturing lynx. – Wildl. Soc. Bull. 31: 980–985.
Google Scholar
165.
Kranzfelder,
P. et al. 2016. Trace DNA from insect skins: a comparison of five extraction protocols and direct PCR on chironomid pupal exuviae. – Mol. Ecol. Res. 16: 353–363.
Google Scholar
166.
Ksiazkiewicz-Parulska,
Z. and
Goldyn,
B. 2017. Can you count on counting? Retrieving reliable data from non-lethal monitoring of micro-snails. – Perspect. Ecol. Conserv. 15: 124–128.
Google Scholar
167.
Lampert,
K. P. et al. 2003. Fine-scale genetic pattern and evidence for sex-biased dispersal in the tungara frog,
Physalaemus pustulosus. – Mol. Ecol. 12: 3325–3334.
Google Scholar
168.
Langtimm,
C. A. et al. 2004. Survival estimates for Florida manatees from the photo-identification of individuals. – Mar. Mamm. Sci. 20: 438–463.
Google Scholar
169.
Law,
P. R. et al. 2013. Using shape and size to quantify variation in footprints for individual identification: case study with white rhinoceros (
Ceratotherium simum). – Wildl. Soc. Bull. 37: 433–438.
Google Scholar
170.
Le Chevalier,
H. et al. 2017. Marking techniques in the marbled newt (
Triturus marmoratus): PIT-tag and tracking device implant protocols. – Acta Herpetol. 12: 79–88.
Google Scholar
171.
Lecq,
S. et al. 2015. Non-lethal rapid biodiversity assessment. – Ecol. Indic. 58: 216–224.
Google Scholar
172.
Lee,
P. S. et al. 2015. Reading mammal diversity from flies: the persistence period of amplifiable mammal mtDNA in blowfly guts (
Chrysomya megacephala) and a new DNA mini-barcode target. – PLoS One 10: e0123871.
Google Scholar
174.
Lechenne,
M. S. et al. 2012. Mortalities due to constipation and dystocia caused by intraperitoneal radio-transmitters in Eurasian lynx (
Lynx lynx). – Eur. J. Wildl. Res. 58: 503–506.
Google Scholar
175.
Li,
J. S. J. et al. 2009. Non-invasive lizard identification using signature curves. – In: Tencon 2009–2009 Ieee Region 10 Conference, Vol. 1–4, pp. 1–5.
Google Scholar
176.
Li,
B. B. V. et al. 2018. Using footprints to identify and sex giant pandas. – Biol. Conserv. 218: 83–90.
Google Scholar
177.
Lindsjö,
J. et al. 2016. Animal welfare from mouse to moose – implementing the principles of the 3Rs in wildlife research. – J. Wildl. Dis. 52: S65–S77.
Google Scholar
178.
Lindsjö,
J. et al. 2019. The dividing line between wildlife research and management – implications for animal welfare. – Front. Vet. Sci. 6: 13.
Google Scholar
179.
Linhart,
P. et al. 2012. Once bitten twice shy: long-term behavioural changes caused by trapping experience in willow warblers
Phylloscopus trochilus. – J. Avian Biol. 43: 186–192.
Google Scholar
180.
Lobo,
N. et al. 2013. Effects of seed quality and abundance on the foraging behavior of deer mice. – J. Mammal. 94: 1449–1459.
Google Scholar
181.
Lobo,
D. et al. 2015. A new method for noninvasive genetic sampling of saliva in ecological research. – PLoS One 10: e0139765.
Google Scholar
182.
Lugg,
W. H. et al. 2018. Optimal survey designs for environmental DNA sampling. – Methods Ecol. Evol. 9: 1049–1059.
Google Scholar
183.
Lund,
T. B. et al. 2012. Public attitude formation regarding animal research. – Anthrozoos 25: 475–490.
Google Scholar
184.
Macpherson,
J. W. and
Penner,
P. 1967. Animal identification I: liquid nitrogen branding of cattle. – Can. J. Comp. Med. 31: 271–274.
Google Scholar
185.
MacRae,
A. M. et al. 2018. Initial evaluation of facial expressions and behaviours of harbour seal pups (
Phoca vitulina) in response to tagging and microchipping. – Appl. Anim. Behav. Sci. 205: 167–174.
Google Scholar
186.
Madden,
J. C. et al. 2014. Application of in silico and in vitro methods in the development of adverse outcome pathway constructs in wildlife. – Phil. Trans. R. Soc. B 369: 20140584.
Google Scholar
187.
Maia,
T. A. et al. 2017. DNA sampling from eggshells and microsatellite genotyping in rare tropical birds: case study on Brazilian merganser. – Genet. Mol. Biol. 40: 808–812.
Google Scholar
188.
Malherbe,
G. P. et al. 2009. Genetic clues from olfactory cues: brown hyaena scent marks provide a non-invasive source of DNA for genetic profiling. – Conserv. Genet. 10: 759–762.
Google Scholar
189.
Maniscalco,
J. M. et al. 2006. Interseasonal and interannual measures of maternal care among individual Steller sea lions (
Eumetopias jubatus). – J. Mammal. 87: 304–311.
Google Scholar
190.
Marino,
L. and
Frohoff,
T. 2011. Towards a new paradigm of non-captive research on cetacean cognition. – PLoS One 6: e24121.
Google Scholar
191.
Martin-Smith,
K. M. 2011. Photo-identification of individual weedy seadragons
Phyllopteryx taeniolatus and its application in estimating population dynamics. – J. Fish Biol. 78: 1757–1768.
Google Scholar
192.
Mathies,
T. et al. 2001. Effects of trapping and subsequent short-term confinement stress on plasma corticosterone in the brown treesnake (
Boiga irregularis) on Guam. – Gen. Comp. Endocrinol. 124: 106–114.
Google Scholar
193.
Maurer,
G. et al. 2010. A ‘feather-trap’ for collecting DNA samples from birds. – Mol. Ecol. Res. 10: 129–134.
Google Scholar
194.
McCarthy,
M. A. and
Parris,
K. M. 2004. Clarifying the effect of toe clipping on frogs with Bayesian statistics. – J. Appl. Ecol. 41: 780–786.
Google Scholar
195.
McGuire,
J. L. et al. 2014. Safety and utility of an anesthetic protocol for the collection of biological samples from gopher tortoises. – Wildl. Soc. Bull. 38: 43–50.
Google Scholar
196.
McLean,
C. M. et al. 2009. Mammalian hair as an accumulative bioindicator of metal bioavailability in Australian terrestrial environments. – Sci. Total. Environ. 407: 3588–3596.
Google Scholar
197.
McLean,
W. R. et al. 2017. Visual lures increase camera-trap detection of the southern cassowary (
Casuarius casuarius johnsonii). – Wildl. Res. 44: 230–237.
Google Scholar
198.
McMahon,
C. R. et al. 2012. Publish or perish: why it's important to publicise how, and if, research activities affect animals. – Wildl. Res. 39: 375–377.
Google Scholar
199.
Meekan,
M. G. et al. 2006. Population size and structure of whale sharks
Rhincodon typus at Ningaloo Reef, Western Australia. – Mar. Ecol. Prog. Ser. 319: 275–285.
Google Scholar
200.
Mellor,
D. J. 2016. Updating animal welfare thinking: moving beyond the ‘five freedoms’ towards ‘a life worth living'. – Animals 6: 21.
Google Scholar
201.
Mettouris,
O. et al. 2016. A newt does not change its spots: using pattern mapping for the identification of individuals in large populations of newt species. – Ecol. Res. 31: 483–489.
Google Scholar
202.
Millspaugh,
J. J. and
Washburn,
B. E. 2004. Use of fecal glucocorticold metabolite measures in conservation biology research: considerations for application and interpretation. – Gen. Comp. Endocrinol. 138: 189–199.
Google Scholar
203.
Millspaugh,
J. J. et al. 2001. Fecal glucocorticoid assays and the physiological stress response in elk. – Wildl. Soc. Bull. 29: 899–907.
Google Scholar
204.
Mingo,
V. et al. 2017. The use of buccal swabs as a minimal-invasive method for detecting effects of pesticide exposure on enzymatic activity in common wall lizards. – Environ. Pollut. 220: 53–62.
Google Scholar
205.
Minteer,
B. A. et al. 2014a. Avoiding (re)extinction. – Science 344: 260–261.
Google Scholar
206.
Minteer,
B. A. et al. 2014b. Specimen collection: plan for the future response. – Science 344: 816–816.
Google Scholar
207.
Mitchell,
E. P. et al. 2018. A new perspective on the pathogenesis of chronic renal disease in captive cheetahs (
Acinonyx jubatus). – PLoS One 13: e0194114.
Google Scholar
208.
Monteiro,
N. M. et al. 2014. Validating the use of colouration patterns for individual recognition in the worm pipefish using a novel set of microsatellite markers. – Mol. Ecol. Res. 14: 150–156.
Google Scholar
209.
Monterroso,
P. et al. 2014. Efficiency of hair snares and camera traps to survey mesocarnivore populations. – Eur. J. Wildl. Res. 60: 279–289.
Google Scholar
210.
Montiglio,
P. O. et al. 2012. Noninvasive monitoring of fecal cortisol metabolites in the eastern chipmunk (
Tamias striatus): validation and comparison of two enzyme immunoassays. – Physiol. Biochem. Zool. 85: 183–193.
Google Scholar
211.
Moreira,
D. O. et al. 2018. Determining the numbers of a landscape architect species (
Tapirus terrestris), using footprints. – Peerj 6: e4591.
Google Scholar
212.
Morinha,
F. et al. 2014. DNA sampling from body swabs of terrestrial slugs (Gastropoda: Pulmonata): a simple and non-invasive method for molecular genetics approaches. – J. Molluscan. Stud. 80: 99–101.
Google Scholar
213.
Mucci,
N. et al. 2014. Cloacal swab sampling is a reliable and harmless source of DNA for population and forensic genetics in tortoises. – Conserv. Gen. Res. 6: 845–847.
Google Scholar
214.
Mulcahy,
D. M. and
Esler,
D. 1999. Surgical and immediate postrelease mortality of harlequin ducks (
Histrionicus histrionicus) implanted with abdominal radio transmitters with percutaneous antennae. – J. Zoo Wildl. Med. 30: 397–401.
Google Scholar
215.
Mulero-Pazmany,
M. et al. 2015. Unmanned aircraft systems complement biologging in spatial ecology studies. – Ecol. Evol. 5: 4808–4818.
Google Scholar
216.
Munshi-South,
J. et al. 2008. Physiological indicators of stress in African forest elephants (
Loxodonta africana cyclotis) in relation to petroleum operations in Gabon, Central Africa. – Divers. Distrib. 14: 995–1003.
Google Scholar
217.
Nagai,
T. et al. 2014. Effectiveness of noninvasive DNA analysis to reveal isolated-forest use by the sable
Martes zibellina on eastern Hokkaido, Japan. – Mammal Study 39: 99–104.
Google Scholar
218.
Nair,
J. V. et al. 2018. An optimized protocol for large-scale in situ sampling and analysis of volatile organic compounds. – Ecol. Evol. 8: 5924–5936.
Google Scholar
219.
Nakamura,
M. et al. 2017. Evaluating the predictive power of field variables for species and individual molecular identification on wolf noninvasive samples. – Eur. J. Wildl. Res. 63: 53.
Google Scholar
220.
Narayan,
E. J. 2013. Non-invasive reproductive and stress endocrinology in amphibian conservation physiology. – Conserv. Physiol. 1: cot011.
Google Scholar
221.
Narayan,
E. et al. 2010. Urinary corticosterone metabolite responses to capture, and annual patterns of urinary corticosterone in wild and captive endangered Fijian ground frogs (
Platymantis vitiana). – Aust. J. Zool. 58: 189–197.
Google Scholar
223.
Nguyen,
H. Q. et al. 2017. Efficient isolation method for high-quality genomic DNA from cicada exuviae. – Ecol. Evol. 7: 8161–8169.
Google Scholar
224.
Nichols,
R. V. et al. 2012. Browsed twig environmental DNA: diagnostic PCR to identify ungulate species. – Mol. Ecol. Res. 12: 983–989.
Google Scholar
225.
NORECOPA 2008. Harmonisation of the care and use of animals in field research, Gardermoen, 21–22 May 2008. A consensus document from the participants, Gardermoen, Norway.
Google Scholar
226.
Nuvoli,
S. et al. 2014. Capture myopathy in a corsican red deer
Cervus elaphus corsicanus (Ungulata: Cervidae). – Ital. J. Zool. 81: 457–462.
Google Scholar
227.
O'Meara,
D. B. et al. 2014. Non-invasive multi-species monitoring: real-time PCR detection of small mammal and squirrel prey DNA in pine marten (
Martes martes) scats. – Acta Theriol. 59: 111–117.
Google Scholar
228.
O'Rorke,
R. et al. 2012. PCR enrichment techniques to identify the diet of predators. – Mol. Ecol. Res. 12: 5–17.
Google Scholar
229.
O'Rorke,
R. et al. 2015. Dining local: the microbial diet of a snail that grazes microbial communities is geographically structured. – Environ. Microbiol. 17: 1753–1764.
Google Scholar
230.
Olah,
G. et al. 2016. Validation of non-invasive genetic tagging in two large macaw species (
Ara macao and
A. chloropterus) of the Peruvian Amazon. – Conserv. Gen. Res. 8: 499–509.
Google Scholar
231.
Olivera-Tlahuel,
C. et al. 2017. Effect of toe-clipping on the survival of several lizard species. – Herpetol. J. 27: 266–275.
Google Scholar
232.
Olson,
Z. H. et al. 2012. An eDNA approach to detect eastern hellbenders (
Cryptobranchus a.
alleganiensis) using samples of water. – Wildl. Res. 39: 629–636.
Google Scholar
233.
Osterrieder,
S. K. et al. 2015. Whisker spot patterns: a noninvasive method of individual identification of Australian sea lions (
Neophoca cinerea). – J. Mammal. 96: 988–997.
Google Scholar
234.
Palmer,
A. N. S. et al. 2008. Foot mucus is a good source for non-destructive genetic sampling in Polyplacophora. – Conserv. Genet. 9: 229–231.
Google Scholar
235.
Panayotova,
I. N. and
Horth,
L. 2018. Modeling the impact of climate change on a rare color morph in fish. – Ecol. Model. 387: 10–16.
Google Scholar
236.
Parmenter,
C. A. et al. 1998. Small mammal survival and trapability in mark–recapture monitoring programs for hantavirus. – J. Wildl. Dis. 34: 1–12.
Google Scholar
237.
Parris,
K. M. et al. 2010. Assessing ethical tradeoffs in ecological field studies. – J. Appl. Ecol. 47: 227–234.
Google Scholar
238.
Paz,
R. C. R. et al. 2015. Fecal cortisol metabolites as indicators of stress in crabeating-fox (
Cerdocyoun thous) in captivity. – Pesqui. Vet. Bras. 35: 859–862.
Google Scholar
239.
Perez,
T. et al. 2011. Improving non-invasive genotyping in capercaillie (
Tetrao urogallus): redesigning sexing and microsatellite primers to increase efficiency on faeces samples. – Conserv. Gen. Res. 3: 483–487.
Google Scholar
240.
Phillips,
R. L. et al. 1996. Leg injuries to coyotes captured in three types of foothold traps. – Wildl. Soc. Bull. 24: 260–263.
Google Scholar
241.
Pichlmuller,
F. et al. 2013. Skin swabbing of amphibian larvae yields sufficient DNA for efficient sequencing and reliable microsatellite genotyping. – Amphib-Reptilia 34: 517–523.
Google Scholar
242.
Pimm,
S. L. et al. 2015. Emerging technologies to conserve biodiversity. – Trends Ecol. Evol. 30: 685–696.
Google Scholar
243.
Pinya,
S. and
Perez-Mellado,
V. 2009. Individual identification and sexual dimorphism in the endangered Balearic midwife toad,
Alytes muletensis (Sanchiz and Adrover, 1981). – Amphib-Reptilia 30: 439–443.
Google Scholar
244.
Player,
D. et al. 2017. An alternative minimally invasive technique for genetic sampling of bats: wing swabs yield species identification. – Wildl. Soc. Bull. 41: 590–596.
Google Scholar
245.
Poirier,
C. and
Bateson,
M. 2017. Pacing stereotypies in laboratory rhesus macaques: implications for animal welfare and the validity of neuroscientific findings. – Neurosci. Biobehav. Rev. 83: 508–515.
Google Scholar
246.
Pollard,
K. A. et al. 2010. Pre-screening acoustic and other natural signatures for use in noninvasive individual identification. – J. Appl. Ecol. 47: 1103–1109.
Google Scholar
247.
Pompanon,
F. et al. 2012. Who is eating what: diet assessment using next generation sequencing. – Mol. Ecol. 21: 1931–1950.
Google Scholar
248.
Poorten,
T. J. et al. 2017. Population genetic structure of the endangered Sierra Nevada yellow-legged frog (
Rana sierrae) in Yosemite National Park based on multi-locus nuclear data from swab samples. – Conserv. Genet. 18: 731–744.
Google Scholar
249.
Powell,
R. A. and
Proulx,
G. 2003. Trapping and marking terrestrial mammals for research: integrating ethics, performance criteria, techniques and common sense. – ILAR J. 44: 259–276.
Google Scholar
250.
Price,
M. R. et al. 2017. Diet selection at three spatial scales: implications for conservation of an endangered Hawaiian tree snail. – Biotropica 49: 130–136.
Google Scholar
251.
Proença-Ferreira,
A. et al. 2019. Drivers of survival in a small mammal of conservation concern: an assessment using extensive genetic non-invasive sampling in fragmented farmland. – Biol. Conserv. 230: 131–140.
Google Scholar
252.
Prugh,
L. R. et al. 2008. Use of faecal genotyping to determine individual diet. – Wildl. Biol. 14: 318–330.
Google Scholar
253.
Prunier,
J. et al. 2012. Skin swabbing as a new efficient DNA sampling technique in amphibians, and 14 new microsatellite markers in the alpine newt (
Ichthyosaura alpestris). – Mol. Ecol. Res. 12: 524–531.
Google Scholar
254.
Putman,
R. J. 1995. Ethical considerations and animal welfare in ecological field studies. – Biodivers. Conserv. 4: 903–915.
Google Scholar
255.
Quinn,
J. H. et al. 2010. Complication associated with abdominal surgical implantation of a rado transmitter in an American badger (
Taxidea taxus). – J. Zoo Wildl. Med. 41: 174–177.
Google Scholar
256.
Ramon-Laca,
A. et al. 2018. Extraction of DNA from captive-sourced feces and molted feathers provides a novel method for conservation management of New Zealand kiwi (
Apteryx spp.). – Ecol. Evol. 8: 3119–3130.
Google Scholar
257.
Rasambainarivo,
F. et al. 2017. Interactions between carnivores in Madagascar and the risk of disease transmission. – EcoHealth 14: 691–703.
Google Scholar
258.
Rasiulis,
A. L. et al. 2014. The effect of radio-collar weight on survival of migratory caribou. – J. Wildl. Manage. 78: 953–956.
Google Scholar
259.
Recio,
M. R. et al. 2011. Lightweight GPS-tags, one giant leap for wildlife tracking? An assessment approach. – PLoS One 6: e28225.
Google Scholar
260.
Reid,
S. M. et al. 2012. Validation of buccal swabs for noninvasive DNA sampling of small-bodied imperiled fishes. – J. Appl. Ichthyol. 28: 290–292.
Google Scholar
261.
Rendall,
A. R. et al. 2014. Camera trapping: a contemporary approach to monitoring invasive rodents in high conservation priority ecosystems. – PLoS One 9: e86592.
Google Scholar
262.
Robinson,
J. L. and
Jones,
I. L. 2014. An experimental study measuring the effects of a tarsus-mounted tracking device on the behaviour of a small pursuit-diving seabird. – Behaviour 151: 1799–1826.
Google Scholar
263.
Rode,
K. D. et al. 2014. Effects of capturing and collaring on polar bears: findings from long-term research on the southern Beaufort Sea population. – Wildl. Res. 41: 311–322.
Google Scholar
264.
Rodgers,
T. W. et al. 2017. Carrion fly-derived DNA metabarcoding is an effective tool for mammal surveys: evidence from a known tropical mammal community. – Mol. Ecol. Res. 17: e133–e145.
Google Scholar
265.
Rognan,
C. B. et al. 2009. Vocal individuality of great gray owls in the Sierra Nevada. – J. Wildl. Manage. 73: 755–760.
Google Scholar
266.
Romano,
M. C. et al. 2010. Stress in wildlife species: noninvasive monitoring of glucocorticoids. – Neuroimmunomodulation 17: 209–212.
Google Scholar
267.
Romiti,
F. et al. 2017. Photographic identification method (PIM) using natural body marks: a simple tool to make a long story short. – Zool. Anz. 266: 136–147.
Google Scholar
268.
Roques,
J. A. C. et al. 2010. Tailfin clipping, a painful procedure: studies on Nile tilapia and common carp. – Physiol. Behav. 101: 533–540.
Google Scholar
269.
Rosner,
S. et al. 2014. Noninvasive genetic sampling allows estimation of capercaillie numbers and population structure in the Bohemian Forest. – Eur. J. Wildl. Res. 60: 789–801.
Google Scholar
270.
Roy,
J. et al. 2014. Challenges in the use of genetic mark-recapture to estimate the population size of Bwindi mountain gorillas (
Gorilla beringei beringei). – Biol. Conserv. 180: 249–261.
Google Scholar
271.
Rudnick,
J. A. et al. 2007. Species identification of birds through genetic analysis of naturally shed feathers. – Mol. Ecol. Notes 7: 757–762.
Google Scholar
272.
Russell,
W. M. S. and
Burch,
R. L. 1959. The principles of humane experimental technique. – Methuen, London.
Google Scholar
273.
Russo,
D. et al. 2017. Collection of voucher specimens for bat research: conservation, ethical implications, reduction and alternatives. – Mammal Rev. 47: 237–246.
Google Scholar
274.
Saito,
M. et al. 2008. Individual identification of Asiatic black bears using extracted DNA from damaged crops. – Ursus 19: 162–167.
Google Scholar
275.
Sakai,
M. et al. 2011. Reactions of Heaviside's dolphins to tagging attempts using remotely-deployed suction-cup tags. – S. Afr. J. Wildl. Res. 41: 134–138.
Google Scholar
276.
Santymire,
R. M. et al. 2018. A novel method for the measurement of glucocorticoids in dermal secretions of amphibians. – Conserv. Physiol. 6: coy008.
Google Scholar
277.
Sawaya,
M. A. et al. 2011. Evaluation of noninvasive genetic sampling methods for cougars in Yellowstone National Park. – J. Wildl. Manage. 75: 612–622.
Google Scholar
278.
Scriven,
J. J. et al. 2013. Nondestructive DNA sampling from bumblebee faeces. – Mol. Ecol. Res. 13: 225–229.
Google Scholar
279.
Shannon,
G. et al. 2013. Effects of social disruption in elephants persist decades after culling. – Front. Zool. 10: 62.
Google Scholar
280.
Shepherd,
G. L. and
Somers,
C. M. 2012. Adapting the buccal micronucleus cytome assay for use in wild birds: age and sex affect background frequency in pigeons. – Environ. Mol. Mutag. 53: 136–144.
Google Scholar
281.
Shyne,
A. 2006. Meta-analytic review of the effects of enrichment on stereotypic behavior in zoo mammals. – Zoo Biol. 25: 317–337.
Google Scholar
282.
Schell,
C. J. et al. 2013. Anthropogenic and physiologically induced stress responses in captive coyotes. – J. Mammal. 94: 1131–1140.
Google Scholar
283.
Schmidt,
K. and
Schwarzkopf,
L. 2010. Visible implant elastomer tagging and toe-clipping: effects of marking on locomotor performance of frogs and skinks. – Herpetol. J. 20: 99–105.
Google Scholar
284.
Schnell,
I. B. et al. 2015. iDNA from terrestrial haematophagous leeches as a wildlife surveying and monitoring tool – prospects, pitfalls and avenues to be developed. – Front. Zool. 12: 24.
Google Scholar
285.
Schoenecker,
K. A. et al. 2015. Estimating bighorn sheep (
Ovis canadensis) abundance using noninvasive sampling at a mineral lick within a national park wilderness area. – West. N. Am. Nat. 75: 181–191.
Google Scholar
286.
Schulte,
U. et al. 2011. Buccal swabs as a reliable non-invasive tissue sampling method for DNA analysis in the lacertid lizard
Podarcis muralis. – N.-W. J. Zool. 7: 325–328.
Google Scholar
287.
Sint,
D. et al. 2015. Sparing spiders: faeces as a non-invasive source of DNA. – Front. Zool. 12: 3.
Google Scholar
288.
Sloman,
K. A. et al. 2019. Ethical considerations in fish research. – J. Fish Biol. 94: 556–577.
Google Scholar
289.
Smith,
G. C. and
Cheeseman,
C. L. 2002. A mathematical model for the control of diseases in wildlife populations: culling, vaccination and fertility control. – Ecol. Model. 150: 45–53.
Google Scholar
290.
Sneddon,
L. U. 2017. Pain in laboratory animals: a possible confounding factor? – ATLA 45: 161–164.
Google Scholar
291.
Sneddon,
L. U. et al. 2017. Considering aspects of the 3Rs principles within experimental animal biology. – J. Exp. Biol. 220: 3007–3016.
Google Scholar
292.
Sonne,
C. et al. 2012. Tissue healing in two harbor porpoises (
Phocoena phocoena) following long-term satellite transmitter attachment. – Mar. Mamm. Sci. 28: E316–E324.
Google Scholar
293.
Soto-Gamboa,
M. et al. 2009. Validation of a radioimmunoassay for measuring fecal cortisol metabolites in the hystricomorph rodent,
Octodon degus. – J. Exp. Zool. 311A: 496–503.
Google Scholar
294.
Stetz,
J. B. et al. 2011. Genetic monitoring for managers: a new online resource. – J. Fish Wildl. Manage. 2: 216–219.
Google Scholar
295.
Stokeld,
D. et al. 2015. Multiple cameras required to reliably detect feral cats in northern Australian tropical savanna: an evaluation of sampling design when using camera traps. – Wildl. Res. 42: 642–649.
Google Scholar
296.
Stratman,
M. R. and
Apker,
J. A. 2014. Using infrared cameras and skunk lure to monitor swift fox (
Vulpes velox). – Southwest. Nat. 59: 500–508.
Google Scholar
297.
Strausberger,
B. M. and
Ashley,
M. V. 2001. Eggs yield nuclear DNA from egg-laying female cowbirds, their embryos and offspring. – Conserv. Genet. 2: 385–390.
Google Scholar
298.
Sukalo,
G. et al. 2013. Novel, non-invasive method for distinguishing the individuals of the fire salamander (
Salamandra salamandra) in capture–mark–recapture studies. – Acta Herpetol. 8: 41–45.
Google Scholar
299.
Sutherland,
W. J. et al. 2004. Bird ecology and conservation: a handbook of techniques. – Oxford Univ. Press.
Google Scholar
300.
Sver,
L. et al. 2016. Camera traps on wildlife crossing structures as a tool in gray wolf (
Canis lupus) management: five-year monitoring of wolf abundance trends in Croatia. – PLoS One 11: e0156748.
Google Scholar
301.
Swanson,
B. J. et al. 2006. Shed skin as a source of DNA for genotyping seals. – Mol. Ecol. Notes 6: 1006–1009.
Google Scholar
302.
Sykes,
A. V. et al. 2017. Refining tools for studying cuttlefish (
Sepia officinalis) reproduction in captivity: in vivo sexual determination, tagging and DNA collection. – Aquaculture 479: 13–16.
Google Scholar
303.
Taslima,
K. et al. 2016. DNA sampling from mucus in the Nile tilapia,
Oreochromis niloticus: minimally invasive sampling for aquaculture-related genetics research. – Aquat. Res. 47: 4032–4037.
Google Scholar
304.
Terio,
K. A. et al. 2004. Evidence for chronic stress in captive but not free-ranging cheetahs (
Acinonyx jubatus) based on adrenal morphology and function. – J. Wildl. Dis. 40: 259–266.
Google Scholar
305.
Terry,
A. M. R. et al. 2005. The role of vocal individuality in conservation. – Front. Zool. 2: 10–10.
Google Scholar
306.
Tete,
N. et al. 2014. Hair as a noninvasive tool for risk assessment: do the concentrations of cadmium and lead in the hair of wood mice (
Apodemus sylvaticus) reflect internal concentrations? – Ecotoxicol. Environ. Saf. 108: 233–241.
Google Scholar
307.
Thaxter,
C. B. et al. 2017. Sample size required to characterize area use of tracked seabirds. – J. Wildl. Manage. 81: 1098–1109.
Google Scholar
308.
Thompson,
D. P. et al. 2018. Vaginal implant transmitters for continuous body temperature measurement in moose. – Wildl. Soc. Bull. 42: 321–327.
Google Scholar
309.
Thornton,
D. H. and
Pekins,
C. E. 2015. Spatially explicit capture–recapture analysis of bobcat (
Lynx rufus) density: implications for mesocarnivore monitoring. – Wildl. Res. 42: 394–404.
Google Scholar
310.
Trefry,
S. A. et al. 2013. Wing marker woes: a case study and meta-analysis of the impacts of wing and patagial tags. – J. Ornithol. 154: 1–11.
Google Scholar
311.
Uher-Koch,
B. D. et al. 2015. Nest visits and capture events affect breeding success of yellow-billed and Pacific loons. – Condor 117: 121–129.
Google Scholar
312.
Vallant,
S. et al. 2018. Increased DNA typing success for feces and feathers of capercaillie (
Tetrao urogallus) and black grouse (
Tetrao tetrix). – Ecol. Evol. 8: 3941–3951.
Google Scholar
313.
Vargas,
M. L. et al. 2009. Noninvasive recovery and detection of possum
Trichosurus vulpecula DNA from bitten bait interference devices (WaxTags). – Mol. Ecol. Res. 9: 505–515.
Google Scholar
314.
Veltheim,
I. et al. 2015. Assessing capture and tagging methods for brolgas,
Antigone rubicunda (Gruidae). – Wildl. Res. 42: 373–381.
Google Scholar
315.
Vermeulen,
F. et al. 2009. Relevance of hair and spines of the European hedgehog (
Erinaceus europaeus) as biomonitoring tissues for arsenic and metals in relation to blood. – Sci. Total. Environ. 407: 1775–1783.
Google Scholar
316.
Vilstrup,
J. T. et al. 2018. A simplified field protocol for genetic sampling of birds using buccal swabs. – Wilson. J. Ornithol. 130: 326–334.
Google Scholar
317.
Vincent,
C. et al. 2001. Photo-identification in grey seals: legibility and stability of natural markings. – Mammalia 65: 363–372.
Google Scholar
318.
Voigt,
C. C. et al. 2005. Blood-sucking bugs as a gentle method for blood-collection in water budget studies using doubly labelled water. – Compar. Biochem. Physiol. 142: 318–324.
Google Scholar
319.
Vucetich,
J. A. and
Nelson,
M. P. 2007. What are 60 warblers worth? Killing in the name of conservation. – Oikos 116: 1267–1278.
Google Scholar
320.
Waits,
L. P. and
Paetkau,
D. 2005. Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. – J. Wildl. Manage. 69: 1419–1433.
Google Scholar
321.
Walker,
K. A. et al. 2010. Behavioural responses of juvenile Steller sea lions to hot-iron branding. – Appl. Anim. Behav. Sci. 122: 58–62.
Google Scholar
322.
Washburn,
B. E. et al. 2003. Using fecal glucocorticoids for stress assessment in mourning doves. – Condor 105: 696–706.
Google Scholar
323.
Waugh,
C. A. and
Monamy,
V. 2016. Opposing lethal wildlife research when nonlethal methods exist: scientific whaling as a case study. – J. Fish Wildl. Manage. 7: 231–236.
Google Scholar
324.
Wechsler,
B. 1991. Stereotypies in polar bears. – Zoo. Biol. 10: 177–188.
Google Scholar
325.
Weiss,
B. M. et al. 2018a. Chemical composition of axillary odor-ants reflects social and individual attributes in rhesus macaques. – Behav. Ecol. Sociobiol. 72: 65.
Google Scholar
326.
Weiss,
B. M. et al. 2018b. A non-invasive method for sampling the body odour of mammals. – Methods Ecol. Evol. 9: 420–429.
Google Scholar
327.
Welbourne,
D. J. et al. 2015. The effectiveness and cost of camera traps for surveying small reptiles and critical weight range mammals: a comparison with labour-intensive complementary methods. – Wildl. Res. 42: 414–425.
Google Scholar
328.
Wey,
T. W. et al. 2015. Stress hormone metabolites predict overwinter survival in yellow-bellied marmots. – Acta Ethol. 18: 181–185.
Google Scholar
329.
Wheat,
R. E. et al. 2016. Environmental DNA from residual saliva for efficient noninvasive genetic monitoring of brown bears (
Ursus arctos). – PLoS One 11: e0165259.
Google Scholar
330.
Wilbert,
T. R. et al. 2015. Non-invasive baseline genetic monitoring of the endangered San Joaquin kit fox on a photovoltaic solar facility. – Endanger. Species Res. 27: 31–41.
Google Scholar
331.
Wilkening,
J. L. et al. 2016. When can we measure stress noninvasively? Postdeposition effects on a fecal stress metric confound a multiregional assessment. – Ecol. Evol. 6: 502–513.
Google Scholar
332.
Wilkie,
S. C. et al. 2018. Trapped river otters (
Lontra canadensis) from central Saskatchewan differ in total and organic mercury concentrations by sex and geographic location. – Facets 3: 139–154.
Google Scholar
333.
Williams,
E. et al. 2018a. A review of current indicators of welfare in captive elephants (
Loxodonta africana and
Elephas maximus). – Anim. Welf. 27: 235–249.
Google Scholar
334.
Williams,
K. E. et al. 2018b. Detection and persistence of environmental DNA from an invasive, terrestrial mammal. – Ecol. Evol. 8: 688–695.
Google Scholar
335.
Wilson,
R. P. and
McMahon,
C. R. 2006. Measuring devices on wild animals: what constitutes acceptable practice? – Front. Ecol. Environ. 4: 147–154.
Google Scholar
336.
Wilson,
R. P. et al. 2015. Pushed to the limit: food abundance determines tag-induced harm in penguins. – Anim. Welf. 24: 37–44.
Google Scholar
337.
Xing,
H. et al. 2019. Identification of signal pathways for immunotoxicity in the spleen of common carp exposed to chlorpyrifos. – Ecotoxicol. Environ. Saf. 182.
Google Scholar
338.
Xu,
C. C. Y. et al. 2015. Spider web DNA: a new spin on noninvasive genetics of predator and prey. – PLoS One 10: e0142503.
Google Scholar
339.
Yeruham,
I. et al. 1996. Skin tumours in cattle and sheep after freeze- or heat-branding. – J. Comp. Pathol. 114: 101–106.
Google Scholar
340.
Yin,
D. Y. and
He,
F. L. 2014. A simple method for estimating species abundance from occurrence maps. – Methods Ecol. Evol. 5: 336–343.
Google Scholar
341.
Yu,
X. J. et al. 2011. Non-invasive determination of fecal steroid hormones relating to conservation practice in giant panda (
Ailuropoda melanoleuca). – Anim. Biol. 61: 335–347.
Google Scholar
342.
Zang,
L. Q. et al. 2013. A novel, reliable method for repeated blood collection from aquarium fish. – Zebrafish 10: 425–432.
Google Scholar
343.
Zemanova,
M. A. 2017. More training in animal ethics needed for European biologists. – Bioscience 67: 301–305.
Google Scholar
344.
Zemanova,
M. A. 2019. Poor implementation of non-invasive sampling in wildlife genetics studies. – Rethink. Ecol. 4: 119–132.
Google Scholar
345.
Zemanova,
M. A. et al. 2018. Slimy invasion: climatic niche and current and future biogeography of
Arion slug invaders. – Divers. Distrib. 24: 1627–1640.
Google Scholar
346.
Zheng,
X. et al. 2016. Individual identification of wild giant pandas from camera trap photos – a systematic and hierarchical approach. – J. Zool. 300: 247–256.
Google Scholar
