Translator Disclaimer
17 March 2020 Towards more compassionate wildlife research through the 3Rs principles: moving from invasive to non-invasive methods
Author Affiliations +

Research in ecology and wildlife biology remains crucial for increasing our knowledge and improving species management and conservation in the midst of the current biodiversity crisis. However, obtaining information on population status often involves invasive sampling of a certain number of individual animals. Marking and sampling practices include taking blood and tissue samples, toe-clipping of amphibians and rodents, or using implants and radio-transmitters – techniques that can negatively affect the animal. Wildlife research may then result in a fundamental conflict between individual animal welfare and the welfare of the population or ecosystem, which could be significantly reduced if non-invasive research practices were more broadly implemented. Implementation of non-invasive methods could be guided by the so-called 3Rs principles for animal research (replace, reduce, refine), which were proposed by Russell and Burch 60 years ago and have become a part of many animal protection legislations worldwide. However, the process of incorporating the 3Rs principles into wildlife research has been unfortunately rather slow and their importance overlooked. In order to help alleviate this situation, here I provide an overview of the most common practices in wildlife research, discuss their potential impact on animal welfare, and present available non-invasive alternatives.

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.


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).


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.


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.


Toe clipping is a classic method for marking small vertebrates such as lizards, amphibians and rodents. Unique marking is achieved by clipping toes in different combinations on different limbs. Although considered harmless by some authors (Grafe et al. 2011, Ginnan et al. 2014), toe-clipping can, in fact, result in reduced survival rate (McCarthy and Parris 2004, Olivera-Tlahuel et al. 2017). It also has negative effect on locomotor performance and endurance (Schmidt and Schwarzkopf 2010) as well as the clinging performance of pad-bearing lizards, which was documented in the Carolina anole Anolis carolinensis (Bloch and Irschick 2005).

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).


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 13 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 13). 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.



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


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


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).


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).

Species abundance

Biodiversity assessment could be done through count surveys and visual sampling (Lecq et al. 2015, Ksiazkiewicz-Parulska and Goldyn 2017). Methodology on estimating species abundance from occurrence maps has been also recently published (Yin and He 2014).

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 13), 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.


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.



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


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


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


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


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


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


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


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


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


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


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


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


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


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


Athreya, R. 2006. A new species of Liocichla (Aves: Timaliidae) from Eaglenest Wildlife Sanctuary. – Indian Birds 2: 82–94. Google Scholar


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


Auger-Methe, M. et al. 2011. Computer-assisted photo-identification of narwhals. – Arctic 64: 342–352. Google Scholar


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


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


Barbknecht, A. E. et al. 2009. Effectiveness of vaginal-implant transmitters for locating elk parturition sites. – J. Wildl. Manage. 73: 144–148. Google Scholar


Barnas, A. et al. 2018a. Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys. – Ecol. Evol. 8: 1328–1338. Google Scholar


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


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


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


Becker, D. J. et al. 2018. Mercury bioaccumulation in bats reflects dietary connectivity to aquatic food webs. – Environ. Pollut. 233: 1076–1085. Google Scholar


Beebee, T. J. C. 2008. Buccal swabbing as a source of DNA from squamate reptiles. – Conserv. Genet. 9: 1087–1088. Google Scholar


Behringer, V. and Deschner, T. 2017. Non-invasive monitoring of physiological markers in primates. – Horm. Behav. 91: 3–18. Google Scholar


Beja-Pereira, A. et al. 2009. Advancing ecological understandings through technological transformations in noninvasive genetics. – Mol. Ecol. Res. 9: 1279–1301. Google Scholar


Bekoff, M. 2002. The importance of ethics in conservation biology: let's be ethicists, not ostriches. – Endanger. Species Update 19: 23–26. Google Scholar


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


Berglund, A. M. M. 2018. Evaluating blood and excrement as bioindicators for metal accumulation in birds. – Environ. Pollut. 233: 1198–1206. Google Scholar


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


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


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


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


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


Blake, M. et al. 2016. DNA extraction from spider webs. – Conserv. Gen. Res. 8: 219–221. Google Scholar


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


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


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


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


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


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


Broquet, T. et al. 2007. Buccal swabs allow efficient and reliable microsatellite genotyping in amphibians. – Conserv. Genet. 8: 509–511. Google Scholar


Brown, M. B. and Brown, C. R. 2009. Blood sampling reduces annual survival in cliff swallows (Petrochelidon pyrrhonota). – Auk 126: 853–861. Google Scholar


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


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


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


Buchanan, K. et al. 2015. Guidelines for the treatment of animals in behavioural research and teaching. – Anim. Behav. 99: I–IX. Google Scholar


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


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


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


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


Carroll, E. L. et al. 2018. Genetic and genomic monitoring with minimally invasive sampling methods. – Evol. Appl. 11: 1094–1119. Google Scholar


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


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


Ceballos, G. et al. 2015. Accelerated modern human-induced species losses: entering the sixth mass extinction. – Sci. Adv. 1: e1400253. Google Scholar


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


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


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


Costello, M. J. et al. 2016. Field work ethics in biological research. – Biol. Conserv. 203: 268–271. Google Scholar


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


Crettaz von Roten, F. 2009. European attitudes towards animal research: overview and consequences for science. – Sci. Technol. Soc. 14: 349–364. Google Scholar


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


Curzer, H. J. et al. 2013. The ethics of wildlife research: a nine R theory. – ILAR J. 54: 52–57. Google Scholar


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


Dalen, L. et al. 2007. Recovery of DNA from footprints in the snow. – Can. Field-Nat. 121: 321–324. Google Scholar


Dalton, R. 2005. Animal-rights group sues over ‘disturbing’ work on sea lions. – Nature 436: 315–315. Google Scholar


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


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


de Abreu, M. S. et al. 2014. Diazepam and fluoxetine decrease the stress response in zebrafish. – PLoS One 9: e103232. Google Scholar


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


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


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


Donnelly, M. A. et al. 1994. Techniques for marking amphibians. – Smithsonian Inst. Press. Google Scholar


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


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


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


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


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


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


Fernandez, S. et al. 2018. Environmental DNA for freshwater fish monitoring: insights for conservation within a protected area. – Peerj 6: e4486. Google Scholar


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


Field, I. C. et al. 2012. Refining instrument attachment on phocid seals. – Mar. Mamm. Sci. 28: E325–E332. Google Scholar


Field, K. A. et al. 2019. Publication reform to safeguard wildlife from researcher harm. – PLoS Biol. 17: e3000193. Google Scholar


Fink, C. et al. 2013. Noninvasive analysis of microbiome dynamics in the fruit fly Drosophila melanogaster. – Appl. Environ. Microbiol. 79: 6984–6988. Google Scholar


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


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


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


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


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


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


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


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


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


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


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


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


Ginnan, N. A. et al. 2014. Toe clipping does not affect the survival of leopard frogs (Rana pipiens). – Copeia 2014: 650–653. Google Scholar


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


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


Gomez, A. et al. 2004. Use of salivary steroid analyses to assess ovariancyclesinanIndianrhinocerosattheNationalZoological Park. – Zoo. Biol. 23: 501–512. Google Scholar


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


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


Gould, M. J. et al. 2018. Density of American black bears in New Mexico. – J. Wildl. Manage. 82: 775–788. Google Scholar


Grafe, T. U. et al. 2011. Putting toe clipping into perspective: a viable method for marking anurans. – J. Herpetol. 45: 28–35. Google Scholar


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


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


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


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


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


Habicher, A. et al. 2013. Tsetse flies as tools for minimally invasive blood sampling. – Wildl. Soc. Bull. 37: 423–427. Google Scholar


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


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


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


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


Harms, V. et al. 2015. Experimental evaluation of genetic predator identification from saliva traces on wildlife kills. – J. Mammal. 96: 138–143. Google Scholar


Harrop, S. 2011. Climate change, conservation and the place for wild animal welfare in international law. – J. Environ. Law 23: 441–462. Google Scholar


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


Herzog, C. J. et al. 2007. Using patterns in track-plate footprints to identify individual fishers. – J. Wildl. Manage. 71: 955–963. Google Scholar


Higashide, D. et al. 2012. Are chest marks unique to Asiatic black bear individuals? – J. Zool. 288: 199–206. Google Scholar


Higham, J. P. et al. 2010. Measuring salivary analytes from free-ranging monkeys. – Physiol. Behav. 101: 601–607. Google Scholar


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


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


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


Hodgson, J. C. et al. 2018. Drones count wildlife more accurately and precisely than humans. – Methods Ecol. Evol. 9: 1160–1167. Google Scholar


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


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


Hood, L. C. et al. 1998. The adrenocortical response to stress in incubating Magellanic penguins (Spheniscus magellanicus). – Auk 115: 76–84. Google Scholar


Horreo, J. L. et al. 2015. Skin sheds as a useful DNA source for lizard conservation. – Phyllomedusa 14: 73–77. Google Scholar


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


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


Huffard, C. L. et al. 2008. Individually unique body color patterns in octopus (Wunderpus photogenicus) allow for photoidentification. – PLoS One 3: e3732. Google Scholar


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


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


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


Chamberlain, D. E. et al. 2009. Avian productivity in urban landscapes: a review and meta-analysis. – Ibis 151: 1–18. Google Scholar


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


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


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


Jackson, S. and Wilson, R. P. 2002. The potential costs of flipper-bands to penguins. – Funct. Ecol. 16: 141–148. Google Scholar


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


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


Jerde, C. L. et al. 2011. ‘Sight-unseen’ detection of rare aquatic species using environmental DNA. – Conserv. Lett. 4: 150–157. Google Scholar


Jett, J. et al. 2017. Tooth damage in captive orcas (Orcinus orca). – Arch. Oral Biol. 84: 151–160. Google Scholar


Jewell, Z. 2013. Effect of monitoring technique on quality of conservation science. – Conserv. Biol. 27: 501–508. Google Scholar


Jewell, Z. C. et al. 2016. Spotting cheetahs: identifying individuals by their footprints. – Jove-J. Vis. Exp. 111: e54034. Google Scholar


Jirkof, P. 2017. Side effects of pain and analgesia in animal experimentation. – Lab Anim. 46: 123–128. Google Scholar


Johnson, M. A. 2005. A new method of temporarily marking lizards. – Herpetol. Rev. 36: 277–279. Google Scholar


Jordan, M. J. et al. 2011. Camera trapping estimates of density and survival of fishers Martes pennanti. – Wildl. Biol. 17: 266–276. Google Scholar


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


Karanth, K. U. et al. 2006. Assessing tiger population dynamics using photographic capture–recapture sampling. – Ecology 87: 2925–2937. Google Scholar


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


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


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


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


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


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


Kendall, K. C. and McKelvey, K. 2012. Hair collection. – In: Long, R. A. (ed.), Noninvasive survey methods for carnivores. Island Press. Google Scholar


Kilkenny, C. et al. 2010. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. – PLoS Biol. 8: e1000412. Google Scholar


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


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


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


Kolbe, J. A. et al. 2003. An effective box trap for capturing lynx. – Wildl. Soc. Bull. 31: 980–985. Google Scholar


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


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


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


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


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


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


Lecq, S. et al. 2015. Non-lethal rapid biodiversity assessment. – Ecol. Indic. 58: 216–224. Google Scholar


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


Lefort, M.-C. et al. 2019. Blood, sweat and tears: a review of non-invasive DNA sampling. – bioRxiv. < >. Google Scholar


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


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


Li, B. B. V. et al. 2018. Using footprints to identify and sex giant pandas. – Biol. Conserv. 218: 83–90. Google Scholar


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


Lindsjö, J. et al. 2019. The dividing line between wildlife research and management – implications for animal welfare. – Front. Vet. Sci. 6: 13. Google Scholar


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


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


Lobo, D. et al. 2015. A new method for noninvasive genetic sampling of saliva in ecological research. – PLoS One 10: e0139765. Google Scholar


Lugg, W. H. et al. 2018. Optimal survey designs for environmental DNA sampling. – Methods Ecol. Evol. 9: 1049–1059. Google Scholar


Lund, T. B. et al. 2012. Public attitude formation regarding animal research. – Anthrozoos 25: 475–490. Google Scholar


Macpherson, J. W. and Penner, P. 1967. Animal identification I: liquid nitrogen branding of cattle. – Can. J. Comp. Med. 31: 271–274. Google Scholar


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


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


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


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


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


Marino, L. and Frohoff, T. 2011. Towards a new paradigm of non-captive research on cetacean cognition. – PLoS One 6: e24121. Google Scholar


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


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


Maurer, G. et al. 2010. A ‘feather-trap’ for collecting DNA samples from birds. – Mol. Ecol. Res. 10: 129–134. Google Scholar


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


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


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


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


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


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


Mellor, D. J. 2016. Updating animal welfare thinking: moving beyond the ‘five freedoms’ towards ‘a life worth living'. – Animals 6: 21. Google Scholar


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


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


Millspaugh, J. J. et al. 2001. Fecal glucocorticoid assays and the physiological stress response in elk. – Wildl. Soc. Bull. 29: 899–907. Google Scholar


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


Minteer, B. A. et al. 2014a. Avoiding (re)extinction. – Science 344: 260–261. Google Scholar


Minteer, B. A. et al. 2014b. Specimen collection: plan for the future response. – Science 344: 816–816. Google Scholar


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


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


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


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


Moreira, D. O. et al. 2018. Determining the numbers of a landscape architect species (Tapirus terrestris), using footprints. – Peerj 6: e4591. Google Scholar


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


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


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


Mulero-Pazmany, M. et al. 2015. Unmanned aircraft systems complement biologging in spatial ecology studies. – Ecol. Evol. 5: 4808–4818. Google Scholar


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


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


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


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


Narayan, E. J. 2013. Non-invasive reproductive and stress endocrinology in amphibian conservation physiology. – Conserv. Physiol. 1: cot011. Google Scholar


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


NC3Rs 2018. Wildlife research. – < Scholar


Nguyen, H. Q. et al. 2017. Efficient isolation method for high-quality genomic DNA from cicada exuviae. – Ecol. Evol. 7: 8161–8169. Google Scholar


Nichols, R. V. et al. 2012. Browsed twig environmental DNA: diagnostic PCR to identify ungulate species. – Mol. Ecol. Res. 12: 983–989. Google Scholar


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


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


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


O'Rorke, R. et al. 2012. PCR enrichment techniques to identify the diet of predators. – Mol. Ecol. Res. 12: 5–17. Google Scholar


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


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


Olivera-Tlahuel, C. et al. 2017. Effect of toe-clipping on the survival of several lizard species. – Herpetol. J. 27: 266–275. Google Scholar


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


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


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


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


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


Parris, K. M. et al. 2010. Assessing ethical tradeoffs in ecological field studies. – J. Appl. Ecol. 47: 227–234. Google Scholar


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


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


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


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


Pimm, S. L. et al. 2015. Emerging technologies to conserve biodiversity. – Trends Ecol. Evol. 30: 685–696. Google Scholar


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


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


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


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


Pompanon, F. et al. 2012. Who is eating what: diet assessment using next generation sequencing. – Mol. Ecol. 21: 1931–1950. Google Scholar


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


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


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


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


Prugh, L. R. et al. 2008. Use of faecal genotyping to determine individual diet. – Wildl. Biol. 14: 318–330. Google Scholar


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


Putman, R. J. 1995. Ethical considerations and animal welfare in ecological field studies. – Biodivers. Conserv. 4: 903–915. Google Scholar


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


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


Rasambainarivo, F. et al. 2017. Interactions between carnivores in Madagascar and the risk of disease transmission. – EcoHealth 14: 691–703. Google Scholar


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


Recio, M. R. et al. 2011. Lightweight GPS-tags, one giant leap for wildlife tracking? An assessment approach. – PLoS One 6: e28225. Google Scholar


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


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


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


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


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


Rognan, C. B. et al. 2009. Vocal individuality of great gray owls in the Sierra Nevada. – J. Wildl. Manage. 73: 755–760. Google Scholar


Romano, M. C. et al. 2010. Stress in wildlife species: noninvasive monitoring of glucocorticoids. – Neuroimmunomodulation 17: 209–212. Google Scholar


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


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


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


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


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


Russell, W. M. S. and Burch, R. L. 1959. The principles of humane experimental technique. – Methuen, London. Google Scholar


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


Saito, M. et al. 2008. Individual identification of Asiatic black bears using extracted DNA from damaged crops. – Ursus 19: 162–167. Google Scholar


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


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


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


Scriven, J. J. et al. 2013. Nondestructive DNA sampling from bumblebee faeces. – Mol. Ecol. Res. 13: 225–229. Google Scholar


Shannon, G. et al. 2013. Effects of social disruption in elephants persist decades after culling. – Front. Zool. 10: 62. Google Scholar


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


Shyne, A. 2006. Meta-analytic review of the effects of enrichment on stereotypic behavior in zoo mammals. – Zoo Biol. 25: 317–337. Google Scholar


Schell, C. J. et al. 2013. Anthropogenic and physiologically induced stress responses in captive coyotes. – J. Mammal. 94: 1131–1140. Google Scholar


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


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


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


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


Sint, D. et al. 2015. Sparing spiders: faeces as a non-invasive source of DNA. – Front. Zool. 12: 3. Google Scholar


Sloman, K. A. et al. 2019. Ethical considerations in fish research. – J. Fish Biol. 94: 556–577. Google Scholar


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


Sneddon, L. U. 2017. Pain in laboratory animals: a possible confounding factor? – ATLA 45: 161–164. Google Scholar


Sneddon, L. U. et al. 2017. Considering aspects of the 3Rs principles within experimental animal biology. – J. Exp. Biol. 220: 3007–3016. Google Scholar


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


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


Stetz, J. B. et al. 2011. Genetic monitoring for managers: a new online resource. – J. Fish Wildl. Manage. 2: 216–219. Google Scholar


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


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


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


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


Sutherland, W. J. et al. 2004. Bird ecology and conservation: a handbook of techniques. – Oxford Univ. Press. Google Scholar


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


Swanson, B. J. et al. 2006. Shed skin as a source of DNA for genotyping seals. – Mol. Ecol. Notes 6: 1006–1009. Google Scholar


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


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


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


Terry, A. M. R. et al. 2005. The role of vocal individuality in conservation. – Front. Zool. 2: 10–10. Google Scholar


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


Thaxter, C. B. et al. 2017. Sample size required to characterize area use of tracked seabirds. – J. Wildl. Manage. 81: 1098–1109. Google Scholar


Thompson, D. P. et al. 2018. Vaginal implant transmitters for continuous body temperature measurement in moose. – Wildl. Soc. Bull. 42: 321–327. Google Scholar


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


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


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


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


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


Veltheim, I. et al. 2015. Assessing capture and tagging methods for brolgas, Antigone rubicunda (Gruidae). – Wildl. Res. 42: 373–381. Google Scholar


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


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


Vincent, C. et al. 2001. Photo-identification in grey seals: legibility and stability of natural markings. – Mammalia 65: 363–372. Google Scholar


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


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


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


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


Washburn, B. E. et al. 2003. Using fecal glucocorticoids for stress assessment in mourning doves. – Condor 105: 696–706. Google Scholar


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


Wechsler, B. 1991. Stereotypies in polar bears. – Zoo. Biol. 10: 177–188. Google Scholar


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


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


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


Wey, T. W. et al. 2015. Stress hormone metabolites predict overwinter survival in yellow-bellied marmots. – Acta Ethol. 18: 181–185. Google Scholar


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


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


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


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


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


Williams, K. E. et al. 2018b. Detection and persistence of environmental DNA from an invasive, terrestrial mammal. – Ecol. Evol. 8: 688–695. Google Scholar


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


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


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


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


Yeruham, I. et al. 1996. Skin tumours in cattle and sheep after freeze- or heat-branding. – J. Comp. Pathol. 114: 101–106. Google Scholar


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


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


Zang, L. Q. et al. 2013. A novel, reliable method for repeated blood collection from aquarium fish. – Zebrafish 10: 425–432. Google Scholar


Zemanova, M. A. 2017. More training in animal ethics needed for European biologists. – Bioscience 67: 301–305. Google Scholar


Zemanova, M. A. 2019. Poor implementation of non-invasive sampling in wildlife genetics studies. – Rethink. Ecol. 4: 119–132. Google Scholar


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


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
© 2020 The Author. This is an Open Access article This work is licensed under the terms of a Creative Commons Attribution 4.0 International License (CC-BY). The license permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Miriam A. Zemanova "Towards more compassionate wildlife research through the 3Rs principles: moving from invasive to non-invasive methods," Wildlife Biology 2020(1), (17 March 2020).
Accepted: 29 January 2020; Published: 17 March 2020

Back to Top