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A rather dry subject; investigating the study of arid-associated microbial communities


Almost one third of Earth’s land surface is arid, with deserts alone covering more than 46 million square kilometres. Nearly 2.1 billion people inhabit deserts or drylands and these regions are also home to a great diversity of plant and animal species including many that are unique to them. Aridity is a multifaceted environmental stress combining a lack of water with limited food availability and typically extremes of temperature, impacting animal species across the planet from polar cold valleys, to Andean deserts and the Sahara. These harsh environments are also home to diverse microbial communities, demonstrating the ability of bacteria, fungi and archaea to settle and live in some of the toughest locations known. We now understand that these microbial ecosystems i.e. microbiotas, the sum total of microbial life across and within an environment, interact across both the environment, and the macroscopic organisms residing in these arid environments. Although multiple studies have explored these microbial communities in different arid environments, few studies have examined the microbiota of animals which are themselves arid-adapted. Here we aim to review the interactions between arid environments and the microbial communities which inhabit them, covering hot and cold deserts, the challenges these environments pose and some issues arising from limitations in the field. We also consider the work carried out on arid-adapted animal microbiotas, to investigate if any shared patterns or trends exist, whether between organisms or between the animals and the wider arid environment microbial communities. We determine if there are any patterns across studies potentially demonstrating a general impact of aridity on animal-associated microbiomes or benefits from aridity-adapted microbiomes for animals. In the context of increasing desertification and climate change it is important to understand the connections between the three pillars of microbiome, host genome and environment.


Water is essential for all known forms of life [1] and a stable arid environment is characterised by low annual precipitation (depending on location from 500- < 10 mm precipitation annually), with desertification occurring with greater loss than gain [2]. Approximately 30% [3] of the land surface area of the Earth is classified as ‘arid’. However even extreme deserts, including both hot and cold extremes and areas with exceptionally little precipitation, are still home to a wide diversity of life from microscopic [4, 5], to large mammals [6, 7] and long lived charismatic flora [8]. It is important therefore to understand how life is able to survive and adapt to the challenge of obtaining and retaining water, along with the other threats to animal life listed in Table 1. Aridity driven adaptations in plants [17, 18] as well as behavioural adaptations in animals [19] have already been well described and will not be addressed in this review. Research has demonstrated the importance of microbial organisms when living as members of associated communities on and in animals; for growth and development [20], dietary necessity [21] and for maintaining expected behaviour [22]. It is reasonable therefore to infer that the microbiota of animals resident in arid environments contributes to their host’s fitness in such harsh conditions. This may be through mechanisms already known from the study of microbiota in model organisms or by unique mechanisms.

Table 1 Examples of environmental factors which make arid environments inhospitable for animal life, and associated challenges faced by living organisms

Here we review the interactions between arid environments and the microbial communities which inhabit them along with the typical stresses these environments present. We specifically highlight and compare studies in arid-adapted animal microbiota, investigating patterns across studies, potentially demonstrating a general impact of aridity on animal-associated microbiomes or benefits from hosting aridity-adapted bacteria, fungi and archaea. We use the terms ‘desert’ and ‘arid’ interchangeably throughout the piece (as naturally occurring arid environments are referred to as deserts), typically naming the desert in question when relevant and indicating if a hot or cold desert is being discussed.

In this review we:

  1. I.

    Review environmental microbiology studies in arid environments through the different environmental factors acting on bacteria, fungi and archaea

  2. II.

    Describe animal physiological adaptations to aridity & highlight animal-associated microbiomes and their roles

  3. III.

    Review arid-associated animal microbiomes, examining two camels in particular and the impact on microbiomes of environmental factors associated with aridity

  4. IV.

    Discuss some of the challenges and opportunities around studying arid animal and environmental microbiomes

Increased global desertification [23,24,25], accelerating climate change [26] and changing land management [27] highlight the importance of better understanding arid ecosystems and animal adaptations to them. There have been comparatively few environmental microbiology studies on arid environments though in the hunt for extremophiles and pharmaceutically useful compounds there have been some focussed investigations of particular locations [28,29,30,31]. Animal-associated microbiota research has often been a component of a larger investigation into a particular host species [32] rather than a systemic approach considering the microbiome as a feature of animal life in arid environments.

Aridity and environmental microbiology

Microorganisms can be found in almost all environments, adapting over millions of years to survive and thrive in conditions ranging from extremely hostile [33,34,35] to resource rich [36, 37]. Prior researchers were limited by the need to culture microorganisms, significantly impacting the scope of investigations on microbial diversity. Recent studies have indicated the importance of ‘culture-omics’ [38, 39] for creating large-scale microbial collections for mechanistic analysis and classifying metagenomic results which could not be assigned purely from bioinformatics. This has allowed investigation of environmental [40] and animal-associated [41] bacteria, fungi and archaea detected through bioinformatics and sequencing, but previously difficult to culture. The ability to sequence environmental samples and analyse genetic material without a need to grow microorganisms has allowed more accurate and large-scale accounting of existing diversity [42,43,44]. With the advent of new techniques and reduced sequencing cost, came a greater understanding of the influence of abiotic and biotic factors acting on microbial communities. Temperature [45,46,47,48], UV exposure [49, 50], salinity [51, 52], humidity [53], pH [54], irradiation [55], pressure [56], pollution [57] and oxygen concentration [58] have all been demonstrated to influence microbiomes.

A similar suite of challenges is encountered in almost all arid environments, foremost amongst these being the lack of free water. These environmental stresses may lead to the establishment of new species, affect the composition of a microbial community (presence or absence of given members) and the relative abundances of species in a community (the level at which members are present).

Desiccation - lack of water

Lack of free water is the defining trait of arid environments, irrespective of temperature [59]. From a microbial perspective the lack of water presents the same dangers as those faced by macroscopic organisms along with uniquely microscopic challenges. In deserts where free water availability is low and the medium in which activity occurs is largely absent, Ho-Kyung Song et. al. [60] found that desiccation led to selection against motility associated proteins within their studied bacteria. They note that these proteins are associated with flagella; and this selective pressure may not be seen with alternative methods of motility. Multiple studies across different natural environments demonstrate reduced microbial diversity with desiccation [61,62,63] compared to sites with greater water availability. This may come from the impaired ability to obtain nutrients from free-moving water [64]. Desiccation can also lead to decreased production of anti-competition compounds. Fierer et. al. hypothesised that the significantly lower production of antimicrobial murein hydrolases, along with reduced abundance of antibiotic resistance genes are associated with the greater environmental pressure on prokaryotic and eukaryotic microorganisms over competition [65] as those compounds would require water for distribution. Le et. al. reported the production of potential osmoprotectants (such as osmoprotective proline) to be upregulated in the microbial communities of hypoliths from the Namib Desert and Antarctica [66]; similar adaptations were observed amongst bacteria living on dry city surfaces like metal and glass in New York [67]. Anderson et. al. found desiccation tolerance of an Archaeon increased when EPS (extracellular polysaccharide) production increased; additionally reporting increased tolerance of heat and oxygen stresses in desiccated versus control cells [68]. Due to environmental challenges, specific reproductive strategies may be employed. For instance, fungi living on extremely dry surfaces using meristematic development in order to reproduce without requiring water for dispersal [69, 70]. Within broader arid environments, relatively moist sites tend towards richer and larger microbial communities [71, 72], demonstrating the intensive selective pressure of aridity. It is worth noting that seemingly desiccated environments can in fact contain tiny water droplets home to bacteria, fungi and archaea surviving in otherwise deadly conditions [73], as well as dormant bacteria and fungi which revive and become metabolically active after an increase in moisture [74].

Temperature - bake or freeze

Aridity and extremes of temperature are commonly found together. Antarctica and the Sahara are two clear examples of desiccation and dangerous temperatures making survival extremely difficult [75]. Multiple studies have investigated microorganisms surviving and thriving in locations with extreme temperatures [76,77,78,79,80,81]. In a hot environment, Armstrong et. al. found that the soil microbial communities of gravel plains in the Namib desert remained constant over time [82], speculating that this is likely due to the stable (though hostile) environmental conditions experienced throughout the majority of the study period. The stress of high temperature has been shown to lead to increased production of heat shock proteins [83]; high temperatures in geothermically active soils in Antarctica have also been shown to influence microbial community composition and may help explain the presence of thermophilic Archaea closely related to those found in similar hot environments thousands of miles away [84]. Cockell et. al. found that higher temperatures limited microbial diversity when other conditions were well-suited for life in and around Hawaiian fumaroles [85]. As expected, high temperatures have been shown to interact with the other stresses associated with aridity to influence microbial community composition - such as favouring endospore forming Firmicutes [86]. This supports the findings by Savage et. al. that elevated temperatures still allowed more diverse microbial communities than those possible when temperatures were combined with other abiotic stresses [87]. In cold environments, production of cold shock proteins has been documented [88]. Antarctic bacteria are known to produce antifreeze proteins [89], Liljeqvist et. al. found a gene predicted to code for production of an antifreeze protein in their metagenomic study of an acid mine drainage stream in northern Sweden [90]. Adaptations to cold temperatures have also been noted in fungi through increased production of unsaturated lipids in the cell membrane [91], maintaining membrane fluidity. Cryotolerant fungi may also accumulate cryoprotectants like glycerol [92].


Drastically reduced coverage from clouds or vegetation in arid environments means exposure to damaging UV light [93, 94]. UV exposure levels have been shown to influence microbial community composition [95]. This includes dry environments where desiccation can lead to greater difficulties in tolerating UV irradiation [96]; though some other studies disagree [97]. Adaptation for UV exposure is typically through pigmentation. As such, environmental sampling of UV irradiated sites in Antarctica [98] and Tibet [99] identified increased pigmentation in UV irradiation tolerant Hymenobacter. A number of studies looking at the microbial residents of solar panels [100, 101]; including panels in Antarctica, the Arctic and the Mediterranean all found Hymenobacter as the most abundant genus [102]. Greater numbers of genes associated with antioxidant production and DNA repair have been noted in Archaea and Bacteria in heavily UV exposed environments [103,104,105]. These observations raise the possibility that extremely effective DNA repair mechanisms may also be a means of adapting to aridity. Pacelli et. al. subjected desiccation resistant Antarctic fungi to gamma radiation levels much greater than any found in nature and speculate that observed tolerance may use known DNA repair mechanisms associated with UV irradiation and dehydration [106]. Investigation by Selbmann et. al. in Antarctic fungi resistant to UV-B exposure led them to suggest that thick and highly melanised cell walls rather than enhanced DNA repair systems were the principal factor in tolerance of UV irradiation [107]. Jones and Baxter review in depth some methods of tolerating UV stress in Archaea which may be translatable into work with other microorganisms [108].


Some arid and semi-arid environments are heavily subjected to salt stress [109]. Intertidal zones and beaches experience either daily coverage with seawater or large quantities of salt deposition from wind off the ocean. Typically, the adaptations employed to protect microorganisms against desiccation also confer protection against salinity - the loss of water being a shared peril. Salinity in arid or other extreme environments can act as an independent factor controlling community composition; with microorganisms needing to be halotolerant in addition to being capable of dealing with other stresses. Management of osmotic potential in a saline environment is a long-term issue and so may require greater dedication of resources than acute salt stress caused by decreasing volumes of water; or employ particular pathways. This was noted by Molina-Menor, et. al. when examining the microbial community of rocks in the intertidal zone of the Mediterranean [110], and distinct microbiomes from more saline areas of larger environments have been recorded [111]. Production of hydrophobins [112] and accumulation of salt-stress specific solutes [113] were observed in fungi from saline environments.

From environmental to animal microbiomes

Comparisons of animal microbiomes to those of their surroundings within extreme environments, such as hot and cold deserts, are currently few in number and not covered in detail in this review. The external conditions of animals in arid environments are liable to be similar to their wider environment. Although behavioural adaptation will alleviate some of the effects of environmental stresses, members of the skin microbiota will be subject to similar stresses as other environmental communities. Salt levels in the intestinal tract of the hot-desert dwelling Fat Sand rat (Psammomys obesus) are similar -at least initially- to those of the saltbush they consume [114], and water content in the faces of desert species is very low. Internal conditions in an animal will differ from the external environment, but potentially not as greatly as between an arid and a wet environment. Therefore arid-adapted animals may host extreme points on different gradients within the larger arid environment, in terms of water availability, temperature (for ectotherms) or salinity; whilst being within the standard range for other environmental variables (e.g. temperature on and within an ectothermic cold desert insect).

Animal adaptation to aridity & animal-associated microbiomes

The same factors (e.g. lack of water, extremes of temperature, restricted energy sources) associated with arid environments influence both microbial and animal communities. In order to survive and thrive within such habitats, animals have developed a suite of physiological and behavioural adaptations. These reduce energy expenditure and water loss [115], use microclimates or seek increased water intake from other sources [116,117,118]. Table 1 provides some examples of animal adaptations in the face of specific stresses; to mirror the focus on mammals in metagenomic studies we focus on non-bird vertebrate adaptations to aridity. A number of animal species restrict their urine production in response to acute water stress, from Merino sheep [119] to ostriches [120] and some toads [121]. These species demonstrate the ability to reduce and concentrate [122,123,124] the amount of urine they produce through specific renal adaptations [125] such as the elongation and enlargement of the renal papilla [10, 126, 127], as well as changes to the distribution of aquaporin proteins [128]. Additionally, animals can store water for use over a longer period, as dromedaries do with their forestomach [129]. Some animals may take advantage of abnormal water sources to survive in arid locations [130], including non-xeric animals [131]. A common behavioural adaptation is to seek shelter, shade or microclimates in hot arid environments to limit heat stress and exposure to dry air; thus reducing evaporative water loss [132]. Switching to a nocturnal rather than diurnal activity pattern is another means of reducing water stress in hot arid environments [133] and of adapting to increasing aridity with climate change [9]. Adaptation for reduced basal metabolic rates in birds along an aridity gradient was uncovered by Tieleman et. al [134]. Low basal energy demands (basal metabolism) are common in hot desert endothermic animals, reducing both the need to forage in hot desert conditions and lung ventilation thereby reducing evaporative water loss [134, 135]. Many endotherms reduce metabolic rate and water loss further through torpor, a state which can last from hours to months and is a reduction of body temperature and other physiological processes [118, 136,137,138]. These changes can be programmed, timed processes or direct responses to environmental conditions [118]. Larger body size may also confer some protection against desiccation, as seen in Anopheles gambiae [139] and camels [140, 141]. Invertebrate survival of extreme dehydration and temperatures is reviewed by Watanabe [142] and Somme [143], showing a suite of morphological, behavioural and physiological changes across a range of species.

While all these studies focused on an organism’s adaptation to aridity, it is important to consider the influence of the microbiome, often called the ‘second genome’, on arid-adapted animals as a potential contributor to aridity tolerance. This is a dynamic relationship, in which the host animals’ adaptations for aridity will directly influence the different niches it provides for potential microbial colonisation and the microbiome may negate the need for host genomic adaptation to aridity. The development of new sequencing methods and increased computational power, coupled with innovative software and analysis approaches have made large scale microbiome investigations more accessible [144, 145], highlighting the vital role they play in host development and health [146]. Research has often been directed to the intestinal microbiomes of ruminants [147,148,149,150,151,152,153] and other commercially valuable species [154,155,156]. By virtue of ease of access, other studies have tended to be on domesticated species [157], or wild species which can be more conveniently reached and investigated [158].

Broadly, arid animals have not been the subject of as much metagenomic research as those found in more hospitable environments. This has led to focus on a small number of species, and less connection across the field between environmental, host and metagenomic factors in arid animals.

Arid animal microbiomes

Beyond environmental microbiomes, and aside from plant-associated microbiomes which are outside the scope of this review (see [159, 160]), the other potential location for microbial communities in arid environments is in association with animal hosts. Here we discuss animal microbiomes influenced by some of the aridity associated factors discussed above, then explore in greater detail Camels and Muskoxen, which have received more investigation than most in this area.

Incidental aridity - animal microbiomes influenced by abiotic factors shared with arid environments

Before moving on to some specific arid-adapted animals it is useful to look at animal microbiomes which may have been influenced by aridity directly or indirectly - or by factors also found in arid environments, similar to the environmental microbiomes discussed above. The comparatively reduced levels of water available in arid environments, whether as humidity, surface moisture or precipitation, limits the ability of macroscopic life to develop [161, 162]. From the perspective of animal host organisms this leads to a tendency for specific diets, becoming more limited as aridity increases and diversity of plant and animal life falls [163, 164]; this has been reported in numerous organisms [165,166,167,168]. The human skin microbiome has distinct correlations between moisture levels and community composition [169, 170]; this could offer some comparative references if skin-microbiome studies of arid-adapted animals are conducted. Diet plays a large, potentially dominant role in establishing the intestinal microbiota of animals [171] and can influence the microbiota of other body areas as well [172, 173].

Limited food and water availability due to arid conditions can lead to concentration of animals in sites with accessible water or a shared source of nutrition - this consumption from the same site and possible close quarters may help explain the proximity based correlation in microbiota composition (likely through range overlap leading to similar microbial exposure) noted by Couch et. al. in Mojave desert-dwelling Bighorn sheep [174] faecal microbiota. Other stresses found in arid environments have been examined in terms of animal-associated microbiomes, both experimentally and in observation of natural conditions. Direct links have been observed between temperature and animal-associated microbiomes in Humpback whales (2 °C to − 2 °C) [175], Fruit Flies (13 °C versus 31 °C) [176], Silkworms (transient exposure to 37 °C after rearing at 25 °C) [177] and Tilapia (24 °C versus 12 °C) [178]. This influence can be profound or act in more subtle ways, as Li et. al. noted in Xenopus tropicalis whereby decreasing temperature altered beta but not alpha-diversity of the gut microbiome [179]. Interestingly, some of these temperature dependent changes in host microbiomes reflect either changes in the host temperature (the fruit flies and silkworms for instance) or in the environmental temperature (Humpback whales). In some instances, these studies have found that the microbiomes subject to influence by heat stress also impact their hosts; Fontaine et. al. finding that temperature induced changes in salamander intestinal microbiota influenced energy uptake from digesting food [180]. Givens found that a change in water temperature surrounding some Fundulus hetereoclitus (Mummichog) corresponded with a change in relative abundance of different species of Vibrio in the intestinal microbiota which may have been connected with increased mortality [181].

Sullam et. al. reported that different levels of salinity influenced the gut microbiota of fish - and that these saltwater fish intestinal communities bore similarities to environmental samples from saltwater [182]. Investigation of Atlantic Salmon found a less diverse intestinal microbiota in those acclimated to saltwater than those living in freshwater [183]. 16S investigation of the exposed facial skin of the Black and Turkey vultures by Mendoza et. al. identified Psychrobacter cryohalolentis and Psychrobacter arcticus, which whilst commonly found in cold environments are known to be halotolerant [184] potentially explaining their presence on the warm but saline surface. Given similarities in environmental microbiomes between saline and arid environments, it is possible that dehydration might influence animal microbiomes in a similar way to salt stress; favouring the same functions and potentially related taxonomic compositions. These studies examine environmental salinity rather than that of internal fluids of the host, whether in the intestinal tract or elsewhere; it could be of interest to assess salinity within the host and potential impacts on the microbiota without an external change in salt levels; tied to consumption of a salt rich diet for instance.

Direct exposure to UV irradiation of animal microbiomes is restricted to those on the external surfaces of the body, skin [185], fur [186], scale [187], etc. Ghaly et. al. found that increased UV irradiation of mouse skin changed the intestinal microbiota - with changes detected at the phylum and genus level which may correspond with increased inflammation [188]. Investigation of New World vultures by Graves et. al. found that extremophiles tolerant of UV irradiation (and desiccation) accounted for the most abundant and third most abundant genera resident on pigmented plumage in a number of studied species [189]; Hymenobacter was the third most abundant genus, and as discussed above, also the most abundant on polar and Mediterranean solar panels as well as UV-irradiated environments in Tibet and Antarctica. Examination of the external surfaces of animals exposed to elevated UV levels, whether through altitude or relative immobility, may also find similarities between these communities and those of environments subject to significant UV irradiation.

Camels and muskoxen, hot and cold arid environments

The single-humped Dromedary (Camelus dromedarius), the double-humped domesticated Bactrian (Camelus bactrianus) and wild Bactrian (Camelus ferus) camels are animals found in hot or cold arid environments, from the Australian Great Sandy Desert [190] to the steppes north of the Gobi Desert [191]. They are excellent cases for comparison of the influences of aridity on animal microbiomes. As with the majority of animal-microbiota studies the focus of the camel investigations has been the intestinal microbiota, ranging from covering multiple sites along the gastrointestinal tract to only sequencing faecal samples. Muskoxen, Ovibos moschatus, are ruminants found in the wild roaming the High Arctic of Greenland, Canada and Alaska; as well as being reared commercially elsewhere in the High Arctic [192,193,194]. They have been investigated as one of many ruminant species to have had their intestinal microbiomes examined, and are of interest in the context of this review as they inhabit a cold arid environment.

Gharaechahi et. al. used 16S rRNA sequencing to study the microbiota of solid and liquid fractions of three female Dromedaries, finding evidence that the core microbial community was highly conserved between the individuals [195], which may be a consequence of limited diets. Interestingly, aridity limiting the diversity of potential energy sources in the diet may explain increased microbial community diversity and richness in Muskox at more northern latitudes as found by Bird et. al. [196]; potentially preventing a small number of bacteria or archaea specialising in an abundant energy source from dominating the community. He et. al. used 16S rRNA sequencing to investigate the microbiota composition along the digestive tract of the Bactrian camel and found a large number of unclassified Ruminococcaceae in the ileum and large intestine; which they suggest might enable survival on salt-tolerant, difficult to digest forage in their arid habitat [197]. An early investigation, utilising metatranscriptomics rather than metagenomics and focusing on rumen eukaryotic residents in the Muskox found enrichment for CAZy gene families [194]; also found to be enriched in the Dromedary intestinal microbiome by Gharechahi & Salekdeh though from prokaryotic sources [198]. This suggests that investigation of the metatranscriptome of the Dromedary may yield similar results - though the contribution of eukaryotic and prokaryotic members of the microbiota may differ. Other work by He et. al. on the Bactrian intestinal microbiota, employing 16S rRNA methods, found increasing complexity and stability of the community with age [199]. They also observed that some seasonal variation of the forestomach microbiota may occur [193]. Shotgun metagenomic sequencing by Gharechahi & Salekdeh to investigate the intestinal microbiome of Dromedaries [198] found a similar pattern for relative abundances of phyla previously identified by Gharechahi et. al. highlighting the difference in relative abundance of Verrucomicrobia detected between Bactrian and Dromedary camels. This may be a consequence of different temperature stresses on the host, different diets or potentially the relative abundance of Verrucomicrobia in the soils of their respective habitats.

These results suggest a general trend in camel species for intestinal microbiota which provide maximum resource extraction from the harsh environment in which they live, with limited and typically static diets - a mutually beneficial arrangement developing between the camels and these microorganisms. It is interesting to note the similarities across these studies, along with the older Dromedary forestomach microbial investigation by Bhatt et. al. [200] as regards the most abundant phyla detected. Salgado-Flores et. al. published the first metagenomic study of the Muskoxen rumen in 2016, utilising 16S rRNA techniques [201]. Bacteroidetes and Firmicutes were the dominant phyla, as with many other sequenced intestinal microbiomes, however they did note that 53.7–59.3% of bacterial sequences couldn’t be characterised. Also, the ratio between relative abundance of Firmicutes and Bacteroidetes was greater as compared to other ruminant intestinal microbiomes, 70.7–81.1%: 16.8–25.3%. This may be an indication that the environment, diet or a combination of the two in their arid habitat favours members of Firmicutes more generally than Bacteroidetes. However, reporting the most abundant phyla is not necessarily the most useful finding to explore given that research has found the same phyla predominant in the intestinal microbiota of humans [165], pigs [155], baleen whales [166] and in fact (at different relative positions) in the soil of the Atacama Desert [202]. In their later work Gharechahi & Salekdeh used shotgun sequencing methods to investigate functional traits of the camel intestinal microbiome. The authors determined that despite taxonomic similarities to a number of published rumen intestinal microbiomes it was functionally distinct from them and more akin to the Moose rumen microbiome in certain regards [198]. Table 2. shows the Bactrian and Dromedary camel metagenomic studies cited in this review including potential links between relatively abundant or notable taxa and their roles. Both Camel species and Muskox share some notable trends, with members of Lachnospiraceae and Prevotellaceae taking up a sizeable proportion of classified bacterial reads. This is likely to be due to similarities in diet courtesy of similar traits in plants needed to survive in both cold and hot arid environments.

Table 2 Summarises metagenomic investigations of camel species including the host species, sequencing technology used, notable prokaryotic taxa detected and postulated roles in the microbiome for the listed taxa. Unless otherwise indicated Dromedary camels were sampled from hot environments, Bactrian from cold; for a indicated studies where no clear location is given for location animals were sampled from

Considerations when studying arid microbiomes

The majority of studies discussed employ 16S rRNA sequencing methods, although a few have also utilised shotgun metagenomics. Many authors have used 16S rRNA approaches due to cost and availability of accessible tools. However 16S rRNA only allows genus level resolution and does not give an indication of functional potential. This is a particular issue when studying highly adapted hosts or environments where it is expected that evolutionary adaptation is at (or predicted to be at) the species/strain and functional level, rather than at genus level or higher. This reflects a trend in which many arid environments or arid-adapted animal microbiomes are sequenced with 16S rRNA and these results published with the caveat that any functional data they present is by necessity derived from the taxonomies they have generated. Frequent use is made of QIIME [203], mothur [204] and PICRUSt [205] - along with 16S rRNA databases such as Greengenes [206, 207] and Silva [208] to highlight what might be expected to be present in functional terms in the microbiome. Though 16S rRNA sequencing currently has advantages in terms of cost and (comparative) ease of use, the limitation to taxonomy-derived functional predictions can be an issue if functional diversity differs significantly from taxonomic measures. Shotgun sequencing enables the direct assessment and investigation of functional diversity within the microbial communities of arid environments, plants and animals.

Deeper sequencing power can create issues when studying novel hosts, as there may be a high proportion of novel microbial taxa present i.e. a high proportion of unidentified reads. This reflects the relative focus of animal-associated metagenomic studies on humans [209], mice [210] and others used as models for medical research [211]. Unless attempting de novo classification methods, taxonomy in metagenomics depends on databases of known (or likely) classifications against which reads from samples can be compared [212]. These are populated by researchers engaging in metagenomic and microbiological studies, thus trend towards easily cultivated or human-associated; though published datasets can be investigated for new genomes [213].

In their latter work, Gharechahi et. al. [198] compared some of the results of their whole genome shotgun sequencing of the camel rumen microbiome to results from their 16S rRNA investigation [195]. Of note are the differences in ability to resolve taxa to the species level, with 21% of the sequences in the metagenomic study being unclassifiable at the species level compared to 51.9% in the 16S rRNA investigation. It is possible that in the intervening years between the studies the growth in size and depth of taxa covered by databases may explain the difference. Both studies found less than 1% of the reads to be classified as Archaea, different methods finding similar results suggesting that Archaea are, proportionally at least, minor contributors to the Dromedary intestinal microbiome. They also speculate as to the differences in abundance of particular phyla depending on the different methodologies employed, which they say may be related to PCR bias. Interestingly for comparison of methods within the same species for metagenomic research, their use of 16S rRNA sequences extracted from metagenomically-assembled-genomes allows for an insight into how more populous and diverse databases can help metagenomic studies.

Work published in recent years highlights the importance in conservation of the microbiomes of animals moving into and out of captivity [214]. Conservation efforts might present an opportunity for shotgun sequencing microbiomes of arid-adapted animals. This would also help expand taxonomic and functional databases employed in metagenomic research. Some of the studies discussed above sampled from multiple locations within the intestinal tract, noting differences between them as well as with the faecal samples they obtained. Similar findings from other species including pigs [215], bats [216] and humans [217] suggest that where possible (and ethically sound) taking samples from internal body sites is necessary to fully understand arid-adapted animal microbiomes. Meng et. al. investigated intestinal microbiota of hot desert-dwelling weevils feeding on plant roots underground, finding that all of the 66 core weevil OTUs could be detected in the soil around the weevils; albeit at lower abundances [218]. This suggests that environmental sampling around any animals investigated might help us understand the origins and development of the microbiome. Where feasible it would be beneficial to obtain samples of the environmental microbiome to see what, if any relationship it has with the animal-associated microbial community. This would allow us to distinguish between transient microorganisms in the organism, environmental contamination or true residents. Ideally comparison across closely related species should be employed, as by Campbell et. al. (though not with arid-adapted organisms) [219], which will allow for better understanding of the interactions between host genome, environment and microbiome in studies of adaptation.

Perspective and conclusion

Instead of lifeless wastes, arid environments are home to organisms from microscopic to enormous [220, 221]. Their living conditions are harsh, but still life is able to survive and thrive. Though diversity drops off as their home becomes more hostile, organisms have been discovered in the depths of Antarctica [222], the Atacama [28] and even in artificially desiccated environments [223]. Adaptations to aridity have been noted in environmental microbiomes as conferring survival advantages against other stresses which co-occur in those environments. As such it is likely that similarities exist between arid microbiomes and those found in hyper-saline, extremely cold, UV irradiated and hot microbiomes; though as moisture levels increase these similarities likely diminish. Within this context it is worth considering the extent to which animals in arid environments provide more hospitable refuges for environmental microorganisms, if potentially they allow for organisms present in small fractions externally to colonise, be fruitful and multiply. Future investigations comparing arid-animal microbiomes to those of their surroundings will need to take account of the impact of faeces and other excretions from the animals which may alter the microbial community in the environment around them [224]. Going forward it is worth considering whether seemingly divergent environmental conditions may in fact contain a similar stress which could impact microbial communities. When investigating environmental microbiomes it could be of benefit (if possible) to take detailed measurements of abiotic stressors and assess whether these may be directly - or through interactions with each other - responsible for community compositions. This may lead to discoveries of shared taxonomic or functional trends from distant and apparently dissimilar locations. It may be useful in addition to sample harsh environments which are not the most extreme, sampling a range of warm pools rather than the hottest spring water for instance; or taking transects across a harsh environment as opposed to multiple samplings from the saltiest or most irradiated sites.

If conducting research on arid animal microbiomes in the future it may be helpful to take environmental samples from sites where the animals feed, rest and otherwise spend their time. This could allow the determination of the extent to which composition of animal-associated microbiomes is purely a consequence of allowing minor members of the wider environmental microbiome to thrive. It may also be interesting to observe in captive animals if differences in diet and microbiome come from the altered diet they consume in captivity or from the different environmental microbiome in which they and their food are kept. Both traditional and novel methods of culturing will have a place in future studies of arid animal and environmental microbiomes, as harsh conditions can be more easily and accurately replicated in the laboratory; enabling the conversion of detected genotypes into observable phenotypes. Functional understanding of arid animal-associated microbiomes may require the use of in vivo models to be completely elucidated; possibly leading to findings which can be translated into industrial applications. In their recent discussion of the ‘Eco-holobiont’ Singh et. al. highlight how environmental factors, environmental microbiomes, animal genomes and animal-associated microbiota may interact to paint a complete picture of the micro-macroscopic relationships shaping the world [225]. Ribeiro et. al. demonstrate that metagenomic investigation can be a crucial component of well-rounded research into the life and adaptation of an arid-adapted animal, along with environmental, metabolic and genomic investigations [32]. Arid environments could offer a very useful proof of concept for this philosophical approach, with clear environmental influences and comparatively simple living communities. As climate change threatens arid environments and their inhabitants around the globe it is increasingly important that we fully understand the functional and taxonomic composition of the microbial communities they host so we can best protect and harness them [226].

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.



Ultra Violet Light


Deoxyribonucleic Acid


Carbohydrate-Active enZYmes


Quantitative Insights Into Microbial Ecology


Phylogenetic Investigation of Communities by Reconstruction of Unobserved States


Polymerase Chain Reaction


Operational Taxonomic Unit


  1. 1.

    Häussinger D. The role of cellular hydration in the regulation of cell function. Biochem J. 1996;313:697–710.

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Barrow CJ. World atlas of desertification. Land Degradation Dev. 1992;3:249.

    Article  Google Scholar 

  3. 3.

    Salem BB. Arid zone forestry: a guide for field technicians. In: FAO Conservation Guide. Rome, Italy: FAO; 1989.

    Google Scholar 

  4. 4.

    Boetius A, Anesio AM, Deming JW, Mikucki JA, Rapp JZ. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat Rev Microbiol. 2015;13:677–90.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Buyanovsky G, Dicke M, Berwick P. Soil environment and activity of soil microflora in the Negev desert. J Arid Environ. 1982;5:13–28.

    Article  Google Scholar 

  6. 6.

    Blix AS. Adaptations to polar life in mammals and birds. J Exp Biol. 2016;219:1093–105.

    PubMed  Article  Google Scholar 

  7. 7.

    Ishida Y, Van Coeverden de Groot PJ, Leggett KEA, Putnam AS, Fox VE, Lai J, et al. Genetic connectivity across marginal habitats: the elephants of the Namib Desert. Ecol Evol. 2016;6:6189–201.

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Taylor NP, Zappi DC. An alternative view of generic delimitation and relationships in tribe Cereeae (Cactaceae). brad. 1989;1989:13–40.

    Article  Google Scholar 

  9. 9.

    Levy O, Dayan T, Porter WP, Kronfeld-Schor N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol Monogr. 2019;89:e01334.

    Article  Google Scholar 

  10. 10.

    Kronfeld N, Shkolnik A. Adaptation to Life in the Desert in the Brown Hare (Lepus capensis). J Mammal. 1996;77:171–8.

    Article  Google Scholar 

  11. 11.

    Silanikove N. The physiological basis of adaptation in goats to harsh environments. Small Rumin Res. 2000;35:181–93.

    Article  Google Scholar 

  12. 12.

    Levy O, Dayan T, Porter WP, Kronfeld-Schor N. Foraging Activity Pattern Is Shaped by Water Loss Rates in a Diurnal Desert Rodent. Am Nat. 2016;188:205–18.

    PubMed  Article  Google Scholar 

  13. 13.

    Schmidt-Nielsen K. Desert Rodents: Physiological Problems of Desert Life. In: Prakash I, Ghosh PK, editors. Rodents in Desert Environments. Dordrecht: Springer Netherlands; 1975. p. 379–88.

    Google Scholar 

  14. 14.

    Wen X, Wang S, Duman JG, Arifin JF, Juwita V, Goddard WA, et al. Antifreeze proteins govern the precipitation of trehalose in a freezing-avoiding insect at low temperature. PNAS. 2016;113:6683–8.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Hazard LC, Lechuga C, Zilinskis S. Secretion by the nasal salt glands of two insectivorous lizard species is initiated by an ecologically relevant dietary ion, chloride. J Exp Zool A Ecol Genet Physiol. 2010;313A:442–51.

    CAS  Article  Google Scholar 

  16. 16.

    Leinaas HP. UV Tolerance, Pigmentation and Life Forms in High Arctic Collembola. In: Hessen DO, editor. UV Radiation and Arctic Ecosystems. Berlin, Heidelberg: Springer; 2002. p. 123–34.

    Google Scholar 

  17. 17.

    Osakabe Y, Osakabe K, Shinozaki K, Tran L-SP. Response of plants to water stress. Front Plant Sci. 2014;5.

  18. 18.

    Nadeem M, Li J, Yahya M, Sher A, Ma C, Wang X, et al. Research Progress and Perspective on Drought Stress in Legumes: A Review. Int J Mol Sci. 2019;20:2541.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  19. 19.

    Kay RNB. Responses of African livestock and wild herbivores to drought. J Arid Environ. 1997;37:683–94.

    Article  Google Scholar 

  20. 20.

    Schnorr SL, Sankaranarayanan K, Lewis CM, Warinner C. Insights into human evolution from ancient and contemporary microbiome studies. Curr Opin Genet Dev. 2016;41:14–26.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    McDonald RC, Watts JEM, Schreier HJ. Effect of Diet on the Enteric Microbiome of the Wood-Eating Catfish Panaque nigrolineatus. Front Microbiol. 2019;10.

  22. 22.

    Lu J, Synowiec S, Lu L, Yu YY, Bretherick T, Takada S, et al. Microbiota influence the development of the brain and behaviors in C57BL/6 J mice. PLoS One. 2018;13:29.

    Google Scholar 

  23. 23.

    Kassas M. Desertification: a general review. J Arid Environ. 1995;30:115–28.

    Article  Google Scholar 

  24. 24.

    Spinoni J, Vogt J, Naumann G, Carrao H, Barbosa P. Towards identifying areas at climatological risk of desertification using the Köppen–Geiger classification and FAO aridity index. Int J Climatol. 2015;35:2210–22.

    Article  Google Scholar 

  25. 25.

    Spinoni J, Micale F, Carrao H, Naumann G, Barbosa P, Vogt J. Global and continental changes of arid areas using the FAO Aridity Index over the periods 1951–1980 and 1981–2010. Geophysical Research Abstracts; 2013.

  26. 26.

    Lal R. 4.10 - Vulnerability of Agroecosystems to Environmental Factors. In: Pielke RA (1st ed). Climate Vulnerability. Academic Press: Oxford, 2013:109–116.

  27. 27.

    Pretty JN, Morison JIL, Hine RE. Reducing food poverty by increasing agricultural sustainability in developing countries. Agric Ecosyst Environ. 2003;95:217–34.

    Article  Google Scholar 

  28. 28.

    Crits-Christoph A, Robinson CK, Barnum T, Fricke WF, Davila AF, Jedynak B, et al. Colonization patterns of soil microbial communities in the Atacama Desert. Microbiome. 2013;1:28.

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Neilson JW, Califf K, Cardona C, Copeland A, van Treuren W, Josephson KL, et al. Significant Impacts of Increasing Aridity on the Arid Soil Microbiome. mSystems. 2017;2:e00195–16 /msys/2/3/e00195–16.atom.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Mohammadipanah F, Wink J. Actinobacteria from Arid and Desert Habitats: Diversity and Biological Activity. Front Microbiol. 2016;6.

  31. 31.

    Schmidt SK, Gendron EMS, Vincent K, Solon AJ, Sommers P, Schubert ZR, et al. Life at extreme elevations on Atacama volcanoes: the closest thing to Mars on Earth? Antonie Van Leeuwenhoek. 2018;111:1389–401.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Ribeiro ÂM, Puetz L, Pattinson NB, Dalén L, Deng Y, Zhang G, et al. 31° South: The physiology of adaptation to arid conditions in a passerine bird. Mol Ecol. 2019;28:3709–21.

    PubMed  Article  Google Scholar 

  33. 33.

    Bryan NC, Christner BC, Guzik TG, Granger DJ, Stewart MF. Abundance and survival of microbial aerosols in the troposphere and stratosphere. ISME J. 2019;13:2789–99.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Ogwu MC, Srinivasan S, Dong K, Ramasamy D, Waldman B, Adams JM. Community Ecology of Deinococcus in Irradiated Soil. Microb Ecol. 2019;78:855–72.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Gorbushina AA. Life on the rocks. Environ Microbiol. 2007;9:1613–31.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Coller E, Cestaro A, Zanzotti R, Bertoldi D, Pindo M, Larger S, et al. Microbiome of vineyard soils is shaped by geography and management. Microbiome. 2019;7:140.

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Lauber CL, Ramirez KS, Aanderud Z, Lennon J, Fierer N. Temporal variability in soil microbial communities across land-use types. Isme J. 2013;7:1641–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Overmann J, Abt B, Sikorski J. Present and Future of Culturing Bacteria. Annu Rev Microbiol. 2017;71:711–30.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Kaeberlein T, Lewis K, Epstein SS. Isolating ‘Uncultivable’ Microorganisms in Pure Culture in a Simulated Natural Environment. Science. 2002;296:1127–9.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Chaudhary DK, Khulan A, Kim J. Development of a novel cultivation technique for uncultured soil bacteria. Sci Rep. 2019;9:1–11.

    Article  CAS  Google Scholar 

  41. 41.

    Browne HP, Forster SC, Anonye BO, Kumar N, Neville BA, Stares MD, et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature. 2016;533:543–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Böttger EC. Rapid determination of bacterial ribosomal RNA sequences by direct sequencing of enzymatically amplified DNA. FEMS Microbiol Lett. 1989;53:171–6.

    PubMed  Article  Google Scholar 

  43. 43.

    Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, et al. Environmental Genome Shotgun Sequencing of the Sargasso Sea. Science. 2004;304:66–74.

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Murgia M, Fiamma M, Barac A, Deligios M, Mazzarello V, Paglietti B, et al. Biodiversity of fungi in hot desert sands. MicrobiologyOpen. 2019;8:e00595.

    PubMed  Article  Google Scholar 

  45. 45.

    Ji P, Rhoads WJ, Edwards MA, Pruden A. Impact of water heater temperature setting and water use frequency on the building plumbing microbiome. ISME J. 2017;11:1318–30.

    PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Cole JK, Peacock JP, Dodsworth JA, Williams AJ, Thompson DB, Dong H, et al. Sediment microbial communities in Great Boiling Spring are controlled by temperature and distinct from water communities. ISME J. 2013;7:718–29.

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Barns SM, Fundyga RE, Jeffries MW, Pace NR. Remarkable archaeal diversity detected in a Yellowstone National Park hot spring environment. Proc Natl Acad Sci U S A. 1994;91:1609–13.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Hugenholtz P, Pitulle C, Hershberger KL, Pace NR. Novel division level bacterial diversity in a Yellowstone hot spring. J Bacteriol. 1998;180:366–76.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Ballaré CL, Caldwell MM, Flint SD, Robinson SA, Bornman JF. Effects of solar ultraviolet radiation on terrestrial ecosystems. Patterns, mechanisms, and interactions with climate change. Photochem Photobiol Sci. 2011;10:226–41.

    PubMed  Article  CAS  Google Scholar 

  50. 50.

    Zaller JG, Caldwell MM, Flint SD, Scopel AL, Salo OE, Ballaré CL. Solar UV-B radiation affects below-ground parameters in a fen ecosystem in Tierra del Fuego, Argentina: implications of stratospheric ozone depletion. Glob Chang Biol. 2002;8:867–71.

    Article  Google Scholar 

  51. 51.

    Mukhtar S, Mirza BS, Mehnaz S, Mirza MS, Mclean J, Malik KA. Impact of soil salinity on the microbial structure of halophyte rhizosphere microbiome. World J Microbiol Biotechnol. 2018;34:136.

    PubMed  Article  CAS  Google Scholar 

  52. 52.

    Zhang Y, Cao C, Guo L, Wu Q, Cui Z. Soil properties, bacterial community composition, and metabolic diversity responses to soil salinization of a semiarid grassland in northeast China. J Soil Water Conserv. 2015;70:110–20.

    Article  Google Scholar 

  53. 53.

    Weinmaier T, Probst AJ, La Duc MT, Ciobanu D, Cheng J-F, Ivanova N, et al. A viability-linked metagenomic analysis of cleanroom environments: eukarya, prokaryotes, and viruses. Microbiome. 2015;3:62.

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Jiménez DJ, Andreote FD, Chaves D, Montaña JS, Osorio-Forero C, Junca H, et al. Structural and Functional Insights from the Metagenome of an Acidic Hot Spring Microbial Planktonic Community in the Colombian Andes. PLoS One. 2012;7.

  55. 55.

    Ogwu MC, Kerfahi D, Song H, Dong K, Seo H, Lim S, et al. Changes in soil taxonomic and functional diversity resulting from gamma irradiation. Sci Rep. 2019;9:1–13.

    Article  CAS  Google Scholar 

  56. 56.

    Kato C, Li L, Nogi Y, Nakamura Y, Tamaoka J, Horikoshi K. Extremely Barophilic Bacteria Isolated from the Mariana Trench, Challenger Deep, at a Depth of 11,000 Meters. Appl Environ Microbiol. 1998;64:1510–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Li Y, Zheng L, Zhang Y, Liu H, Jing H. Comparative metagenomics study reveals pollution induced changes of microbial genes in mangrove sediments. Sci Rep. 2019;9:1–11.

    Article  CAS  Google Scholar 

  58. 58.

    Noll M, Matthies D, Frenzel P, Derakshani M, Liesack W. Succession of bacterial community structure and diversity in a paddy soil oxygen gradient. Environ Microbiol. 2005;7:382–95.

    CAS  PubMed  Article  Google Scholar 

  59. 59.

    Packer E, Scher S, Sagan C. Biological contamination of Mars II. Cold and aridity as constraints on the survival of terrestial microorganisms in simulated Martian environments. Icarus. 1963;2:293–316.

    Article  Google Scholar 

  60. 60.

    Song H-K, Shi Y, Yang T, Chu H, He J-S, Kim H, et al. Environmental filtering of bacterial functional diversity along an aridity gradient. Sci Rep. 2019;9:1–10.

    Article  CAS  Google Scholar 

  61. 61.

    Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B, et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. PNAS. 2015;112:15684–15,689.

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Zeng Q, An S, Liu Y, Wang H, Wang Y. Biogeography and the driving factors affecting forest soil bacteria in an arid area. Sci Total Environ. 2019;680:124–31.

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Feng W, Zhang Y, Yan R, Lai Z, Qin S, Sun Y, et al. Dominant soil bacteria and their ecological attributes across the deserts in northern China. Eur J Soil Sci. 2020;71:524–35.

    CAS  Article  Google Scholar 

  64. 64.

    Chen D, Saleem M, Cheng J, Mi J, Chu P, Tuvshintogtokh I, et al. Effects of aridity on soil microbial communities and functions across soil depths on the Mongolian Plateau. Funct Ecol. 2019;33:1561–71.

    Article  Google Scholar 

  65. 65.

    Fierer N, Leff JW, Adams BJ, Nielsen UN, Bates ST, Lauber CL, et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. PNAS. 2012;109:21390–21,395.

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Le PT, Makhalanyane TP, Guerrero LD, Vikram S, Van de Peer Y, Cowan DA. Comparative Metagenomic Analysis Reveals Mechanisms for Stress Response in Hypoliths from Extreme Hyperarid Deserts. Genome Biol Evol. 2016;8:2737–47.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Afshinnekoo E, Meydan C, Chowdhury S, Jaroudi D, Boyer C, Bernstein N, et al. Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics. Cell Systems. 2015;1:72–87.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Anderson KL, Apolinario EE, Sowers KR. Desiccation as a Long-Term Survival Mechanism for the Archaeon Methanosarcina barkeri. Appl Environ Microbiol. 2012;78:1473–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Antonelli F, Esposito A, Calvo L, Licursi V, Tisseyre P, Ricci S, et al. Characterization of black patina from the Tiber River embankments using Next-Generation Sequencing. PLoS One. 2020;15:e0227639.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    De Leo F, Antonelli F, Pietrini AM, Ricci S, Urzì C. Study of the euendolithic activity of blackmeristematic fungi isolated from a marble statue in the Quirinale Palace’s Gardens inRome, Italy. Facies. 2019;65:18.

    Article  Google Scholar 

  71. 71.

    Goberna M, Pascual JA, García C, Sánchez J. Do plant clumps constitute microbial hotspots in semiarid Mediterranean patchy landscapes? Soil Biol Biochem. 2007;39:1047–54.

    CAS  Article  Google Scholar 

  72. 72.

    Aguilera LE, Armas C, Cea AP, Gutiérrez JR, Meserve PL, Kelt DA. Rainfall, microhabitat, and small mammals influence the abundance and distribution of soil microorganisms in a Chilean semi-arid shrubland. J Arid Environ. 2016;126:37–46.

    Article  Google Scholar 

  73. 73.

    Grinberg M, Orevi T, Steinberg S, Kashtan N. Bacterial survival in microscopic surface wetness. eLife. 8.

  74. 74.

    Placella SA, Brodie EL, Firestone MK. Rainfall-induced carbon dioxide pulses result from sequential resuscitation of phylogenetically clustered microbial groups. PNAS. 2012;109:10931–10,936.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Scambos TA, Campbell GG, Pope A, Haran T, Muto A, Lazzara M, et al. Ultralow Surface Temperatures in East Antarctica From Satellite Thermal Infrared Mapping: The Coldest Places on Earth. Geophys Res Lett. 2018;45:6124–33.

    Article  Google Scholar 

  76. 76.

    Kanokratana P, Chanapan S, Pootanakit K, Eurwilaichitr L. Diversity and abundance of Bacteria and Archaea in the Bor Khlueng Hot Spring in Thailand. J Basic Microbiol. 2004;44:430–44.

    PubMed  Article  Google Scholar 

  77. 77.

    Wilkins LGE, Ettinger CL, Jospin G, Eisen JA. Metagenome-assembled genomes provide new insight into the microbial diversity of two thermal pools in Kamchatka, Russia. Sci Rep. 2019;9:15.

    Article  CAS  Google Scholar 

  78. 78.

    Inskeep WP, Rusch DB, Jay ZJ, Herrgard MJ, Kozubal MA, Richardson TH, et al. Metagenomes from High-Temperature Chemotrophic Systems Reveal Geochemical Controls on Microbial Community Structure and Function. PLoS One. 2010;5:e9773.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  79. 79.

    Dai D, Rhoads WJ, Edwards MA, Pruden A. Shotgun Metagenomics Reveals Taxonomic and Functional Shifts in Hot Water Microbiome Due to Temperature Setting and Stagnation. Front Microbiol. 2018;9.

  80. 80.

    Chan CS, Chan K-G, Tay Y-L, Chua Y-H, Goh KM. Diversity of thermophiles in a Malaysian hot spring determined using 16S rRNA and shotgun metagenome sequencing. Front Microbiol. 2015;6.

  81. 81.

    Sharp CE, Brady AL, Sharp GH, Grasby SE, Stott MB, Dunfield PF. Humboldt’s spa: microbial diversity is controlled by temperature in geothermal environments. ISME J. 2014;8:1166–74.

  82. 82.

    Armstrong A, Valverde A, Ramond J-B, Makhalanyane TP, Jansson JK, Hopkins DW, et al. Temporal dynamics of hot desert microbial communities reveal structural and functional responses to water input. Sci Rep. 2016;6:1–8.

    Article  CAS  Google Scholar 

  83. 83.

    Noronha MF, Lacerda Júnior GV, Gilbert JA, de Oliveira VM. Taxonomic and functional patterns across soil microbial communities of global biomes. Sci Total Environ. 2017;609:1064–74.

    CAS  PubMed  Article  Google Scholar 

  84. 84.

    Soo RM, Wood SA, Grzymski JJ, McDonald IR, Cary SC. Microbial biodiversity of thermophilic communities in hot mineral soils of Tramway Ridge, Mount Erebus, Antarctica. Environ Microbiol. 2009;11:715–28.

    CAS  PubMed  Article  Google Scholar 

  85. 85.

    Cockell CS, Harrison JP, Stevens AH, Payler SJ, Hughes SS, Kobs Nawotniak SE, et al. A Low-Diversity Microbiota Inhabits Extreme Terrestrial Basaltic Terrains and Their Fumaroles: Implications for the Exploration of Mars. Astrobiology. 2019;19:284–99.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Filippidou S, Wunderlin T, Junier T, Jeanneret N, Dorador C, Molina V, et al. A Combination of Extreme Environmental Conditions Favor the Prevalence of Endospore-Forming Firmicutes. Front Microbiol. 2016;7.

  87. 87.

    Savage AM, Hills J, Driscoll K, Fergus DJ, Grunden AM, Dunn RR. Microbial diversity of extreme habitats in human homes. PeerJ. 2016;4.

  88. 88.

    Varin T, Lovejoy C, Jungblut AD, Vincent WF, Corbeil J. Metagenomic Analysis of Stress Genes in Microbial Mat Communities from Antarctica and the High Arctic. Appl Environ Microbiol. 2012;78:549–59.

    PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Muñoz PA, Márquez SL, González-Nilo FD, Márquez-Miranda V, Blamey JM. Structure and application of antifreeze proteins from Antarctic bacteria. Microb Cell Factories. 2017;16.

  90. 90.

    Liljeqvist M, Ossandon FJ, González C, Rajan S, Stell A, Valdes J, et al. Metagenomic analysis reveals adaptations to a cold-adapted lifestyle in a low-temperature acid mine drainage stream. FEMS Microbiol Ecol. 2015;91.

  91. 91.

    Hayes MA. The Geomyces Fungi: Ecology and Distribution. BioScience. 2012;62:819–23.

    Article  Google Scholar 

  92. 92.

    Su Y, Jiang X, Wu W, Wang M, Hamid MI, Xiang M, et al. Genomic, Transcriptomic, and Proteomic Analysis Provide Insights Into the Cold Adaptation Mechanism of the Obligate Psychrophilic Fungus Mrakia psychrophila. G3: Genes, Genomes. Genetics. 2016;6:3603–13.

    CAS  Google Scholar 

  93. 93.

    Castenholz RW, Garcia-Pichel F. Cyanobacterial Responses to UV Radiation. In: Whitton BA, editor. Ecology of Cyanobacteria II: Their Diversity in Space and Time. Dordrecht: Springer Netherlands; 2012. p. 481–99.

    Google Scholar 

  94. 94.

    Worrest RC, Háder D-P. Overview of the Effects of Increased Solar UV on Aquatic Microorganisms. Photochem Photobiol. 1997;65:257–9.

    Article  Google Scholar 

  95. 95.

    van de Water JAJM, Courtial L, Houlbrèque F, Jacquet S, Ferrier-Pagès C. Ultra-Violet Radiation Has a Limited Impact on Seasonal Differences in the Acropora Muricata Holobiont. Front Mar Sci. 2018;5.

  96. 96.

    Hansen AA, Merrison J, Nørnberg P, Lomstein BA, Finster K. Activity and stability of a complex bacterial soil community under simulated Martian conditions. Int J Astrobiol. 2005;4:135–44.

    CAS  Article  Google Scholar 

  97. 97.

    Fahimipour AK, Hartmann EM, Siemens A, Kline J, Levin DA, Wilson H, et al. Daylight exposure modulates bacterial communities associated with household dust. Microbiome. 2018;6:175.

    PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Sedláček I, Pantůček R, Králová S, Mašlaňová I, Holochová P, Staňková E, et al. Hymenobacter amundsenii sp. nov. resistant to ultraviolet radiation, isolated from regoliths in Antarctica. Syst Appl Microbiol. 2019;42:284–90.

    PubMed  Article  CAS  Google Scholar 

  99. 99.

    Dai J, Wang Y, Zhang L, Tang Y, Luo X, An H, et al. Hymenobacter tibetensis sp. nov., a UV-resistant bacterium isolated from Qinghai–Tibet plateau. Syst Appl Microbiol. 2009;32:543–8.

    CAS  PubMed  Article  Google Scholar 

  100. 100.

    Dorado-Morales P, Vilanova C, Peretó J, Codoñer FM, Ramón D, Porcar M. A highly diverse, desert-like microbial biocenosis on solar panels in a Mediterranean city. Sci Rep. 2016;6:1–9.

    Article  CAS  Google Scholar 

  101. 101.

    Porcar M, Louie KB, Kosina SM, Van Goethem MW, Bowen BP, Tanner K, et al. Microbial Ecology on Solar Panels in Berkeley, CA, United States. Front Microbiol. 2018;9.

  102. 102.

    Tanner K, Martí JM, Belliure J, Fernández-Méndez M, Molina-Menor E, Peretó J, et al. Polar solar panels: Arctic and Antarctic microbiomes display similar taxonomic profiles. Environ Microbiol Rep. 2018;10:75–9.

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Portero LR, Alonso-Reyes DG, Zannier F, Vazquez MP, Farías ME, Gärtner W, et al. Photolyases and Cryptochromes in UV-resistant Bacteria from High-altitude Andean Lakes. Photochem Photobiol. 2019;95:315–30.

    CAS  PubMed  Article  Google Scholar 

  104. 104.

    Albarracín VH, Pathak GP, Douki T, Cadet J, Borsarelli CD, Gärtner W, et al. Extremophilic Acinetobacter Strains from High-Altitude Lakes in Argentinean Puna: Remarkable UV-B Resistance and Efficient DNA Damage Repair. Orig Life Evol Biosph. 2012;42:201–21.

    PubMed  Article  CAS  Google Scholar 

  105. 105.

    Kurth D, Belfiore C, Gorriti MF, Cortez N, Farias ME, Albarracín VH. Genomic and proteomic evidences unravel the UV-resistome of the poly-extremophile Acinetobacter sp. Ver3. Front Microbiol. 2015;6.

  106. 106.

    Pacelli C, Bryan RA, Onofri S, Selbmann L, Zucconi L, Shuryak I, et al. Survival and redox activity of Friedmanniomyces endolithicus, an Antarctic endemic black meristematic fungus, after gamma rays exposure. Fungal Biol. 2018;122:1222–7.

    CAS  PubMed  Article  Google Scholar 

  107. 107.

    Selbmann L, Isola D, Zucconi L, Onofri S. Resistance to UV-B induced DNA damage in extreme-tolerant cryptoendolithic Antarctic fungi: detection by PCR assays. Fungal Biol. 2011;115:937–44.

    CAS  PubMed  Article  Google Scholar 

  108. 108.

    Jones DL, Baxter BK. DNA Repair and Photoprotection: Mechanisms of Overcoming Environmental Ultraviolet Radiation Exposure in Halophilic Archaea. Front Microbiol. 2017;8.

  109. 109.

    Raheja PC. Aridity and Salinity. In: Boyko H, editor. Salinity and Aridity: New Approaches to Old Problems. Dordrecht: Springer Netherlands; 1966. p. 43–127.

    Google Scholar 

  110. 110.

    Molina-Menor E, Tanner K, Vidal-Verdú À, Peretó J, Porcar M. Microbial communities of the Mediterranean rocky shore: ecology and biotechnological potential of the sea-land transition. Microb Biotechnol. 2019;12:1359–70.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

    Khomutova TE, Borisov AV. Estimation of microbial diversity in the desert steppe surface soil and buried palaeosol (IV mil. BC) using the TRFLP method. J Arid Environ. 2019;171:104004.

    Article  Google Scholar 

  112. 112.

    Zajc J, Liu Y, Dai W, Yang Z, Hu J, Gostinčar C, et al. Genome and transcriptome sequencing of the halophilic fungus Wallemia ichthyophaga: haloadaptations present and absent. BMC Genomics. 2013;14:617.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Pérez-Llano Y, Rodríguez-Pupo EC, Druzhinina IS, Chenthamara K, Cai F, Gunde-Cimerman N, et al. Stress Reshapes the Physiological Response of Halophile Fungi to Salinity. Cells. 2020;9:525.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  114. 114.

    Degen AA. Energy requirements of the fat sand rat (Psammomys obesus) when consuming the saltbush, a triplex halimus: a review. J Basic Clin Physiol Pharmacol. 1993;4:13–28.

    CAS  PubMed  Article  Google Scholar 

  115. 115.

    Sato H, Ichino S, Hanya G. Dietary modification by common brown lemurs (Eulemur fulvus) during seasonal drought conditions in western Madagascar. Primates. 2014;55:219–30.

    PubMed  Article  Google Scholar 

  116. 116.

    Randall JA. Behavioural adaptations of desert rodents (Heteromyidae). Anim Behav. 1993;45:263–87.

    Article  Google Scholar 

  117. 117.

    Schwimmer H, Haim A. Physiological adaptations of small mammals to desert ecosystems. Integrative Zool. 2009;4:357–66.

    Article  Google Scholar 

  118. 118.

    Kronfeld-Schor N, Dayan T. Thermal Ecology, Environments, Communities, and Global Change: Energy Intake and Expenditure in Endotherms. Annu Rev Ecol Evol Syst. 2013;44:461–80.

    Article  Google Scholar 

  119. 119.

    Macfarlane WV, Morris RJH, Howard B, McDonald J, Budtz-Olsen OE. Water and electrolyte changes in tropical Merino sheep exposed to dehydration during summer. Aust J Agric Res. 1961;12:889–912.

    CAS  Article  Google Scholar 

  120. 120.

    Levy A, Perelman B, Grevenbroek MV, Creveld CV, Agbaria R, Yagil R. Effect of water restriction on renal function in ostriches (Struthio camelus). Avian Pathol. 1990;19:385–93.

    CAS  PubMed  Article  Google Scholar 

  121. 121.

    Shoemaker VH. The stimulus for the water-balance response to dehydration in toads. Comp Biochem Physiol. 1965;15:81–8.

    CAS  PubMed  Article  Google Scholar 

  122. 122.

    Coleman JC, Downs CT. Variation in urine concentrating ability and water balance of the Black-tailed Tree Rat Thallomys nigricauda, along an aridity gradient. Comp Biochem Physiol A Mol Integr Physiol. 2009;154:508–13.

    CAS  PubMed  Article  Google Scholar 

  123. 123.

    Bassett JE. Habitat aridity and urine concentrating ability of nearctic, insectivorous bats. Comp Biochem Physiol A Comp Physiol. 1986;83:125–31.

    CAS  PubMed  Article  Google Scholar 

  124. 124.

    Korine C, Vatnick I, van TIG, Pinshow B. New observations on urine contents in water-deprived Negev Desert rodents. Can J Zool. 2003;81:941–5.

    Article  Google Scholar 

  125. 125.

    Sands JM, Layton HE. Advances in Understanding the Urine-Concentrating Mechanism. Annu Rev Physiol. 2014;76:387–409.

    CAS  PubMed  Article  Google Scholar 

  126. 126.

    Diaz GB, Ojeda RA. Kidney structure and allometry of Argentine desert rodents. J Arid Environ. 1999;41:453–61.

    Article  Google Scholar 

  127. 127.

    Urity VB, Issaian T, Braun EJ, Dantzler WH, Pannabecker TL. Architecture of kangaroo rat inner medulla: segmentation of descending thin limb of Henle’s loop. Am J Phys Regul Integr Comp Phys. 2011;302:R720–6.

    Google Scholar 

  128. 128.

    Bozinovic F, Gallardo PA, Visser GH, Cortés A. Seasonal acclimatization in water flux rate, urine osmolality and kidney water channels in free-living degus: molecular mechanisms, physiological processes and ecological implications. J Exp Biol. 2003;206:2959–66.

    CAS  PubMed  Article  Google Scholar 

  129. 129.

    Hoppe P, Kay RN, Maloiy GM. Proceedings: The rumen as a reservoir during dehydration and rehydration in the camel. J Physiol Lond. 1976;254:76P–7P.

    CAS  PubMed  Google Scholar 

  130. 130.

    Letnic M, Webb JK, Jessop TS, Florance D, Dempster T. Artificial water points facilitate the spread of an invasive vertebrate in arid Australia. J Appl Ecol. 2014;51:795–803.

    Article  Google Scholar 

  131. 131.

    Dawson TJ, McTavish KJ, Munn AJ, Holloway J. Water use and the thermoregulatory behaviour of kangaroos in arid regions: insights into the colonisation of arid rangelands in Australia by the Eastern Grey Kangaroo (Macropus giganteus). J Comp Physiol B. 2005;176:45.

    PubMed  Article  Google Scholar 

  132. 132.

    Schmidt-Nielsen B, Schmidt-Nielsen K. A complete account of the water metabolism in kangaroo rats and an experimental verification. J Cell Comp Physiol. 1951;38:165–81.

    CAS  PubMed  Article  Google Scholar 

  133. 133.

    Lockard RB. Seasonal Change in the Activity Pattern of Dipodomys spectabilis. J Mammal. 1978;59:563–8.

    Article  Google Scholar 

  134. 134.

    Tieleman BI, Williams JB, Bloomer P. Adaptation of metabolism and evaporative water loss along an aridity gradient. Proc R Soc Lond Ser B Biol Sci. 2003;270:207–14.

    Article  Google Scholar 

  135. 135.

    Schmidt-Nielsen B, Schmidt-Nielsen K. Pulmonary water loss in desert rodents. Am J Physiol Legacy Content. 1950;162:31–6.

    CAS  Article  Google Scholar 

  136. 136.

    Geiser F. The role of torpor in the life of Australian arid zone mammals. Austr Mammal. 2004:125–34.

  137. 137.

    Cooper CE, McAllan BM, Geiser F. Effect of torpor on the water economy of an arid-zone marsupial, the stripe-faced dunnart (Sminthopsis macroura). J Comp Physiol B. 2005;175:323–8.

    CAS  PubMed  Article  Google Scholar 

  138. 138.

    Nowack J, Stawski C, Geiser F. More functions of torpor and their roles in a changing world. J Comp Physiol B. 2017;187:889–97.

    PubMed  PubMed Central  Article  Google Scholar 

  139. 139.

    Fouet C, Gray E, Besansky NJ, Costantini C. Adaptation to Aridity in the Malaria Mosquito Anopheles gambiae: Chromosomal Inversion Polymorphism and Body Size Influence Resistance to Desiccation. PLoS One. 2012;7:e34841.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. 140.

    Schmidt-Nielsen B, Schmidt-Nielsen K, Houpt TR, Jarnum SA. Water Balance of the Camel. Am J Physiol Legacy Content. 1956;185:185–94.

    CAS  Article  Google Scholar 

  141. 141.

    Schmidt-Nielsen K. The physiology of the camel. Sci Am. 1959;201(6):140–51.

  142. 142.

    Watanabe M. Anhydrobiosis in invertebrates. Appl Entomol Zool. 2006;41:15–31.

    CAS  Article  Google Scholar 

  143. 143.

    Somme L. Invertebrates in Hot and Cold Arid Environments. Berlin Heidelberg: Springer-Verlag; 1995.,583-1..

    Google Scholar 

  144. 144.

    Davenport CF, Tümmler B. Advances in computational analysis of metagenome sequences. Environ Microbiol. 2013;15:1–5.

    CAS  PubMed  Article  Google Scholar 

  145. 145.

    Kawalia A, Motameny S, Wonczak S, Thiele H, Nieroda L, Jabbari K, et al. Leveraging the Power of High Performance Computing for Next Generation Sequencing Data Analysis: Tricks and Twists from a High Throughput Exome Workflow. PLoS One. 2015;10:e0126321.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  146. 146.

    Heijtza RD, Wang SG, Anuar F, Qian Y, Bjorkholm B, Samuelsson A, et al. Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci U S A. 2011;108:3047–52.

    Article  Google Scholar 

  147. 147.

    Stewart RD, Auffret MD, Warr A, Wiser AH, Press MO, Langford KW, et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat Commun. 2018;9:11.

    Article  CAS  Google Scholar 

  148. 148.

    Falentin H, Rault L, Nicolas A, Bouchard DS, Lassalas J, Lamberton P, et al. Bovine Teat Microbiome Analysis Revealed Reduced Alpha Diversity and Significant Changes in Taxonomic Profiles in Quarters with a History of Mastitis. Front Microbiol. 2016;7:14.

    Article  Google Scholar 

  149. 149.

    Ma C, Sun Z, Zeng B, Huang S, Zhao J, Zhang Y, et al. Cow-to-mouse fecal transplantations suggest intestinal microbiome as one cause of mastitis. Microbiome. 2018;6.

  150. 150.

    Wilkinson TJ, Huws SA, Edwards JE, Kingston-Smith AH, Siu-Ting K, Hughes M, et al. CowPI: A Rumen Microbiome Focussed Version of the PICRUSt Functional Inference Software. Front Microbiol. 2018;9:10.

    Article  Google Scholar 

  151. 151.

    Dill-McFarland KA, Weimer PJ, Breaker JD, Suen G. Diet Influences Early Microbiota Development in Dairy Calves without Long-Term Impacts on Milk Production. Appl Environ Microbiol. 2019;85.

  152. 152.

    Chen SY, Wang J, Peng DD, Li G, Chen J, Gu XH. Exposure to heat-stress environment affects the physiology, circulation levels of cytokines, and microbiome in dairy COWS. Sci Rep. 2018;8:11.

    Article  CAS  Google Scholar 

  153. 153.

    Hess M, Sczyrba A, Egan R, Kim TW, Chokhawala H, Schroth G, et al. Metagenomic Discovery of Biomass-Degrading Genes and Genomes from Cow Rumen. Science. 2011;331:463–7.

    CAS  PubMed  Article  Google Scholar 

  154. 154.

    Jin W, Li Y, Cheng YF, Mao SY, Zhu WY. The bacterial and archaeal community structures and methanogenic potential of the cecal microbiota of goats fed with hay and high-grain diets. Antonie Van Leeuwenhoek. 2018;111:2037–49.

    CAS  PubMed  Article  Google Scholar 

  155. 155.

    Lamendella R, Santo Domingo JW, Ghosh S, Martinson J, Oerther DB. Comparative fecal metagenomics unveils unique functional capacity of the swine gut. BMC Microbiol. 2011;11:103.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  156. 156.

    Rudi K, Angell IL, Pope PB, Vik JO, Sandve SR, Snipen LG. Stable Core Gut Microbiota across the Freshwater-to-Saltwater Transition for Farmed Atlantic Salmon. Appl Environ Microbiol. 2018;84:9.

    Google Scholar 

  157. 157.

    Alessandri G, Milani C, Mancabelli L, Longhi G, Anzalone R, Lugli GA, et al. Deciphering the Bifidobacterial Populations within the Canine and Feline Gut Microbiota. Appl Environ Microbiol. 2020;86.

  158. 158.

    Antwis RE, Lea JMD, Unwin B, Shultz S. Gut microbiome composition is associated with spatial structuring and social interactions in semi-feral Welsh Mountain ponies. Microbiome. 2018;6:11.

    Article  Google Scholar 

  159. 159.

    Compant S, Samad A, Faist H, Sessitsch A. A review on the plant microbiome: Ecology, functions, and emerging trends in microbial application. J Adv Res. 2019;19:29–37.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  160. 160.

    Cheng YT, Zhang L, He SY. Plant-Microbe Interactions Facing Environmental Challenge. Cell Host Microbe. 2019;26:183–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  161. 161.

    Thakur D, Chawla A. Functional diversity along elevational gradients in the high altitude vegetation of the western Himalaya. Biodivers Conserv. 2019;28:1977–96.

    Article  Google Scholar 

  162. 162.

    Arroyo MTK, Squeo FA, Armesto JJ, Villagran C. Effects of Aridity on Plant Diversity in the Northern Chilean Andes: Results of a Natural Experiment. Ann Mo Bot Gard. 1988;75:55–78.

    Article  Google Scholar 

  163. 163.

    Arnan X, Arcoverde GB, Pie MR, Ribeiro-Neto JD, Leal IR. Increased anthropogenic disturbance and aridity reduce phylogenetic and functional diversity of ant communities in Caatinga dry forest. Sci Total Environ. 2018;631–632:429–38.

    PubMed  Article  CAS  Google Scholar 

  164. 164.

    Liu B, Sun J, Liu M, Zeng T, Zhu J. The aridity index governs the variation of vegetation characteristics in alpine grassland, Northern Tibet Plateau. PeerJ. 2019;7.

  165. 165.

    Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14.

    CAS  Article  Google Scholar 

  166. 166.

    Sanders JG, Beichman AC, Roman J, Scott JJ, Emerson D, McCarthy JJ, et al. Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nat Commun. 2015;6:8.

    Article  CAS  Google Scholar 

  167. 167.

    Singh RK, Chang H-W, Yan D, Lee KM, Ucmak D, Wong K, et al. Influence of diet on the gut microbiome and implications for human health. J Transl Med. 2017;15.

  168. 168.

    King CH, Desai H, Sylvetsky AC, LoTempio J, Ayanyan S, Carrie J, et al. Baseline human gut microbiota profile in healthy people and standard reporting template. PLoS One. 2019;14:e0206484.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  169. 169.

    Grice EA, Kong HH, Renaud G, Young AC, Bouffard GG, Blakesley RW, et al. A diversity profile of the human skin microbiota. Genome Res. 2008;18:1043–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  170. 170.

    Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. Bacterial Community Variation in Human Body Habitats Across Space and Time. Science. 2009;326:1694–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  171. 171.

    Martínez-Mota R, Kohl KD, Orr TJ, Denise Dearing M. Natural diets promote retention of the native gut microbiota in captive rodents. ISME J. 2019;14(1):1–12.

  172. 172.

    Chiarello M, Auguet J-C, Bettarel Y, Bouvier C, Claverie T, Graham NAJ, et al. Skin microbiome of coral reef fish is highly variable and driven by host phylogeny and diet. Microbiome. 2018;6:147.

    PubMed  PubMed Central  Article  Google Scholar 

  173. 173.

    Antwis RE, Haworth RL, Engelmoer DJP, Ogilvy V, Fidgett AL, Preziosi RF. Ex situ Diet Influences the Bacterial Community Associated with the Skin of Red-Eyed Tree Frogs (Agalychnis callidryas). PLoS One. 2014;9:8.

    Article  CAS  Google Scholar 

  174. 174.

    Couch CE, Arnold HK, Crowhurst RS, Jolles AE, Sharpton TJ, Witczak MF, et al. Bighorn sheep gut microbiomes associate with genetic and spatial structure across a metapopulation. Sci Rep. 2020;10:6582.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  175. 175.

    Bierlich KC, Miller C, DeForce E, Friedlaender AS, Johnston DW, Temporal AA. Regional Variability in the Skin Microbiome of Humpback Whales along the Western Antarctic Peninsula. Appl Environ Microbiol. 2018;84:15.

    Google Scholar 

  176. 176.

    Moghadam NN, Thorshauge PM, Kristensen TN, de Jonge N, Bahrndorff S, Kjeldal H, et al. Strong responses of Drosophila melanogaster microbiota to developmental temperature. Fly (Austin). 2017;12:1–12.

    Article  Google Scholar 

  177. 177.

    Sun Z, Kumar D, Cao G, Zhu L, Liu B, Zhu M, et al. Effects of transient high temperature treatment on the intestinal flora of the silkworm Bombyx mori. Sci Rep. 2017;7:1–15.

    Article  CAS  Google Scholar 

  178. 178.

    Kokou F, Sasson G, Nitzan T, Doron-Faigenboim A, Harpaz S, Cnaani A, et al. Host genetic selection for cold tolerance shapes microbiome composition and modulates its response to temperature. eLife. 2018;7:e36398.

    PubMed  PubMed Central  Article  Google Scholar 

  179. 179.

    Li J, Rui J, Li Y, Tang N, Zhan S, Jiang J, et al. Ambient temperature alters body size and gut microbiota of Xenopus tropicalis. Sci China Life Sci. 2019.

  180. 180.

    Fontaine SS, Novarro AJ, Kohl KD. Environmental temperature alters the digestive performance and gut microbiota of a terrestrial amphibian. J Exp Biol. 2018;221:7.

    Article  Google Scholar 

  181. 181.

    Givens CE. A Fish Tale: Comparison of the Gut Microbiome of 15 Fish Species and the Influence of Diet and Temperature on its Composition. Athens; 2012:227.

  182. 182.

    Sullam KE, Essinger SD, Lozupone CA, O’Connor MP, Rosen GL, Knight R, et al. Environmental and ecological factors that shape the gut bacterial communities of fish: a meta-analysis. Mol Ecol. 2012;21:3363–78.

    PubMed  Article  Google Scholar 

  183. 183.

    Dehler CE, Secombes CJ, Martin SAM. Seawater transfer alters the intestinal microbiota profiles of Atlantic salmon (Salmo salar L.). Sci Rep. 2017;7:1–11.

    CAS  Article  Google Scholar 

  184. 184.

    Mendoza MLZ, Roggenbuck M, Vargas KM, Hansen LH, Brunak S, Gilbert MTP, et al. Protective role of the vulture facial skin and gut microbiomes aid adaptation to scavenging. Acta Vet Scand. 2018;60:19.

    Article  Google Scholar 

  185. 185.

    Lavrinienko A, Tukalenko E, Mappes T, Watts PC. Skin and gut microbiomes of a wild mammal respond to different environmental cues. Microbiome. 2018;6:209.

    PubMed  PubMed Central  Article  Google Scholar 

  186. 186.

    de FRA, Milanelo L, Bondan EF, Bentubo HDL. Filamentous fungi isolated from the fur microbiota of callitrichids kept in captivity in Brazil. zamd. 2015;46:350–4.

    Article  Google Scholar 

  187. 187.

    Walker DM, Leys JE, Grisnik M, Grajal-Puche A, Murray CM, Allender MC. Variability in snake skin microbial assemblages across spatial scales and disease states. ISME J. 2019;13:2209–22.

    PubMed  PubMed Central  Article  Google Scholar 

  188. 188.

    Ghaly S, Kaakoush NO, Lloyd F, Gordon L, Forest C, Lawrance IC, et al. Ultraviolet Irradiation of Skin Alters the Faecal Microbiome Independently of Vitamin D in Mice. Nutrients. 2018;10:1069.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  189. 189.

    Graves GR, Matterson KO, Milensky CM, Schmidt BK, O’Mahoney MJV, Drovetski SV. Does solar irradiation drive community assembly of vulture plumage microbiotas? Anim Microbiome. 2020;2:24.

    Article  Google Scholar 

  190. 190.

    Saalfeld WK, Edwards GP. Distribution and abundance of the feral camel (Camelus dromedarius) in Australia. Rangel J. 2010;32:1–9.

    Article  Google Scholar 

  191. 191.

    Tulgat R, Schaller GB. Status and distribution of wild Bactrian camels Camelus bactrianus ferus. Biol Conserv. 1992;62:11–9.

    Article  Google Scholar 

  192. 192.

    Bergmann GT, Bates ST, Eilers KG, Lauber CL, Caporaso JG, Walters WA, et al. The under-recognized dominance of Verrucomicrobia in soil bacterial communities. Soil Biol Biochem. 2011;43:1450–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  193. 193.

    He J, Li GW, Hai L, Ming L, Yi L, Guo FC, et al. An analysis of the forestomach bacterial microbiota in the bactrian camel. J Camel Pract Res. 2019;26:71–9.

    Article  Google Scholar 

  194. 194.

    Qi M, Wang P, O’Toole N, Barboza PS, Ungerfeld E, Leigh MB, et al. Snapshot of the Eukaryotic Gene Expression in Muskoxen Rumen—A Metatranscriptomic Approach. PLoS One. 2011;6:e20521.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  195. 195.

    Gharechahi J, Zahiri HS, Noghabi KA, Salekdeh GH. In-depth diversity analysis of the bacterial community resident in the camel rumen. Syst Appl Microbiol. 2015;38:67–76.

    PubMed  PubMed Central  Article  Google Scholar 

  196. 196.

    Bird S, Prewer E, Kutz S, Leclerc L-M, Vilaça ST, Kyle CJ. Geography, seasonality, and host-associated population structure influence the fecal microbiome of a genetically depauparate Arctic mammal. Ecol Evol. 2019;9:13202–13,217.

    PubMed  PubMed Central  Article  Google Scholar 

  197. 197.

    He J, Yi L, Hai L, Ming L, Gao W, Ji R. Characterizing the bacterial microbiota in different gastrointestinal tract segments of the Bactrian camel. Sci Rep. 2018;8:654.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  198. 198.

    Gharechahi J, Salekdeh GH. A metagenomic analysis of the camel rumen’s microbiome identifies the major microbes responsible for lignocellulose degradation and fermentation. Biotechnol Biofuels. 2018;11:216.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  199. 199.

    He J, Hai L, Orgoldol K, Yi L, Ming L, Guo F, et al. High-Throughput Sequencing Reveals the Gut Microbiome of the Bactrian Camel in Different Ages. Curr Microbiol. 2019;76:810–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  200. 200.

    Bhatt VD, Dande SS, Patil NV, Joshi CG. Molecular analysis of the bacterial microbiome in the forestomach fluid from the dromedary camel (Camelus dromedarius). Mol Biol Rep. 2013;40:3363–71.

    CAS  PubMed  Article  Google Scholar 

  201. 201.

    Salgado-Flores A, Bockwoldt M, Hagen LH, Pope PB, Sundset MA. First insight into the faecal microbiota of the high Arctic muskoxen (Ovibos moschatus). Microbial Genomics. 2016;2:e000066.

    PubMed  PubMed Central  Article  Google Scholar 

  202. 202.

    Maza F, Maldonado J, Vásquez-Dean J, Mandakovic D, Gaete A, Cambiazo V, et al. Soil Bacterial Communities From the Chilean Andean Highlands: Taxonomic Composition and Culturability. Front Bioeng Biotechnol. 2019;7.

  203. 203.

    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  204. 204.

    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl Environ Microbiol. 2009;75:7537–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  205. 205.

    Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  206. 206.

    DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  207. 207.

    McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.

    CAS  PubMed  Article  Google Scholar 

  208. 208.

    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.

    CAS  PubMed  Article  Google Scholar 

  209. 209.

    Consortium THMJRS. A Catalog of Reference Genomes from the Human Microbiome. Science. 2010;328:994–9.

    Article  CAS  Google Scholar 

  210. 210.

    Xiao L, Feng Q, Liang S, Sonne SB, Xia Z, Qiu X, et al. A catalog of the mouse gut metagenome. Nat Biotechnol. 2015;33:1103–8.

    CAS  PubMed  Article  Google Scholar 

  211. 211.

    Thomas AM, Segata N. Multiple levels of the unknown in microbiome research. BMC Biol. 2019;17:48.

    PubMed  PubMed Central  Article  Google Scholar 

  212. 212.

    Walsh AM, Crispie F, O’Sullivan O, Finnegan L, Claesson MJ, Cotter PD. Species classifier choice is a key consideration when analysing low-complexity food microbiome data. Microbiome. 2018;6:50.

    PubMed  PubMed Central  Article  Google Scholar 

  213. 213.

    Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.

    CAS  PubMed  Article  Google Scholar 

  214. 214.

    Wei F, Wu Q, Hu Y, Huang G, Nie Y, Yan L. Conservation metagenomics: a new branch of conservation biology. Sci China Life Sci. 2019;62:168–78.

    CAS  PubMed  Article  Google Scholar 

  215. 215.

    Liu Y, Zheng Z, Yu L, Wu S, Sun L, Wu S, et al. Examination of the temporal and spatial dynamics of the gut microbiome in newborn piglets reveals distinct microbial communities in six intestinal segments. Sci Rep. 2019;9:1–8.

    Article  CAS  Google Scholar 

  216. 216.

    Ingala MR, Simmons NB, Wultsch C, Krampis K, Speer KA, Perkins SL. Comparing Microbiome Sampling Methods in a Wild Mammal: Fecal and Intestinal Samples Record Different Signals of Host Ecology, Evolution. Front Microbiol. 2018;9.

  217. 217.

    Araújo-Pérez F, McCoy AN, Okechukwu C, Carroll IM, Smith KM, Jeremiah K, et al. Differences in microbial signatures between rectal mucosal biopsies and rectal swabs. Gut Microbes. 2012;3:530–5.

    PubMed  PubMed Central  Article  Google Scholar 

  218. 218.

    Meng F, Bar-Shmuel N, Shavit R, Behar A, Segoli M. Gut bacteria of weevils developing on plant roots under extreme desert conditions. BMC Microbiol. 2019;19:311.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  219. 219.

    Campbell TP, Sun X, Patel VH, Sanz C, Morgan D, Dantas G. The microbiome and resistome of chimpanzees, gorillas, and humans across host lifestyle and geography. ISME J. 2020;14(6):1–16.

  220. 220.

    Cowan DA, Hopkins DW, Jones BE, Maggs-Kölling G, Majewska R, Ramond J-B. Microbiomics of Namib Desert habitats. Extremophiles. 2019.

  221. 221.

    Leggett KEA. Home range and seasonal movement of elephants in the Kunene Region, northwestern Namibia. Afr Zool. 2006;41:17–36.

    Article  Google Scholar 

  222. 222.

    Cary SC, McDonald IR, Barrett JE, Cowan DA. On the rocks: the microbiology of Antarctic Dry Valley soils. Nat Rev Microbiol. 2010;8:129–38.

    CAS  PubMed  Article  Google Scholar 

  223. 223.

    Duc MTL, Dekas A, Osman S, Moissl C, Newcombe D, Venkateswaran K. Isolation and Characterization of Bacteria Capable of Tolerating the Extreme Conditions of Clean Room Environments. Appl Environ Microbiol. 2007;73:2600–11.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  224. 224.

    Kuznetsova TA, Kam M, Khokhlova IS, Kostina NV, Dobrovolskaya TG, Umarov MM, et al. Desert Gerbils Affect Bacterial Composition of Soil. Microb Ecol. 2013;66:940–9.

    PubMed  Article  Google Scholar 

  225. 225.

    Singh BK, Liu H, Trivedi P. Eco-holobiont: a new concept to identify drivers of host-associated microorganisms. Environ Microbiol. n/a.

  226. 226.

    Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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LH is funded by a Wellcome Trust Investigator Award (no. 100/974/C/13/Z) and an Institute Strategic Programme Gut Microbes and Health grant no. BB/R012490/1 and its constituent projects BBS/E/F/000PR10353 and BBS/E/F/000PR10356. DT and WH were supported by the BBSRC, Institute Strategic Programme Grant [BB/J004669/1], BBSRC Core Strategic Programme Grant [BB/P016774/1]. PO is funded by the BBSRC as part of the Doctoral Training Partnership programme, code: CT471J03B.

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PO conducted the review, prepared and wrote the manuscript. LH, WH and DT were responsible for the conception of the review, along with PO. NK-S, LH and WH provided considerable revisions to the work during writing. All authors read and approved the final manuscript.

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Correspondence to Peter Osborne.

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Osborne, P., Hall, L.J., Kronfeld-Schor, N. et al. A rather dry subject; investigating the study of arid-associated microbial communities. Environmental Microbiome 15, 20 (2020).

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