Andrew C Espenlaub

Abdullah I Alhassan

Shuji Nakamura

Claude Weisbuch

James S Speck

Auger-generated hot carrier current in photo-excited forward biased single quantum well blue light emitting diodes Andrew C. Espenlaub, 1 Abdullah I. Alhassan, 1 Shuji Nakamura, 1 Claude Weisbuch, 1,2 and James S. Speck We report on measurements of the photo-modulated current-voltage and electroluminescence characteristics of forward biased single quantum well, blue InGaN/GaN light emitting diodes with and without electron blocking layers. Low intensity resonant optical excitation of the quantum well was observed to induce an additional forward current at constant forward diode bias, in contrast to the usual sense of the photocurrent in photodiodes and solar cells, as well as an increased electroluminescence intensity. The presence of an electron blocking layer only slightly decreased the magnitude of the photo-induced current at constant forward bias. Photo-modulation at constant forward diode current resulted in a reduced diode bias under optical excitation. We argue that this decrease in diode bias at constant current and the increase in forward diode current at constant applied bias can only be due to additional hot carriers being ejected from the quantum well as a result of an increased Auger recombination rate within the quantum well. Published by AIP Publishing. https://doi.org/10.1063/1.5021475 Blue InGaN/GaN light emitting diodes (LEDs) form the basis of efficient solid-state white lighting. One mechanism limiting LED bulb efficiency is the "efficiency droop" phenomenon, where the efficiency of an LED decreases above a certain operating current density. Therefore, to achieve a given light output power efficiently, large area devices, or several small area devices, are used to reduce the operating current density.

Several mechanisms for LED efficiency droop have been proposed (reviewed elsewhere [1][2][3][START_REF] Shim | III-Nitride Based Light Emitting Diodes and Applications[END_REF] ), including carrier overshoot of the quantum well (QW) active region, thermionic escape of carriers out of the QW(s), and non-radiative Auger recombination in the QW(s). Recently, high-energy electron emission into ultra-high vacuum (UHV) from the surface of an operating blue LED has been reported, [START_REF] Iveland | [END_REF] confirming the presence of Auger recombination. These electroemission spectroscopy experiments require a UHV environment, a custom large area device and contact geometry, and a clean cesiated surface. The technique is also only sensitive to hot electrons although both hot electrons and hot holes may be produced by Auger processes within the LED, via electron-electron-hole (eeh) or electron-hole-hole (ehh) processes, respectively. Therefore, a complementary, and simpler, technique for detecting Auger recombination, sensitive to both types of hot carriers, would be of interest to further study the efficiency droop in InGaN LEDs.

In this letter, we report on a benchtop measurement of the modulation of the current-voltage (IV) characteristic and electroluminescence (EL) intensity of single quantum well (SQW) blue InGaN/GaN LEDs under resonant optical excitation of the QW. A schematic of the observed changes in the IV characteristic is shown in Fig. 1. Additional forward current is observed in forward bias under optical excitation, in LEDs with and without an AlGaN electron blocking layer (EBL). We attribute this additional forward current to a photo-induced increase in the Auger-generated hot carrier current within the device.

The fate of the hot carriers produced by Auger recombination is typically absent from discussions of carrier transport in InGaN/GaN LEDs. However, as early as the 1980s, it was recognized that hot carrier generation in the active region of GaInAsP/InP double-heterostructure LEDs and lasers could lead to additional minority carrier current in those devices. 6 R€ omer et al. [START_REF] Omer | Proc. SPIE[END_REF] made an attempt to account for possible recapture of the ejected hot carriers back into the QW(s) of InGaN/GaN blue LEDs, but the carrier escape direction was not discussed. Also, Vampola et al. [START_REF] Vampola | [END_REF] considered "Auger-assisted electron overflow" as a potential explanation for luminescence from a QW embedded in the p-region of electrically injected blue LEDs. FIG. 1. A schematic showing the observed effect of resonant optical excitation of the QW on the IV characteristic of a SQW c-plane blue InGaN/GaN LED. At voltages below the open-circuit voltage, V OC , the photo-induced current is in the conventional, reverse direction. At higher forward voltages, the photo-induced current is observed to switch the direction. Li et al. [START_REF] Li | Green light emitting diodes under photon modulation[END_REF] reported a photo-induced forward current in a forward biased green multiple quantum well (MQW) InGaN/ GaN LED. A very high excitation power density was used ($500Â higher that in this work), and the potential role of hot carriers in the photo-induced current was not discussed. Ahn et al. [START_REF] Ahn | [END_REF] reported measurements of the photo-induced forward current in blue MQW LEDs. The photo-induced current was attributed to "carrier spill-over"-escape of carriers from the QWs by thermionic emission in the forward direction-but no alternative mechanisms were discussed. As such, their measurements do not provide more information than the embedded p-side QW experiment reported in the paper by Vampola et al., [START_REF] Vampola | [END_REF] where it was concluded that "… the mechanism responsible for electron overflow is undetermined and may be different for the two test structures. Both traditional overflow mechanisms and Auger-assisted electron overflow mechanisms are capable of explaining this data."

0003

Complicating the interpretations in the previous reports of forward photo-induced current are that the LEDs analyzed had MQW active regions. It is well known that carriers are not uniformly injected into each of the QWs in a MQW LED. 11 In contrast, the absorption of the optical excitation is nearly uniform across all the QWs, making the photoresponse a weighted average of each of the QWs, all with different carrier densities.

The measurements reported herein were performed on blue SQW c-plane InGaN/GaN LEDs with peak EL emission wavelengths of 450 6 5 nm from 1 A/cm 2 to over 100 A/cm 2 . The epitaxial layers were grown on flat sapphire by metalorganic chemical vapor deposition, at UCSB. The device layers in the LEDs without an EBL were, in the order grown after the buffer layer, 1.1 lm of Si:GaN, a 6 period (8 nm/ 3 nm) GaN/In 0.05 Ga 0.95 N superlattice, 43 nm of Si:GaN, 7 nm of unintentionally doped (UID) GaN, a single 3 nm UID In 0.16 Ga 0.84 N QW, 7 nm of UID GaN, 200 nm of Mg:GaN, and a pþ Mg:GaN contact layer, as shown in Fig. 2(b). In the LEDs with an EBL, the first 8 nm of the Mg:GaN layer were instead Mg:AlGaN.

LEDs were fabricated from 1 cm 2 pieces, and the devices were left on-wafer. They had circular etched 0.1 mm 2 mesas, Ti/Au n-contacts completely surrounding the mesas, and $30% transparent Pt/Au p-contacts for near ideal current spreading. The device was probed with off-mesa contact pads. The p-pad metallization was brought off the mesas with 300 nm of SiO 2 as an insulating underlayer to prevent shorting on the mesa sidewall. The p-contact covered $75% of the mesa area, and $87.5% of that injected area was exposed to the laser excitation through the contact.

A 100 mW continuous wave (CW) 403 nm Coherent Cube laser was used to resonantly excite carriers selectively into the blue-emitting QW. A series of neutral density filters was used to adjust the incident power. A 1 mm diameter pinhole was placed after the filters to limit the beam area to a known value. A calibrated silicon photodiode was used to measure the beam power after the pinhole. A schematic of the experiment is shown in Fig. 2(a).

The back of the LED die was coated with an absorbing colloidal graphite paint to limit multiple pass reflections of the laser light through the QW of the device. To reduce selfheating under high forward current densities, the LED die was attached with thermally conducting paste to an aluminum heat sink and a thermoelectric stage controlled to 25 C.

The LED was driven with a Keithley 2400 sourcemeter, in a 4-wire configuration. A 0.26 X resistor network was placed in series with the LED to allow the measurement of the current through the LED (and its modulation) without adding too much to the series resistance of the circuit. The order in which the data were measured was randomized by the computer control program to transform any remaining systematic error from self-heating in the LED into random scatter in the measured data.

A biased silicon photodiode fitted with simple collection optics was used to collect a portion of the LED EL emission. A long wave pass filter prevented scattered laser light from impinging on the detector. The effective blocking of the pump light was confirmed with the LED biased in reverse, where the photoluminescence of the QW is below the noise floor of the detector. The absence of photoluminescence of the diode in reverse bias is indicative of effective carrier sweep out.

Because the photo-induced current in the LED was small compared to the total forward current, lock-in detection was necessary to measure the modulated LED current and relative EL intensity. The laser light was chopped at $83 Hz, and a lock-in amplifier was used to detect the modulated signals. The response times of the photodiode and the sourcemeter were much shorter than the period of the modulated optical excitation.

During the measurement, the room lights were kept off. The intensity of the laser light was controlled to be low enough (5.7-6.0 W/cm 2 ) to produce a linear response in the LED with pump power.

Several LEDs, with and without EBLs, were analyzed and found to have very similar optical and electrical characteristics, including the measured photo-induced currents under forward bias. The photo-induced voltage drop, DV, and the dynamic resistance (derivative of the IV characteristic) are shown in Fig. 3 shown are the normalized relative external quantum efficiencies (EQEs) of the same LEDs.

The conventional photocurrent at À1 V, which serves as the lower bound on the optical generation rate of carriers in the QW, was measured to be <20 mA/cm 2 in all devices. This lower bound is exact for a 100% escape probability in reverse bias and is a very good approximation in III-nitride solar cell structures optimized for high escape efficiency. 12,13 The LEDs discussed herein were not optimized. Nonetheless, the measured values of the conventional photocurrent are consistent with an absorption coefficient similar to values reported in the literature (of the order of $1 Â 10 5 cm À1 ), 14 assuming 100% escape probability and 30% p-contact transparency. That the photo-induced forward current exceeds the generation rate of carriers in the QW throughout most, if not all, of the droop regime of the diode (current densities տ5-10 A/ cm 2 ) suggests that the increase in current results from a nonlinear process.

Figure 3(c) shows the normalized increase in the relative EL intensity of the LEDs under forward bias, in voltage control. That the EL increases implies that the carrier density in the QW is increasing. Additionally, the relative EL increase provides an approximation of the relative increase in the carrier density in the QW, which is small at current densities higher than where the external quantum efficiency (EQE) peaks. Thus, the photo-excitation is only a small positive perturbation to the QW carrier densities.

The measured drop in voltage, DV, at constant current [Fig. 3(a)] and the increase in current density, DJ, at constant voltage [Fig. 3(b)] are consistent with the dynamic resistance of the diode [Fig. 3(a)].

Since the photo-excitation resonantly pumps the QW, photo-induced carriers must escape the QW for there to be any photo-induced current through the device, at constant diode bias. Current which overshoots the QW entirely is not directly affected by the resonant photo-excitation of the QW.

We consider three possible carrier escape mechanisms for the QWs: (1) tunneling, (2) thermionic emission, and (3) hot carrier production by Auger recombination.

As the measured photo-induced current is in the forward direction, it cannot be due to carriers tunneling out of the active region. Tunneling escape, if present, must produce reverse current as there are no available states for carriers to tunnel into in the forward direction [Fig. 4(a)].

Thermionic emission of electrically injected carriers out of the QW will contribute to forward current if any of the escaping carriers travel in the forward direction (towards the p-side for electrons or the n-side for holes). Electrically injected carriers escaping in the reverse direction do not produce a net current as they just cancel the forward injection current, as shown in Fig. 4(b).

On the other hand, thermionic escape of photo-excited carriers, generated within the QW, can only produce a net reverse current as the majority of escaping carriers are swept out by the residual built-in field of the junction, as shown in Fig. 4(c). Thus, the photoresponse to optical modulation of the thermionic emission rate of carriers out of the QW can (c) thermionic emission in the conduction band of a SQW LED, at a sufficient injected carrier density in the QW to screen the built-in and piezoelectric fields. The situation for the valence band is analogous. (a) Tunneling escape out of the QW can only occur in the reverse direction as there are no final states in the forward direction. (b) Thermionic emission of electrically injected carriers produces a net forward current equal to the fraction, f, which escape forward. (c) Thermionic emission of photo-excited carriers produces a net current equal to the net forward escape rate. This current can only ever be in reverse, on net, because f < 0.5 due to the higher barrier to escape in the forward direction.

only be a net reverse current and cannot be responsible for the observed increase in forward current.

So, as tunneling and thermionic emission cannot be the source of the observed photo-induced forward current, only escape of Auger-generated hot carriers remains as an explanation. Unlike thermionic escape, Auger-induced escape of photo-excited carriers can cause a net increase in the forward current because it is an inherently multi-body process which can involve both electrically injected and photo-excited carriers.

Increased Auger recombination in the photo-excited state is additionally consistent with the observed increase in EL intensity, as well as with the lack of a significant reduction in the photo-induced current upon insertion of an EBL into the LED. This is because transport of high energy hot carriers should be relatively unaffected by an EBL, which provides a barrier that is smaller than the kinetic energy of Auger-generated hot carriers. The blocking efficiency of an AlGaN EBL for non-hot carriers is controversial however, 15,16 and the lack of a significant reduction is not on its own a confirmation that Auger-generated hot carriers are the cause of the photo-induced forward current. It is nonetheless useful information since the opposite result, a significant reduction in photo-induced current, would have ruled out hot carriers as the cause-it is hard to imagine carriers with such high energy being blocked by the AlGaN layer.

The way in which Auger-generated hot carriers produce forward current in an LED will now be discussed in more detail. Figure 5 shows how the different types of recombination in the QW affect the net forward current through the LED, in the steady state. Figures 5(a)-5(c) depict recombination of electron-hole pairs which have been electrically injected, and Figs. 5(d)-5(f) depict recombination of optically injected electron-hole pairs. For Shockley-Read-Hall (SRH) and radiative recombination, there is only the forward current required to inject the recombining carriers. For electrically injected electron-hole pairs, this is one electron of current through the circuit per recombination event (full, filled circles in the figure). For optically injected electronhole pairs, no current needs flow for SRH or radiative recombination.

The situation is different for Auger recombination. For brevity, only eeh Auger recombination is depicted [in Figs. 5(d) and 5(f)] although the situation is analogous for ehh Auger recombination. For purely electrical carrier injection, not only does the recombining pair of carriers need to be injected from the contacts but also the eventual hot carrier (an electron for eeh and a hole for ehh Auger). This leads to a higher net forward current per recombination event [Fig. 5(c)].

It is difficult to assess the fraction of hot carriers which will travel in the forward direction. However, if the simple approximation is made that half of the hot carriers travel in each direction, as in Fig. 5, then it is easy to see that the net forward current will be one half electron more per recombination event than for either SRH or radiative recombination. A fraction different from one half will produce a proportionally different net forward current per Auger recombination event.

In the case of photo-excitation of carriers into the QW, because Auger recombination is a multi-body process unlike thermionic emission, forward current will still be induced in the device if Auger recombination is occurring in the QW. This current, due to the hot carriers produced by Auger recombination involving both electrically injected and photo-excited carriers, as shown in Fig. 5(f), corresponds to the photo-induced forward current that has been measured.

The observed photo-induced current is a measure of the sum of the eeh and ehh Auger rates in the QW because both hot electrons and hot holes will produce forward current. This sets the present measurement apart from the electroemission measurements of high energy carriers reported by Iveland et al., [START_REF] Iveland | [END_REF] which were only sensitive to eeh Auger processes and only measured the small fraction of hot carriers that survived to be emitted from a cesiated p-GaN surfaced.

In summary, we have described measurements of changes in the IV characteristic and EL intensity under resonant optical excitation of the QW layer in a blue SQW c-plane InGaN/ GaN LED under forward bias. The photo-induced current was in the forward direction when the LED was forward biasedopposite to the usual direction of the reverse bias photocurrent in solar cells and photodiodes. The EL intensity of the LED increased upon optical excitation, indicating that the carrier density in the QW layer increased, as well as all recombination rates, including the Auger rates.

The magnitude of the photo-induced forward current was not observed to decrease significantly upon inserting an AlGaN EBL into the LED structure, indicating that the photo-induced current was not due to carrier escape by tunneling or thermionic escape but instead due to hot carriers generated by Auger recombination in the QW active region of the LEDs. (d), (e), and (f) show the average current flow per recombination event, in the steady state, for an optically injected electron-hole pair, for SRH, radiative, and eeh Auger, respectively. It is assumed for definiteness that the fraction of forward escaping hot carriers is exactly half, though this need not be the case. The band diagrams are not to scale and are for illustration purposes only. Filled disks: electrons; empty circles: holes.

141106-4

Espenlaub et al.

Appl. Phys. Lett. 112, 141106 (2018)

1 1

 1 Materials Department, University of California, Santa Barbara, California 93106-5050, USA 2 Laboratoire de Physique de la Matie `re Condens ee, CNRS, Ecole Polytechnique, 91128 Palaiseau, France (Received 4 January 2018; accepted 22 March 2018; published online 3 April 2018)

  (a) for representative LEDs, with and without an EBL. The photo-induced current, DJ, at constant applied voltage is shown in Fig. 3(b), for the same LEDs. The magnitude of DJ was observed to decrease only slightly in the LEDs with an EBL, compared to those without. Also

FIG. 2 .

 2 FIG. 2. (a) A schematic of the experimental apparatus, showing the circuit for measuring the photo-induced current at constant voltage. To measure the voltage drop at constant current, the voltage across the LED was measured instead of just the voltage across the resistor network. (b) A schematic of the epitaxial layers in the LEDs measured.

FIG. 3 .

 3 FIG. 3. (a) The absolute value of the voltage drop, DV (symbols), obtained from measurements under current control, as well as the dynamic resistance of the diode (lines), for representative LEDs with (circles/full line) and without (triangles/dashed line) an EBL. (b) The photo-induced current density, DJ (symbols), obtained from measurements under voltage control, as well as the normalized external quantum efficiency (EQE) (lines), for the same LEDs as in (a). (c) The normalized increase in the EL intensity, DL/L, obtained from measurements under voltage control, for the same LEDs as in (a).

FIG. 4 .

 4 FIG.4. Schematics depicting (a) tunneling escape and (b) and (c) thermionic emission in the conduction band of a SQW LED, at a sufficient injected carrier density in the QW to screen the built-in and piezoelectric fields. The situation for the valence band is analogous. (a) Tunneling escape out of the QW can only occur in the reverse direction as there are no final states in the forward direction. (b) Thermionic emission of electrically injected carriers produces a net forward current equal to the fraction, f, which escape forward. (c) Thermionic emission of photo-excited carriers produces a net current equal to the net forward escape rate. This current can only ever be in reverse, on net, because f < 0.5 due to the higher barrier to escape in the forward direction.

  This work was supported by the US Department of Energy Solid-State Lighting Program under Agreement No. DE-EE0007096 and the KACST-KAUST-UCSB Solid State Lighting Program (SSLP).

FIG. 5 .

 5 FIG. 5. (a), (b), and (c) show the average current flow per recombination event, in the steady state, with only electrical injection of carriers into the QW, for Shockley-Read-Hall (SRH), radiative, and eeh Auger, respectively.(d), (e), and (f) show the average current flow per recombination event, in the steady state, for an optically injected electron-hole pair, for SRH, radiative, and eeh Auger, respectively. It is assumed for definiteness that the fraction of forward escaping hot carriers is exactly half, though this need not be the case. The band diagrams are not to scale and are for illustration purposes only. Filled disks: electrons; empty circles: holes.


Mark Viney
email: viney@liverpool.ac.uk

Louise Cheynel

Gut immune responses and evolution of the gut microbiomea hypothesis

Keywords: Evolution, Microbiome, IgA

The gut microbiome is an assemblage of microbes that have profound effects on their hosts. The composition of the microbiome is affected by bottom-up, among-taxa interactions and by topdown, host effects, which includes the host immune response. While the high-level composition of the microbiome is generally stable over time, component strains and genotypes will constantly be evolving, with both bottom-up and top-down effects acting as selection pressures, so driving microbial evolution. Secretory IgA is a major feature of the gut's adaptive immune response, and a substantial proportion of gut bacteria are coated with IgA, though the effect of this on bacteria is unclear. Here we hypothesise that IgA binding to gut bacteria is a selection pressure that will drive the evolution of IgA-bound bacteria, so that they will have a different evolutionary trajectory than those bacteria not bound by IgA. We know very little about the microbiome of wild animals and even less about their gut immune responses, but it must be a priority to investigate this hypothesis to understand if and how host immune responses contribute to microbiome evolution.

The gut microbiome

As is now well known, the gut microbiome comprises a very large collection of prokaryotic and eukaryotic cells, though here we will focus on the largest component, the bacteria. The microbiome provides a number of services to its host and so it is crucial for host health and wellbeing [START_REF] Guzman-Bautista | Bacteria-immune cells dialog and the homeostasis of the systems[END_REF], but some members of the microbiome can also harm the host. The microbiome is a complex mixture, whose composition varies among individuals, can change over time, and can be perturbed by a range of factors.

Most of what we know about the gut microbiome comes from studies of laboratory animals (mainly mice), people, and some livestock species. While there are clear advantages of using laboratory animals, for example when they capture relevant traits of another species (e.g. humans), or of the same species in a different environment (e.g. the wild), there are also problems. Not only do laboratory animals' microbiomes differ substantially among laboratories and suppliers but the microbiome can affect other host traits, such as immune function and resistance to infection, giving aberrant results [START_REF] Belkaid | Role of the microbiota in immunity and inflammation[END_REF][START_REF] Ivanov | Specific microbiota direct the differentiation of IL-17-producing T-helper cells in the mucosa of the small intestine[END_REF]. For example, laboratory mice that would otherwise be killed by an Influenza A virus infection in the lung survive if they have received a gut microbiome transplant from wild mice [START_REF] Rosshart | Wild mouse gut microbiota promotes host fitness and improves disease resistance[END_REF]; lab mice that normally immunologically reject the gut worm Trichuris are fully susceptible to it if they live in outdoor enclosures and acquire a wild microbiome [START_REF] Leung | Rapid environmental effects on gut nematode susceptibility in rewilded mice[END_REF]. Therefore, there must be some concern that the work of the large international community studying laboratory animals' microbiomes might be discovering phenomena that are only relevant in the laboratory and missing impacts of the microbiome that occur in wild animals. This, in turn, suggests that to understand animal microbiomes that it is necessary (and perhaps ideal) to study the microbiome in wild animals directly.

That said, we know rather little about wild animals' microbiomes, except that they are usually different than lab-maintained con-specifics, and that they can be more diverse than lab animals' [START_REF] Bowerman | Effects of laboratory domestication on the rodent gut microbiome[END_REF] for example c. 30 -50% more diverse in wild mice (Shannon diversity index and abundance by phyla; [START_REF] Rosshart | Wild mouse gut microbiota promotes host fitness and improves disease resistance[END_REF][START_REF] Bowerman | Effects of laboratory domestication on the rodent gut microbiome[END_REF][START_REF] Bär | Strong effects of lab-to-field environmental transitions on the bacterial intestinal microbiota of Mus musculus are modulated by Trichuris muris infection[END_REF][START_REF] Graham | Naturalizing mouse models for immunology[END_REF][START_REF] Viney | The gut microbiota of wild rodents: Challenges and opportunities[END_REF][START_REF] Yeung | Altered immunity of laboratory mice in the natural environment is associated with fungal colonization[END_REF][START_REF] Suzuki | Host genetic determinants of the gut microbiota of wild mice[END_REF]) (Box 1). Wild animals' microbiomes are different likely because wild animals differ in a wide range of factorssuch as diet, reproductive status, infections, immune state etc.that are often heavily constrained in laboratory animals, and these factors affect the composition of the microbiome. In the wild, individual animals will also quantitively vary in these factors, so driving inter-individual variation in their microbiomes. While this among-individual variation in microbiomes might seem unhelpful noise it is actually the result of different biology of individual hosts, so showing the breadth of factors that act and interact to affect the microbiome.

Controlling the composition of the microbiometop-down and bottom-up processes

Clearly the composition of the microbiome is importantthe human microbiome is generally stable over time and deviation from this can cause harm to the host [START_REF] Faith | The long-term stability of the human gut microbiota[END_REF]. Therefore, understanding the processes that control the composition of the microbiome is of interest and importance. However, in beginning to think about this, it is very important that we are clear about what is meant A c c e p t e d M a n u s c r i p t 6 by composition of the microbiome. The microbiome composition can be measured, and so defined, in different ways, which can be (i) simply the species composition, (ii) other abundancecontrolled measures, or (iii) bacterial phylogenetic diversity (Box 1). Thus, when thinking about the control of the composition of the microbiome we need to be clear about what aspect or measure of the composition is being considered.

A useful way to approach thinking about the composition of the microbiome is to recognise that it is an ecosystem, where direct and indirect interactions between bacterial taxa in the microbiome affect the presence and abundance of bacterial taxa. For example, the human preterm-infant microbiome is assembled in a predictable pattern, with much of this driven by among-species interactions [START_REF] Rao | Multi-kingdom ecological drivers of microbiota assembly in preterm infants[END_REF][START_REF] Madi | Community diversity is associated with intraspecies genetic diversity and gene loss in the human gut microbiome[END_REF]. Thus, while species are acquired from the environment somewhat haphazardly, their establishment and subsequent growth within the microbiome depends on the presence of other species, which therefore generates a certain predictability in the composition of the assembled microbiome [START_REF] Coyte | Ecological rules for the assembly of microbiome communities[END_REF]. Experimental introduction of microbial taxa into mice also shows that order-of-arrival in the gut affects colonization success [START_REF] Martínez | Experimental evaluation of the importance of colonization history in early-life gut microbiota assembly[END_REF]. These among-species interactions are, in the language of ecology, bottom-up processes. While these among-species interactions can be seen, for example, in the establishment to the preterm-infant microbiome, these process are actually occurring all the time within microbiomes and so contributing to determining the composition and structure of the microbiome.

It is self-evident that the gut microbiome exists within its host and so host physiology and immune state create (and change) the environment in which the microbiome exists. In this way the host can alter the composition of the microbiome. Again, in the language of ecology, the microbiome is therefore affected by top-down host factors. For example, a seasonal change in a wild animal's diet could lead to a change in the species composition of the microbiome because the different food present in the gut will favour some bacterial a species and disfavour others. Also, one can envisage that reproductive hormonal changes in female animals could result in a change in gut physiology thereby also altering the species composition of the microbiome. There are also host genetic effects on the microbiome, which are another manifestation of top-down processes. In wild mice (Mus musculus domesticus) the genetic distance among individual mice predicted the composition of their microbiome, and these effects persisted in animals when they were in a common laboratory environment [START_REF] Suzuki | Host genetic determinants of the gut microbiota of wild mice[END_REF].These effects also can be seen by analysis of the heritability (the proportion of the among-individual variation in microbiome composition that is attributable to host genetics) of the microbiome composition. In humans, one study found that the abundance of 5 % of taxa in the microbiome were heritable, whereas in a second study it was of 37 % of taxa [START_REF] Turpin | Association of host genome with intestinal microbial composition in a large healthy cohort[END_REF][START_REF] Goodrich | Genetic determinants of the gut microbiome in UK twins[END_REF]. The composition of the baboon gut microbiome has also been found to be heritable, though generally with low values, though the heritability changed over seasons etc. pointing to the effects of the wider environment on the microbiome [START_REF] Grieneisen | Gut microbiome heritability is nearly universal but environmentally contingent[END_REF]. In lab mice, some 20 quantitative trait loci have been found that affect the composition of the microbiome [START_REF] Benson | Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors[END_REF]. But any top-down change in the composition of the microbiome will also have consequences for the bottom-up, among-species interactions in the microbiome [START_REF] Foster | The evolution of the host microbiome as an ecosystem on a leash[END_REF].

A c c e p t e d M a n u s c r i p t 7 In summary, both bottom-up and top-down processes affect the composition of the microbiome and, presumably, its function and so its contribution to host health, wellbeing and evolutionary fitness. Therefore, understanding how the composition of the microbiome is determined is necessary to understand the normal biology of animals.

Bacterial evolution

It is axiomatic that bacteria in the microbiome will evolve, as all organisms do. In thinking about this, there are at least two conceptions of microbiome evolution. One is that microbiome evolution is a quantitative change in the composition of the microbiome, that is a change in its alpha diversity (Box 1). This could occur because of both bottom-up processes (as hosts acquire new bacteria and then there are different among-species interactions) and / or top-down processes, due to changes in the host diet, physiology, immune state etc. [START_REF] Foster | The evolution of the host microbiome as an ecosystem on a leash[END_REF][START_REF] Garud | Evolutionary dynamics of bacteria in the gut microbiome within and across hosts[END_REF][START_REF] Scanlan | Microbial evolution and ecological opportunity in the gut environment[END_REF]. A second conception of microbiome evolution is the molecular evolution of bacterial taxa within the microbiome [START_REF] Foster | The evolution of the host microbiome as an ecosystem on a leash[END_REF][START_REF] Garud | Evolutionary dynamics of bacteria in the gut microbiome within and across hosts[END_REF][START_REF] Scanlan | Microbial evolution and ecological opportunity in the gut environment[END_REF]. In thinking about this we need to focus on genotypes (or strains [START_REF] Lieberman | Detecting bacterial adaptation within individual microbiomes Phil[END_REF]) of bacteria, recognising that it is these (rather than species) that are competing with each other and that are subject to natural selection. As the evolution of genotypes occurs the bacterial species will still exist and persist in the microbiome, but the species' component genotypes will have changed.

Different bacterial genotypes will evolve by processes of mutation and horizontal gene transfer [START_REF] Arnold | Horizontal gene transfer and adaptive evolution in bacteria[END_REF], which will occur on short time scales, and so within the lifespan of the host. The potential for evolution is huge; for mutations only, given the large population size of many bacteria in the microbiome, their relatively small genomes, and assuming a conservative mutation rate then every position in the genome could be theoretically be mutated in every bacterial generation [START_REF] Lieberman | Detecting bacterial adaptation within individual microbiomes Phil[END_REF].

There has been some study of evolution of bacteria within the microbiome. In humans, over timescales of multiple months there is evolutionary change of resident bacterial strains (with the acquisition of new strains occurring only rarely) [START_REF] Garud | Evolutionary dynamics of bacteria in the gut microbiome within and across hosts[END_REF]. A 6 year-long study of laboratory mice colonised with 12 bacterial taxa, showed that the overall composition of this simple microbiome was stable, but that there was evolution of within-taxa strains that then co-existed, though a change in host diet brought about a rapid shift in the microbiome's composition [START_REF] Yilmaz | Long-term evolution and short-term adaptation of microbiota strains and sub-strains in mice[END_REF]. As Escherichia coli adapted to the gut of laboratory mice, soft genetic sweepsthe multiple occurrence of beneficial mutationsoccurred in the E. coli population, with inter-genotype competition, but this bacterial evolution differed in young and old mice, with patterns suggesting that the old mouse gut was a comparatively more harsh environment for E. coli [START_REF] Barroso-Batista | The first steps of adaptation of Escherichia coli to the gut are dominated by soft sweeps[END_REF][START_REF] Barreto | The landscape of adaptive evolution of a gut commensal bacteria in aging mice[END_REF]. In a longitudinal study of a single human microbiome, focussing on 36 bacterial species, there were different levels of genetic variation within each species that changed over time with this likely being driven by selection [29]. Though in contrast, longitudinal study of a single clone of E. coli in a single human microbiome found limited genetic diversity and no evidence of selection [START_REF] Ghalayini | Evolution of a dominant natural isolate of Escherichia coli in the human gut over the course of a year suggests a neutral evolution with reduced effective population size[END_REF]. Analysis of some bacterial species in the gut microbiome of people from 14 different (urban vs.

A c c e p t e d M a n u s c r i p t 8 rural) populations showed that horizontal gene transfer was very common and frequent, and that this occurred more in individuals from urban settings [START_REF] Groussin | Elevated rates of horizontal gene transfer in the industrialized human microbiome[END_REF]. Within the human microbiome, Bacteroides fragilis was found to be genetically diverse, with this diversity having arisen within individuals, with horizontal gene transfer selection having occurred [START_REF] Zhao | Adaptive evolution within gut microbiomes of healthy people[END_REF]. Analysis of the genomic evolution of 42 bacterial pan genomes found that genes coding for secreted products evolved faster than other genes [START_REF] Nogueira | Rapid evolution of the sequences and gene repertoires of secreted proteins in bacteria[END_REF]. These secreted proteins are, presumably, how bacteria interact with each other but also how they interact with their host, and so evidence of their accelerated evolution is particularly relevant to bacteria in the gut microbiome.

In summary, there is good evidence that bacteria in the microbiome are genetically diverse, that this diversity can change and that selection can act, changing the bacterial genotypes comprising the bacterial species, therefore changing the composition of the microbiome.

The gut immune response and IgA

The gut has a fully functioning immune system that, together with other host physiological processes, maintains gut integrity in the face of the large microbial population resident in the gut. The core of this gut immune system is the gut-associated lymphoid tissue, particularly Peyer's patches (PP), which occur along the length of the gut, and mesenteric lymph nodes. Epithelial microfold cells and dendritic cells sample antigens of microbes that are associated with, or attempting to penetrate, the intestinal epithelium, resulting in induction of T and B cells in the PPs [START_REF] Hooper | Interactions between the microbiota and the immune system[END_REF][START_REF] Reboldi | Peyer's patches: organizing B-cell responses at the intestinal frontier[END_REF]. T cell responses remain confined to the intestinal tissue, whereas B cells produce IgA that is secreted across the intestinal epithelium. Secreted IgA is a dimer, covalently bound to a secretory component [START_REF] Hooper | Interactions between the microbiota and the immune system[END_REF]. Therefore, within the gut lumen itself, the only adaptive immune response is secreted IgA. The gut microbiome is necessary for the production of IgA, shown by its near absence in germ-free animals [START_REF] Macpherson | The bilateral responsiveness between intestinal microbes and IgA[END_REF][START_REF] Macpherson | The immune geography of IgA induction and function[END_REF][START_REF] Lindner | Diversification of memory B cells drives the continuous adaptation of secretory antibodies to gut microbiota[END_REF].

IgA is abundant, with 80 % of all human plasma cells secreting IgA, with the gut containing c. 70 % of all antibody-producing plasma cells [START_REF] Bunker | Innate and adaptive humoral responses coat distinct commensal bacteria with immunoglobulin A[END_REF][START_REF] Sender | The total mass, number, and distribution of immune cells in the human body[END_REF] and humans secrete c. 5 g of IgA into the gut daily [START_REF] Slack | Functional flexibility of intestinal IgAbroadening the fine line[END_REF]. Normally other antibody isotypes are not present in the gut, though this can occur when there is gut pathology (e.g. gut IgG in Chron's disease, Ulcerative Colitis; Inflammatory Bowel Disease [START_REF] Travis | Predicting outcome in severe ulcerative colitis[END_REF]) or partly replacing IgA in settings of IgA deficiency (which 0.2 % of the Caucasian population are, which is not lethal, though individuals can have a range of symptoms) [START_REF] Fadlallah | Microbial ecology perturbation in human IgA deficiency[END_REF]. However, whether or not IgA deficiency occurs in wild animal populations (and if so with what prevalence) isn't known.

When an antibody response develops, immunoglobulin genes can undergo somatic hypermutation (SHM), which increases the affinity of the antibody for the target antigenthis is the standard view of how a circulating immune response develops. However, lab mouse studies suggest that not all the gut IgA response appears to be affinity matured in this way, and so rather the gut IgA response consists of both high-affinity (sometimes called 'induced') IgA (resulting from SHM) and poly-reactive (sometimes called 'natural') IgA responses (not resulting from SHM A c c e p t e d M a n u s c r i p t 9 processes) [START_REF] Pabst | IgA and the intestinal microbiota: the importance of being specific[END_REF][START_REF] Okai | High-affinity monoclonal IgA regulates gut microbiota and prevents colitis in mice[END_REF]. Therefore, lab mouse IgA responses are generated in a T-cell independent, some in a T-cell dependant, fashion though the significance of these observations continues to be discussed [START_REF] Pabst | IgA and the intestinal microbiota: the importance of being specific[END_REF]. However, in humans (and which are, immunologically, perhaps closer to wild animals) then there is clear evidence of SHM in intestinal IgA responses so that these responses are T-cell dependent [START_REF] Spencer | The human intestinal IgA response; burning questions[END_REF]47].

IgA, as other antibodies, can bind antigen through its Fab regions, but IgA also has non-canonical binding, via parts of the antibody molecule other than the Fab regions. For IgA, much of the noncanonical binding occurs via the extensive glycosylation of the IgA molecule [START_REF] Pabst | IgA and the intestinal microbiota: the importance of being specific[END_REF]. This glycosylation makes IgA protease resistant, so prolonging its half-life in the gut. Further, this noncanonical binding might be important in IgA's interaction with bacteria, in two ways. Firstly, the surface of many bacteria are themselves glycosylated, which might therefore be important in IgA's binding to bacteria. Secondly, these glycan-based interactions might allow IgA molecules to recognise multiple bacterial species. Specifically, it has been found that IgA monoclonal antibodies can recognise multiple bacterial species (though perhaps most plasma cells produce antigen-specific IgA) [START_REF] Pabst | IgA and the intestinal microbiota: the importance of being specific[END_REF]48,49]. This phenomena may be because of interactions between glycans on the antibody molecule and bacterial glycans, and/or that antibodies recognise glycan epitopes, and / or that there is cross reactivity between non-glycan epitopes.

But, all of this work, and so our understanding of gut immune responses in general, and IgA responses in particular, has come from work in laboratory animals and people. Nothing, as far as we are aware is known about wild animals' gut immune responses. In wild mice, Mus musculus domesticus, faecal IgA concentrations have been measured. The faecal IgA concentration of wild mice varied extensively (2 -1,178 μg / g faeces), with the very high concentrations most likely in older animals [50], this variation was more so than lab mouse controls (130-380 μg / g faeces), though overall there was no difference between mice from these 2 sources [50]. Pet shopacquired mice have higher serum IgA concentrations than lab mice [51].

Overall, the IgA response appears to corral the microbiome to the gut and can alter its composition [START_REF] Guzman-Bautista | Bacteria-immune cells dialog and the homeostasis of the systems[END_REF][START_REF] Macpherson | The bilateral responsiveness between intestinal microbes and IgA[END_REF][START_REF] Okai | High-affinity monoclonal IgA regulates gut microbiota and prevents colitis in mice[END_REF]52,53,54]. For example, in antibody-deficient mice the gut microbiome has a different composition compared with control animals [54]; though in contrast, the gut microbiome maintained a similar composition in IgA replete and IgA deficient people, suggesting a more limited role of IgA in moulding the composition of the microbiome [START_REF] Fadlallah | Microbial ecology perturbation in human IgA deficiency[END_REF]. Here though, it is important to be clear about what measure of microbiome composition (Box 1) are being considered. Comparing evolution of E. coli strains in immunologically normal and immune deficient (Rag2 -/-, lymphocyte deficient) mice showed that these different immune environments (interacting with their microbiomes) provided different selective environments for E. coli [55]. The microbiome of a range of immune-deficient mice (Rag1 -/-, B and T cell deficient; lghm -/-, B cell deficient; Cde3 -/-, T cell deficient) had a lower species richness compared with immunologically normal controls [54]. Laboratory mice either unable to class switch to produce IgA nor undergo SHM (AID -/-), or mainly SHM deficient (AID G23S ), had a severely altered microbiome (compared with normal controls) though reconstitution of normal IgA function restored microbiome composition [56,57]. In these studies using immunodeficient animals where the microbiome is altered compared with nonimmunodeficient animals, then these effects may be IgA-dependant but may also be due to other, A c c e p t e d M a n u s c r i p t 10 wider effects of the immunodeficiency on the animals physiology. IgA clearly interacts with bacteria in the microbiome, because many bacteria are coated with it [START_REF] Bunker | Innate and adaptive humoral responses coat distinct commensal bacteria with immunoglobulin A[END_REF]58,59,60,61]. Flow cytometry has been used to separate bacteria that are bound by IgA, and those that are not, henceforth IgA + and IgA -bacteria, respectively. The UniFrac distance (Box 1) of IgA + and IgA - bacteria from laboratory mice differed, though this difference was reduced or removed in Cde3 -/- (T cell deficient) mice [62].

Flow cytometry-based separation of IgA + and IgA -bacteria, followed by DNA sequencing [58] has been used to understand what bacteria are in each fraction, and whether IgA binding is directed to pathogenic (or potentially pathogenic [START_REF] Belkaid | Role of the microbiota in immunity and inflammation[END_REF]) bacteria. The results of this have shown that many (perhaps most) of the IgA + bacteria are commensal, though some appear to be involved in gut pathology [58,60]. In our own studies of the faecal microbiome of wild mice, we have flow cytometry-separated and 16S sequenced IgA + and IgA -bacteria (Figure 1). For 34 Bacilli ASVs there was one enriched in the IgA + fraction, 4 enriched in the IgA -fraction, with the remaining 29 not enriched in either way. One interesting question that needs to be addressed is the extent to which the same bacterial genotypes or ASVs are in IgA + or IgA -fractions among different hosts and across different populations of hosts. The extent of the inter-individual host variation of gut IgA responses and how these are directed or not to bacterial genotypes, especially in wild animals, is a key unknown.

The functional effects of IgA's binding to bacteria isn't yet fully known [START_REF] Guzman-Bautista | Bacteria-immune cells dialog and the homeostasis of the systems[END_REF]63,64,[START_REF] Huus | Diversity and dynamism of IgA-microbiota interactions[END_REF] but it has been shown to clump bacteria [63,66], inhibit bacterial cell division [START_REF] Moor | High-avidity IgA protects the intestine by enchaining growing bacteria[END_REF], provide an altered within-gut niche for the bacteria [START_REF] Donaldson | Gut microbiota utilize immunoglobulin A for mucosal colonization[END_REF][START_REF] Hooper | Interactions between the microbiota and the immune system[END_REF][START_REF] Mcloughlin | Host selection of microbiota via differential adhesion[END_REF], and change bacterial gene expression and metabolism, including of bacterial-derived toxins [63, [START_REF] Pabst | IgA and the intestinal microbiota: the importance of being specific[END_REF][START_REF] Donaldson | Gut microbiota utilize immunoglobulin A for mucosal colonization[END_REF][START_REF] Hooper | Interactions between the microbiota and the immune system[END_REF][START_REF] Mcloughlin | Host selection of microbiota via differential adhesion[END_REF][START_REF] Nakajima | IgA regulates the composition and metabolic function of gut microbiota by promoting symbiosis between bacteria[END_REF][START_REF] Pabst | Secretory IgA in the coordination of establishment and maintenance of the microbiota[END_REF].

In summary, the bacterial microbiome is necessary to generate what becomes a large gut IgA response that, overall, keeps the microbiome in the gut, with IgA able to affect the composition of the microbiome. IgA directly binds to a proportion of the bacterial cells in the microbiome, with this IgA binding potentially affecting the IgA-bound bacteria.

What is the effect of the IgA response?

Or to put this question in a more biologically rigorous form, what is the evolutionary fitness advantage of making a gut IgA response? Immune responses are energetically expensive to make and so animals will only invest in them if they bring benefit [START_REF] Benson | Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors[END_REF]. Therefore, we can surmise that gut IgA responses are being made because they do something useful, important and beneficial to the host, which could include controlling the microbiome's composition to the host's benefit. Presumably, the gut host immune response is seeking to maintain gut integrity, to attack and neutralise pathogens, but while maintaining the ability to absorb nutrition from the gut. Clearly, the IgA and associated gut cellular response does protect the integrity of the gut and keep the bacterial microbiome in the gut, where it belongs, but this must be a balance of having a sufficiently highly activated immune response to stop or ameliorate the harm that pathogens can A c c e p t e d M a n u s c r i p t 11 cause, but not one that interferes with nutrition. But beyond this, generating an IgA response that favours bacteria that are beneficial to the host and disfavouring bacteria that are non-beneficial (or even harmful), must maximise host fitness. With this perspective, is the binding of IgA to a proportion of the bacterial cells in the microbiome the attempt of hosts to choose between different bacteria to create a microbiome that maximises its contribution to host fitness? But of course the bacteria in the microbiome are not passive partners, because they are also evolving and seeking to maximise their fitness. This leads to the inevitable question, what is the evolutionary effect of the gut IgA response on gut bacteria? We propose the hypothesis that IgA's binding to bacteria acts as a selective force so that IgA + bacteria have an altered fitness compared with IgA -bacteria. With IgA acting as a selective force, then we predict that the IgA + bacteria will evolve molecularly faster, or at least differently, compared with IgA -bacteria. For this idea to be correct the binding of IgA to bacteria needs to alter bacterial fitness in some way, compared with bacteria not bound by IgA. Because bacteria have a very short generation time compared with their hosts, this IgA-driven evolution would occur during the host's life. Indeed, there is the possibility that some of the already-observed evolution of bacteria in the microbiome (section 3, above) may be the result of IgA responses against bacteria in the microbiome.

In conceptual support of our hypothesis (and indeed its inspiration) are the multiple examples of host immune responses imposing selection pressures on pathogens, driving their molecular evolution. Well studied examples include: Streptococcus pneumoniae, Neisseria meningitidis, Staphylococcus aureus, Plasmodium falciparum (the malaria parasite), Influenza A, Dengue, HIV, Hepatitis C, Foot and Mouth Disease Virus, and Mycoplasma gallisepticum [START_REF] Lipsitch | Patterns of antigenic diversity and the mechanisms that maintain them[END_REF][START_REF] Cobey | Immune history and influenza virus susceptibility[END_REF][START_REF] Grenfell | Unifying the epidemiological and evolutionary dynamics of pathogens[END_REF][START_REF] Gupta | Exploring the evolution of diversity in pathogen populations[END_REF][START_REF] Nogueira | Rapid evolution of the sequences and gene repertoires of secreted proteins in bacteria[END_REF][START_REF] Allen | Selective escape from CD8+ T-cell responses represents a major driving force of human immunodeficiency virus type 1 (HIV-1) sequence diversity and reveals constraints on HIV-1 evolution[END_REF][START_REF] Weedall | Detecting signatures of balancing selection to identify targets of anti-parasite immunity[END_REF][START_REF] Callaghan | The effect of immune selection on the structure of the meningococcal opa protein repertoire[END_REF][START_REF] Young | Severe infections emerge from commensal bacteria by adaptive evolution[END_REF][START_REF] Williams | Incomplete host immunity favors the evolution of virulence in an emergent pathogen[END_REF][START_REF] Petrova | The evolution of seasonal influenza viruses[END_REF]. In these systems, host immune responses kill or constrain certain antigenic types (i.e. strains [START_REF] Ellegaard | Beyond 16S rRNA community profiling: intra-species diversity in the gut microbiota[END_REF]) that hosts have previously encountered, so facilitating the emergence of new strains. Immune responses therefore drive changes in these populations of pathogens. This immune-mediated selection can be detected by phylogenetic analysis of strains across a host populationthe common process driving strain divergence is immune-dependent selection, but the infection biology of each pathogen (prevalence, infectivity, persistence etc.) modifies these phylogenetic relationships [START_REF] Grenfell | Unifying the epidemiological and evolutionary dynamics of pathogens[END_REF]. In our hypothesis, the gut IgA response directed at bacteria within the microbiome is the evolutionary selective force and driving the microbiome's evolution.

While IgA responses are directed to gut pathogens it appears that many commensal bacteria are also bound by IgA, and so our hypothesis is that there will be IgA driven evolution of commensal bacteria within the microbiome.

Testing the hypothesis of IgA driven evolution of gut bacteria

There are a number of approaches that can be used to investigate this hypothesis. The most obvious approach is to longitudinally sample the microbiome of hosts, then separate and metagenomically sequence the IgA + and IgA -fractions and measure the molecular evolution of genotypes across time. This would be a direct test of the hypothesis that bacteria in the IgA + A c c e p t e d M a n u s c r i p t 12 fraction have a different molecular evolutionary trajectory than those in the IgA -fraction. This could be done using laboratory animals, though we would favour doing this in wild populations, recognising the different immune responses of wild animals compared with those of laboratory animals [50,85]. But there are other approaches that could be used. One approach would be to transplant artificially marked bacterial strains into animals and to track the evolution of those strains while also measuring the IgA and other immune responses directed towards them. This approach could involve different bacterial taxa and include mixtures of differently marked strains. Also, the IgA dependency of any observed effects could be tested by using immune-manipulated and knock-out mice, potentially transplanted with wild mouse microbiomes [START_REF] Rosshart | Wild mouse gut microbiota promotes host fitness and improves disease resistance[END_REF].

Significance of IgA driven evolution of the bacterial microbiome

If our hypothesis is correct and IgA + bacteria have an altered evolutionary trajectory compared with IgA -bacteria, this process will drive changes to the genotypes comprising bacteria species, and so change the composition of the microbiome. Because individual animals will make their own individual immune responses, the IgA selection pressure will probably differ among individual hosts, so that the same bacterial species may be evolving differently among individual hosts. Such inter-individual effects are likely to be more obvious and important in wild animals, rather than in laboratory animals.

Because of the importance of the microbiome in health and wellbeing, there is considerable interest in manipulating the microbiome of people and animals for therapeutic purposes. The principal approach to this is to change the composition of the microbiome by adding new bacteria (by ingestion of bacterial mixes or by microbiome transplant) or by the use of antibiotics. IgA driven bacterial evolution could lead to the failure of attempts to manipulate the composition of the microbiome. That is, one could introduce a bacterial strain of therapeutic interest, but its subsequent within-host evolution could result in the loss of the anticipated therapeutic benefit. Equally, but in contrast, immune-driven evolution of the microbiome could potentially be used therapeutically. That is, there is the potential to immune-drive the evolution of bacterial genotypes for host therapeutic benefit.

Summary and conclusion

The gut bacteria microbiome is important for animals' normal health. While the microbiome has been extensively studied in people, livestock and laboratory animals, there has been very limited study of it in wild animals. The composition of the microbiome can be affected by both bottom-up and top-down processes, and the bacterial genotypes that comprise bacterial species can evolve, with this evolution itself contributing to changes in the microbiome. The gut immune response, particularly the gut IgA response, affects the gut microbiome, with IgA binding to a proportion of bacterial cells in the microbiome. There are a number of potential effects of this binding on the bacteria, but here we hypothesise that this IgA binding will act as selective pressure on IgA-bound bacteria so driving their molecular evolution, thus contributing to changes in the composition of Sequencing. DNA Sequencing has revealed the complex composition of the microbiome. The most widely-used approach to determine the composition of the microbiome is sequence analysis of fragments of (multi copy) ribosomal RNA (rRNA) coding genes, 16S for prokaryotes and 18S for eukaryotes. Metagenomic sequencing can also be used, which determines more (or all) of the relevant microbial genome. Bioinformatic analyses of rRNA or metagenomic sequence data can then try to identify the relevant taxon, but this is only possible if the focal taxon has previously been sequenced. Alternatively, the sequence data can be analysed agnostic to taxon identity. A common and attractive approach here is to use Amplicon Sequence Variants (ASV), which are groups of common, near-identical, sequences. Both taxon identification and ASVs can be used in measures of alpha and beta diversity, below.

Alpha diversity. Alpha diversity measures the microbiome of an individual host. It can be measured simply by taxon or ASV richness, which is a direct count of the number of taxa or ASVs. Richness does not account for the relative abundance of different taxa / ASVs, but there are other measures that do, for example Shannon's Index and the Chao-1 index. Richness can be increased by the presence of very rare taxa / ASVs (for example a single individual), whereas these other metrics are less sensitive to rare taxa / AVSs. Beta diversity. Beta diversity measures the microbiome as taxa or AVSVs at the scale of a group or population of hosts. A commonly used metric is the Bray-Curtis dissimilarity, which calculates the dissimilarity between two populations, with 0 showing that the two populations are identical, and 1 showing that they are completely dissimilar. If we consider a single host's microbiome as one population, then Bray-Curtis is used for the microbiomes of a group or population of hosts and measures the overall (dis)similarity of the microbiome of this group of hosts.

UniFrac. The diversity of the microbiome can also be measured by the UniFrac statistic. This measures the phylogenetic similarity among the microbiomes that are being compared. In so doing this is wholly different from Bray-Curtis and other measures of beta diversity, which do not consider the phylogenetic relationship of the taxa in the microbiome.

Quantification. The now standard approach to analysing the composition of the microbiome is to use the abundance of sequences belonging to a taxon or ASV as the measure of its abundance. From this, the composition of the microbiome is then commonly described proportionally; that is, each taxon or ASV is reported as some proportion of the whole microbiome. This approach is therefore, unable to account for changes in the absolute abundance of the microbiome, or changes in the abundance of component taxa. For example, compare two individuals differing 2fold in the number of bacteria of taxon A. The proportionate method would show a greater A c c e p t e d M a n u s c r i p t 15 proportion of A, but also a consequent decrease in the proportional abundance of other taxa. But a fully quantitative method would show that taxon A was twice as abundant, while, for example, all other taxa maintained the same abundance, therefore giving very different measures of microbiome composition than the proportionate approach. Where studied, the microbial load of individuals' microbiomes has been found to vary, and so too the abundance of different microbial taxa [86].

A

  c c e p t e d M a n u s c r i p t 13 the microbiome. Although much remains to be investigated about immune responses and the microbiome of wild animals, there will likely be considerable inter-individual variation in wild animals' IgA responses such that any IgA-based selection that bacteria face will vary across individual hosts. It should be of considerable interest to investigate the role of gut IgA responses in driving the evolution of wild animals' microbiomes and to understand how this contribute to the biology of wild animals.

A c c e p t e d M a n u s c r i p t 14 Box 1 .

 141 Measuring microbiome diversity

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27 Legend to Figure 1 Figure 1 .

 2711 Figure 1. IgA targeting of Bacilli ASVs. Faecal bacteria from 6 wild mice (Mus musculus domesticus) were prepared, stained with anti-IgA and FACS sorted into IgA + and IgA -fractions, which were then 16S sequenced, assigned to ASVs, and IgA probability ratios (after [59]) calculated for 34 ASVs. For the 5 highlighted ASVs the first to third quartile values are above (red) or below (blue) zero. Whiskers show minimum and maximum probability ratio values. Detailed methods are in Supplementary Information.

Figure 1

 1 Figure 1

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Acknowledgments

We would like to thank land owners for generous access for our trapping work, and Kathryn Else for samples from the IgMi mice.

Ethical approval

This work was approved by the University of Liverpool Animal Welfare and Ethical Review Body.

Funding

There are no funders to report for this manuscript.

Data availability

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Conflicts of interest

The authors declare no conflicts of interest.