06-discussion.qmd

# Discussion {#chap-discussion}

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## Summary

This is a chapter to discuss the limitations and potential of biofilm models
and calculus in archaeological research. And to reflect on the many,
many questions that arose during my research. In fact there were more
new questions that arose than were answered. Yay, science!
I will also reflect on the limitations of model biofilms. They are quite 'sterile'
systems. After all, who can survive on sugar water and raw starch grains twice a
day? Not to mention that we would quickly
become dehydrated from drinking distilled or ultrapure water.
I discuss the role of dental calculus in paleodietary reconstructions more
generally, and lay out the challenges we will need to address going forward;
some of which could be addressed with an oral biofilm model.
:::

:::

<!-- remind the reader why they suffered through the dissertation -->
Archaeological researchers are presented with a unique challenge.
Because time eventually degrades everything, the archaeological record will always
be incomplete. Barring the invention of time travel---and depending on your position
on travelling back to a time before time travel is invented---we are limited in
our ability to fill these gaps in our knowledge.
Consider it a puzzle that needs to be put back together. The only problem is that
some pieces are permanently missing, while the rest are mostly broken.
Researchers will attempt to complete the puzzle by fixing the broken pieces with
scientific analyses, and recreate the missing pieces based on what we can see
from the broken pieces.
To further complicate things, the methods we use to recreate the broken pieces
may not be able to entirely accurately recreate the pieces, which results in pieces
that look like they fit, but are actually different from the originals.
Dental calculus is an example of a puzzle with many missing and broken pieces.
Even if we analysed dental calculus from a living person, we would still not
be able to completely recreate the entirety of that person's diet by only looking
at the food debris within the dental calculus. For whatever reason, some of the things
we eat will leave traces on our teeth, while some will not. Now add to that a few
hundred or thousand years in the ground with physical and chemical processes
that are constantly degrading the organic material, and the picture becomes even
murkier.
We can show something is there if we detect it. But what about the things we don't
detect? Were they not there, or could we not detect them? If they weren't there,
why weren't they there? If the thing in
question was consumed, but not entrapped in the dental calculus; why is this
the case?

As shown in [Chapter 1](#fig-plot-and-wordclouds), dental calculus has become a
very popular substance within archaeological research. One of its primary uses is
to reconstruct the diet of past populations. It's not surprising why this is the
case. It forms and grows inside our mouth over time, and it is in direct contact
with everything we put in our mouth. However, there is limited systematic and
fundamental research and experimentation being conducted within the fields that
make use of archaeological dental calculus.
There are of course exceptions
[@powerRepresentativenessDental2021; @powerChimpCalculus2015; @leonardPlantMicroremains2015; @sotoCharacterizationDecontamination2019; @fagernasMicrobialBiogeography2021; @trompEDTACalculus2017; @velskoHighConservation2023; @velskoMicrobialDifferences2019],
but they have not addressed the full extent of dental calculus limitations
(nor should they). This type of research should aim to validate aspects of our
current analytical methods on synthetic materials or through
detailed observation and documentation of dietary habits in living humans
(or non-human primates), and critically evaluate the patterns of information we
extract. Methods-validation has also been conducted on archaeological material
[@fagernasMicrobialBiogeography2021; @trompEDTACalculus2017; @modiCalculusMethodologies2020],
but these studies are limited by the fact that we have no way of knowing what
the original diet looked like. At least not at the resolution necessary to really
scrutinize the results of a method. All we have are pieces of information from the,
likely incomplete, dietary remains that ended up in the calculus, and from
contextual remains, such as animal bones, food residues, and plant remains, both
macro- and microscopic. And even then we have no way of saying for certain whether
the materials were included in the diet, or just there because our somewhat
crucial requirement for oxygen means the oral cavity is not a closed system
[@radiniFoodPathways2017].


<!-- summary of the dissertation goals and outcomes -->


<!-- how does this dissertation address the problems/aims stated earlier? -->

<!-- this is basically also in intro. Need to figure out where it fits best -->
In this dissertation, I have mainly focused on the development, validation, and
application of an oral biofilm model and its potential for informing
archaeological research. I have shown that it was possible to develop a
protocol for an oral biofilm model with a relatively simple setup, and
use it to grow artificial dental calculus, and that it can serve
as a reasonable proxy to natural dental calculus
[[Chapter 3](#byoc-valid); @bartholdyAssessingValidity2023).
I demonstrated
how the oral biofilm model can answer questions and identify hidden biases
related to using dental calculus for paleodietary reconstructions, specifically
addressing the identification and quantification of starch granules. The results
from this study showed that what goes in, doesn't necessarily come out. And the
loss of information is not evenly distributed across the different types of
starches, depending on size and morphology [[Chapter 4](#byoc-starch); @bartholdyInvestigatingBiases2022].
In [Chapter 5](#mb11CalculusPilot) I present a study that goes beyond the model
and looks at
archaeological dental calculus. This is, after
all, a dissertation in archaeology. We analysed dental calculus samples from
a rural Dutch archaeological site in Middenbeemster, using ultra high performance
liquid chromatography tandem mass spectrometry (UHPLC-ESI-MS/MS). This allowed
us to identify a number of residues from plants that may have been consumed for
nutrition, medicine, recreation, or all of the above.


## The dental calculus model

While the use of oral biofilm models in dental research is well-established, even
long-term calcifying models to produce dental calculus, they never made it into
archaeological research, at least not to the extent that the results or protocols
of these models were published (that I could find).
The oral biofilm model outlined in this dissertation is by no means the ultimate
solution to save us from the limitations of archaeological dental calculus, but
may provide a small step towards understanding them a little better, and
hopefully promote further exploration through systematic fundamental research.
The goal of developing a dental calculus model was to explore core aspects of
how we use dental calculus in paleodietary research, with a
relatively simple setup that is accessible to most labs in archaeological science.
The idea is to take a step back and really scrutinise our current methods for
interpreting diet from dental calculus. What the field has accomplished so far is
undeniably impressive, but there are many things we still don't understand. Some
of the things we don't understand are on a very basic level, such as how plant
microremains become trapped inside calculus, how much of what we consume ends up
inside calculus, and to what extent our current methods are able to accurately
extract that information.

<!-- from the Introduction chapter
I chose to use a 24-well plate with a plastic substratum suspended from a lid. The model
was inoculated with whole saliva, which can be more difficult to control, but more
closely replicate the complex dynamics between oral bacteria during biofilm
formation, such as metabolic dependencies [@roderStudyingBacterial2016; @mcbainBiofilmModels2009].
My choice of model was partially driven by
available facilities and financial limitations, and partially by the benefits of being
able to generate a large number of samples under similar, adjustable conditions. 
It's arguably also more realistic for the facilities and finances of most archaeological
departments and grants.
Our model uses a simplified high-throughput setup more commonly seen in shorter-term
biofilm models that mainly focus on dental plaque [@extercateAAA2010; @tianUsingDGGE2010].
Using oral biofilm models to grow dental calculus is in
no way a novel concept. In fact, it has been applied for decades to study
the growth and mineralisation of biofilms
[@fehrVitroCalculus1960; @middletonVitroCalculus1965; @sissonsMultistationPlaque1991].-->

The model we chose was a simple model using a shaking incubator and a 24 deepwell
plate with the plastic lids as a substratum. The artificial saliva we used was
based on the basal modified medium used by Sissons and colleagues
[-@sissonsMultistationPlaque1991; -@sissonsPHResponse1994; -@sissonsArtificialPlaque1997]
to grow dental calculus. We also made use of their calcifying solution, calcium phosphate
monofluorophosphate urea (CPMU) to speed up the mineralisation process (natural
dental calculus can take weeks, even months, to form). To make sure the calculus
we were growing in the lab was a good model for calculus grown naturally, we
sequenced the DNA of our model calculus and compared it to samples from various
sites inside the human mouth, including dental plaque and calculus.
The bacterial composition of our model calculus samples had a strong oral signature,
but was distinct from other natural oral samples, including modern dental plaque
and calculus. The main difference between natural samples and model calculus was
that the natural samples were more heterogeneous in composition, which is
expected when comparing natural and lab-grown samples. Natural samples had a larger
number and variety of microbes compared to the model calculus. This was reflected
in the aerotolerance of dominant microbes in model calculus, which were largely
anaerobes, while the most abundant microbes in natural samples were aerobes
and facultative anaerobes.
The natural samples also had a more diverse representation of bacteria from all
stages of biofilm development, including early- middle-, and late-colonisers, while
model calculus samples were predominantly late-colonisers
([**Chapter 3**](#byoc-valid), @bartholdyAssessingValidity2023).
Results from our metagenomic analysis were similar to a comparable *in vitro*
biofilm model.
In their study, the authors also used a 24-well plate with pooled saliva as
inoculate. The growth medium was similar but also contained a sheep's-blood serum,
and the samples were only grown for 24 hours [@edlundUncoveringComplex2018].
As with our model, the comparison with natural oral samples showed a lower overall
richness and diversity, and a distinct microbial profile
([**Chapter 3**](#byoc-valid), @bartholdyAssessingValidity2023). Given that our results
are similar to a short-term biofilm model, we may be replacing the medium too
often (every three days), and not allowing communities to establish more
complex metabolic pathways that are normally present in mature biofilms.
To resolve this and other issues, our protocol will benefit from further refinement.
Using serum in the medium may help to establish thicker and more stable biofilms,
and allow slow-growing organisms to become more established [@ammannZurichBiofilm2012].
Filter-sterilising the heat-sensitive solutions that are not autoclaved, such as
CPMU and starch solutions, may prevent environmental contamination from entering
the biofilm during the setup, such as members of the *Enterococcus* genus.
While these are commonly present in oral samples, they were significantly more
abundant in our samples than the natural oral samples to which we compared them.
Once changes to the model setup, the model will have to be re-validated, as the
concentrations of nutrients, let alone the type of nutrients, will impact the
community composition of the biofilms [@edlundBiofilmModel2013].

We also used Fourier Transform Infrared (FTIR) spectroscopy to assess the mineral
content of our model and compare it to natural dental calculus, both modern and
archaeological.
Our analysis showed that, after 25 days of growth, our biofilm model produced a
substance that is chemically very similar to both modern and
archaeological calculus. 
It is interesting that the mineral composition was so similar to natural calculus
given the unique microbial profile. It suggests that the mineralisation occurs
in a predictable manner regardless of the microbial profile, if conditions are
favourable. Even in the absence of the known mineraliser, *Corynebacterium matruchotii*.
The crystallinity of the model calculus also matched the
archaeological sample we used as a comparison, though with a slightly less ordered
structure. This may be related to the age differences in model calculus compared
to archaeological calculus. Not only did the archaeological calculus spend a few
hundred years maturing in the ground, allowing crystals to expand into the
gaps created by degraded organic matter [@weinerBiologicalMaterials2010], but
given the known lack of oral hygiene practices in the past, the calculus was surely
older than 25 days before being buried.
We also only analysed a single archaeological sample, so we don't know how
representative this sample is of archaeological samples in general. Perhaps this
was a particularly under- or over-mineralised sample. It would be more appropriate
to compare to the modern reference samples, since we are actually trying to
recreate something that mimics natural modern calculus, not something that has
been buried for hundreds of years or more. Unfortunately we didn't have access
to new modern samples and couldn't produce modern calculus grind curves for this
analysis.

### Model application

After establishing that our model dental calculus mimics, at least to some extent,
the real deal, we assessed what biases may occur in starch incorporation.
It is a mistake to think you can solve any major problems just with potatoes
[@adamsLifeUniverse2002], so we also included wheat starch in the model to
cover a wider range of granule shapes and sizes.
Put simply, we added a known amount of starch granules---well, to the extent we
could estimate the large quantities in our starch solutions without counting
every single granule---to our
biofilm over the course of the 25-day experiment. 
Starch solutions were added on day nine of the experiment. This was a somewhat
arbitrary decision; we only needed to ensure that there was enough separation
between the last saliva donation and the introduction of starch treatments.
We did this to prevent our starch counts from being affected by $\alpha$-amylase
activity from the donated saliva, thereby getting somewhat 'pure' counts from
the added starches. However, we found no evidence of the model
retaining $\alpha$-amylase from the donated saliva, there is no reason the
starch treatments couldn't start sooner in the experiment. For future experiments
looking at the effect of amylase activity, it's important to still keep this
under consideration, as amylase activity from natural saliva can fluctuate
in individuals throughout the day based on both physical and psychological
influences [@naterHumanAmylase2005]. Controlling the level of amylase activity
in the experiment is more easily done with amylase artificiallsupplier of scientificy added to the
model. Amylase can be purchased from your local supplier of scientific equipment
along with some overpriced sugar and baking soda. If it's not 'analytical grade'
it's not

At the end of the experiment, we dissolved the calculus
and counted the number of starches that were inside. Those who are familiar
with previous dietary research on archaeological dental calculus will probably
not be surprised that the number of starches we extracted was nowhere near the
amount we put in. More interestingly, though, the size of the starch granules
influenced the outcome; fewer large starches were extracted than what was put
in the model during growth. This could be related to how starch
granules are trapped in
biofilms in the first place, where size and/or surface morphology of the starch
granules could influence the likelihood of being retained in the biofilm.
We also found that a very, VERY, low proportion of the starch granules that we
'fed' our samples actually made it into the dental calculus; only 0.06% to 0.16%
of granules from the treatment solutions were extracted from the dental calculus
([**Chapter 4**](#byoc-starch), @bartholdyInvestigatingBiases2022). Given how few
actually make it in, this may suggest that evidence for dietary starches are
the result of repeated exposure to a large quantity of granule-containing foods.

### Model limitations {#disc-model-limitations}

<!-- Limitations of this and other biofilm models -->
So far I have covered what our biofilm model can do. It is equally important to
talk about what our model can't do. After all, we demand rigidly defined areas
of doubt and uncertainty [@adamsHitchhikersGuide2002].
While we have a high degree of control and reproducibility, especially when
compared to *in vivo* models, there are certain conditions we cannot regulate with
our current setup. This includes environmental conditions such as CO~2~ and oxygen
availability, which rely on the conditions in the lab where the experiments take
place. To some extent, the bacterial communities within a biofilm can generate
favorable conditions in a local environment through metabolic processes---one of
the adaptive benefits from being part of a biofilm---but these are still somewhat
dependent on the extrinsic environment in which they are situated. Biofilms on
hard tissues will differ in composition from those found on soft tissues. And 
biofilms found closer to the front of the mouth will differ from those found
towards the back
[@marshDentalPlaque2005; @kolenbranderOralMultispecies2010; @proctorSpatialGradient2018; @palmerCoaggregationInteractions2003].
This difference is also something that is difficult to mimic in a single
experimental setup; as is the ability to control salivary flow rates and circadian
rhythms, both of which can influence the growth of plaque
[@proctorSpatialGradient2018; @dawesCircadianRhythms1972].

The effect of circadian differences in microbiome between individuals
can influence replication of the microbial composition of our model, which will
be limited by our use of whole saliva as inoculum rather than using a handful of
select species. This means microbial
profiles of the biofilms may change between (or even within) experiments, since
the microbial composition of our saliva can vary slightly throughout the day, and
the formation and composition therefore depends on the time of day the saliva is
collected. It can also differ between donors. We reduced these limitations in our
experiments by collecting samples from a single donor at the same time of day
for each inoculation, but this will still cause differences between experiments.

The absence of $\alpha$-amylase in our model may have affected the microbial
composition of our biofilms.
Our model has no renewable source for $\alpha$-amylase once the inoculations
have been completed. There are streptococcal species present in the model that
are known for their ability to bind amylase
[@nikitkovaStarchBiofilms2013; @haaseComparativeGenomics2017];
however, we did not investigate whether the strains present in our
model contain these genes.
Starch solutions were only introduced on day 9 of the experiment. Prior to this,
all samples were treated with the sucrose solution. The absence of starch during
inoculation could have suppressed bacterial production of amylase-binding proteins
[@nikitkovaEffectStarch2012]. Frequent medium replacements may also be clearing
out all of the unbound host salivary amylase. We don't know exactly why
$\alpha$-amylase is absent, and need to look into this. In the meantime, this
absence opens up opportunities to examine its role in the incorporation process
of dietary materials (see [below](#bfmodels-in-arch)).

A well-known limitation of biofilm models in general is the difficulty in capturing
the diversity and complexity of the natural oral biome. Diversity and complexity
may be represented as interspecies communities and complex metabolic dependencies
between organisms within the communities, or as an environmental complexity
determined by nutrient availability, host immune-responses to biofilms, and
fluctuating microenvironments across the biofilm in response to these factors
[@edlundUncoveringComplex2018; @bjarnsholtVivoBiofilm2013]. These limitations
can be mitigated by complex experimental setups, but at the cost of lower
throughput and higher financial cost.
Increasing the number of species included in a model can approach the diversity
found in the natural microbiome, but still falls short of capturing the complete
diversity [@edlundBiofilmModel2013], and the use of whole saliva introduces
another set of limitations (as discussed above).

Then of course there's the inevitable limitation that we're dealing with a model.
An attempt to recreate the real thing under controlled conditions, allowing us to
test a variety of circumstances and see what the outcome might look like in the
real world. These are generalisations that may not be comparable to any specific
real-world case, but allow us to view and quantify processes that can be difficult
to access in natural systems. The very isolated and controlled model setup also
deviates from the natural conditions in our mouths. Many of the biofilm's natural
predators are not present in our setup. Plaque is constantly at risk of removal
by the tongue, salivary flow, oral hygiene practices, even the act of
chewing---processes which help shape the biofilm (this is counterintuitive since
they are processes of removal) [@shawCommonalityElastic2004].

### Further model validation

Going forward, we aim to further assess the validity of our model, as well as
optimise the protocol. While we have established that our model is capable
of forming a mineral composite comprising a largely oral
microbiome, there are properties that we have yet to determine. Just because
the bacteria in our model are identified as oral, doesn't mean they necessarily
behave like communities of natural oral bacteria. By determining the functional
and metabolic profiles of the bacteria and communities within our model,
we hope to get further insights on metabolic dependencies, production of metabolic
by products, and gene expression in our model<!--elaborate?-->. As a result we will be able to
further optimise the protocol to more closely mimic the natural oral biome.

There are also other conditions within our model that we need to determine,
such as monitoring physiological responses to changing conditions. For example,
after carbohydrates have been consumed, there is a dip in the pH within the oral
cavity as the carbohydrates are consumed by bacteria, which release acidic
by-products. This occurs within the first few hours of consuming carbohydrates,
after which the saliva will work to balance the pH back to pre-carbohydrate
levels, also known as the 'Stephan curve' [@stephanStudiesChanges1947].
By acting as a buffer and restoring the oral pH-level, saliva can help prevent
high levels of acid from demineralising the tooth surface and causing caries.
Since our model is fed both with sucrose and starch, it is important to know
that the pH levels don't permanently drop to levels that are unfavourable to mineral
supersaturation and plaque mineralisation.

Since FTIR only addresses the overall mineral composition, we will need to further
investigate whether there are any other structural/chemical differences between
our model and natural calculus that may be caused by microbial profiles, and
microscopically examine the model to determine the micro-architecture.

### Potential biofilm model applications in archaeology {#bfmodels-in-arch}
<!-- future directions and potential of the biofilm model for archaeological research -->
<!-- move to end of chapter? or above limitations? -->
<!-- reflect on future of dental calculus in archaeology and the place for a biofilm model -->

Biofilm models are an untapped resource in archaeological research, especially
for dental calculus research. Coupled with existing validation methods to address
current dental calculus limitations, the proverbial sky is the limit.
This section describes some possible archaeological applications for a biofilm
model, but is certainly not complete. It is mainly comprised of questions
that arose during the experiments I conducted, as well as during the analysis of
archaeological material, that I was unable to address in this dissertation due
to time constraints. Hopefully these questions can be answered by myself or others
in the future.

<!--How it can improve our understanding of the mechanism by which dietary compounds become trapped in dental calculus?-->
The main question that came up during experiments concerns the mechanism of
incorporation of dietary compounds, especially starch granules, in dental calculus.
How does it actually happen?
This seemingly simple question is particularly challenging, and one that I hadn't
prepared for in my experimental design. Going forward
it will be an important question to answer, as it may influence the likelihood
of certain compounds to become trapped in dental calculus, and at what point during
the formation and mineralisation process this occurs. By staggering the treatments
during the experiment, we may be
able to see if the rate of incorporation varies during biofilm growth, and whether
or not particles can penetrate the surface of the calculus after it has mineralised.
If not, this could mean the layered structure is indicative of chronological
consumption events.
If so, what is the size limit? Can starches infiltrate dental calculus post-burial,
or is this limited to smaller molecules? And do the chemical/physical properties
of molecules and microremains (amylopectin content of starch granules, polarity
and hydrophobicity of molecules, etc)
influence their ability to become incorporated or penetrate the mineralised surface?
This question of incorporation also came up during the analysis of archaeological
dental calculus in
[**Chapter 5**](#mb11CalculusPilot) [@bartholdyMultiproxyAnalysis2023].
Based on the presence of many metabolites, it seems that this may not have been
during consumption, but rather during excretion through saliva, or, put more
simply, when the molecules are on their way out of the body again. This makes some
sense, since
food actually spends relatively little time in our mouth while we're eating, and
significantly longer travelling through our body. This may also explain
the very low retention of starch granules we found in [**Chapter 4**](#byoc-starch).
It seems that most of the starch granules are swallowed, while few become lodged
in our teeth/plaque and are eventually trapped in dental calculus. Without looking
into the mechanism by which starches and other food molecules are incorporated
into dental plaque, we are always going to be guessing (albeit educated guesses)
what is happening archaeologically.

An important question to address within the framework of incorporation pathways,
is what role bacteria
play in the incorporation of dietary material,
and whether differing bacterial profiles have an impact on the retention of
dietary molecules and microremains. It is likely that they will cause differential
retention given that they make use of a lot of the food that passes through our
mouths with the help of digestive enzymes [@rogersRoleStreptococcus2001]. The
important question to answer is how, and, to what extent, they influence this
process. A systematic approach would be to set up multiple experiments with
different sets of defined consortia grown under the same conditions.
On a related note, the absence of host salivary $\alpha$-amylase activity in our
model (as shown in [**Chapter 4**](#byoc-starch), @bartholdyInvestigatingBiases2022)
provides an opportunity to explore the effect of various amylase levels on the
incorporation and retention of dietary compounds, especially starches, in dental
calculus. Alpha-amylase can be purchased from most laboratory supply
companies, and can therefore be added to the model and explored as a controlled
variable. Some bacteria have the ability to bind $\alpha$-amylase in order to
use the degradation products of starches as nutrients
[@nikitkovaEffectStarch2012; @rogersRoleStreptococcus2001], so the abundance of
these bacteria coupled with $\alpha$-amylase activity will likely influence
starch retention.

Finally, it's worth noting how important it is to be able to generate an unlimited
number of samples for validating current methods and developing new ones.
Archaeological dental calculus is a finite material and should be treated as such.
We should know exactly what we're doing when we are analysing samples. If not,
then model dental calculus would be a great substance to try out new things,
and even for training researchers on the range of methods at our disposal.

## Dental calculus in archaeology and future challenges

<!-- What needs to be done going forward -->

<!-- outline main challenges before going into detail -->
<!-- contamination and authentication -->
<!-- differential degradation of molecules -->
<!-- mechanisms of entrapment in dental calculus -->
<!-- what is from diet, and what is not? -->
<!--inherent bias of sampling from calculus-formers?-->

Dental calculus has provided unique perspectives on multiple activities of humans
in the past, from dietary practices to the evolution of the oral microbiome.
Researchers continue to find innovative ways to extract information from a material
that was once discarded.
It is uniquely situated to address diet because of its direct interaction with
everything that enters (and exits) our mouth, some of which leaves clues behind
that are embedded within the calculus itself. There are, however, still limitations
to address to further unlock the potential of dental calculus to reconstruct past
dietary activities. Probably the main challenge we face in archaeology, let alone
studies of dental calculus, is identifying contamination versus the authentic
remains left behind from the past.
A challenge more specifically related to dental calculus, is understanding why
some things are retained in dental calculus, and why others are not.
<!--Another challenge  is differential degradation of materials and molecules-->
Finally, we should continue to optimise our sampling and analytical methods to
make sure we are getting the most out of these small deposits of minerals, bacteria,
food debris, and whatever else made its way into the mouth during life.



### Incorporation pathways

As discussed above, one of the main challenges of working with dental calculus is
our lack of
understanding of incorporation pathways. We need to know how
exogenous material becomes trapped inside, and to what extent the processes within
the oral cavity cause damage to, or completely eliminate, the dietary compounds.

The incorporation pathway for larger particles (relatively speaking), such as dietary
starches and phytoliths, is likely during consumption of foods that contain them.
What exactly about their morphology or physicochemical properties
allows them to enter and become trapped is still unknown.
<!-- this is largely repeated in the section ## incorporation pathways -->
The surfaces of starch granules mainly contain polar phospholipids
[@cornejo-ramirezStructuralCharacteristics2018], making the
phospholipid bilayer of a starch granule compatible with, or even attracted to, a
biofilm consisting largely of water. Conversely, hydrophobic molecules might be
less likely to associate with a biofilm, and therefore be underrepresented in
any analysis on dental calculus, if they are present at all. Once starch granules
become attached, the repeated process of biofilm growth
would result in the starch molecules becoming trapped between two biofilm layers,
increasing the likelihood of retention.
Once trapped inside the biofilm, retention of the dietary particles depends on
the ability to avoid digestive enzymes that are commonly used by the communities
of bacteria to break down the macromolecules into more manageable sizes.
This gap in our knowledge is also why we
don't understand why the remains of some plant species are overrepresented while
others are underrepresented. We know that this happens, but not why. 
Smaller molecules
may be able to hitch a ride through diffusion
channels that transport nutrients into the biofilm [@flemmingBiofilmMatrix2010],
although biofilms are known for their ability to limit diffusion of specific
molecules, such as antibiotics [@stewartAntimicrobialTolerance2015].
Diffusion of molecules has been explored clinically, but mainly focusing on
antibacterial agents
[@stewartAntimicrobialTolerance2015; @takenakaDiffusionMacromolecules2009; @maModelingDiffusion2010].
So far nothing has been done to explore the dietary perspective in which we're
interested.

The
incorporation pathway may also be heavily influenced
by mode of consumption. If someone
was chewing tobacco or storing coca in their cheeks, the most likely place to
detect nicotine or cocaine, the principal alkaloids of these plants, would be
in dental calculus deposits on the molars. However, mucous-rich saliva,
produced by the sublingual and submandibular glands (located in
the front of the mouth), preferentially binds toxins [@doddsHealthBenefits2005],
making the anterior teeth a good hypothetical target for detecting these compounds.

Another potential pathway is the presence of molecules in dental calculus as a
result of excretion from the body through the saliva. If you consider the amount
of time you spend with food (or other things) in your mouth, it is relatively short.
A few minutes at most? Whereas the time spent in your body is much longer, as food
molecules enter the bloodstream and are distributed throughout the body.
The molecules can then re-enter the mouth through the saliva and spend
significantly more time in the mouth the second time around, as excretion may take
days [@leeOralFluid2011]. At this point the
original compounds may have been broken down by, for example, the liver or
kidneys, in which case mainly the metabolites will be present. The plausibility
of finding molecules via this pathway depends on the size of the molecules and
the ability to diffuse from serum/plasma to saliva and enter the oral cavity<!--rephrase and elaborate-->. Given this incorporation pathway, the molecules are, hypothetically,
more likely to be secreted in higher concentrations through the serum-rich saliva
of the parotid glands, located next to the molars [@doddsHealthBenefits2005].
<!--Saliva is mixed and flows throughout the mouth, making the entire dentition
possible...-->
Molecules originating from this pathway would mean that it, unfortunately, wouldn't
be possible to determine the mode of consumption (e.g. chewing vs. smoking) based
on the mass spectrometric results alone, but would require further analysis of the
dentition to identify. For example, if nicotine is detected, it would be useful
to identify tooth staining and periodontal disease caused
by tobacco smoking [@nessEpidemiologicStudy1977]. It would also require
relying on contextual materials found at the site, but that's something which
should be done anyway.
To bridge this essential gap in our knowledge, further testing through systematic
sampling of different parts of the dentition is needed.



### Identification of fragmented remains

<!-- and differential degradation?-->

Identifying and quantifying plant microremains has a particular set of challenges,
even before the food has entered our mouth.
Humans have become reliant on processing foods to aid digestion and to maximise
the energy acquired from eating. Unfortunately, this also means that the
microremains are put through various damaging processes during preparation
[@graneroStarchTaphonomy2020]. Pre-cooking processing
may already render starch granules unidentifiable [@liInfluenceGrinding2020].
During cooking, starch
granules are, at best, modified and, at worst, completely destroyed depending on
the cooking method [@henryCookingStarch2009]. The granules that survive the cooking
process are then submitted to further harm in the oral cavity by the act of chewing
and the presence of digestive enzymes. After death, the starch granules that are
trapped in dental calculus will have to resist degradation from the burial
environment, including bacteria, fungi, and water damage [@graneroStarchTaphonomy2020].
To add final insult to injury, further damage can occur during excavation
and processing of the dental calculus [@trompEDTACalculus2017], and even during
preparation for microscopic identification [@graneroStarchTaphonomy2020].
Through all this, there are still dietary molecules and microremains that somehow
survive hundreds-to-thousands of years inside dental calculus, and remain identifiable.
Our next challenge is to determine how to interpret these remaining microremains.
To date, most experimental methods have addressed the damage and modifications
occurring to microremains present on tools and cooking utensils
[@maMorphologicalChanges2019; @liInfluenceGrinding2020; @langejansRemainsDay2010],
and not in the context of dental calculus. Given the added processes affecting
the survival and morphology of microremains unique to the oral cavity, this
context is very important.
Validation conducted on archaeological remains will suffer from the same limitations
as *in vivo* studies, namely the variability of dental calculus growth. The
variability can affect comparisons between
two or more individuals, as well as between dental calculus deposits within the
oral cavity of a single individual. The human oral cavity is home to many unique
environments causing differences in the chemical and bacterial makeup of dental
calculus [@hayashizakiSiteSpecific2008; @fagernasMicrobialBiogeography2022].
Our best option to control these many factors and explore the precise nature of
their individual impact on the incorporation and retention of dietary materials
in dental calculus, is to isolate these factors in separate, controlled
experiments in a lab.

Methods developed more recently offer us the ability to make identifications on
a much smaller scale. The 'omics' approaches can be used to detect many compounds
which are otherwise invisible to the naked, microscopically-aided, eye.
There are still limitations to these methods. Ancient DNA (aDNA) and paleoproteomics
are limited by the low amount of diet-related genetic material present in dental
calculus compared to an overwhelming number of host-associated genomes related to
the millions of
microbes inhabiting the oral cavity. Further complicating the matter is the
inability to assign damaged DNA sequences to a single precise species designation,
and instead relying on low resolution estimates [@mannHaveSomething2023].
Similar issues are encountered in protein identification [@hendyAncientProtein2021].


Adding to the challenge is the fact that not all materials will degrade in a
similar manner. Some materials/molecules are more robust than others. To what
extent, then, can we interpret the difference between the abundance, or even
presence and absence, of materials detected within and between individuals?
We know that the stability of molecules plays a role in
what will ultimately be detectable by mass spectrometry. The chances of finding
principal pharmacologically active or psychoactive constituents of plants, such
as morphine or tetrahydrocannabinol, are relatively slim since these molecules are
unstable and have a hard enough time surviving decades, let alone (pre-)historic
timescales [@lindholstLongTerm2010]. Protein and bacterial abundances are also
impacted by differential degradation [@hendyAncientProtein2021].
This makes it hard to determine whether the quantities of molecules are an
accurate reflection of the quantities during life, which in turn complicates
interpretations we make on the health and diet of individuals.


### Contamination and lab processing
<!-- contamination: environmental and laboratory -->

It has been shown that dental calculus preserves well, and that little external
contamination enters the calculus after burial [@warinnerPathogensHost2014].
Dental calculus is a robust material. After all, it's made from a lot of the same
material as bone. It can clearly provide good
protection to the microremains and various molecules trapped inside, and survive
thousands of years [@yatesOralMicrobiome2021; @henryNeanderthalCalculus2014].
It is, however,
not impenetrable. In fact, it can be quite porous [@friskoppComparativeScanning1980; @powerSynchrotronRadiationbased2022].
This means it's important to consider what may have been originally trapped
within the calculus during life, and what could have entered post-mortem.
The proportions of original
to exogenous material may also change with time, depending on the physicochemical
properties of the molecules. It seems that small hydrophilic molecules are
more often lost from dental calculus than larger hydrophobic molecules, suggesting
postmortem movement of water through the substrate [@velskoDentalCalculus2017].
In addition, these molecules may also be present as contamination in labs or
in the burial environment. I cannot stress enough how important it is to collect
control samples from surrounding soil <!--(found this out the hard way) -->and to replicate
findings in separate labs, with clear identification of potential contaminants
[@crowtherDocumentingContamination2014].

In the study from [**Chapter 5**](#mb11CalculusPilot) [@bartholdyMultiproxyAnalysis2023],
we detected various compounds in dental
calculus using UHPLC-MS/MS, including salicylic acid, a phytohormone from willow
trees (*Salix alba*, for example) with medicinal properties. Willow bark
has long been known for its medicinal properties, and is present in many common
foods. It is therefore not surprising that we found it in the dental calculus
of people from the 19th century. We also know, however, that salicylic acid is
abundant and very mobile in soil. With this in mind, how do we interpret our
findings? There are currently no standards for authenticating results from
GC/LC-MS/MS analyses on archaeological samples<!--are there?-->. Research in aDNA
uses, among other things,
damage patterns from the sequences to determine whether a sequence is old or not,
and there are many tools available to accomplish this, such as decontam [@Rdecontam],
PMD tools [@skoglundSeparatingEndogenous2014], HOPS [@hublerHOPSAutomated2019],
and cuperdec [@yatesOralMicrobiome2021]. Similarly paleoproteomic research
can look at markers of degradation, such as deamidation [@ramsoeDeamiDATESitespecific2020].
We attempted to provide a method to authenticate our finds by plotting the
quantity of compounds in three washes and comparing these quantities with the
quantity extracted from the calculus itself. We expect to see a decrease in
quantities over the three washes as surface contaminants are removed, and a
subsequent increase in quantity as the calculus is dissolved and the compounds
that were embedded within the calculus are extracted [@bartholdyMultiproxyAnalysis2023].
This assumes that the embedded compounds were incorporated during life, and does
not in any way verify that the molecules are actually old.
So what does this mean for our interpretations? Until we can find a way
to separate external contamination from authentic compounds from the past,
and quantify the extent of external contamination in dental calculus, we can
say that they most likely consumed plants containing salicylic acid, but that
we also cannot rule out contamination from the burial environment as a source.
It's most likely a combination of both.

We also included modern synthetic compounds that we know would not have been
present in the past. These included MDMA, Fentanyl, Amphetamine, and others.
We detected cocaine in nine individuals. Cocaine is not a modern compound,
since it has been used for millennia in the Americas
[@indriatiCocaPrehistoric2001; @springfieldCocaineMetabolites1993; @abucaCocaTrade2019],
however, it didn't become known to Europeans until colonisation in the late 15th
century, and was only widely adopted in the late 19th century after cocaine
was isolated by Albert Niemann [@abucaCocaTrade2019; @marianiCoca1886].
This complicated things. Cocaine is an alkaloid found naturally in the leaves of
various species of coca plants. While we wouldn't expect a rural population from
19th century Netherlands to have access to coca leaves, it wasn't impossible to
imagine. It was commonly observed to prevent fatigue and suppress appetite,
potentially useful to farmers. There was some Dutch presence in South America
with the Dutch West Indies, and they even established the
*Nederlandsche Cocainefabriek* in Amsterdam in 1900 [@bosHistoryLicit2006].
Given the possible impact of such a finding, we analysed new samples from the
same individuals in a separate lab on different equipment. We were unable to
detect cocaine in any of the replicated individuals, and it was probably a case
of some sort of lab contamination that managed to slip past our blanks
[@bartholdyMultiproxyAnalysis2023].
Upon further research, we were unable to find historic evidence of coca leaf-use
in Europe for anything other than study, and the only small-scale botanical imports
were recorded prior to the late 19th century (the most recent individuals included
in our study were buried in the 1860s). Coca leaves are also susceptible to decay
during travel and may not have been viable for their intended use once they arrived
in Europe [@abucaCocaTrade2019].

Contamination is widely recognised as a risk in all aspects of archaeological
research, including paleobotany [@crowtherDocumentingContamination2014] and
aDNA
[@llamasFieldLaboratory2017; @cooperAncientDNA2000; @gilbertBiochemicalPhysical2005; @gilbertAssessingAncient2005; @knappSettingStage2012], often because of bold
claims made in the past (no specifics will be mentioned here).
Protocols for dental calculus sampling include various steps for decontaminating
dental calculus, and range from brushing the surface to UV-radiation and sonication.
However, the use of liquids for decontamination may be problematic when there
are plans to do biomolecular analyses [@velskoDentalCalculus2017].
Sodium hydroxide (NaOH) has been suggested
as a better decontamination solution based on testing on synthetic precipitates
of calcium phosphate (the principal component of dental calculus)
[@sotoCharacterizationDecontamination2019]. It's not clear how valid this approach
is since the synthetic dental calculus was grown without bacteria, and they're
generally responsible for the channels (supplying nutrients)
in dental calculus that would allow a decontaminating agent to seep into the
calculus and affect the microremains. Nevertheless, it is a step in the right
direction.

After decontamination, the dental calculus is dissolved to extract the remains
trapped inside.
The exact method for dissolving dental calculus inside depends
on the type of analysis being done. The most
commonly used chemicals for extracting starches from dental calculus are
hydrochloric acid (HCl) and
ethylenediaminetetratetraacetic acid (EDTA).
HCl has long been the preferred method
for decalcification of dental calculus for extraction of plant microremains
[@hardyRecoveringInformation2018; @hardyDentalCalculus2016].
However, there was no apparent testing on the original use of HCl
[@middletonImprovedMethod1990],
which was originally developed for extraction of phytoliths, which are very
resistant to chemical degradation [@cabanesPhytolithAnalysis2020]. It has since
become clear that dental calculus is also a rich source of starch granules
[@henryCalculusSyria2008; @cummingsMayanCalculus1997], though it's not entirely
clear how resistant starch granules are to degradation by acids. It was briefly
mentioned in @henryCalculusSyria2008 that weak solutions of HCl would not affect
starch granules, but more recent research suggests
that EDTA can recover more material from archaeological dental calculus than HCl
[@trompEDTACalculus2017] and cause less damage to the starches
[@lemoyneCalculusPretreatments2021].
Validation of methods on archaeological material is difficult since we don't
really know the starting point. <!-- move to biofilm applications? -->

One way to explore the external contamination of calculus and how it may affect
already present compounds and microremains, is to set up an experiment where
model calculus samples containing known quantities of compounds (and controls without anything)
are buried for different periods of time (within a reasonable timeframe).
We originally attempted this, but the model calculus protocol was not ready
and the model calculus samples were not sufficiently mineralised to survive in
the ground. The initial biofilm growth and burial are included in a blog post
(https://www.leidenarchaeologyblog.nl/articles/spit-tartar-and-burial-an-experiments-diary),
but no further results were written up because of the aforementioned issue with
the protocol, and intrusion by a pandemic.
This particular failure motivated me to revise the protocol and properly
validate the grown model dental calculus (see [**Chapter 3**](#byoc-valid) and @bartholdyAssessingValidity2023).

<!--The effect of lab processing methods on dental calculus and the remains contained
within has been addressed in previous research.
EDTA has been adopted as a decalcifying solution and described as a 'gentler
decalcifying agent' based on a comparison of quantities of starches and phytoliths
extracted from archaeological dental calculus [@trompEDTACalculus2017].-->

There is an art, or rather, a knack to decontamination and dissolution of dental
calculus.
The knack lies in learning how to make sure all contaminants are removed and
authentic material is dislodged from the minerals, and preventing further
degradation of the authentic materials crucial to our understanding of past
dietary activities.
<!-- dissolution of the calculus,  -->
To continue the laboured analogy from the beginning of this
chapter; we don't want to cause any more damage to the already broken puzzle
pieces.
Since it's clear that water can potentially clear out some of the original molecules
from dental calculus, we need to be careful with lab cleaning and processing methods,
and more extensive research on the effects of processing methods needs to be done.



### Deliberate and efficient sampling and analysis

<!-- intro -->
Dental calculus has many advantages over other elements from skeletal remains,
especially when it comes to dietary reconstructions.
With dental calculus we can more reliably argue that the substances we find within
are the result of direct consumption. Dental calculus is, after all, formed inside
our mouth, which is, famously, used during the act of eating.
It would be hard to justify the
presence of plant microremains found on any other element of skeletal remains as
a result of consumption. Any starches found outside of dental calculus, even within
the enamel of teeth, would likely have gotten there after death as the result of
environmental contamination.
This doesn't mean
we can throw caution to the wind and interpret everything in dental calculus as
food [@radiniFoodPathways2017], but it is one of the likelier scenarios.
<!--A wider range of analytical methods available.--> 

Because the formation of dental calculus is continuous throughout life, the
information we extract about diet more likely reflects a broader time frame, but
given the potential for many growth disruptions and removal, it probably reflects
dietary patterns closer to the individual's death (depending on the size of the
deposit).
That being said, other skeletal elements also have advantages over dental calculus
that should be considered when studying diet.
When it comes to studying the childhood of adult individuals, dental calculus
would not be applicable. This is because of the aforementioned cycle of potential
mechanical disruptions, and the fact that dental calculus is uncommon in younger
individuals. Any calculus visible on an adult skeleton is unlikely to have
formed during childhood.
Here, enamel represents the most appropriate choice. Enamel is formed during
childhood and remains largely unchanged during life [@hillsonDentalAnthropology1996],
so any dietary influences from childhood during the time of enamel formation,
which spans around 28 weeks *in utero* to around 16 years (*ex utero*, of course)
[@hillsonDentalAnthropology1996],
will be present in the enamel of the adult dentition. Similarly, bone and dentine
(depending on where you sample the dentine)
have a slower turnover, and represent a more stable source of dietary
patterns. And since they are generally not exposed to environmental contamination
during life (otherwise you're in trouble), they may, in some cases, be more
reliable. However, methods using these skeletal elements
suffer from a low resolution, since they can generally "only" (highly exaggerated
air quotes since it's still incredibly useful) offer insights into
very broad dietary trends [@katzenbergStableIsotope2008], whereas methods used on
dental calculus can be much more specific<!-- (pun intended)-->, sometimes even
incredibly so [@hendyProteomicCalculus2018; @scottExoticFoods2021]. Others have
also noted that the source of collagen protein in dental calculus, the primary
target for stable isotope analyses, can be difficult to determine given all the
microorganisms residing in plaque and dental calculus. This leaves questions
about what the isotopes are actually saying about diet, if anything
[@salazar-garciaDentalCalculus2014; @priceTestingValidity2018], and may be more
related to dental disease or contamination from other archaeological materials
[@mackiePreservationMetaproteome2017].

If sheer quantity of DNA is what
you're after then there really is no better substance than dental calculus. It
is estimated to
contain up to 170 times more DNA in archaeological samples compared to dentine
samples from the same tooth. The main difference is the presence of microbial DNA.
For human host DNA, the abundance in dentine is typically higher, though more
variable.
Dental calculus contains limited host DNA, which may be difficult to capture
given the lower relative abundance compared than bacterial DNA, and it can be
more fragmented [@mannDifferentialPreservation2018; @ziesemerGenomeCalculus2018].
This difference is due to the nature of the two substances. During life, plaque
is primarily made up of bacteria, while dentine does not contain any bacteria.
The exception is in some cases of oral disease, such as periodontitis, where the
presence of bacteria is a byproduct of the disease process. Since dental calculus
is also a trap for food debris, dental calculus can contain plant DNA and food
proteins
[@warinnerEvidenceMilk2014; @fagernasMicrobialBiogeography2022; @hendyProteomicCalculus2018; @scottExoticFoods2021].
The problem with detecting dietary DNA in dental calculus
is the same as for human host DNA; there is very little of it, and it may be
highly damaged.
This causes problems when trying to identify the source of the DNA. If the DNA
sequences are not long enough to distinguish between multiple related sources
(e.g. mammals), then interpretations can be made difficult [@mannHaveSomething2023].
That being said, as our techniques develop and we accumulate more complete reference
databases that allow us to make more robust identifications on smaller DNA fragments,
dental calculus can become even more of a treasure trove of information than it
is already.

Detecting metabolites in dental calculus has its own set of considerations.
Until now, the most common separation method for analysing metabolites has been
using high temperatures to vaporise samples into a gas phase
(the 'GC' in GC-MS)  and decompose metabolites within samples 
for subsequent identification by mass spectrometry (MS). The benefit being large
reference databases used to identify various compounds. However, it may not be
the best option for
every use-case, and the high temperatures required can cause problems, such as
degradation of the compounds. Some
metabolites, particularly alkaloids, are less volatile, and are therefore not
easily vaporised and detected following derivatization [@zimmermanBiomolecularArchaeology2023].
This is not a great feature when looking for
potentially interesting dietary and non-dietary alkaloids. Methods using liquid
chromatography coupled with mass-spectrometry (LC-MS) use lower temperatures and
are able to detect these compounds directly, without the step of derivatization
[@sorensenSensitiveDetermination2017; @rustichelliSimultaneousSeparation1996].
This reduces sample preparation time,
but comes at a higher cost, financially for instrumentation and operators (a
serious consideration for archaeological budgets).
<!-- targeted vs. untargeted approach?-->

If dental calculus is the best substance for the particular research goal, then
it's important to maximise the information extracted from the samples, and minimise
the amount of sample needed.
Since dental calculus has become the target for many different types of analyses
and studies, there have been attempts to unify extraction protocols for different
analyses to save on time and minimise destructive sampling, such as a combined
extraction protocol for aDNA and proteomics [@fagernasUnifiedProtocol2020] and
aDNA and plant microremains [@modiCalculusMethodologies2020]. 
The sequence of analyses should also be considered, as some 'non-destructive'
techniques may cause invisible damage to the samples. For example, high-powered
imaging techniques involving radiation may affect the quantity and quality of
extracted DNA [@immelEffectXray2016]. We should continue
to explore ways to minimise the amount of material required to conduct our
studies.


While they are abundant in the past, dental calculus deposits are quite small,
ranging from less than one to around a hundred milligrams. It is therefore
important to make our sampling as efficient as possible so we can retain some of
the material for future analyses and replication.
Many of the analytical methods used on dental calculus required destruction of at
least part of the sample. When deciding to perform destructive analyses, it is
important to consider the goal of the research. Dental calculus may not be suitable
for all purposes. It's important to select the right tool for the job.
There are likely better sites on the human body to sample for
human DNA. And while it has been preferentially targeted due to the fact that it's
technically considered an ectopic growth and is not given the same ethical scrutiny
as skeletal material, maybe it should. After all, it does contain human DNA, and
our microbiomes are unique.

## Thoughts on the future

<!-- so what implications the model has for future interpretations of archaeological
material. What will it bring to the future? especially for biomolecular methods development
and interpretation of microremains quantities - -->

It's hard to imagine the future of dental calculus to be anywhere else than
in the hands of biomolecular methods.
Further refinement of our methods will identify and address current weaknesses
and improve our interpretations. Such method validation should be performed on a
model with known input, to accurately assess the outcomes and biases of our analytical
methods. Something that cannot be achieved using archaeological dental calculus.
By validating what we see in an artificial substrate with known input, we can
accelerate our knowledge and
start to make bolder interpretations that are grounded in systematic experimentation.

A model can provide insights on many of the challenges listed above, including
differential degradation of remains (starches, metabolites, DNA, proteins, etc.),
likelihood of incorporation and retention during life.
What does it mean when we find X number of potato starches and Y number of
grass phytoliths in dental calculus? What does it mean when we detect certain ratios
of metabolites and can we use that to identify a source?
<!-- move to potential model applications?-->
Model calculus is potentially a useful material to test the recovery
rates of unified protocols compared to separating samples and analyses.
Using robust materials as a control, it would be possible to track the process
from incorporation
to extraction and quantification without worrying about what was lost to enzymatic
and acidic damage. An example of such a material is palynospheres, black ceramic
spheres which are used as marker grains because they are resistant to chemical and
mechanical degradation. They were created as an alternative to *Lycopodium* spore
tablets in places where you might expect to find indigenous *Lycopodium* spores
[@kitabaBlackCeramic2017].


The wide range of analytical methods that can provide important insights on
dental calculus require a similarly wide range of expertise. Inter-disciplinary
collaboration is an absolute must for analyses involving a deep understanding of
scientific methods, as well
as continuous communication between archaeologists and other fields to understand
the limitations and strengths of methods and interpretations in an archaeological
context. Lists of authors on archaeological papers are growing; as they should.
Paleoproteomics has already shown that it's possible to detect very specific
information about dietary molecules present in dental calculus, down to the type
of food, its source, and method of processing [@hendyProteomicCalculus2018]. It
also has the advantage over DNA in that proteins seem to preserve for longer.
Further development of reference databases and analytical methods is continuously
improving the fields of paleoproteomics and (oral) metagenomics by increasing
quantity of, and confidence in, species identifications of dietary sources
and improved methods for authenticating truly ancient sources of materials.
It will be exciting to see where these fields can lead us as they mature.

Another area which may lead to exciting discoveries is accessing the layered
structure of dental calculus through high-powered imaging techniques
[e.g. @powerSynchrotronRadiationbased2022].
We know that the formation of a biofilm is sequential, with new layers of
biofilm continuously forming on the already established layers.
Sequential analysis of dental calculus layers might therefore be able to determine
a sequence of incorporation events for dietary material in dental calculus.
However, since we can't yet access information about the age of
occurrence of the seemingly haphazard mineralisation events in dental plaque, it
is difficult to envision a scenario where we can talk about dietary activities
and the age of individuals. Until then, though, it will still be beneficial to be
able to generate a sequence of deposition events and talk about the dietary
material found in each layer.

<!--don't get too distracted by shiny new technology-->
Amidst a scientific revolution, it's important to remember that there are things
that can be said about dental calculus without using biomolecular or microscopic
methods. Not to mention, visually scoring calculus deposits is cheaper and
requires no specialised equipment.
The presence of dental calculus and the size of the deposit can be
meaningful. For example, @yaussyCalculusSurvivorship2019 found a decreased
survivorship in individuals with dental calculus formations. Past populations
are also a well-suited target to explore the relationship between dental diseases,
such as dental calculus and periodontitis; and between dental diseases and diet,
since oral hygiene interventions were less widespread in the past.
Therefore, it's crucial to record the deposit *in situ* before
proceeding with destructive sampling. This means taking photos and scoring the
deposit using existing methods, such as [@brothwellDiggingBones1981], and recording
detailed information allowing researchers to filter out unnecessary information
in downstream analyses rather than missing out on something that was never recorded.
Ideally,
each surface of the tooth should be scored separately to retain the most
information for future analyses, and allows calculating a dental calculus
index [@greeneQuantifyingCalculus2005]. Calculating an index with calculus scored
on multiple surfaces of the teeth allows us to
reveal more patterns related to the presence and absence of dental calculus,
such as uneven distribution within the dental arcade, allowing more fine-grained
comparisons between populations and within different groups in the same population.
<!-- higher resolution of calculations needed. 
e.g. not just looking at caries in an individual, but by tooth type,
and calculus by quadrant, given the pitfalls of a single index/ratio per
individual.
The teethr R package to help with this.
-->
No analytical method should be considered the be-all and end-all of our analytical
toolkit. Results should not be considered in isolation. The best approach
considers multiple angles and makes use of multiple lines of evidence to reach
robust interpretations. Not only multiomic approaches, but studies that
incorporate the entire spectrum of archaeological analyses.
<!-- As with all research interpretations, the best evidence is more evidence. And -->
<!-- a pinch of common sense. Ideally, studies should consider what other materials -->
<!-- are present, such as animal bones and food-related pottery, and whether the presence -->
<!-- of a food makes sense. -->
<!-- If you hear hoofbeats, look for horses, not zebras (unless you're currently somewhere -->
<!-- you would expect to find them). -->
<!-- If you see cocaine in Post-Medieval Europe, look for contamination, not early -->
<!-- adoption of the coca leaf. -->

<!-- working more openly-->
Working with new scientific methods and improving our analytical approaches is
only one small way to contribute to existing knowledge of dental calculus, and
may be unproductive if the method has already been tested by other labs. Moving
past the disregard for 'null' results will prevent researchers from conducting
the same experiments (as other labs) and expecting a different result.
Registered Reports allow researchers to apply a method and have it
guaranteed to be published in a journal, not because the results were deemed
"positive" or "novel", but because their methodology was sound and their results
contribute to a robust, scientific foundation of knowledge
[@chambersInsteadPlaying2014; @nosekRegisteredReports2014].
Opening our methods will facilitate faster improvements to existing protocols,
as well as open up opportunities for researchers in smaller labs. Here I'm not
talking about vague, cryptic methods sections in papers, but detailed protocols
accessible to anyone with the necessary materials and equipment. Platforms like
protocols.io are a great solution (e.g. 
[10.17504/protocols.io.bvt9n6r6](dx.doi.org/10.17504/protocols.io.bvt9n6r6)
and [10.17504/protocols.io.dm6gpj9rdgzp/v1](dx.doi.org/10.17504/protocols.io.dm6gpj9rdgzp/v1)).
Adopting more open research practices will also make it easier to incorporate
multiple proxies in research studies,
as this will no longer be limited to those with access to enough material and range
of materials to conduct large-scale analyses [such as @yatesOralMicrobiome2021].
Ensuring that we publish our
data in a manner that is Findable, Accessible, Interoperable, and Reusable (FAIR)
will promote reproducibility and replication, two crucial aspects of scientific
research [@wilkinsonFAIRGuiding2016]. Creating communities that can promote these
practices within specific fields and subfields can be effective in creating
relevant standards and fostering
an environment that promotes equitable research practices. This has been
realised by the SPAAM community and Open Phytoliths with
[AncientMetagenomeDir](https://zenodo.org/record/7789069) and the
[FAIR Phytoliths Project](https://zenodo.org/record/6435441), respectively.
Unfortunately, many of these initiatives fall on researchers early in their careers
out of a need for more resources or sheer enthusiasm for what they do. There are
still very few incentives for organising these resources and practicing
Open Science, and instead rewarding fast science and measures of impact that have
somehow been assigned as important. Out-dated reward systems are preventing the
widespread adoption of open practices and disproportionately impacting young
scholars and early career researchers.



## Concluding remarks

In my dissertation I set out to put dental calculus under the microscope,
scrutinising what we know about dental calculus, what we think we know, and what
we need to know.
To do this I created a model system that allowed me, and will allow myself and others,
to address fundamental processes involved in all aspects relevant to the dental
calculus analytical lifecycle. Processes including formation and growth, exposure
to dietary and non-dietary materials, burial with subsequent degradation of original
materials and the colonisation of materials and molecules from the burial environment,
decontamination and extraction of materials trapped within the calculus, and many
more.
With the help of co-authors, the model dental calculus was examined to ensure
that the bacterial and mineral
compositions were sufficient to mimic an oral environment and closely resemble
natural dental calculus. We deemed this to be satisfactory, but further validation
is absolutely encouraged.
The model calculus system was put into action to see what it could contribute to
the use of methods to extract and quantify plant microremains from dental calculus.
It showed that there is more to the process than dietary input, and that size,
morphology, and physicochemical properties of granules may have an impact on
what we ultimately end up seeing in archaeological dental calculus.
We applied a new method, previously validated on cadavers, to explore the use
of dietary and non-dietary alkaloids and metabolites in a rural Dutch population
from the 19th century. Detection of mundane everyday compounds, such as those
present in tea and coffee, has never been more exciting! Even the absence of
compounds raises a number of questions about why they were absent, and if they
were ever there to begin with. Contamination is omnipresent in archaeological
studies, especially those employing biomolecular methods. Ours was no exception,
with the possible, but unlikely, detection of cocaine.
Overall there were more questions generated during the various projects than I
could possibly hope to answer over the duration of a PhD program (plus a little
extra), and there is a clear need to address many challenges going forward,
some of which may be addressed with oral biofilm models.

<!-- summary of future potential -->
I have no doubt that we have just scratched the surface of what dental calculus
can do to inform us about past activities, diet and otherwise.
Novel analyses and biomolecular techniques have already taken us beyond what
was likely imagined back when archaeological dental calculus was discarded.
Microscopy, metagenomics, and paleoproteomics have already provided incredibly detailed
insights into the dietary activities of people in the past, and will
undoubtedly continue to improve our understanding.
Before we can achieve any of these things, though, we need to take a closer look
at how dental calculus incorporates these markers of diet, and what biases the
mechanisms of incorporation may cause.
Advances in dental calculus and dietary reconstructions will require a deeper
understanding of the substance.
How it behaves under certain conditions and how it interacts with the material
and environments with which it comes into contact.
This dissertation provides one possible solution to the need for more fundamental
research required to understand these processes,
adding to our toolkit of method-validation, which already includes ethnographic
research, and experimental archaeology.


We already understand that we are limited in what we can say about diet in
the past from dental calculus, especially from a quantitative perspective.
It's not enough to identify the problems, but rather
to identify the causes of the problems and their implications. With more systematic
research answering more fundamental questions, maybe we can move beyond
these limitations and be a little bolder in our interpretations.
How can we possibly expect to understand diet from
archaeological dental calculus if we don't understand fundamental processes that
lead to dietary components ending up in dental calculus in the first place?
Basically, we need to ask more stupid questions. They are probably not stupid;
it's more likely that they point out fundamental assumptions that we have been
making without actually going through the trouble of testing them. After all,
"You can't possibly be a scientist if you mind people thinking that you're a fool"
[@adamsLongThanks2002].

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