2021-03-03 Jordan Brown - Seasonality from Shellfish: a Case Study in Archaeological Quantification

berkeley. edu/arf you can make a donation and your your donation will be tripled in support of archaeology thanks for that sarah so before i introduce our speaker today i just wanted to say that next week we will have the pleasure of being joined by katie kinkoff who will be talking about her experience as a new professor in the csu system and her ongoing research on materializing disability justice so please join us for that back here at the virtual arf today we are joined by jordan brown who is also my brown bag co-organizer this semester and he is a geo archaeologist working on his phd in the department of anthropology at uc berkeley he works primarily in pre and proto-historic southwest asia in what is now eastern jordan and northern iraq however he also has an abiding interest in statistical methods in archaeological data analysis and the integration of archaeological ecological geomorphological and climatic data so while i look forward to hearing about his work in southwest asia some other time today we’ll be hearing about a project a little closer to home about a novel statistical approach for determining the harvesting seasonality of shellfish on the california coast this is a collaborative project drawing on many different data sets so towards the end of today’s talk we’ll be joined i believe by a couple of guest stars familiar faces to many of you so definitely stay tuned for that also this work will be presented at a session at the society for california archaeology meetings this friday afternoon along with other work by folks from the cal lab and the ahma mutson tribal band so check that out and without further ado take it away jordan thanks so much lucy um it’s a pleasure to be here in something other than my usual capacity um and of course uh do stay tuned for those those special guests um you saw one of them on the screen just now um but uh yeah so so what i’ll be talking about today um is a case study and archaeological quantification um looking at shellfishing seasonality on the california central coast during the holocene and there are a lot of different aspects to this project and to the research that’s related to it i’m going to be focusing fairly narrowly on the isotopes and how to quantify them since that reflects how i got involved in this project um but before i launch into that i i do want to just give a quick uh thanks to the the cal lab for getting me involved in the first place it’s about a year ago now that um we started chatting about this kind of stuff and the sca presentation session that lucy mentioned has been about that long deferred so this has turned into a longer project which has been fortunate um as i hope you’ll agree um so yeah very very many thanks to to kent and and rob cuthrell and and mike cronin and alec apodaca for their work on this and you’ll get to hear from some of them uh towards the end so uh why are we interested in this study here um why are we interested in in shellfish on the coast in this case we’re talking specifically about california mussel which are really sort of the dominant uh rocky intertidal species here and and very very important in uh in the gathering that that we see throughout the holocene important research resource for folks um well the uh sort of best single argument perhaps you can make is there are enormous shell mounds all on coast and shellfish feature very prominently in that and and that’s sort of a you know a shorthand for a lot of other things that are going on with these with these organisms and people’s relationships to them and the environments within which they grow um that uh that we’re gonna be interested in here um some sort of uh broader questions to be thinking about again i’m gonna be focusing fairly narrowly on uh sort of this as a math problem um today but but we’ll get hints at some of the other other stuff um i was thinking about how does shellfishing seasonality harvest seasonality change uh through time how is it um related to variation in in space across the region um and where you know how are these activities concentrated during the year that’s the sort of basic meaning of seasonality that i’m using here not strictly and you know one of four seasons um and uh and how does that relate to all manner of uh social factors trade networks uh economic uh structures um food waste uh anything you like really there’s this is uh pretty deeply integrated in in life on the coast and uh and then finally and and and perhaps uh most importantly is um you know looking at these long-term archaeological data sets um particularly that are you know these are the the sites and and the ancestors of the folks in the almond woodson tribal band and um and looking for you know lessons from those ancestors for how we uh steward the coast today um and particularly how the tribal members of the amah mutsun can be involved in that and and in fact leading those efforts by looking to these long-term data sets um so uh i will give a brief overview of what’s going on in in seasonality determination from shellfish the basic thing is that we’re drilling out samples of the shell carbonate and relying on the fact that there is a very well studied relationship between the oxygen isotopic content of uh of carbonate that’s precipitated by uh by mollusks in this case california muscle and the temperature of the water in which that uh that carbonate is is precipitated and uh and so this allows us to to look at temperature changes over the life of a muscle um very very far back in time so this is a little picture of our basic sampling scheme courtesy of alec apodaca and and a little picture of a thin section as well where you can see sort of the different growth bands these creatures grow by accretion one band on top of the other as they as they age and so they’re sort of creating a a biotic calendar in a way of sea surface temperature in the form of carbonate oxygen isotope composition uh now i want to give a quick history of seasonality analysis uh using isotopic data sets um well particularly in shellfish um and it is a it has a long history at least relative to the relatively young field of stable isotope geochemistry one of the very first papers that discussed this carbonate oxygen isotope paleo thermometer as it’s referred to um it’s from yuri at all this is the the dude who invented uh radiocarbon dating so thanks harold um he was very very busy um and uh and in this paper they sampled um from the growth rings of a of a fossil bell knight um and uh uh i’m not sure if it’s exactly a specimen but some uh this species became the sort of standard uh for isotope analysis of carbon in general anyway they plot up their values and they get this nice curve and they say we think this is seasons that’s why we see ws here for winter summer um and uh so that was an exciting thing to realize and um a couple of decades later nicholas shackleton realized that not only was that a fancy thing for paleo climate studies it was also great for archaeological studies um and uh this is a little graph from the very very first archaeological isotope seasonality study where um shackleton sampled um uh modern shells um of a particular genus uh or species um forgetting the name at the moment um not my list california honest not not california muscle um and establish sort of what the um distribution of isotope values was over the whole course of the year um and then sampled some archaeological specimens and said look their isotope values cluster on one side of the year this is um a uh a positive uh isotope value so um this is this is cold um uh i won’t go too deeply into into that but um winter harvesting is what he concluded um and uh and then somewhat after that um killing lee and berger and then uh berger perhaps and then killingly um again both both geochemists with some archaeological interests um take a look at actually specifically california muscle um in southern california and say we think we can do one better than shackleton shackleton just said this harvesting was happening during cold part of the year i can’t say exactly when these analyses are too uncertain killingly says no look if we take a bunch of samples not just you know a couple from the very end of the archaeological shell uh if we take many we can trace this whole curve through a long span of time um and you know you have to make some assumptions about how fast the shell is growing but but we feel we can do this and then we go to our archaeological shells and say huh where in the squiggle do they end up this is where they’re harvested and made estimates of a monthly resolution on the basis of that um shackleton didn’t love this um and thought that they were spending way too much money on sampling uh for conclusions that were far too uncertain and um in an attempt to demonstrate this uh deeth and colleagues colleagues included uh shackleton um plotted uh took samples on the same day from you know shells uh modern shells and said look all three of these isotope profiles were harvested on the same day do you really think you can say to a monthly degree of resolution you know what when when a single archaeological shell was harvested so that’s their their skepticism here i would say that there’s a little bit of shape um similarity in this um and killingly’s argument was about shape um so i i think it it doesn’t fully end the end the discussion as you’ll see my opinion is today um so let’s bring us up to the present day uh very quickly um the two major techniques in use are so-called terminal growth band plus x sampling tgb plus x which means you take the final growth band value one isotope sample here and then you move back at regular intervals say one millimeter two millimeters two millimeters in our case um and uh and you plot those values in in a sort of time series um and this spacing is relatively coarse as you can see here um and some other authors have pointed out that you might be missing important parts of the trend important parts of the shape and and so there are authors who uh advocate micro sampling and and micro milling um in order to really resolve um as clearly as possible uh the isotopic variation and shell over time but there’s a trade-off here and the the authors that i’m mentioning uh recognize this um because this approach is a lot more costly and a lot more time consuming um and it provides some benefits but then you have a real trade-off to think about uh in regards to archaeological site and and region sampling because if we’re trying to say something about how people interact with the resource that’s all along the coast um then just getting one shell right is not enough um so that’s why i am uh interested in the the terminal growth band tgb plus x approach um if we can do a better job of understanding the uncertainties that are involved here and of trying to get useful estimates of seasonality out without um sort of pre-dividing up the year into um sort of bins that we can see easily um in the samples um this is a problem that shackleton confronted and that everybody who has ever done a tgb plus x study has confronted you’re going shell by shell saying like okay i see an uh high value at the start high temperature value once i’ve converted it from the isotopes and then it goes up but then it goes down and i’m guessing at how much time that includes and so i’m trying to match that up to a seasonal curve but there’s a lot of of sort of expert judgment required and that’s also hard to scale if we want to do a large sample study so what are our goals in in sort of revising these methods or or perhaps trying to build upon them is a better way to put it um the the sort of king king goal perhaps that all these things could fall under is reproducibility um this is the concept of other researchers being able to re-analyze your data using your same methods but perhaps varying some assumptions and part of this is automation making sure that it’s not just you saying here i plotted this thing but trying to make that an automatic process if you can which also helps with scalability really trying to get a handle on the uncertainties involved because we know there’s a great deal of uncertainty even just the baseline we’re using for the annual sea surface temperature curve uh in our region of interest is an important decision we’ll see that later and then also uh transparency and accessibility having especially because this is a collaborative process in many ways not only with other researchers but also with the um woodson tribal band um and directed towards you know their stewardship goals um the the folks who are you know sort of having our audience here um are both re you know uh all of these groups um really need to have the ability to evaluate what we know and how well we think we know it um in in an interactive and and um and sort of clear-cut way um that so we don’t end up with like isotopes just seeming like you know this is the dictate from on high and uh either take it or leave it but yeah so this is this is an important thing you’ll see coming up throughout um so the very basic thing that you’ve got to do um is when you’re trying to get uh seasonality out of out of shell isotopes is get from the isotopes to sea surface temperature which we have an equation for and then from distance along a shell into calendar time which is a bit of a thornier problem for this pilot study that i’m going to be talking about uh today we make a simple assumption of a constant on average growth rate um of a millimeter and a half per month um which is plausible for california muscle on the central cross central coast um but there are lots of things that i would like to look at in more detail about this later but for now that’s what we do and this can get you from a comparison of your archaeological oxygen isotope data in a shell to a historical reference data set of sea surface temperature which we have thanks to the hopkins marine station at pacific grove at the southern end of monterey bay so um to give a sense of the the specifics of of of our approach that’s sort of the general things that you’ve got to do um one way or another but uh what’s sort of particular about what we do um is to take our historical data and transform that into simulated isotope space which is to say that since we have this historical data set of what sea surface temperature was doing over a long period of time we could in principle predict what a shell should look like that was you know harvested on any given day given the assumptions of our sea surface temperature and isotope relationship and of our growth rate assumptions um for for this taxon um so if we do that then we can say okay well here’s the temperature values that should be relevant to these different growth bands that we’re sampling in our tgb plus four in this case approach so terminal growth band plus four others back in time um and here’s what a shell that’s harvested on that day i think it was oh i think it was like may first that i picked um what that isotope profile should look like and we can do that for a whole database um and so we’ve got oh may 10th here’s your may 10th squiggle and the isotope values that you hypothesize here’s a september 11th here’s january 13th these are all totally arbitrary just um pulled out of the the broader data set i do this for the whole the whole like 100 year span that we have at pacific grove uh hopkins temperature data um and as you can see these are the different growth bands uh time proceeds from left to right and uh and then the next thing that you can do which is also somewhat particular to our approach is to um standardize each of these uh growth profiles simulated shell growth profiles um and say we know that there’s you know perhaps like some overall you know centennial trends in sea surface temperature change we don’t want those to affect our seasonality analysis so we’re going to just say we want the shape we don’t care about the specific values so we’re just getting the shape out here plus or minus from the average it’s a very simple approach um and then we take our archaeological shell over here um i believe this issue shell number nine um and we compare it to our uh historical database and say does it match um where can we find matches um and then what we do is we plot up those matches because we know the day on which those simulated shells were simulated to have been harvested um and so we can make that plot of like here’s here’s where we find matches for shell number nine um and so we see you know this histogram presenting certain peaks throughout the year and that gives us a guess at when this shell was harvested so then we do that for all 40 of our specimens or potentially quite many more this is really just the click of a button that’s that nice automation thing and we get a sense for between these different sites um the uh three sites are scr seven ser-14 and sma-216 scr seven uh is a middle holocene site you know on the on the order of of uh 6 000 years ago um six to four or so um and uh scr 14 is uh as well as sma 216 are both late holocene sites um so primarily over the last uh last millennium um and uh all these sites are are just north of of santa cruz so and this is worth noting on the opposite side of monterey bay from uh the temperature data set and in a more open coastline environment so anyway this is the the histograms that we see and i submit that this is real uncertainty about when these shells were harvested um what we can then do is combine those histograms into general pictures of harvesting activity as we think you know based on this very small sample size um in these uh sites hey alec what’s going on well uh i like i’ll i’ll uh i’ll bring you and you and mike on in a little bit sorry to give give away our special guests but if you want to hop off for the moment you can yeah you gotta zoom pro over here um but uh anyway so uh we see these sort of seasonal curves um from the histograms here’s if we plot a kernel kernel density estimate um over those data points just really interpolating um densities to them uh and and we see some trends here um now uh that’s exciting the the basic uh basic takeaway here is well kind of kind of works these might be real things but um i’ll come back i’ll come back to that in a moment uh getting ahead of myself um so uh just another fun thing that you can do if you assume that um if you go back to these plots and you say well let’s take the modal value um for each of these shells the the highest sort of count of um of of matches on these in these histograms um for our archaeological shells then you can take that as like our best guess at the the specific time of year um that each of these shells was harvested and if you do that then you can sort of count back along this uh growth band here right and you can say okay well we assume that date because from our the mode of that histogram then we go back and we can say based on our growth rate how far we think that was so we can take a guess at what this day was and so on um and then you can treat each shell as a time series and plot up the temperature values as though you had a real little thermometer hanging out in you know in this region of of the santa cruz coast um hundreds and thousands of years ago um and uh what is fun to see about this is at least for the for the subplots b through e there’s something that resembles actually a seasonal sea surface temperature curve it’s sort of got the right shape which is encouraging that we’re not just you know that there isn’t something going terribly wrong in our analyses somewhat as an an independent check on this um it’s not entirely independent but but part of what makes it somewhat independent is the fact that we actually plotted this in terms of um the absolute temperature values um rather than those relative ones and the curves still seem to hang together somewhat um and they’re hovering around some reasonable temperature values for the most part one thing that i will note is these five dots up here represent one shell which has some very balmy temperatures for the santa cruz coast that’s in celsius over here so pretty pretty toasty waters there getting up close to 20 degrees celsius that’s those are are off by some factor but we actually kind of know what factor they’re off by because there’s this gap here and despite the fact that the absolute temperature values the absolute isotope values don’t match up with what we’d expect we picked up on the shape and our analysis assigned it to a plausible time of the year similar to these strands down here which are you know plausible absolute temperature values one thing that killingly and berger did in their 1979 piece was note that carbon isotopes can also be used as a potential uh proxy for upwelling intensity um this is a complex relationship that i don’t have time to go into but um i made the plot and it’s kind of fun to to take a look at and and speculate about um that this is something we could potentially try to take trace also um with this this same data set um and there’s uh you know it’s just sort of an example of of how much not only information on you know human interaction with the environment but also on on other environmental variables that that may impact um you know these these uh seascapes um over the long term how much of that sort of information is is sort of ensconced in in these these little shells so fun stuff all right so uh now we get to some some caveats which is uh this is a mini caveat you notice there are missing shells here they didn’t find good matches in our historical temperature data set and that is unfortunately the nature of a limited um a limited data set even though it’s 100 years long there’s only so many temperature conditions that you go through it’s not necessarily a you know a balanced representation of different uh annual and multi-decadal cycles um el nino variation is not necessarily the full range of it is not necessarily included in in that century and and we know there’s also changes over the holocene and sea surface temperature so some of the shells didn’t find good matches at least within the goal posts i set up which there is a better way of doing this um full disclosure i was plotting this stuff up this week um so i didn’t have time to to work out all the kinks yet but so that’s also why all the all the graphs have labels in like lowercase and saying things like day um by the way day day 300 of the year is about november first so you know um just to give you some bearing there um anyway so what do you do about this well you try to come up with a true functional relationship between the variables that you’re interested in again not something i’ll go into in great depth here um but i took a hack at it oh this is the real way to do it um yeah this is so foreshadowing of collaborations um my i’ll show you my version first this is this is my sort of hacky way of trying to get estimates for these shells that we’re missing 2 4 5 7 10 and 23.

you can get an estimate that’s great it does affect somewhat the overall distribution but not hugely but the other thing that’s going on here is that i’m essentially still treating each of the growth band samples as like an independent observation which they’re not because those the shape is really what we’re interested in and those shapes hang together so this is a just a sort of tag like how you might go about this the real way to do it um is can i go back there we go um is to do uh some fun bayesian statistics which is a favorite theme but i’ll leave for another time in any detail which is to say we’ve got one growth band estimate here z is for the isotope value and w gives us a sense of over the course of the year and the curve here is a representation of isotopic variation throughout the year according to this temperature data set that we have and there’s noise in that curve but if you plot a line through it at your temperature value or your isotope value then you get regions of sort of increased likelihood and if you take not only one but several for example five uh growth band measurements then uh you can start to build up a sharper estimate of when the shell was was harvested um the time access is backwards here but just besides the point um so moving on um plausibility this is plausibility analysis um do our conclusions make sense remembering that this is a very small sample size only 40 shells and those shells are split across three different sites and five different sort of chunks of radiocarbon time um and so the things that we want to look to here are other archaeological data sets and archaeological models that have been developed um for this region and this time period the central coast over the over the mid to late holocene we want to look at historical accounts both oral and written what do folks know already about the way that harvesting was done on this part of the coast that’s really important information and especially as we get um into building a proper asian model this is something that we can incorporate as prior prior information that’s a technical term in bayesian stuff but basically means what do you already know about the phenomenon you’re interested in and now you’re doing an experiment and adding your your data analysis to that um and and the thing that we’ll look at today as well is the robustness of our conclusions to changing our assumptions which is another nice thing that automation lets us do so i’ll just briefly say since i am i am not truly a californianist though i am a californian um uh but with no no real expertise about shellfishing alas um they taste good um the um and i’m hoping that this this mathematical analysis will will send me on the way to really securing a reliable source of of muscles in my life um so that’s that’s perhaps a you know ulterior motive that i should reveal um anyway so i’ll note very briefly that talking to the the real californianists in the room um they’re the trends that we see taking place over the over the holocene based on that analysis that i showed you these these plots earlier this plot this plot scares people a little bit because it it uh not scares but raises uh questions um because it shows sort of a a year-round harvesting signature in the in the middle of scene and that sort of changes in the late halls and that’s not really what we expect necessarily to to see happen for various reasons um i’ll you know and maybe tempered by various things but uh i’ll let the the experts talk about that um but i think what’s important to mention here is that we know we’re making some pretty big assumptions about growth rate and about the proper sort of reference data set to compare things with and so those are things that i can vary in my nicely automated analysis so that’s what i’ll do now so we changed the growth rate i said one and a half millimeters per month well here are some comparisons of the conclusions and how they change for each shell if you assume one millimeter a month or two millimeters a month which are both totally plausible growth rates with very much within the likely range of variation and the changes are not huge um in fact it shows up a match for the shell number two which is kind of nice but uh you know there are some changes here that are that are relevant um by you know a couple of months um here and there uh that we need to account for and and certainly changes in sort of the the sharpness of the of the conclusion um how how uncertain we are about it um now of course things shouldn’t get more uncertain when you vary the parameters so in fact what you would want to do and this is why again a bayesian approach will be fun is you can build this into the model you can say well we think the growth rate is centered at one and a half millimeters a month but there’s a bell curve of variation around it perhaps um and then you can have that show up explicitly in your posterior distributions as it’s called um sort of what you bayesian analysis sort of goes with your prior what you think you know already your data what you learned in your experiment experiment and posterior information which is what you think you know based on what you knew before and now the experiment that you’ve done so um anyway you can get this stuff into the into the the sort of final density distribution um and here’s the effect of growth rates on the on the distributions by sight again not huge but but not not negligible um so this is something to think about now another important thing to consider is our sea surface temperature data set and we used pacific grove which is as i said at the southern end of monterey bay granite canyon is another recording station that doesn’t have quite as long a data set which is why we didn’t use it to begin with it’s only 1975 onward so but but that station is a little bit further south along the coast so further from our archaeological sites but perhaps in a more similar marine environment um and so it might actually be that that granite canyon is a more appropriate comparison um you know this is something to investigate but here we see this can affect the seasonality estimations by a great deal almost in every case it’s a couple of months um and and again this is something we would want to build into the analysis like how likely do we think you know we can sort of break down what we think the sst curve sea surface temperature curve was um uh across the whole region that we think gathering may have been going on uh this shellfish harvesting activity for a given site right there’s lots of things that we have to think about in terms of like how are people interacting with this specific piece of archaeological evidence that uh you know in the past um and making those things explicit in our models for for this sort of analysis is is really helpful um so again this is something that that we could potentially build in but just to give a sense of the overall effect um pacific grove this confusingly is different colors pacific grove now the the hopkins south monterey bay location that we use for our first analysis um that’s in pink the granite canyon data that is the re-analysis um is in blue um and uh and as we see there this gives us a quite a different picture um and and makes us think well it’s actually probably pretty important to get a handle on this you know what the particular temperature environment was that that these muscles were being harvested in um what they were growing in because it significantly affects our conclusions um and and especially given that we have this other archaeological evidence in these archaeological models that say you know we should expect a different signature than the one we got from pacific grove then that’s really something that we need to bring in again sort of as prior information in that in that bayesian terminology so this is you know this is sort of the reason for taking this explicit um uncertainty modeling approach and um it allows you to integrate with other data sets so that brings me to uh you know a discussion here of where we’re going with this and and in in all cases that could be termed discussed in terms of collaborations um so on the one hand you know we’ve got to um do some experiments to understand uh muscle uh california muscle growth rates on the central coast better there’s a pretty thin literature on that right now most of the studies have been done either the north west coast or um in southern california but um south of the the the byte south of santa barbara which are manifestly different um uh different ocean conditions um so we got to do some do some experiments um and uh and we hope to really connect those experiments with um collaborators both in the the alma mater tribal band working specifically so that the experiments really like tie into ongoing stewardship work that they’re uh they’re developing around uh around uh seascape stewardship and again we’ll have alec and mike on to talk a bit about that um and and also hopefully to to work on um work on these questions in association with the hopkins marine station um and the good folks from from stanford who who staff it um and uh and know a lot about these these organisms and these ecosystems and uh and also have sort of a historical ecological interest um in this stuff um but have to my knowledge at least not had the chance to work with archaeological data sets uh and not yet had the chance to work with the knowledge that the mutsun have um about these about these practices and and uh and how they tie in with sort of these social and ecological questions that go together and yeah traditional uh resource management methods and that sort of thing um and another element of this further approach is modeling um i mentioned um my friend gabriel lewis who’s a statistician over at the university of massachusetts amherst um and a very fine bayesian has been working on on this problem trying to develop that more explicit mathematical functional model that i was uh sketching briefly and there are a lot of you know all of the things that we’ve talked about as important assumptions that we need to you know sort of balance here those things all need to go into the model um and that is the wonderful uh potential of of basically statistics is to really allow us to to to make to make clear to the world to to admit our our full uncertainty without um just becoming lost in in limbo of not being able to conclude anything so so hoping to i hope that uh nicholas shackleton will will not um come down to to to put this mac on my my analyses here and then thinking is always good this is particularly thinking with archaeologists about the nature of these sites and thinking with our tribal partners about the nature and function of each of these sites and places landscape um and how that relates to our quantitative analyses here we want to make sure that we are sampling not only the shells in a sensible way but also the sites and the regions and thinking about the sort of statistical properties of the the um analyses that we’re trying to do and the data itself and and its properties and um and what we’re trying to say about it so so these are some some important questions that um now sort of having a proof of concept i hope um of this of this method um that that we can get into um and and try to build upon it um so uh finally i brought a picture from our very own mike crone here really the the goal here is to you know to to use this this knowledge from from the past um and and for the woods and travel ban from ancestors um to uh to nuance and inform the way that that um that all of us folks who live along the coast here today interact with with these ecosystems and which with these organisms and and with ourselves in relationship to um to to our our landscape and our environments um and having these sort of detailed um you know molluscum uh archives um in which these important uh environmental variables are recorded and then more um sort of more elaborately having these detailed archaeological archives that were you know constructed very intentionally um by the indigenous folks uh native californians living along these along these uh coastlines um as you know very clear testaments to a particular way of interacting with these these coastal ecosystems and resources and managing them and reacting to changes um in uh you know in sort of external perhaps ocean climatic variables um and also conditioning those changes um buffering them and and all sorts of things like that so um this is what we’re sort of all interested in in getting at here um and and that’s very much a collaborative project so um in light of that i will now uh bring on my my special guests um who are very much uh at the core of this project that i am somewhat of an interloper in uh so i just want to thank everybody at the the cal lab here um at berkeley um and uh of course ken lightfoot for for occasioning this sort of broader uh research uh research theme um and uh and also uh of course the the collaborators at the umitzen tribal brand and and uh and land trust for for you know being willing to entrust these these archives of of um of ancestral practices to to to to us to work with um and uh yeah i hope for a lot of a lot more fruitful collaboration in the future thank you all so much thank you jordan that was an excellent presentation and uh really impressive just how you’ve you know taken on the problem of isotopes and archaeological assemblages from a nuanced uh perspective and you know contributing to a broader body of work about seasonal sight use and patterns and traditional resource harvesting practices very novel approaches and as you mentioned uh very useful for the ongoing efforts of the travel ban it’s there’s an active project right now that i’m working on as a consultant with the tribe that’s integrating archaeological information such as this into efforts of restoring coastal stewardship from for people who were largely removed from their traditional coastlines and shorelines so in a lot of cases traditional resource management practice knowledge and traditional ecological knowledge like timing of harvest locations extent size preference and just harvesting profiles in general are largely lost in the canon of ethnobiological information of the tribe holds so this sort of analysis can provide these key links for uh cultural resource practitioners who are trying to restore traditional food waste and just relationships to the coast that span yeah but also have a very kind of seasonally prescribed uh means of interacting so i think really great stuff and uh and exciting and very useful um get to see you know developing projects um with more sampling with a larger more robust data set to see how more how refined we can get these interpretations might pass it over to alec apodaca on that because he’s a you know he did a lot of sampling for these these individual shells and see what ways to refine this method thanks jordan thanks mike and yeah i’ll just follow up on that i think that you know jordan’s call for some some more experimental studies is really warranted and you know the the beauty about that is that if with the alma moots and land trust and nama moot and tribal band um going out and having controlled experiments of intertidal plots will open the door for just a lot more other things and you know right now the alma mutan tribal band is really interested in monitoring the effects of climate change ocean acidification and pollutants that are affecting a lot of the organisms on the coast and i feel that you know just starting with monitoring the growth rates of muscle shells over the the course of let’s say one year or two years or three years um this is going to provide a lot of information into how we interpret the isotopic results um once we apply it to archaeology so you know i i think that you know it’s it’s fruitful we can probably sit back and and feel that isotopes are are fraught with a lot of uncertainty and we can you know feel that we need to tread lightly and we don’t want to make interpretations but i think the the future is very bright and we’re headed in that direction thanks alec um i know we don’t have a whole lot of time left but uh folks have questions uh be happy to answer them either for me or for alec or mike um lightfoot our very own uh has a question in the chat which i will ask um can you make any interpretations about muscle harvesting for the archaeological sites at this time and what is explicitly your next step besides eating more muscles well i’m glad i’m glad kent caught the the primary aim of this talk um so i mean what i can speak to is uh what the the analysis says um so you know i’ll go back to um some of the plots here uh don’t bother to do presenter view just so let’s see a little more clearly um so this is sort of the the the basic conclusion um by sight and also by by time period um sort of chunked up into where the radiocarbon dates associated with this context cluster so what this says to me is that you know we’re seeing during most of the time that through most of the context in scr seven that we looked at um a distinct likelihood of harvesting throughout the year um at the oldest time period maybe a more pronounced sort of winter spring peak and then looking at the the late holocene sites um perhaps this is just a single well perhaps two shells so i wouldn’t give it a whole lot of credence um but this uh platinum plot v um shows scr-14 which is a an an inland site you know a couple of kilometers inland from from the other from the coast um uh it shows a winter harvesting emphasis um and uh and then the the coastal site from the same time period but further north on the coast uh some tens of kilometers not too much more than that um uh has a has a spring emphasis um and so mostly i think what this shows us is that we’re making some strong assumptions um because of course if we go down to uh the graph here where we use a different temperature data set that changes the conclusions pretty significantly um scr seven in particular um no longer is throughout the year as much much lower harvesting densities in in winter um and much higher sort of concentrated in spring and summer so um i think the the main inclusion conclusion to be done drawn here is that you know we probably need larger samples that’s no surprise we only have 40 shells and only like 15 from our our biggest sites and that’s split up between these different uh time periods that are separated by you know almost a thousand years each um uh so we wouldn’t expect to like get the whole signature of the site in that um and then also we know that assuming our uh temperature data sets is uh the data set that we we choose is important and has some pretty big leverage on the on the conclusion so that’s that’s where i would leave it cool um christine also has a question christine hasdorf uh you partially answered it she asks can you say if they were harvested year-round or seasonally um and then specifically asks what about the red tide is that something you’ve looked into uh yes um so uh the red tide is uh harmful algal blooms um going on um and and red tide is a natural phenomenon or that’s what it’s meant to distinguish from from what i’ve heard the term habs habs um harmful algal blooms which are which are not necessarily of sort of an on a natural cycle um uh that we absolutely think is is a is a likely reason that people would be avoiding shellfish at certain times during the year as they become toxic to eat um and uh and folks would have certainly been aware of these uh these red tides the thing to consider is that they are significantly associated with um not only uh sort of solar forcings um in terms of kicking up photosynthesis but also upwelling and that’s something that can vary through time and so that’s that’s part of the reason that i uh showed the the carbon isotopes because that that could be a way of starting to get at that um and there are also other geochemical signatures sclera chemical signatures in these shells that that might offer potential um avenues towards that so that’s a very good question um and then uh yeah i remember to can’t ask for for next steps i would say that um both i’d say two next steps that come immediately to mind one is to apply these analyses which are all nice and automated to existing uh isotope data that’s already in the literature um there’s thousands of isotope determinations um thousands of shells um from various sites that have been recorded in a way that is absolutely digestible by this program it just will take somebody to tabulate them and clean the data but that would be a very quick way to bump up sample size not necessarily specific at these sites but but over the region as a whole and could give us a sense of of what those broader patterns are and then as a twin to that um is the stuff that alex talking about of you know working with the tribe and um and developing these uh these experimental uh plots that can also uh work as um you know a sort of learning teaching learning testing ground um for these traditional resource management techniques and and and knowledges that that folks have and and uh and want you know a venue for um so so yeah so i think those are kind of the two two places to go uh to go next great well those are the only questions i’m seeing right now we’re already a little over time um but uh yeah thank you all so much for a fun talk about a place that i think many of us are personally acquainted with so makes it a little bit more relatable even if we can’t necessarily travel there at the moment.