Insights from Assessing Management Impacts on the Soil Microbiome

so now we all transition to conversations around the soil microbiome by Dr Liz Riki who is a soil microbiome scientist with the soil Health Institute with a background in microbiology and engineering currently Dr Ricky has developing DNA sequence based indicators to describe changes in soil functionality not captured by current soil health indicators previously she has served as a project scientist on the soil Health institute's North American project to evaluate soil Health measurements where she led those efforts for the northern Midwest and Northeast regions in the U.S I'll turn things over to you Dr Ricky thank you Dr Odom for the introduction and also your support of this project from the start so let's see here share screen all right can you see my screen I think you can hear me hopefully I'll take that as a yes like Diana uh before I get started today I'd just like to say I didn't quite know what I was getting myself into uh working on the North American project but it's been a blast from day one working with all the soil Health Institute scientists as well as our different partnering scientists all right so not a surprise to many in the crowd today that over the past few decades there's been quite a few different biological indicators that have been developed by researchers in private Labs alike to assess the biological Health soil health these can kind of be grouped into three main categories the first being differences in microbial the size of microbial communities the second being available resource pools for microbial consumption and the third being broad scales of broad scale measurements of microbial activity and while these uh there's a number of these measurements they're often difficult to interpret with the interpretation kind of coming down to more is better so today my talk will focus on a better understanding of the 24-hour potential carbon mineralization measurement which is one of the recommended measurements by the soil Health Institute some of y'all may be more familiar with the measurement is a CO2 burst or soil respiration but really what we're capturing in this burst is this microbial response to this re-wetting of an air dry sieve soil so when we re-wet this soil microwaves consume cells that were lice during drying others that consume cytoplasmic substances that were released during that re-wetting phase and then finally the consumption of organic residues that were released from that sieving procedure earlier on so what do we know about this measurement we know that this potential carbon mineralization measurement is generally greater in reduced tillage systems we know that it's moderately correlated with the size of the microbial community in the soil but we also know that tillage is capable of altering microbial community structure so in the literature we find significant and insignificant differences due to tillage on that microbial community structure but as others have mentioned you know different variations can cause some of these differences those being related to differences in Sample sampling time and processing differences between the tillage implements that were used in the studies and then the cropping system history has this tillage Implement only been introduced for two years or has it been there for 10 years and then finally looking at differences in inherent properties and climates can we compare a tillage system in New York to a tillage system here in Iowa so our goal today was to really try and Link changes in that microbial community structure from differences in tillage two differences in potentially mineralizable carbon across North America and to do that we have three objectives the first being we really need to Define differences on tillage and community structure across these different sites and then secondly we really wanted to see can we find similar community members that are enriched under no-till systems across these different climates and soil types and then finally we want to see if we can identify a set of organisms that are enriched in no-till and also influential in this 24-hour carbon mineralization measurement so to do that we utilize data collected as part of the North American project to evaluate soil Health measurements and this consisted of these 124 long-term research sites spread across Canada the US and Mexico and from those long-term sites we collected over 2 000 experimental units from paired treatments so whether that be care treatments looking at differences in tillage cover crops residue retention different management treatments like that all of the samples were collected for to uniform depth of 15 centimeters and then we collected quite a bit of management data along with each of those treatments today I'll be focusing on a subset of those sites though that had Direct tillage comparisons and also replicated treatments uh within those comparisons so uh those sites uh on the map on the right are uh distinguished by those white stars so you can see we had quite quite a range of different soils and climates that were included in this study uh the the tillage implements were kind of categorized we've categorized them into three different uh groupings the first being a minimum tillage and so those are going to be primarily your no-till or very undisturbed systems the moderate College category uh represents most of your kind of reduced tillage system so whether that be strip till or maybe in other every other year tillage uh and then the intense grouping is going to be your chisel plows your mold floor plows things that really disturb the system so once we had those groupings we kind of performed two different sets of analyzes the first one was we wanted to see where we were seeing differences in community structure between the treatments so a side-by-site basis and then secondly we wanted to look and see which organisms were enriched in these undisturbed soils so and then just kind of take a step back the two measurements that we are focusing on today are that first that 24-hour potentially mineralizable carbon which was performed on air dried two millimeter sieved soil and then secondly the uh the 16s rrna amplicon sequencing which was performed on eight millimeters of fresh soil and what it really does is it gives you kind of an idea of that bacterial and archaeal fingerprint within the soil profile now we'll get into some of the results so first I want to talk about what we saw when we compared community structure and minimum tillage systems versus intense tillage systems so we had 14 sites that we were able to compare and out of those 14 sets 11 had significantly different Community structures due to tillage kind of interesting the three sites that did not contain significant differences were all wheat based rotations and I will note that these sites represented different climates and soil properties so we don't think that that was the driving factor of why we weren't seeing significant differences the figure on the right on the slide is a detrended correspondence analysis which is similar to principal coordinate analyzes if you're more familiar with that type of figure but in essence what we're looking at is each each symbol here represents the treatment average microbial fingerprint for a given either intense tillage which is represented by a circle or a minimum tillage treatment which is represent in by a triangle and what you'll notice is in the figure we have different kind of groupings or Pockets or clumps of triangles and circles but when you look across the graph which is colored by soil PH we see that that is overall the main driving Factor but within those different groupings of pH we see these different clusters so the next question we wanted to ask was can we find these uh or excuse me can we find sequences that represent these different microbes that are enriched consistently across these different climates and soil types and it's pretty cool turns out that we can we found uh in our data set that there are six 717 different sequences that were enriched under minimum tillage when compared to intense tillage uh and so these these sequences on average represented about 33 of the microbes and minimum tillage and 16 of the microbes in intense tillage the figure on the right shows the relative abundance of these different sequences in each of the different treatments for each of the different sites and you'll notice uh the different cars colors on the bars and those represent the different phylum uh or broad scale groups of bacteria and Archaea and what you'll notice is it wasn't just one type of organism that was enriched under minimum tillage when compared to intense tillage but quite a few different types foreign so now to move on I'd like to touch on the kind of less intense tillage comparisons so these results were a little bit less clear than the comparisons uh with between intense and minimum tillage so only four out of seven sites uh when comparing minimum and moderate tillage contains significantly different Community structures and we saw pretty similar results when we compared moderate intense site comparisons with just about half containing significantly different Community structures so not as clear when we look at it like this but when we take these different sites and group them by pH we start to untangle a little bit of the mystery so what we saw was those sites that contained a site average pH between 5.8 and 6.5 we did not see significant differences in community structures but the sites that had a pH below 5.8 or above 6.5 did have significant differences in community structure so what we see here is that those inherent that inherent pH Factor may cause more resilience in microbial community structure at the slightly acidic pH and I'm moving on to that last piece of the puzzle here seeing if we can relate uh there's there's uh organisms that were enriched under no-till to these increases in carbon mineralization so we performed a machine learning model to identify those microbes that were influential in carbon mineralization measurements and so the figure that you see here contains the top 10 most important sequences in modeling carbon mineralization so you can see from the different colors of the bars in the graph that once again quite a few different organisms were influential in this carbon mineralization measurement uh you'll notice the green the dark green color appears quite a bit uh and that's the phylum proteobacteria and those overall contributed the most uh to the measurement and then if you take a look it might have to squint a little bit but the stars that appear above some of the different bars those represent sequences that where are not only important in modeling carbon mineralization but we're also enriched under minimum tillage so an overall 44 of those sequences uh that were important in the model were also enriched under minimum tillage and to take this kind of one step further we wanted to see uh out of those sequences that are really important if those ones that were enriched under minimum tillage are they correlated with increases in carbon mineralization and what we found was an overwhelming yes they were correlated they're small R squares but positively correlated and significant so where you see the plus signs over the bars now those different sequences were positively correlated with the measurement and those that have a negative symbol were negatively correlated with the carbon mineralization measurement so pretty interesting that we're seeing that those that were enriched under minimum tillage were also positively correlated on their own with the carbon mineralization measurement so what does this all kind of mean you know kind of break it down now so when we think about microbes that are associated with no-till and increases in this carbon mineralization measurement we kind of group them by different traits that they have so some of the traits that we found in the groups of these organisms were first that they were slow growing uh most were adapted to lower but consistent nutrient concentrations so what I mean by this is they're able to survive without extraneous inputs of uh inorganic fertilizers organic fertilizers biomass amendments they're able to prosper with what they have in the soil next a lot of them were able to produce extracellular polymeric substances commonly known as kind of microbial glues that help in soil aggregation and then finally the set of microbes many of them were able to obtain necessary nutrients for from the environment rather than building everything themselves in turn early so once again kind of being able to utilize what they have around them so in conclusion we saw that tillage intensities create measurable differences in microbial communities at the Continental scale that below ground wheat biomass May mimic that tillage residue incorporation uh so we think we want to dig into that more hopefully with maybe some of our partnering scientists but we did see that there is proof of similar enrichment across climates and soils we did note that the soils do matter a bit when the differences in disturbance are smaller to kind of recap it we didn't see significant differences in community structure in those slightly acidic PHS so those may be more resilient to physical disturbance and then finally the bacteria and Archaea and these no-till systems had adapted to prosper given their set of situations and this adaptation likely leads to these increases in carbon mineralization measurements and so with that I'd really like to thank all president and former Shi staff it's been such a great team to work with and then also our partnering scientists from all the different long-term sites this wouldn't have been possible without you and then finally the financial support of the Samuel Roberts Noble Foundation General Mills and the foundation for food and agriculture research thank you so much for that Liz I think we do have time for at least one question and then there are other questions in the chat so um I think one of the questions that I thought was interesting is uh your thoughts on whether these microbial molecular indicators will eventually substitute actual parameters that are measured in the lab in the future oh well I'll say personally I hope so as we're working on it now at the soil Health Institute but our goal isn't necessarily to substitute but to identify different indicators or indicators of functions that aren't necessarily captured right now so for instance uh indicators that relate to different ecosystem services like reductions in greenhouse gas emissions or reductions in nitrate losses some of those other functions that we're not capturing here with a set of measurements thank you so much for that and there are a few other questions so if you don't mind seeing if some of those you can answer as well then again encouraging folks to continue putting questions in the chat there's a lot of great stuff happening in the Q a thank you Dr Ricky thanks again Dr Adam