Increasing agricultural production while also preserving biodiversity and ecosystem services is difficult and likely only possible with an understanding of the spatial variation of soil communities as pertains to soil health. A spatial biological indicator of soil biodiversity could provide an important link between soil condition, biodiversity, and resultant ecosystem services, but such an indicator is currently lacking. Soil microarthropods such as springtails (Collembola) are extremely common and dense in the upper soil (up to hundreds of thousands per square meter) and are as much a part of the soil as they are in contact with it. They are therefore likely to serve as an excellent biological indicator of soil conditions. This project will sample the Collembola communities in several land use types, including two types of agriculture: “conventional” and “agroecological.” The composition of Collembolan communities will be used to quantify the link between these arthropods and indicators of soil nutrition, including carbon and nitrogen pools, with the aim of developing a Collembolan-based biotic index of soil health. The spatial variation in soil conditions and in Collembola communities will be mapped within sampled fields and made available to participating farmers. In doing so, this project will lay the basic groundwork for future applications of a bioindicator and a biological liaison of soil health.
The project ultimately aims to develop an efficacious biotic index based on springtail communities. Similar to pollution indices based on aquatic invertebrates, the presence or absence of Collembola, certain genera therein, or how many there are (abundance) should be able to rapidly indicate the overall nutritional state of the environment (in this case, the top soil). Additionally, we intend to cross-validate such an indicator with spatial statistics, in order to assess how well-matched soil conditions are to Collembolan community metrics over space and time. With this being of necessary concern and interest to farmers, we plan to continue engaging Indiana farmers for the development of such an index, at no expense to the farmers other than in allowing us to sample from their fields. In the process, we hope to not only inform involved farmers of the state of both their soils and soil biodiversity, but we will also:
1.) Use this as an opportunity to educate about cryptic soil biodiversity, with Collembola as our model arthropod, garnering interest in the prospective bioindicator by recruiting further collaboration for the project.
2.) Continue the tradition of both NCR-SARE and Purdue Extension in developing and encouraging sustainable and innovative methods of agricultural monitoring among local farmers.
Field study sites for the overall dissertation project fall under five different land-use categories located within Indiana, chosen both for their assumed ability to best represent the dominant landscapes that can be encountered in the state’s geopolitical boundaries, and on a gradient of increasing land-management “intensity:” secondary-growth forest, clearcuts or successional forest shrub, restored native prairie, agroecological farmland, and conventional farmland. Each of these land-use categories were surveyed with three transects to generate a representative picture of Collembolan communities and soil dynamics within each, for a total of 15 transects per sampling year. However, due to SARE’s interest in agricultural pursuits, the grant provided funding only for the two agricultural landscapes (which constituted 6 of the 15 transects).
It was decided that a conventional farmland or field was defined as those under monocultural crop production, utilizing conventional pest management strategies, and, for ease of comparison, all fields in this category grew either corn, soy, or a corn-soy rotation. An agroecological farm was qualified as being either USDA-certified organic or otherwise employing alternative agricultural techniques or practices associated with “sustainable” or “traditional” agricultural production, such as utilizing permaculture-type practices or cultivating diverse, polycultural vegetable crops. Six different fields across five different farms (with two of the fields being under the same management) were surveyed in this study, with each field containing one of the three transects per either the agroecological or conventional category. All of these are located in counties across northern Indiana. The agroecological farms included in the study are, briefly, as follows: an USDA-certified organic corn-soy monoculture (coded as SHO in the data), and two, small-operation polycultural farms, Perkins’ Good Earth Farm (coded PC) and Lanes’ End Farm (coded LAN), respectively, either of which grow a variety of specialty or heirloom vegetable and fruit varieties. The conventional cropland included two monocultural fields under a corn-soy rotation (SHC and WRN), and one monocultural field of continuous soy (coded LNG). All of these farms are located in counties across northwestern Indiana.
Two sampling years have so far been conducted, with the first year occurring in the summer of 2017 and the second in the summer of 2019. The second sampling year (2019) was limited to only the two agricultural land-use sites, as per the stipulations of the grant. Both years made use of the same transect sampling method, utilizing a novel grid-like transect in the form of a partial-Fibonacci spiral (Figure 1). This spiral conformation was selected as being best suited for the spatial component of our project, given that it eliminates directional bias, maintains good coverage, and maximizes intersample distance variation (Fortin and Dale, 2005). Each spiral had 20 sampling points, ranging approximately 80 meters in diameter at the furthest points of the spiral. This distant was selected for as ideal both for the golden ratio calculations placing the five core points of the spiral at distances similar to those found on typical grid or line transect sampling for Collembola (approximately 3 to 5 meters apart), while also not exceeding the areas of our smallest landscape units (our small-scale production polycultural farms). All of our transects were randomly placed within the research areas by selecting random coordinates in R.
At each point of the transects, one large soil core (approximately 13 cm wide x 20 cm deep) and two to three smaller cores (3 cm wide by 20 cm deep) were taken within an approximately one meter-squared area, centering around the large core. The smaller cores were subsequently pooled to average the soil properties within the given area. This resulted in a total of 120 large cores and 120 small cores (after pooling) collected during either sampling year for our agricultural land-use sites, totaling 240 large and 240 small cores for both years of sampling. The larger core is dedicated to the extraction of Collembola, either via a sugar flotation procedure modified from Snider and Snider (1997) following soil-washing as for the 2017 sample collection, or, as for the 2019 samples, using a Tullgren funnel extraction method. A morphological identification system was decided upon for Collembolan identification, first to the superfamily level and then to the tribe or genus, if necessary, to obtain good taxonomic resolution of the sampled areas while avoiding time-consuming species-level identifications.
The pooled, smaller cores were devoted to chemical and physical analyses of the soil properties. Chemical measurements to determine some of the most pertinent physical and organic properties of the soil included: percent soil moisture content (taken on site); pH; soil texture; labile carbon (POXC using infrared spectroscopy); and an inorganic nitrogen measurement (using a SEAL analytic machine). Taken together, the data obtained on these soil properties and Collembolan community metrics is intended to be used quantify any relationships that exist between them using nonlinear regression and redundancy analysis, whereas an asymmetric bioindicator procedure (IndVal) will then be used to examine the specificity and fidelity of the biota to these conditions. Additionally, variography and kriging will be used via R and QGIS to examine the spatial extent over which both the Collembolans and the soil conditions vary, allowing us to project how Collembolan communities change across space within these landscapes, and how well matched the biota and conditions are to assess how suitable the biotic index is for describing both intra- and inter-site variation across our different land-use categories.
To date, all 120 of the Collembola cores collected from the 2017 sampling season have been washed, floated via sugar flotation, and sorted to superfamily/family. The following taxa were represented in the data: Onychiruidae, Entomobryidae, Isotomidae, Sminthuridae, and Hypogastruridae. Another category of “unidentified” was used to indicate Collembolans which could be identified as such but which were too damaged to initially determine their family under a dissection microscope. Two replicates per sample are currently being run for the inorganic nitrogen extraction assay, with all but 20 run to completion and the data returned for the year of 2017, while the labile carbon analysis and pH tests are currently underway. The soil texture assay has yet to be performed, while the Collembolan cores collected last summer (2019) are presently being sorted to superfamily/family for a second year of data to compare, spatial and temporally, to the first.
While we still have yet to compile data on all of the soil condition variables, we are presently performing summary statistics and preliminary spatial analyses on the family-level taxonomic data collected for 2017. Only two out of 120 data points returned no Collembola specimen, and these were excluded from our analyses. A Welch’s T-Test was first performed for both Collembolan diversity (calculated using the Shannon diversity index) and abundance, comparing the agroecological specimens against those found in the conventional fields. On average, the agroecological sites had slightly higher diversity, with a mean score of 0.647 versus 0.503 in the conventional sites, though this was just short of statistical significance (t = 1.9126, df = 111.44, p-value = 0.05837). Because we are at such a high taxonomic level, however, we take this as a promising sign that identification of the specimens down to genus as originally planned will reveal how diverse these communities are from one another. In terms of abundance, the average for all agroecological sites was 65.78, whereas the conventional average was 26.33, with the t-test indicating a significant effect (t = 2.6404, df = 65.406, p-value = 0.01034). When represented visually, as in Figures 2 and 3 below, we can see that all of the agroecological sites (coded as LAN, PC, and SHO) have higher abundances than two of the three conventional sites (coded LNG and SHC).
As to the remaining conventional site (WRN), it demonstrated a higher or similar average abundance to two of the three agroecological sites (LAN and SHO), and, on its own, accounted for most of the total abundance for the conventional sites, given that both LNG and SHC often had few Collembolans per sampling point (Figure 4). Similarly, one of the agroecological farm sites (coded PC) contributed to most of the total abundance for the agroecological land-use category, also shown in Figure 4.
How these two sites (PC and WRN) differed from the other two selected farmlands in their category is still being explored, given that the dataset is still being compiled. It is possible, based on what we have observed and tested so far, that the higher nitrite/nitrate concentrations found in the soil samples from these divergent sites may play a role or otherwise indicate some quality of the soil conducive to higher Collembolan abundance. Figure 5 displays a site-by-site bar chart of the nitrite/nitrate concentration (mg N/L), including the first replicate only given that the second replicate has not been completed. It can be seen based on this preliminary data that WRN has a higher average nitrite/nitrate concentration as compared to the other two conventional farms, and it is within a comparable range to two of the three agroecological farms (LAN and PC). An analysis into the intra- and inter-site variation in these dynamics, as well as cross-listing with qualitative notes on each site’s cultivation history, should yield a more complete explanation for such peculiarities.
Because our Collembolan specimens are presently sorted to only the family level, we have not as yet been able to gain a “high resolution” picture of the site’s diversity and differences between them. An NMDS analysis using Bray-Curtis dissimilarity that was run on the family level data, for instance, demonstrated significant overlap between the two agricultural types, as each “dimension” in the analysis was a Collembolan family that generally had representation in either kind of field, even though abundances varied. This reflected the results of the t-test that indicated non-significance for diversity between the two types. We are currently conducting spatial statistics using variography and kriging to visualize the distribution of the Collembola in relation to soil physico-chemical properties for the 2017 data, though we anticipate, similar to the NMDS results, that a lower level of identification will be needed to discern strong relationships within each site in regards to the Collembola taxa present and the surrounding soil.
Educational & Outreach Activities
Due to the ongoing processing and compilation of data for the project, journal publications and Purdue Extension press releases or documents are still pending. One brief, on-site talk was given to interns during a visit to one of the agroecological farm sites participating in the project (Perkins’ Good Earth Farm) in the summer of 2019, however, in which the project’s purpose, its details, and its desired outcomes were discussed. A manuscript based on the spatial distribution of the Collembola is currently being drafted, with a possible submission date pending the completion of family-level identification and sorting of the data collected during 2019.