Introduction

One of the foremost theories on distribution of species on earth is the habitat heterogeneity hypothesis. There is an overwhelming body of evidence that supports the idea that more heterogeneous habitats can support more species diversity. Habitat heterogeneity, or small-scale changes in resource composition and structural complexity, provides more possible niche space for organisms to occupy and exploit (Tews et al, 2004). Some factors that determine heterogeneity may include rain, the amount of vegetation in an area, soil type and others. The small-scale changes create locally partitioned areas (Cramer and Willig, 2005). With more niche space, there are more levels of possible resources for locally adapted species to use (Allouche, 2012). This means that there are more niches available in a smaller area, which makes the area larger ecolog­ically but not spatially (Davidowitz and Rosenzweig, 1998). In biogeog­raphy, this theory is used to explain community assemblages, diversity gradients and many other observed phenomena. The idea of habitat heterogeneity can explain why some regions experience faster turnover and beta diversity (Cramer and Willig, 2005). The habitat heterogeneity hypothesis is seen repeatedly as ecological space and niches special­ization help determine where species can live (Knope, 2015). Habitat heterogeneity conceptualized as an extension of niche space will support higher species richness, or more different species in the same area as a less heterogeneous habitat (Zhiyong et al, 2015). Species richness is a commonly used measure of diversity in studies and surveys that quantifies simply the number of species in an area, and is important for exploring local saturation therefore making species richness a logical measure to look at habitat heterogeneity (Gotelli and Colwell, 2001).

Earth has latitudinal diversity gradients that explain overall di­versity, and many hypotheses attempt to explain the mechanisms behind these gradients (Pianka, 1966). The trend in diversity across the planet holds that many measures of diversity decline away from the equator. While humans are not planets and have no equator, there is growing research that claims there may be diversity gradients and community assemblage rules that help determine what species of microbes live where on the human planet. Humans have many different microbiota, or communities of microbes living on and in their bodies (Kong, 2011). These microbiota are beneficial in digestion, excretion, protection, and many other functions (Kong and Segre, 2012). One of the most extensive microbiota in the human body is the skin microbiota. Defined as the entire collection of microbes (bacteria, archaea, fungi, viruses, and mites) that reside in and on human skin, the skin microbiota has immense diversity. The skin contains over 1 million bacteria per square centimeter (Chen 2013). These bacteria play many roles in human health, from protecting the body against invading microbes to causing health issues such as acne, the skin microbiota is a diverse group of microbes that has many functions (Kong and Segre 2012).

Just as the planet has diversity rules and gradients that help determine what species can live where, there are commonly accepted ideas about the diversity makeup of the skin microbiome. There are indi­vidual differences in microbiota, these differences are due to a variety of factors such as sex, age, interaction with other people, and endogenous host factors (Costello et al, 2009). There are also factors general to most humans that can affect microbial diversity, such as location of the area on the body and temperature (Costello et al, 2009). However, these distribu­tions can change due to disturbances such as showering or structurally different habitats such as more hair (Schommer and Gallo, 2013). The structural changes experienced by the microbes on human skin are anal­ogous to heterogeneous habitats and represent changing niches (Grice and Segre 2011). Structural differences may be one of the most deter­minate factors in the organization of the skin microbiome, and the most common structural difference is the presence of hair. At the base of each strand of hair there is a follicle that can be full of sebum or oil, creating dif­fering moisture levels and different available resources (Grice and Segre 2011). This habitat heterogeneity creates more available niches for the microbes to fill and often areas with a higher concentration of hair follicles may have more species richness than low hair areas (Capone 2011). The changing habitats of the human body may be analogous to the differing levels of habitat heterogeneity that affect the planetary species richness.

We predict that the richness of morphospecies found in human hair (above ear/hairline) vs non-hairy skin areas (inside of elbow) will be sta­tistically significantly different. The richness in hairy areas will be higher than in non-hairy areas due to the sebaceous excretions. The differences between hair and non-hair can parallel the differences of habitat hetero­geneity with different niche spaces and ecological resource availability. We predict that the hairier area will have higher morphospecies richness and diversity due to the increased habitat heterogeneity of these areas caused by hair follicles and an abundance of sebum. We also predict that communities that are the most different in hair density will be the least similar in species composition due to environmental differences. We will test these predictions by sampling skin microbiomes and comparing the species richness.

Methods

We randomly selected and sampled 20 male, brown-haired college students in the Donnelley and Lee Library of Lake Forest College in Lake Forest, IL. We thus controlled for sex and age of participant be­cause both are known to affect human microbiome composition (Kong & Segre, 2012). Each participant was swabbed with a sterile Q-tip at three cutaneous locations along a hair density gradient: 1) just behind the ear at the hairline (highest hair density) 2) the outer shin approximately 10 cm from the ankle (medium hair density), and 3) the inner forearm three cm below the elbow (lowest hair density). Each location and individual was swabbed using medium pressure for 3 seconds back and forth across a line approximately 3 cm long. Each participant was also asked how many hours ago they had last showered and we recorded their answer. We recorded each sample with a unique identifier for the participant and the location site. Arm and leg site was photographed using a smartphone camera with a ruler for reference (Fig.1). The number of hair follicles with­in a 3 cm by 3 cm square for both arm and leg sites were then counted. Due to poor depth resolution of the images, hair strands that were within the square were assumed to originate from follicles in that area.

Figure1. Example image of shin hair quantification.

 

 The swab was then submerged and swirled in PBS. To plate the samples, they were first diluted with PBS to a 1:20 dilution. The dilut­ed sample was mixed well and 50 ul of solution was pipetted onto an agar plate. 5-7 glass beads were added and the plate was swirled vigorously for 10 seconds. These samples were plated no more than an hour after collection. The plates were then left in the incubator at 36ºC for 72 hours at which point growth was stopped by placing the inoculated dishes in a refrigerator. The dishes were removed from the refrigerator and examined in order to determine morphospecies richness, abundance, and diversity. Morphospecies were determined to be relatively reliable proxies for taxonomic unit species (Derraik, et al., 2010). To categorize morphospecies, the form, margin, size, color, and elevation of each colony were recorded, and 16 distinct morphospecies were identified (Table SI. 1). Each mor­phospecies was then given a unique name.

The abundance (total number of colonies for each morphospe­cies) and richness (number of morphospecies) of each plate were record­ed. Then, the Jaccard similarity index, which measures the proportion of species shared between two sites relative to the total number of species of both sites, was calculated between each site for each individual, eg: between the shin and the forearm site of one person, using the formula described in Muellenberg-Dombois & Ellenberg (1974). Then, the three indices calculated for each person were then compiled and an ANOVA was used to test whether the hairline and forearm sites were less similar within individuals than the similarity between the forearm and the shin and the shin and the hairline. The morphospecies diversity of each sample was also calculated in Excel using the Shannon-Wiener diversity index, which incorporates both richness and abundance to measure the evenness of a community as described in Spellerberg and Fedor (2003). To determine whether morphospecies richness and/or diversity was pre­dicted by sampling location site, we used ANCOVA tests with the number of hours since they last showered added to the model as a covariate to control for changes in microbial presence due to washing (Kong & Segre, 2012). We then used multiple linear regression to test whether the hours since the last shower or the hair density for the arm or leg predicted the richness or diversity of morphospecies within those sites. All statistical tests were performed in R Version 3.3.1 and all figures were drawn in Microsoft Excel 2016.

Results

Richness

When tested with an ANCOVA, sampling location was not a significant factor in predicting morphospecies richness (F2,54=0.573, p=0.565, Fig. 2). A linear regression of the number of hours since the last shower taken by the participant also failed to predict species richness at any site (R²=.03, p=0.088200, Fig. 3). Linear regression of hair density for both the forearm (Fig. 4) and shin (Fig. 5) revealed that it did not pre­dict species richness for either site (R²=0.061, p=0.1063 and R²=0.024, p= 0.381).

Figure 2. Average morphospecies richness for each sampling location. Error bars represent standard error.

 

Figure 3. Morphospecies Richness vs. hours since last shower for each sampling location.

 

Figure 4. Morphospecies Richness vs. Hair density for the forearm

 

Figure 5. Morphospecies Richness vs. Hair density for the shin

 

Diversity

The ANCOVA did not reveal any significant difference in morphospecies diversity between sampling locations of different hair densities (F2,56=0.565, p=0.572, Fig. 6), and the number of hours since showering also had no effect (R²=0.026, p=0.116, Fig.7). The multiple linear regression models for arm hair density and for leg hair density (both with hours since shower as an additional factor) were both nonsignificant (arm site: R²=0.1414, p=0.106, Fig. 8; leg site: R²=0.002, p=0.381, Fig. 9).

Figure 6. Average Shannon-Weaver Diversity Index for each sampling location. Error bars represent standard error.

 

Figure 7. Shannon-Weaver Diversity Index vs. Hours since last shower

 

Figure 8. Shannon-Weaver diversity index vs. leg hair density

 

Figure 9. Shannon-Weaver diversity index vs. Forearm hair density

 

Identity and frequency of occurrence

A bar chart was made comparing morphospecies identity with frequency, or number of times the morphospecies was observed for each site (Fig. 10). For all three sampling sites, Round White Ones were the most frequently observed, with 12 observations in the head and shin locations and 8 observations in forearm samples. Several morphospecies were found in only one or two sites. In general, the frequency of observa­tion from the forearm was similar to the patterns of frequency seen from the head location but the shin sampling site seemed to be more unique.

Figure 10. Morphospecies Identity vs. frequency showing the number of times the morphospecies appeared on an inoculated dish after incubation.

 

Similarity

The ANOVA between the Jaccard indices, as calculated between each pair of sites within each individual, revealed no significant difference in the similarity between the hairline and forearm sampling site, which was predicted to be the least similar), and the similarity between the hairline and shin site and similarity between the forearm and shin sites (F2,57=0.566, p-0.571, Fig. 11). Therefore, no pair of sites sampled was more or less similar in morphospecies composition than any other pair of sites from the same individual.

Figure 11. Jaccard Similarity index for each sampling site. Error bars represent standard error.

 

Discussion

We first looked at the overall morphospecies richness between locations, and found no significant differences in richness between sampling sites. Our results do not support our hypothesis that there will be higher morphospecies richness in the hairier sites as there was no correlation between number of hairs in a 3 cm square and morphospecies richness. Testing the hypothesis on Shannon-Weiner diversity indices did not yield any different results and also failed to support our hypothesis, both overall and when accounting for hair. Including hours since last shower in the model did not improve the results. These results may be due to the fact that microbes on human skin have few if any barriers to their dispersal across human bodies and the skin microbiome experienc­es constant gene flow (Costello, et al., 2012) This constant gene flow may ensure that there are few species that go extinct once they have colo­nized an area due the availability of new genetic information and new in­dividuals (Ellstrand and Rieseberg, 2016). While there is apparent niche selection among human microbiota, this view of the microbiome does not account for why intrapersonal differences in microbiomes are not as im­portant or impactful as interpersonal variation (Grice et al., 2008). These results may have implications that habitat heterogeneity does not impact the human skin microbiome like changing habitats affect animal diversity.

Looking at frequency of morphospecies occurrence, RWO were the most commonly seen morphospecies, which may indicate that RWO is the best dispersalist or the most generalist of the identified morphospecies. Additionally, the observation of the forearm having more unique species may be seen because the forearm is more exposed to a variety of surfaces in everyday activities. The shin may not have closely followed the general pattern seen in the head and forearm locations due to distance from both other sites and may indicate distance degrada­tion. Distance decay states that the further away two sites are, the more differences there are between their community makeup species turnover increases (Nekola and White, 1999). The differing patterns in the shin may indicate the occurrence of distance decay in the skin microbiome but testing this would require more accurate species identification.

The nonsignificant results obtained by the ANOVA between Jaccard similarity measures indicate that there is no turnover between sites and beta diversity remains relatively stable within the human micro­biome. While there were different patterns of species frequency seen in the shin, this difference was not seen overall and there were no differ­ences when comparing the Jaccard similarity between any sites. Overall, the lowest Jaccard result was between the shin and the head and the most similar sites were the shin and forearm, but neither similarity was significantly different from the shin or arm being compared to the head location. This may be attributed to the limitation in size of both our sample and the human body. Complete turnover may require a larger area (Leg­endre, Borcard, and Peres-Neto, 2005) or it may be that alpha diversity is playing a larger role, although that seems unlikely (Harrison, Ross, and Lawton, 1992).

In general, it seems that habitat heterogeneity does not play a large role in skin microbiota diversity, although the habitats we chose may not have exhibited differing levels of heterogeneity as we assumed. Additionally, our non-significant results may be due to issues with our methods. It is believed that the swab method we employed does not provide accurate representations of microbial diversity (Favero et al., 1968). An additional issue our study may have exhibited are poor classification standards; using enterotubes, staining techniques, and other microbiota identification methods may have improved the accuracy of our study. Notably, some researchers have posited that microbes that live in sebum and hair follicles, our habitat changes, may be anaerobic and so would not have grown on our sample plates as we incubated the plates in atmospheric oxygen conditions (Kong and Segre, 2012). Additionally, if we were to repeat this study, we would sample individuals from a wider geographic range as all students sampled are residents of Lake Forest College and may have microbes local to the college or town (Kong, 2011). In the future, we would like to continue to look at the skin microbiome and apply biogeographic methods to it, potentially testing theories of island biogeography using cell phones or personal computers as islands. Diver­sity gradients, community assembly, and niche exploitation are important concepts when studying the human microbiome and more research needs to be done on these topics to discover assembly rules for skin microbiome.

References

Allouche, O., Kalyuzhny, M., Moreno-Rueda, G., Pizarro, M., & Kadmon, R. (2012). Area– heterogeneity tradeoff and the diversity of ecological communities. Proceedings of the National Academy of Sciences of the United States of America, 109(43), 17495– 17500.

Capone, K. A., Dowd, S. E., Stamatas, G. N., & Nikolovski, J. (2011). Diversity of the human skin microbiome early in life. Journal of Investigative Dermatology, 131(10), 2026-2032.

Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J., & Relman, D. A. (2012). The application of ecological theory toward an understanding of the human microbiome. Science, 336(6086), 1255-1262.

Cramer, M. J., & Willig, M. R. (2005). Habitat heterogeneity, species diversity and null models. Oikos, 108(2), 209-218.

Currie, D. J. (1991). Energy and large-scale patterns of animal-and plant-species richness. The American Naturalist, 137(1), 27-49.

Davidowitz, G., & Rosenzweig, M. L. (1998). The latitudinal gradient of species diversity among North American grasshoppers (Acrid­idae) within a single habitat: a test of the spatial heterogeneity hypothesis. Journal of Biogeography, 25(3), 553-560.

Ellstrand, N. C., & Rieseberg, L. H. (2016). When gene flow really mat­ters: gene flow in applied evolutionary biology. Evolutionary applications, 9(7), 833-836.

Favero, M. S., McDade, J. J., Robertsen, J. A., Hoffman, R. K., & Edwards, R. W. (1968). Microbiological sampling of surfaces. Journal of Applied Bacteriology, 31(3), 336-343.

Gotelli, N. J., & Colwell, R. K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology letters, 4(4), 379-391.

Grice, E. A., Kong, H. H., Renaud, G., Young, A. C., Bouffard, G. G., Blakesley, R. W., … & Segre, J. A. (2008). A diversity profile of the human skin microbiota. Genome research, 18(7), 1043- 1050.

Grice, E. A., & Segre, J. A. (2011). The skin microbiome. Nature Reviews Microbiology, 9(4), 244-253.

Harrison, S., Ross, S. J., & Lawton, J. H. (1992). Beta diversity on geographic gradients in Britain. Journal of Animal Ecology, 151-158.

Kong, H. H. (2011). Skin microbiome: genomics-based insights into the diversity and role of skin microbes. Trends in molecular medi­cine, 17(6), 320-328.

Kong, H. H., & Segre, J. A. (2012). Skin microbiome: looking back to move forward. Journal of Investigative Dermatology, 132(3), 933-939.

Knope, M. L., Heim, N. A., Frishkoff, L. O., & Payne, J. L. (2015). Limited role of functional differentiation in early diversification of ani­mals. Nature communications, 6.

Legendre, P., Borcard, D., & Peres-Neto, P. R. (2005). Analyzing beta diversity: partitioning the spatial variation of community compo­sition data. Ecological Monographs, 75(4), 435- 450.

Mueller-Dombois, D. & Ellenberg, H. (1974) Aims and methods of vegeta­tion ecology. John Wiley and Sons, New York.

Nekola, J. C., & White, P. S. (1999). The distance decay of similarity in biogeography and ecology. Journal of Biogeography, 26(4), 867-878.

Pianka, E. R. (1966). Latitudinal gradients in species diversity: a review of concepts. The American Naturalist, 100(910), 33-46.

Schommer, N. N., & Gallo, R. L. (2013). Structure and function of the hu­man skin microbiome. Trends in microbiology, 21(12), 660-668.

Spellerberg, I. F., & Fedor, P. J. (2003). A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’ Index. Global ecology and biogeography, 12(3), 177-179.

Tews, J., Brose, U., Grimm, V., Tielbörger, K., Wichmann, M. C., Schwa­ger, M., & Jeltsch, F. (2004). Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. Journal of biogeography, 31(1), 79-92.

Yang, Z., Liu, X., Zhou, M., Ai, D., Wang, G., Wang, Y., … & Lundholm, J. T. (2015). The effect of environmental heterogeneity on species richness depends on community position along the environ­mental gradient. Scientific reports, 5.

Supplemental Information

Table SI.1. Table Showing classification of morphospecies.

Related Links:

• 135-138_Broeker_PrimaryArticle (PDF)