Guest post by Tyler Poppenwimer (University of Tennessee) and Michael Van Nuland (University of Tennessee)
A major challenge for predicting the strength and direction of plant-soil feedbacks (PSF) is understanding the interactions between plant traits, belowground communities, and the soil processes both compartments influence. A new article by Ke et al. (2015) uses a modeling approach to test how interactions between litter- and root-mediated pathways can influence which plant and soil biota traits are important mechanisms that drive PSF. Here, we will unpack their model in a simple way and touch on some of the interesting ecological and evolutionary implications. The model developed by Ke et al. attempts to capture the complexities involved in PSF by considering six major elements wherein each element interacts. Although highly complex, we can easily illustrate this model by following the movement of nitrogen through the system. In seedlings and adults, nitrogen is obtained through both a natural ability and mycorrhizal-mediated pathway. Both seedlings and adults then fix carbon in an amount proportional to their acquisition of nitrogen. Additionally, adults use nitrogen to produce seedlings and litter whilst seedlings use it to mature to adulthood. Plants do not survive indefinitely and eventually die via natural or pathogenic causes. Once deceased, they produce litter proportional to their carbon biomass. Once this biomass has been converted into litter, either through death or production thereof, it decomposes and nitrogen is deposited into the soil. Once in the soil, nitrogen can be acquired by mycorrhizae or plants and the cycle repeats. Although this is the main system, the model also includes the additional actions of competition between pathogens and mycorrhizae, mycorrhizal and pathogenic uptake of nutrients from the plants, light competition among adults and seedlings, and abiotic fluctuations of soil nitrogen. 
The model is necessarily complex – take a look at Figure 1 if you don’t believe us. This is so that it can realistically capture the interactions of major elements that structure PSF. Their use of differential equations wherein each element depends on the actions of the others greatly increases the realism of their model. For example, the production of seedlings depends on the adult’s uptake of nitrogen from the soil, which is (in part) dependent on mycorrhizae (whose actions can be influenced by pathogenic competition), where pathogenic competition is dependent on the amount of nitrogen obtained from deceased plants. Yeah, lots of moving parts. Further strengthening their model, they present a partially closed system where a majority of the nitrogen must be recycled in an efficient fashion to avoid breakdowns. Although strengthened by the aforementioned qualities, like all models it contains potential weaknesses. A potential weakness is their assumption that nitrogen is evenly distributed in the soil such that all plants have equal access to all nitrogen. Additionally, it is assumed that nitrogen uptake is not proportional to the biomass of the plant, it is only proportional to the life stage (seedling or adult). In spite of these shortcomings, the model is able to realistically capture the interactions of PSF, highlighting the role of soil microbes in plant-soil conditioning and the reciprocal effects of soils on plant survival. Plant phenotypes construct soil niches, which given phenotypic variation results in differing soil conditions that are the basis of PSF. Given this well-known fact, it is surprising that relating plant traits to PSF is relatively rare. Ke et al. show that it is high-time to incorporate plant traits if we are to boost our understanding when it comes to predicting feedbacks (Kardol et al. 2015). They illustrate the strength of this approach by correlating litter decomposability with PSF strength based on differing traits of the soil community (e.g., mycorrhizal or pathogen heavy). It might also prove useful to explore how the other plant traits considered in their model would relate to PSF strength. For instance, how might the pattern change if plant defense traits (tissue C:N or pathogen mortality rate) were used to predict PSF strength from similar literature data? It might be expected (as their model suggests) that defense traits would be more important for determining PSF resulting from pathogens or mycorrhizae (Figure 2). Similarly, as the authors point out, simplifying mycorrhizae down to a positive effect on plants goes against biological realism a bit, since root-associated fungi function along a parasitism-mutualism continuum (Johnson et al. 1997). It might be interesting to somehow program that for similar models in the future, whereby plant trait variation would alter fungal traits along a negative-positive scale, thereby affecting PSF outcomes. 
The authors predict that invaders with high litter decomposability perform better with increasing mycorrhizal abundance. However, this might not be universal – especially when we consider the example of invasive pines, which have tough leaves and low litter decomposability. In areas where pines have been introduced and spread quickly, it has been shown that the invasion is dependent on the presence of mycorrhizal associations (i.e., a change in relative abundance of mycorrhizae from 0 to 1+; Dickie et al. 2010). What we think is an important take-away though, is that plant-microbe interactions (mediated by traits) are increasingly being recognized as having strong implications for species range dynamics – both from an experimental and now from a modeling standpoint. There are some interesting evolutionary implications from the paper that we won’t discuss in detail here, but are worthwhile to ponder (and discuss in the comments section!). For example, if plant litter chemistry is heritable, then the fact that root-associated communities dictate the trait effect on PSF enters into the realm of quantitative and population genetics. In this sense, the Ke et al. findings are consistent with previous work on the mechanistic basis of plant-soil linkages that result in evolutionary interactions (Kylafis and Loreau 2008, Schweitzer et al. 2014), but add a new level of specificity and complexity by avoiding the familiar black-box perspective to tackle some of the major soil biotic interactions. Overall, we thought this was an excellent paper from multiple standpoints and would love to hear your thoughts! References Dickie IA et al. (2010) Co‐invasion by Pinus and its mycorrhizal fungi. New Phytologist 187: 475-484. Johnson NC et al. (1997) Functioning of mycorrhizal associations along the mutualism–parasitism continuum*. New Phytologist 135: 575-585. Kardol P et al. (2015) Peeking into the black box: a trait-based approach to predicting plant-soil feedback. New phytologist 206: 1-4. Ke P-J et al. (2015) The soil microbial community predicts the importance of plant traits in plant–soil feedback. New Phytologist 206: 329-341. Kylafis G & Loreau M (2008) Ecological and evolutionary consequences of niche construction for its agent. Ecology letters 11: 1072-81. Schweitzer JA et al. (2014) Are there evolutionary consequences of plant–soil feedbacks along soil gradients? Functional Ecology 28: 55-64.
