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# Invasions eliminate the legacy effects of substrate history on microbial nitrogen cycling

Elsa Abs, Michaeline B N Albright, Steven D Allison

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Elsa Abs, Michaeline B N Albright, Steven D Allison. Invasions eliminate the legacy effects of substrate history on microbial nitrogen cycling. Ecosphere, 2024, 15 (1), 10.1002/ecs2.4754. hal-04417852

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# Invasions eliminate the legacy effects of substrate history on microbial nitrogen cycling

Elsa Abs<fnref n="1" /> | Michaeline B. N. Albright<fnref n="2" /> | Steven D. Allison<fnref n="1" />,<fnref n="3" />

**Correspondence**

Elsa Abs

Email: eabs@uci.edu

**Funding information**

US Department of Energy, Office of Science, BER, Grant/Award Number: DE-SC0020382; Los Alamos National Laboratory, Grant/Award Numbers: 20180746PRD3, 563732; H2020 Marie Skłodowska-Curie Actions, Grant/Award Number: 891576; U.S. Department of Energy Office of Biological and Environmental Research, Grant/Award Number: 2018LANLF255

**Handling Editor:** Michael C. Duniway

**Abstract**

Changes in substrate quality driven by climate, land use, or other forms of global change may represent a strong selective force on microbial communities. Invasion of new taxa into a community through dispersal, evolution, or recolonization could impact the outcome of this environmental selection. Here, we simulated substrate change with a trait-based model of microbial litter decomposition (DEMENTpy) to assess the legacy effects of past substrate quality and the impact of selection by a new substrate on community decomposition activity. Simulations were run with different levels of invasion, including invasion from communities long-adapted to the new substrate. Legacy effects were evident with substrate change for native communities differing in composition. Protein was the only substrate that exerted a strong enough selective force to affect community composition. Legacy effects disappeared when invaders came from substrates similar to the new substrate. Together, our simulations demonstrate that substrate quality changes associated with global change can lead to legacy effects on substrate degradation. In decomposing plant litter, such legacy effects can occur if substrate inputs shift to higher protein content and if invasion is low.

**KEYWORDS**

biogeochemistry, environmental microbiology, global change, invasion, legacy effect, nitrogen cycling, soil ecology

## INTRODUCTION

Global change may cause substantial shifts in plant composition (Franklin et al., 2016). Aboveground vegetation diversity strongly influences soil microbial community composition and organic matter decay rates, largely through effects on litter quality (Zak et al., 2003). However, it remains unclear how microbial communities will respond to these changes in litter quality or how these responses will, in turn, affect soil carbon and

nutrient cycling. Invasion of new microbial diversity through local movement, dispersal, or de novo evolution may play a role in the resilience and adaptation of microbial communities to litter change (Shade et al., 2012).

Invasion via dispersal, defined here as the random introduction of microbial individuals into a community, has been studied in ecological contexts, primarily with a focus on its role as a mediator of microbiome assembly (Evans et al., 2017; Walters & Martiny, 2020). Invading microbes can represent up to 60% of the biomass of a

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Authors. Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of America.

<footnote n="1">¹Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA</footnote>

<footnote n="2">²BioScience Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA</footnote>

<footnote n="3">³Department of Earth System Science, University of California, Irvine, California, USA</footnote>
community and can consist of individuals from nearby to thousands of kilometers away (Van der Gucht et al., 2007). The effect of such invasion on microbiome function, however, is less well understood. Albright et al. (2020) found that the identities of resident community members and distant invaders were stronger determinants of community function (respiration, substrate degradation) than the invasion quantity and frequency. Invaders from afar can facilitate an influx of new taxa, introducing functions that are absent in the resident community and that enhance carbon and nutrient cycling.

Few studies, however, have explored the impact of invasion on microbiome function in the context of environmental change. Empirical studies employing transplant-based climate change simulations have found that microbial communities and functions may be determined by both the past climate, owing to legacy effects, and the new climate owing to contemporary environmental selection (Glassman et al., 2018). In a modeling study, Wang and Allison (2021) were able to reproduce these climate-driven legacy effects on community composition and soil organic carbon (SOC) decomposition. They found that local invasion removed the legacy effects of drought on soil organic decay even in cases of severe drought. In their experiment, allowing taxa to recolonize after drought prevented extinction and loss of function. Both of these studies were performed with single substrates (grassland litter), highlighting the need for further studies of substrate legacies.

Many studies have shown that substrate chemistry shapes its decomposition (Chapin et al., 2011; Schnecker et al., 2019). More recent work has demonstrated that past exposure to substrates can influence current substrate decomposition. Strickland et al. (2009) conducted a common garden experiment using microbial communities collected from sites with different aboveground vegetation and tested each community's ability to decompose substrates from each of the sites. They found that past substrate makeup determined microbial community composition and that decomposition was greater when microbes encountered the same substrate again. Given that global models already integrate the dynamics of aboveground vegetation, we aim to increase the understanding of microbial functioning in relation to litter substrate changes. This knowledge could be useful for the next generation of Earth system models.

To investigate the effects of invasion on microbial communities experiencing substrate change, we used the Decomposition Model of Enzymatic Traits (DEMENT) model of litter decomposition by microbes (Allison et al., 2010; Wang & Allison, 2021). This mechanistic numerical model represents the driving processes at play in the microbial decomposition of organic matter. As the

composition of a microbial community is an important determinant of its function, DEMENT includes diverse taxa with distinct traits, enzymes with distinct production costs and activity kinetics, and 12 substrate components that differ in their carbon (C), nitrogen (N), and phosphorus (P) ratios, including hemicellulose, lignin, and nucleic acids.

Although little is known about the impact of invasion on microbial function in the context of changes in litter quality, we formulated hypotheses based on previous microbial transplant experiments (Albright et al., 2020; Wang & Allison, 2021). We hypothesized that differences in litter substrate chemistry would select for distinct communities that have discrete functions. We further hypothesized that differences in community composition would lead to legacy effects on substrate degradation when those communities are transplanted onto a new litter substrate. Finally, we hypothesized that the legacy effects would disappear if we allowed invasion by microbial communities from substrates chemically similar to the new substrate (Figure 1).

# MATERIALS AND METHODS

## Model description

DEMENT is a spatially explicit individual- and trait-based microbial model that simulates leaf litter decomposition emerging from both cell-level physiology and community-level interactions (Allison, 2012). We used the more recent Python version of the code, DEMENTpy, which incorporates explicit drought tolerance mechanisms (Appendix S1: Figure S1) (Wang & Allison, 2021). We initialized the simulation on a spatial grid of 100 × 100 boxes (1–10 µm on a side) representing the surface of a litter substrate. The 12 compounds that compose leaves, such as cellulose and lignin, were initially homogeneously distributed on the grid. In our study, we randomly placed 100 taxa onto this grid that differed in their trait values, such as their monomer maximal uptake rates or the number and type of enzymes they produce. Bacterial taxa had an initial biomass of 1 mg cm⁻³ and a 1% probability of occupying each grid box. Fungal taxa had an initial biomass of 25 mg cm⁻³ and a 0.04% probability of occupying each grid box. As such, at the start of the simulation, there were an average of 0.52 taxa and 1 mg cm⁻³ of biomass (bacteria or fungi) per grid box.

Each day during the simulation, microbes produced enzymes, substrate compounds were decomposed into monomers, and these monomers diffused on the grid. Microbes then took up the monomers, grew, produced
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<figure><img src="image_5.png" /><figcaption>**FIGURE 1** Conceptual illustration of the key hypotheses of the present study. We simulate the effect of substrate change and invasion treatments on (a) microbial community composition (phases I and II) and (b) substrate degradation (phase II), as illustrated by a community native to forest litter transplanted onto desert litter. We hypothesized that forest litter would select for different taxa than desert litter. When transplanting forest native communities onto desert litter, the change in substrate will change the composition of the community such that its substrate degradation will ultimately be closer to that of communities native to desert litter (contemporary selection by the environment). However, the loss of taxa adapted to desert litter during the first phase on forest litter will cause contemporary selection to play out differently (legacy effect) unless invaders arrive from a substrate similar to desert litter. Conversely, adding invaders from a site with more dissimilar substrate chemistry could adversely impact substrate degradation (maladaptation).</figcaption></figure>
metabolites, reproduced, and/or died. Each taxon produced one or more enzymes. Each enzyme was assigned zero, one, or several substrate compounds to degrade. Each substrate compound was decomposed into one type of monomer. Taxa membrane transporters were monomer-specific. A taxon was assumed to possess the transporter type(s) for the enzyme(s) it produced. Reproduction occurred when the initial biomass of a taxon doubled, with one daughter cell staying in the same grid box, and the other randomly moving to one of the four adjacent grid boxes. Taxa also produced osmolytes that determined their probability of death depending on litter moisture.

We held temperature and moisture constant for this study at 15.7°C and -0.01 MPa, respectively (Earth averages), in order to focus on the sole effect of litter change. Enzyme kinetics depended on temperature as per the Arrhenius equation. Each year, the assembled microbial community was placed on fresh litter substrate, with a reinitialized substrate distribution, no monomers, and no enzymes. There was no additional substrate input between day 0 and day 365. DEMENT simulated six years of decomposition by a complex community on a grid of 1 mm² in ~20 h. A more detailed description of the model is available in Wang and Allison (2021).

## Litter substrate chemistry

To test whether litter substrate chemistry affects microbial community composition and function, we allowed 10 communities to randomly assemble on each of five different substrates (phase I in Figure 2). The chemistry parameters for this simulation were derived from Baker and Allison (2017) and correspond to the leaf litter chemistry of five sites along a Californian elevation gradient (desert, scrubland, grassland, pine oak, subalpine) (Figure 3; Appendix S1: Table S1). The same sites were used for the climate transplant experiment performed by Glassman et al. (2018). We used litter chemistry from these sites to generate realistic substrates, facilitate comparison with empirical findings, and make our results relevant for ecosystem-scale models. We calculated average annual substrate decay rates and relative abundances for all taxa. Taxa were assumed to be specialists, producing only three enzyme types per taxon, to increase the chances of observing strong environmental selection. Preliminary runs revealed that both outputs stopped varying by year 3 of the simulation, so we compared the average community composition and decay rates in year 3. To assess community composition, we assigned taxa to functional groups based on the litter compounds they decomposed. This grouping is relevant for our analysis

because DEMENT taxa are defined solely by their traits, and multiple taxa may have traits allowing them to decompose a given substrate.

## Invasion

To test the effect of invasion following a change in litter substrate chemistry, we initialized a new set of simulations (phase II in Figure 2) using each of the 50 microbial communities assembled in phase I. To simulate changes in leaf substrate chemistry, we transplanted each community to one of the other four substrates. To simulate invasion, we merged the community with a pool of invaders equivalent to 40% of the size of one of the final communities from phase I. Invaders could come from the same community or from a different one. We also ran a simulation using transplanted communities with no invaders as a control experiment. The invasions were conducted only with transplants that showed differences in litter decay. As in phase I, assembly stabilized around years 2 or 3, so we stopped the simulations at the end of year 3. We then compared substrate decay rates and relative abundances of microbial functional groups defined by enzyme-based substrate preferences. These variables were averaged over year 3 within substrate chemistry and invasion treatments. Plots and data analyses were performed in Google Colaboratory. Using Google Colaboratory's Python module "Statsmodels," we tested for significant differences across treatments with ANOVA followed by post hoc *t* tests. To fit the relationship between microbial growth rate and initial biomass, we used Google Colaboratory's Python module "SciPy optimize," which provides the residual and sum of squares to calculate the coefficient of determination, *R*². We used Google Colaboratory's Python function " numpy.polyfit" to fit the relationship between microbial growth rate and total microbial biomass. Numpy.polyfit takes a dataset and a polynomial function of any degree and returns an array of coefficients that minimizes the squared error.

## RESULTS

### Substrate composition does not affect overall substrate decay

Substrate chemical composition had no significant impact on overall decay or total microbial biomass (Figure 4). Because all compounds occurred in all litter types, all microbial functional groups were still present in each inoculated community. By three years after
<figure><img src="image_6.png" /><figcaption>FIGURE 3 Leaf litter substrate chemistry. Bar plots for each substrate chemistry: desert (D), scrubland (Sc), grassland (G), pine oak (Po), subalpine (Sa). The five bars correspond to five sites along an elevation gradient in California. Colors correspond to the different substrate components. Data are derived from Baker and Allison (2017). They correspond to the sum of carbon, nitrogen, and phosphorus density (in milligrams per cubic centimeter), and have been normalized such that the sum of all substrate components is equal between sites (300 mg cm⁻³).</figcaption></figure>

inoculation, contemporary selection had overcome most initial differences in community composition. Even when there were differences in decay rates of specific litter compounds, those differences offset one another. For example, the D to Po community had a lower hemicellulose decay rate but higher protein decay rate than the Po to Po community, leading to no significant difference in overall decay rates.

## Substrate composition affects hemicellulose and protein decay

Substrate composition did affect the decay of two compounds: hemicellulose and protein. Hemicellulose decay levels were 96% lower in desert litter relative to other substrates, although the biomass of hemicellulose degraders in desert communities was not significantly different from that in other communities (Figure 5a,b). In contrast, protein decay was 93% lower in pine oak and subalpine litter. These substrates contained 76% lower protein levels, along with a 66% lower biomass of protein

degraders relative to other substrate communities (Figure 5c,d). Overall, protein content had both a direct impact on protein decay rate and an indirect effect through changes in the relative abundance of protein decomposers, whereas hemicellulose content only affected hemicellulose decay directly (no indirect effect of shifts in microbial composition).

## Substrate change leads to legacy effects only on protein decay

Transplants increased the variance in litter decay between replicates but there was no significant difference in average litter decay (Figure 4). When we examined transplants between substrates that differed in hemicellulose and/or protein decay levels, two distinct patterns emerged. When hemicellulose content shifted, hemicellulose decay at the end of the three-year simulation was the same as that for communities native to the final litter composition (Figure 5a, desert-to-scrubland substrate transplant, *p* = 0.39). Likewise, the biomass of hemicellulose degraders was the same in native and transplanted communities (Figure 5b). When the substrate shifted from low to high protein content, however, we saw a legacy effect. Protein decay after 3 years remained the same as it did prior to substrate change despite higher protein availability (Figure 5c, pine oak-to-desert transplant).

## Invasion eliminates the legacy effects on protein decay

The biomass of protein degraders exhibited the same response as did the protein degradation activity. The biomass of protein degraders in pine oak communities transplanted onto desert substrate remained the same as in the native pine oak communities despite an increase in protein availability (Figure 5d). This substrate legacy effect on community composition and function was eliminated, however, when invaders from protein-rich

<caption>FIGURE 2 Conceptual framework of the simulation experiment. The experimental setup used to test the factors driving the relationship between invasion and microbial community composition and function in the context of substrate change. In phase I, 50 microbial communities (MCs) were created from 10 randomly assembled communities run on five different substrates (D, desert; Sc, scrubland; G, grassland; Po, pine oak; Sa, subalpine) for three years, by which time community composition had stabilized, under standardized climatic conditions (soil temperature = 15.7°C, soil moisture = -0.01 MPa). For example, MC-D1 is the microbial community selected on desert litter with stochasticity generator (seed) 1. In phase II, we allowed for invasion of each of the 50 communities generated in phase I, and we simulated the activities of these communities for three years on substrates different from those on which they were run in phase I (n = 5 residents × 4 substrates × (5 invaders + 1 control) × 10 seeds = 1200 simulations). For example, for MC-Sc invading MC-D, we sampled MC-Sc1 and added it to MC-D1 (the resident), repeating for each of the 10 stochastically assembled communities.</caption>
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** The effects of substrate (phase I) and substrate transplant (phase II) on litter average decay rate (a) and total microbial biomass (b). Average litter decay rate was calculated as the average decay rate of the 12 chemical substrates weighted by their average total (sum of carbon, nitrogen, and phosphorus) mass over the grid for year 3. The first five boxplots show the results from year 3 of phase I; the others show the results from year 3 of phase II for all transplants. The communities in phase I are assembled randomly, and the communities in phase II are the ones selected by the five substrates in phase I as indicated by the x-axis line noted "Resident" (D, desert; Sc, scrubland; G, grassland; Po, pine oak; Sa, subalpine). The boxplot midlines represent the median, box hinges are the first and third quartiles, and whiskers extend up to 1.5 times the interquartile range (or to the furthest data point if they are within that range) with outliers located beyond the whiskers.</figcaption></figure>

communities (desert, scrubland, grassland) were added, but it was unchanged by the addition of invaders from protein-poor communities (pine oak, subalpine) (Figure 6a). In line with these results, invader communities derived from protein-rich substrates contained significantly higher protein-degrader biomass relative to invader communities derived from protein-poor substrates (Figure 6b). We did not find significant differences in protein degradation or degrader biomass between the “home substrate” (desert) and the two others (scrubland and grassland) (Figure 6b). Therefore, the pattern we observed is not entirely consistent with a “home-field advantage” (Fanin et al., 2021).

**Positive frequency dependence of growth among protein degraders**

We found that the growth rate of protein degraders increased exponentially with their initial biomass (Figure 7a). If their initial biomass in the whole grid was under 140 mg, their growth was negative or zero, whereas above 140 mg, their growth was positive. This explains why we observed a legacy effect for communities native to protein-poor substrates, but not for invaders derived from protein-rich substrates that contain more protein degraders. We observed no significant relationship between the total initial
<figure><img src="image_8.png" /><figcaption>**FIGURE 5** Effects of substrate and substrate change on decay and biomass. Initial communities are assembled randomly, and communities after substrate change have experienced selection by the first substrate. (a) Hemicellulose decay averaged over the grid and over year 3 for five substrates; desert (D), scrubland (Sc), grassland (G), pine oak (Po), and subalpine (Sa); and for two substrate transplants: desert to scrubland (D→Sc: hemicellulose-poor to hemicellulose-rich) and scrubland to desert (Sc→D: hemicellulose-rich to hemicellulose-poor). (b) The biomass of hemicellulose degraders at baseline (day 0 of phase I), and averaged over the grid and over year 3 of phase I for each substrate, and over year 3 of phase II for the two substrate transplants (D→Sc and Sc→D). (c) Protein decay averaged over the grid and over year 3 for the five substrates, and for two substrate transplants: desert to pine oak (D→Po: protein-rich to protein-poor) and pine oak to desert (Po→D: protein-poor to protein-rich). (d) The biomass of protein degraders at baseline (day 0 of phase I), and averaged over the grid and over year 3 of phase I for each substrate, and over year 3 of phase II for the two substrate transplants (D→Po and Po→D). Letters above the boxplots represent significant differences between treatments. The boxplot midlines represent the median, box hinges are the first and third quartiles, and whiskers extend up to 1.5 times the interquartile range (or to the furthest data point if they are within that range) with outliers located beyond the whiskers.</figcaption></figure>

microbial biomass of nonprotein degraders and the growth rate of protein degraders (Figure 7b). These results suggest that the interaction between protein degraders is positive, and that the effect of nonprotein degraders on protein degraders at the community level is neutral.

DISCUSSION

In anticipation of global change, many studies have focused on temperature and moisture as drivers of decomposition (Chase et al., 2017; Glassman et al., 2018; Waldrop & Firestone, 2006; Wang & Allison, 2021). Yet increasing
<figure><img src="image_9.png" /><figcaption>**FIGURE 6** Effect of invasion on protein decay and protein degrader biomass. (a) Protein decay averaged over the grid and over year 3 of phase I on pine oak litter (Po), and over year 3 of phase II on desert litter (D) after 3 years of phase I on Po and with all invasion treatments: no invasion (NoInv), invaders from desert communities (Inv-D), invaders from scrubland communities (Inv-Sc), invaders from grassland communities (Inv-G), invaders from pine oak communities (Inv-Po), and invaders from subalpine communities (Inv-Sa). (b) Biomass of protein degraders in initial invader communities for all invasion treatments. Letters above the boxplots represent significant differences between treatments. The boxplot midlines represent the median, box hinges are the first and third quartiles, and whiskers extend up to 1.5 times the interquartile range (or to the furthest data point if they are within that range) with outliers located beyond the whiskers.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**FIGURE 7** Effect of the initial biomass of (a) protein degraders and (b) nonprotein degraders on protein degrader growth rates. Growth rates were calculated as log(final biomass/initial biomass)/T, where T is the duration of the simulations (1095 days). Protein degrader growth rates were an exponential function of their initial biomass ($R^2 = 0.86$ vs. $R^2$ of a linear fit = 0.63) (a), whereas there was no significant relationship with the total initial microbial biomass of nonprotein degraders (b).</figcaption></figure>

evidence indicates that changes in plant composition will happen on ecological timescales (Harte & Shaw, 1995; Klein et al., 2004; Knapp et al., 2002; Yang et al., 2011) and will directly influence soil microbe-mediated carbon and nutrient cycling (Hooper & Vitousek, 1997; Tilman et al., 1997). Transplant experiments represent an ideal means of quantifying contemporary environmental selection due to changing litter chemistry along with legacy effects of prior

selection (Albright & Martiny, 2018; Chase et al., 2021). We simulated microbial transplants with a spatially explicit, trait-based model to quantify how substrate chemistry acts as a selective force and interacts with community invasion during litter decay.

Our first hypothesis was that different litter substrates would select for distinct microbial communities with different functions. We found partial support for this hypothesis.
Although our simulated substrates were chemically distinct, only the abundance of protein-degrading microbes varied significantly among the communities growing on the litter substrates. Total microbial biomass and overall decay rates did not differ significantly across the litter types. Empirical studies have found evidence that different litter composition selects for different microbial communities (Yan et al., 2018) and rates of litter decay (Fanin & Bertrand, 2016). We found that the variation in hemicellulose and protein contents across litter types had consequences for the decay rates of those specific compounds. In contrast, the contents of the other compounds did not vary enough between litter types to affect their decay rates. These findings align with prior studies illustrating the key role of substrate quality, such as the ratio of neutral detergent fiber (comprising hemicellulose and lignin) to crude protein, in governing soil decay processes (Blagodatskaya & Anderson, 1998; Cyle et al., 2016; Evans et al., 2017).

We found that litter types with lower protein content had lower abundances of protein-degrading microbes along with lower protein decay rates. In turn, these differences in microbial community composition resulted in legacy effects after transplantation, in line with our second hypothesis. Empirical studies have observed similar legacy effects (Albright & Martiny, 2018; Chase et al., 2021; Glassman et al., 2018; Martiny et al., 2017), and mathematical models further substantiate these findings (Evans et al., 2017; Wang & Allison, 2021).

Consistent with our third hypothesis, we found that the legacy effects disappeared when invasion induced a shift in community composition. These findings echo the empirical results from Albright et al. (2020), suggesting that the effect of dispersal on microbiome functioning may vary from insignificant to substantial, contingent on the identity of the invasive taxa. Similarly, using a version of our model, Wang and Allison (2021) deduced that invasion could potentially attenuate the legacy effects of drought. Significantly, our research represents the first theoretical demonstration of this phenomenon in relation to changes in litter quality.

Some studies have identified maladaptation as a potential consequence of invasions due to mass effects, whereby species that are not optimally adapted to local conditions manage to colonize and maintain populations through continued dispersal (Leibold & Chase, 2017). However, our research did not detect any instances of maladaptation arising from invasions. Poorly adapted invaders might be unable to grow given that even species with higher fitness relied on positive frequency dependence for their growth.

The frequency dependence of protein degrader growth we found is likely driven by N dynamics. Although all taxa can take up mineralized N from the grid, N is a limiting

nutrient because most taxa degrade only litter compounds that do not contain N. On the one hand, when protein degraders are not abundant, most of the mineralized N they produce diffuses across the grid and is taken up by nonprotein degraders. This “cheating” (protein degraders’ N taken by nonprotein degraders) translates to low protein degrader fitness because they pay the cost of enzyme production but do not receive its benefit. On the other hand, when protein degraders are abundant, the inorganic N they produce and that diffuses on the grid can benefit other protein degraders. Both synergistic and competitive interactions are expected in microbiomes with extracellular enzyme secretion as a “public good” (West et al., 2007). Allison (2005) and Kaiser et al. (2015) both revealed how the presence of cheaters may constrain carbon and nutrient dynamics. Our study extends these findings by revealing a specific form of cheating that occurs with N-containing substrates and their microbial degraders.

Our results also demonstrated positive frequency-dependent selection (PFDS), as the growth of protein degraders (a proxy for fitness) increased when their initial abundance was higher. Frequency-dependent selection is expected in microbial communities because strong selection, spatial structuring, and cooperation-dependent growth are common in microbial populations (Ross-Gillespie et al., 2007). Both negative and positive frequency-dependent selection have been observed in cooperative microbial communities with potential cheating (Healey et al., 2016; Rendueles et al., 2015). Note that our simulations only allowed initial protein degrader frequency to vary between 0 and 0.1. It is thus possible that we would observe negative frequency-dependent selection at higher frequencies. Even so, we observed an exponential fitness function, meaning that the growth of cooperators, when initially rare, accelerates as their population biomass increases. This result contrasts with Healey et al. (2016) who found no change in fitness at low microbial population densities.

For this study, we made some assumptions that should be explored more in the future. For example, we made microbial taxa specialists by allowing them to degrade at most three substrate compounds out of 14. If we parameterized microbes as generalists such that they could produce all types of enzymes and have access to all substrate components, we would expect no selection of specific taxa, and therefore no greater variation in community composition between substrates as compared with within one substrate. We would similarly not expect substrate change or invasion to have any impact on community composition, and specific substrate decay rates would only be determined by litter chemistry. In the future, we could investigate the degree of specialization under which substrate protein content no longer selects for differences in community composition. To apply our
results across different climatic conditions, additional
simulations would need to account for environmental
variation in temperature and moisture.

Our results have potential applications in systems
where legacy effects might constrain the microbial
response to environmental changes. For example, land
managers could leverage microbial inoculation as a
means of enhancing soil carbon sequestration (Metting
et al., 2001; Trivedi et al., 2013). Still, it remains a chal-
lenge to establish the most cost-efficient means of modi-
fying microbiome activity to promote the soil carbon
storage while reducing CO₂ or CH₄ release into the atmo-
sphere. Our results suggest that the management of abi-
otic conditions (water, temperature, nutrients) may need
to be coupled with the inoculation of specific functional
groups of microbes to achieve this goal. Such a strategy
may be particularly important in N-limited soil where
adding N-rich organic amendments may not be sufficient
to stimulate the growth of N-degrading microbes without
boosting their frequencies through inoculation.

AUTHOR CONTRIBUTIONS

Elsa Abs was involved in conceptualization (lead), inves-
tigation (lead), supervision (lead), validation (equal),
writing—original draft (lead), and writing—review and
editing (lead). Michaeline Albright was involved in con-
ceptualization (equal) and writing—review and editing
(equal). Steven D. Allison was involved in validation
(equal) and writing—review and editing (equal).

ACKNOWLEDGMENTS

Funding was provided by the European Union's Horizon
2020 Research and Innovation Programme under the
Marie Sklodowska-Curie grant agreement number
891576 to Elsa Abs; the US Department of Energy, Office
of Science, BER under award DE-SC0020382, and sub-
contract 563732 from Los Alamos National Laboratory to
Steven D. Allison. Michaeline B. N. Albright was
supported by a Los Alamos National Laboratory Postdoc
Fellowship 20180746PRD3 and by SFA grant
2018LANLF255 from the U.S. Department of Energy
Office of Biological and Environmental Research.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

Data and code (Abs, 2023) are available from Zenodo:
https://doi.org/10.5281/zenodo.10230922.

ORCID

Michaeline B. N. Albright https://orcid.org/0000-0002-4528-0312

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Abs, Elsa, Michaeline B. N. Albright, and Steven D. Allison. 2024. "Invasions Eliminate the Legacy Effects of Substrate History on Microbial Nitrogen Cycling." *Ecosphere* 15(1): e4754. https://doi.org/10.1002/ecs2.4754