An official website of the United States government
Abstract Soil biota are increasingly recognized as a primary control on litter decomposition at both local and regional scales, but the precise mechanisms by which biota influence litter decomposition have yet to be identified.There are multiple hypothesized mechanisms by which biotic communities may influence litter decomposition—for example, decomposer communities may be specially adapted to local litter inputs and therefore decompose litter from their home ecosystem at elevated rates. This mechanism is known as the home‐field advantage (HFA) hypothesis. Alternatively, litter decomposition rates may simply depend upon the range of metabolic functions present within a decomposer community. This mechanism is known as the functional breadth (FB) hypothesis. However, the relative importance of HFA and FB in litter decomposition is unknown, as are the microbial community drivers of HFA and FB. Potential relationships/trade‐offs between microbial HFA and FB are also unknown.To investigate the roles of HFA and FB in litter decomposition, we collected litter and soil from six different ecosystems across the continental US and conducted a full factorial litter × soil inoculum experiment. We measured litter decomposition (i.e. cumulative CO2‐C respired) over 150 days and used an analytical model to calculate the HFA and FB of each microbial decomposer community.Our results indicated clear functional differences among decomposer communities, that is, litter sources were decomposed differently by different decomposer communities. These differences were primarily due to differences in FB between different communities, while HFA effects were less evident.We observed a positive relationship between HFA and the disturbance‐sensitive bacterial phylum Verruomicrobia, suggesting that HFA may be an important mechanism in undisturbed environments. We also observed a negative relationship between bacterial r versus K strategists and FB, suggesting an important link between microbial life‐history strategies and litter decomposition functions.Microbial FB and HFA exhibited a strong unimodal relationship, where high HFA was observed at intermediate FB values, while low HFA was associated with both low and high FB. This suggests that adaptation of decomposers to local plant inputs (i.e. high HFA) constrains FB, which requires broad rather than specialized functionality. Furthermore, this relationship suggests that HFA effects will not be apparent when communities exhibit high FB and therefore decompose all litters well and also when FB is low and communities decompose all litters poorly. Overall, our study provides new insights into the mechanisms by which microbial communities influence the decomposition of leaf litter. Read the freePlain Language Summaryfor this article on the Journal blog.
more »
« less
This content will become publicly available on March 26, 2026
Scaling from microsite to landscape to resolve litter decomposition dynamics in globally extensive drylands
Abstract Decomposition is the transformation of dead organic matter into its inorganic constituents. In most biomes, decomposition rates can be accurately predicted with simple mathematical models, but these models have long under‐predicted decomposition in globally extensive drylands.We posit that the exposed surface conditions characteristic of drylands make litter decomposition uniquely subject to microsite‐specific environmental controls and spatially variable microbial communities. As such, decomposition in dryland ecosystems—which are characterized by extremes in temporal heterogeneity of climate conditions and spatial heterogeneity of vegetation cover with corresponding microclimate variability—is a prime example of a macrosystems process that can be addressed by merging field data with new predictive process models operating across a hierarchical continuum of spatial scales and process resolutions.A macrosystems approach offers promise to reconcile model‐measurement discrepancies by integrating observations and experiments across multiple scales, from microsites (e.g. shrub sub‐canopy or intercanopy) to regions (e.g. across a 100s of km2study site with complex topography, precipitation and temperature) and ultimately to a continental perspective (e.g. North American drylands).Recent developments in technology and data availability position the scientific community to integrate laboratory, field, modelling and remote sensing approaches across a hierarchical range of scales to capture the spatiotemporal distribution of litter and environmental conditions needed to predict decay dynamics at the micro‐to‐macroscale. This multi‐scale approach promises a path forward to resolving a longstanding disconnect between measured data and modelled processes in dryland litter decomposition.Dryland litter decomposition presents an excellent case study for resolving spatially and temporally complex biogeochemical dynamics through a hierarchical, multidisciplinary macrosystems approach.We focus on dryland litter decomposition, but the hierarchical, multidisciplinary macrosystems approach we outline shows great potential for resolving other spatially and temporally complex biogeochemical processes across a wide range of ecosystems. Read the freePlain Language Summaryfor this article on the Journal blog.
more »
« less
- PAR ID:
- 10647745
- Publisher / Repository:
- British Ecological Society
- Date Published:
- Journal Name:
- Functional Ecology
- ISSN:
- 0269-8463
- Subject(s) / Keyword(s):
- carbon cycling decay macrosystems biology microsite photodegradation semi-arid spatial heterogeneity temporal heterogeneity
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Litter decomposition is a key ecological process that determines carbon (C) and nutrient cycling in terrestrial ecosystems. The initial concentrations of C and nutrients in litter play a critical role in this process, yet the global patterns of litter initial concentrations of C, nitrogen (N) and phosphorus (P) are poorly understood.We employed machine learning with a global database to quantitatively assess the global patterns and drivers of leaf litter initial C, N and P concentrations, as well as their returning amounts (i.e. amounts returned to soils).The medians of litter C, N and P concentrations were 46.7, 1.1, and 0.1%, respectively, and the medians of litter C, N and P returning amounts were 1.436, 0.038 and 0.004 Mg ha−1 year−1, respectively. Soil and climate emerged as the key predictors of leaf litter C, N and P concentrations. Predicted global maps showed that leaf litter N and P concentrations decreased with latitude, while C concentration exhibited an opposite pattern. Additionally, the returning amounts of leaf litter C, N and P all declined from the equator to the poles in both hemispheres.Synthesis: Our results provide a quantitative assessment of the global concentrations and returning amounts of leaf litter C, N and P, which showed new light on the role of leaf litter in global C and nutrients cycling.more » « less
-
Abstract The temporal stability of plant productivity affects species' access to resources, exposure to stressors and strength of interactions with other species in the community, including support to the food web. The magnitude of temporal stability depends on how a species allocates resources among tissues and across phenological stages, such as vegetative growth versus reproduction. Understanding how plant phenological traits correlate with the long‐term stability of plant biomass is particularly important in highly variable ecosystems, such as drylands.We evaluated whether phenological traits predict the temporal stability of plant species productivity by correlating 18 years of monthly phenology observations with biannual estimates of above‐ground plant biomass for 98 plant species from semi‐arid drylands. We then paired these phenological traits with potential climate drivers to identify abiotic contexts that favour specific phenological strategies among plant species.Phenological traits predicted the stability of plant species above‐ground biomass. Plant species with longer vegetative phenophases not only had more stable biomass production over time but also failed to fruit in a greater proportion of years, indicating a growth–reproduction trade‐off. Earlier leaf‐out dates, longer fruiting duration and longer time lags between leaf and fruit production also predicted greater temporal stability.Species with stability‐promoting traits began greening in drier conditions than their unstable counterparts and experienced unexpectedly greater exposure to climate stress, indicated by the wider range of temperatures and precipitation experienced during biologically active periods.Our results suggest that bet‐hedging strategies that spread resource acquisition and reproduction over long time periods help to stabilize plant species productivity in variable environments, such as drylands. Read the freePlain Language Summaryfor this article on the Journal blog.more » « less
-
Our understanding of carbon and nutrient dynamics in globally vast and socioeconomically critical dryland ecosystems lags behind mesic systems. Litter decomposition models consistently underestimate measured decomposition in these regions. Both models and measurements largely represent spatially dominant intercanopy areas; however, little litter resides in these interspaces as transport vectors move litter to other microsites such as beneath plant canopies and buried in soil. Abiotic and biotic conditions differ among microsites, but few studies have characterized microsite impacts on decomposition. We collated data on microsites where litter accumulates. In microsites with sufficient available data, we used meta-analysis to test hypotheses on decomposition relative to litter in intercanopy spaces. Decomposition was lower under woody plant canopies than in intercanopy spaces. Buried litter decomposed faster than surface litter. There was no difference in decomposition between surface litter and litter suspended aboveground to simulate standing dead. All microsite contrasts had exceptions, suggesting that site-specific characteristics influence microclimate and subsequent decomposition. Extrapolation of decomposition rates to the landscape-level (using estimates of microsite-specific decomposition rates multiplied by litter pools), suggests that decomposition estimates based on intercanopy data alone underrepresent landscape-level decomposition. Thus, despite advances in the understanding of mechanistic decomposition drivers in drylands advancing, most studies are spatially unrepresentative analyses in intercanopy areas and this will underestimate decomposition at the landscape level. Expanding the ecological relevance of decomposition processes to be useful for predicting larger-scale carbon and nutrient dynamics requires improved characterization of dryland litter distribution, coupled with a mechanistic understanding of decomposition in microsites where litter accumulates.more » « less
-
Summary Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations.Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty‐eight CNNs were trained on subsets of manually defined ground‐truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm2). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry.The CNN approach gave a precision‐recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species.We provide aLeafVeinCNNsoftware package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.more » « less
