Abstract
Above- and belowground systems are linked via plant chemistry. In forested systems, leaf litter chemistry and quality mirror that of green foliage and have important afterlife effects. In systems where belowground inputs dominate, such as grasslands, or in ecosystems where aboveground biomass is frequently removed by burning or harvesting, foliar traits may provide important information regarding belowground inputs via exudates and fine-root turnover. Many, if not most, of the plant traits that drive variation in belowground processes are also measurable via remote sensing technologies. The ability of remote sensing techniques to measure fine-scale biodiversity and plant chemistry over large spatial scales can help researchers address ecological questions that were previously prohibitively expensive to address. Key to these potential advances is the idea that remotely sensed vegetation spectra and plant chemistry can provide detailed information about the function of belowground processes beyond what traditional field sampling can provide.
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8.1 Framework
Remote sensing (RS) of belowground processes via aboveground ecosystem properties and plant foliar traits depends upon (1) the ability to quantify ecosystem productivity and relevant plant attributes—including plant chemical composition and diversity—and (2) tight linkages between above- and belowground systems. These linkages can occur through the effects of aboveground inputs into belowground systems and/or through relationships between above- and belowground attributes and, in turn, between belowground relationships between plant roots and microbial communities and processes (i.e., fine-root turnover, mycorrhizal associations). The increasing ability of remotely sensed information to accurately measure productivity, ecologically important plant traits (Serbin and Townsend, Chap. 3, this volume; Wang et al. 2019), and plant taxonomic, functional, and phylogenetic diversity (Wang et al. 2019; Schweiger et al. 2018; Gholizadeh et al. 2019) creates new opportunities to observe terrestrial ecosystems. While the focus of RS tools is generally on aboveground vegetation characteristics, the tight linkage between above- and belowground systems through productivity and foliar chemistry means that many belowground processes can be inferred from remotely sensed information. Here, we focus on how the productivity and composition of foliar traits in plant communities influence belowground processes such as decomposition and nutrient cycling. We specifically consider foliar traits that are increasingly measurable via airborne RS. Using two case studies, one in a clonal aspen (Populus tremuloides) forest system and one in a manipulated grassland biodiversity experiment, we demonstrate that plant foliar traits and vegetation cover, as measured via plant spectra (Wang et al. 2019), can provide critical information predictive of belowground processes.
8.2 How Are Belowground Processes and Microbial Communities Influenced by Aboveground Properties?
Belowground processes—including decomposition and nutrient cycling, which are mediated by microbial biomass, composition, and diversity—are heavily influenced by both the amount and chemistry of aboveground inputs. Quantifying the amount and quality of foliar components is a major aspect of trait-based ecology, which seeks to use functional traits, rather than taxonomic classification, to determine organisms’ contributions to communities and ecosystems. Trait-based ecology has inherent strengths, including the ability to consider biological variation across both phylogenetic and spatial scales (Funk et al. 2017). While there is a range of accepted trait-based approaches in plant sciences (Funk et al. 2017), the emergence of the leaf economic spectrum (Wright et al. 2004) and the whole plant economic spectrum (Reich 2014) has clearly demonstrated that plant traits are important to ecosystem processes across multiple biological and spatial scales. Further, employing a trait-based approach to explore the relationships among plant function, biodiversity, and belowground processes allows us to take advantage of recent advances in RS to accurately measure plant traits across large spatial scales.
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A.
Decomposition and Nutrient Cycling—The productivity, composition, and diversity of aboveground communities influence belowground processes, in part through decomposition of leaf litter (Gartner and Cardon 2004; Hättenschwiler et al. 2005), root litter (Bardgett et al. 2014; Laliberté 2017), and root exudates (Hobbie 2015; Cline et al. 2018) and also through effects on soil organic matter (SOM) properties (Mueller et al. 2015) and soil physical structure (Gould et al. 2016). Several seminal reviews outlining the importance of biodiversity to ecosystem function (BEF) have focused specifically on the afterlife effects of litter diversity on decomposition (Hättenschwiler et al. 2005; Gessner et al. 2010).
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B.
Microbial Community Composition —Variation in the quantity and quality of organic inputs into belowground systems drives variation in belowground microbial communities and functioning (de Vries et al. 2012). Differences in aboveground communities are mirrored by those in belowground communities (Wardle et al. 2004; De Deyn and van der Putten 2005; Kardol and Wardle 2010). Across multiple spatial and taxonomic scales, variation in belowground microbial communities is driven by variation in plant traits associated with the leaf economic spectrum (de Vries et al. 2012). In general, fungi dominate decomposition of complex, low-quality substrates, while bacteria favor labile, high-quality substrates (Fig. 8.1, Bossuyt et al. 2001; Lauber et al. 2008). Microorganisms release extracellular enzymes, which degrade organic molecules outside of their cells, and likely differ among groups of microorganisms (Schneider et al. 2012). As a consequence, microbial composition and diversity are expected to influence decomposition and nutrient cycling. Most litter decomposition appears to be driven by fungal members, with Ascomycota dominating early degradation of cellulose and hemicellulose, followed by colonization by lignin-degrading Basidiomycota (Osono 2007; Schneider et al. 2012). Although lignin decomposition is dominated by fungal groups, some bacteria also degrade lignin (Kirby 2006; López-Mondéjar et al. 2016). Bacteria not directly involved with litter decomposition target the low molecular weight carbohydrates provided by fungal-derived extracellular enzymes (Allison 2005). The degradation of aromatic polyphenolics is largely limited to fungal member of the Basidiomycota phylum (Floudas et al. 2012). The wide structural variation among tannins (see section on carbon (polyphenols) results in a wide range of effects on specific microbial members (Kraus et al. 2003).
Complex, recalcitrant compounds are typically degraded by fungi, while soluble, labile substrates are catabolized by bacteria
A challenge in predicting belowground processes such as decomposition and nutrient cycling from the diversity and quality of leaf litter inputs is that such an approach must also consider the diversity and function of belowground microbial communities. Belowground mycorrhizal communities can increase net primary production (NPP) and drive variation in plant communities (Wardle et al. 2004). Given the influence of plant traits on belowground processes, biodiversity may drive variation in decomposition through top-down (microbially driven) rather than bottom-up (substrate driven) forces (Srivasta et al. 2009). Several reviews have addressed the importance of belowground community diversity to ecosystem processes (e.g., Hättenschwiler et al. 2005; Gessner et al. 2010; Phillips et al. 2013; Bardgett and van der Putten 2014). Belowground diversity can influence aboveground factors such as NPP (Wardle et al. 2004; Eisenhauer et al. 2018) that then have important feedbacks to belowground processes. Decomposition is driven by a combination of both the microbial community and the quality and quantity of litter that those communities receive (e.g., Keiser et al. 2013; García-Palacios et al. 2016).
8.3 Mechanisms by Which Aboveground Vegetation Attributes Influence Belowground Processes
Aboveground community composition and vegetation chemistry are tightly linked with belowground communities through belowground inputs and subsequent decomposition and nutrient uptake (Hobbie 1992; Wardle et al. 2004). Plant biomass, structure, and chemical composition are all important drivers of belowground processes to such an extent that plant traits may be the dominant control on litter decomposition, outweighing the influence of climate even over large spatial scales (Cornwell et al. 2008).
8.3.1 Total Aboveground Inputs
Standing aboveground biomass and NPP are among the most important attributes of vegetation that impact belowground systems (Chapin et al. 2002) and are widely measured via RS techniques with increasing accuracy (Kokaly et al. 2009; Serbin et al., this issue). Belowground respiration is tightly linked with aboveground productivity (Högberg et al. 2001), and leaf litter can provide roughly half of organic inputs into some belowground systems (Coleman and Crossley 1996). The amount of aboveground biomass can be critical to litter decomposition (Lohbeck et al. 2015) and microbial community function and diversity (Fierer et al. 2009; Cline et al. 2018), and its influence may surpass the effects of plant quality, as measured by plant chemistry and functional traits (Lohbeck et al. 2015).
Plant traits related to biomass, such as leaf area index (LAI), are also linked to belowground processes, with belowground carbon (C) turnover peaking at intermediate LAI levels (Berryman et al. 2016; others). Importantly, LAI can be measured with RS products over large spatial scales (Serbin et al. 2014; Lausch et al., Chap. 13 this volume, Morsdorf et al. Chap. 4). While there have been few explicit links of remotely sensed LAI to soil respiration (but see Huang et al. 2015), the conceptual link has been recognized for decades (Landsberg and Waring 1997). Other remotely sensed variables tightly coupled with biomass, including vegetation cover (Wang et al. 2019), also predict soil respiration (Fig. 8.4).
The effects of biomass on belowground processes have been recognized by ecologists employing RS to estimate belowground C stocks (e.g., Bellassen et al. 2011). Across large scales, aboveground biomass is generally correlated with belowground root biomass (Cairns et al. 1997). While aboveground biomass is commonly measured, the calculation of belowground biomass is less common and is often limited to estimates of shoot biomass as a simple proportion of aboveground biomass (Mokany et al. 2006). Nonetheless, the belowground estimates based on aboveground measurements can be useful for estimating above- and belowground C stores via RS products over large spatial scales (Saatchi et al. 2011). Allocation of C to belowground systems varies among systems, with annual grassland systems differing from forested biomes in their allocation patterns of NPP (Litton et al. 2007). There are also large differences in above- and belowground linkages according to site fertility. In fertile sites the majority of NPP returned to the soil as labile fecal matter, whereas in infertile systems most NPP returned as recalcitrant plant litter (Wardle et al. 2004).
8.3.2 Chemical Composition of Vegetation
Beyond variation in total organic inputs to soil, variation in plant chemical composition is critical to belowground ecosystem processes. The physiological traits that comprise the plant economic spectrum developed by Wright et al. (2004) have important afterlife affects for belowground systems (Cornwell et al. 2008; Freschet et al. 2012; see review by Bardgett 2017). Variation in litter chemical quality can produce marked, long-term effects on litter decomposition rates and nutrient cycling in underlying soils, and litter quality has long been identified as key factor in determining decomposition rates (Tenney and Waksman 1929). Litter chemistry generally mirrors canopy chemistry (Hättenschwiler et al. 2008), making canopy chemistry a viable metric to estimate litter chemistry and subsequent belowground decomposition and nutrient cycling patterns. Aside from aboveground biomass, leaf nitrogen (N) and lignin content are often the dominant plant traits that drive variation in belowground process, particularly leaf litter decomposition (Aber and Mellilo 1982; Cadisch and Giller 1997), and both of these traits are readily derived from spectroscopy at multiple scales (Wessman et al. 1988; Serbin et al. 2014; Schweiger et al. 2018; Wang et al. 2019). RS of additional leaf traits important to belowground processes, such as plant secondary chemistry, is also increasingly measured via RS techniques (Kokaly et al. 2009; Asner et al. 2014; Serbin et al., this issue).
Nitrogen
Foliar N is often the most important leaf trait driving variation in decomposition across biomes (Diaz et al. 2004; Cornwell et al. 2008; Handa et al. 2014). In some biomes leaf N is the only known leaf trait associated with leaf decomposition among wide ranges of species (Jo et al. 2016). Because canopy N has a tight correlation with plant carbon capture through photosynthesis, aboveground biomass, and belowground processes such as decomposition and N cycling rates, it is among the most common canopy traits measured via RS platforms (Martin and Aber 1997; Wessmen et al. 1998; Kokaly and Clark 1999; Martin et al. 1998, 2008; Ollinger et al. 2002; Townsend et al. 2003; Kokaly et al. 2009; Vitousek et al. 2009; Ollinger et al. 2013).
Leaf N is directly linked to plant productivity because most plant N is associated with metabolically active proteins, including RuBisCo. Leaf N content is driven by a trade-off between the benefits of increased photosynthetic potential and the costs associated with acquiring N along with the increased risk of herbivory (Diaz et al. 2016). In addition, leaf N can be indicative of plant growth strategies (Wardle et al. 2004). Most short-term decomposition studies indicate that leaf N increases leaf litter decay (Cornwell et al. 2008). However, as decomposition progresses, leaf N may negatively affect the latter stages of decomposition, possibly due to interactions with lignified substrates (Berg 2014; discussed in brief below).
Carbon quality (lignin)
The second most abundant natural polymer following cellulose is lignin, a complex phenolic polymer that wraps in and out of the structural polysaccharides in cell walls (Cadisch and Giller 1997). Due to its central roles in both aboveground biomass and belowground decomposition, lignin has been targeted as an important plant trait for RS techniques (Wessman et al. 1988; Serbin et al. 2014; Serbin and Townsend, Chap. 3). While lignin is a polyphenolic compound comprised of linked phenols (Horner et al. 1988), it is considered separate from other polyphenols because lignin is a primary structural component, whereas other polyphenols are a subset of secondary metabolites not directly involved with plant growth. The structure role of lignin and its low solubility also merit distinction from other polyphenolics when considering belowground processes (Hättenschwiler and Vitousek 2000). Lignin concentrations are negatively correlated with decomposition rates (Meentemeyer 1978; Melillo et al. 1982; Horner et al. 1988). The recalcitrant nature of lignin is due, in part, to its irregular structure and low energy yield, which largely limits its degradation to white-rot fungus members of Basidiomycota (Chapin et al. 2002).
The interaction of N and lignin during decomposition is not straightforward because N limits the early stages of decomposition, whereas lignin limits the latter stages of decomposition (Burns et al. 2013). Newly senesced leaves are composed largely of polysaccharides of holocellulose and lignin. High N availability will stimulate holocellulose decomposition in the early stages of decomposition but will then retard lignin decomposition in later stages of decomposition leading to lignified soil organic matter (SOM), potentially due to white-rot fungi favoring low N conditions (Berg 2014). The degradation of lignin is often a rate-limiting step during the later stages of decomposition because it protects cell wall polysaccharides physically and chemically (Talbot et al. 2012). Despite the changing roles that leaf N and lignin have over the course of decomposition, litter quality metrics such as C:N and lignin: N can explain variation in decomposition, with decomposition rates increasing with N in the early stages, but decreasing with N in the later stages, and decreasing with lignin (Fanin and Bertrand 2016).
While lignin almost universally retards decomposition, there is a large amount of variation within lignin compounds based on the proportion of specific monomers that varies across major plant groups (Thevenot et al. 2010). Angiosperm lignin tends to degrade more quickly than does gymnosperm lignin due to the specific identities of constituting moieties of lignin in each species (Higuchi 2006). The compact nature of gymnosperm lignin subunits is thought to protect them from enzymatic degradation (Hatakka and Hammel 2010). Functional measurements of lignin are often made via either acid digestion or thioglycolic acid methods that can then be used to calibrate spectroscopic methods (Brinkmann et al. 2002; Schweiger et al. 2018).
Carbon quality (polyphenols)
In some ecosystems non-lignin carbon compounds (e.g., phenolics) explain more variation in decomposition than does either N or lignin (Hättenschwiler et al. 2011). Phenolics are the most widely distributed class of secondary plant metabolites and interact strongly with several aspects of nutrient cycling (Hättenschwiler and Vitousek 2000). Simple phenolics can prime (Fontaine et al. 2007), while large complex polyphenolics can retard (Coq et al. 2010) decomposition. Carbon quality—including the chemical composition of polyphenolics—can be more important to litter decomposition than is litter nutrient concentration (Hättenschwiler and Jørgensen 2010). Plant polyphenolics can be accurately measured via near-infrared spectroscopy (NIRS; Rupert-Nason et al. 2013), and by airborne imaging spectroscopy (Kokaly et al. 2009; Asner et al. 2014; Madritch et al. 2014; Serbin and Townsend, Chap. 3).
Though typically considered primarily for their aboveground defensive properties, phenolics in plant residues (leaf litter and roots) can have large influences on decomposition. Simple phenolics can increase soil respiration by providing a simple carbon source for microorganisms (Horner et al. 1988; Schimel et al. 1996; Madritch et al. 2007). Tannins are defined, in part, by their ability to bind to proteins (Bate-Smith 1975). The attributes of nonstructural polyphenolics that make them effective plant pathogen defenses also affect nonpathogenic fungi and microbes once litter enters the detrital food web; tannins do not discriminate between enzymes of plant pathogenic fungi or decomposing fungi. If tannins bind covalently with proteins to form polyphenolic-protein complexes, they become highly recalcitrant, and only basidiomycetes with polyphenol oxidase and earthworms can take advantage of these complex N sources (Hättenschwiler and Vitousek 2000). The inhibitory role of tannins on soil enzymes varies with specific tannin structure, which varies widely among species (Triebwasser et al. 2012). Tannins also have a limited ability to bind with carbohydrates and cellulose to form recalcitrant complexes (Horner et al. 1988; Kraus et al. 2003). The ability of polyphenolics to complex with proteins and other biochemicals is the primary method by which they influence soil respiration, litter decomposition, and soil N fluxes.
In addition to their influence on decomposition, nonstructural polyphenolics (which do not include lignin) influence N cycling by binding to and promoting retention of N-rich compounds including ammonium, amino acids, and proteins (Hättenschwiler and Vitousek 2000). Ayres (1997) suggested that condensed tannins may be more important to N cycling than to herbivore defense, since condensed tannins frequently have no anti-herbivory activity. Hättenschwiler et al. (2011) also proposed that polyphenolics, and tannins in particular, may be an important N conservation and recovery strategy for some species. This appears to be the case in Populus tremuloides systems, where high-tannin genotypes recovered more N than did low-tannin genotypes, especially when under severe herbivory (Madritch and Lindroth 2015). The high reactivity and branching structure of reactive hydroxyl sites also allow polyphenolics to complex with clay particles in soil and thereby influence several micronutrients in addition to N (Schnitzer et al. 1984).
Variation in plant phenolics is driven by several interacting factors. In general, polyphenolic concentrations in foliage are highest during the summer months (Feeny 1970). Summer coincides with both the onset of herbivory and the highest levels of photosynthetic activity. Herbivory-induced polyphenolic production is a well-documented aspect of plant-insect interactions (Herms and Mattson 1992; Baldwin 1994). The composition and quantity of phenolics vary among taxa at small and large phylogenetic scales. At large phylogenetic scales, condensed tannins are common in woody plants but almost absent in herbaceous species (Haslam 1989). At narrow phylogenetic scales, the concentration of polyphenolics is also under genetic control, and often there is considerable variation within the same species that can have important influences on belowground processes including litter decomposition and nutrient cycling (Lindroth et al. 2002; Schweitzer et al. 2005; Madritch et al. 2006, 2007).
8.3.3 Plant Diversity
Plant diversity, which can be accurately remotely sensed at some spatial scales (Wang et al. 2019; Gholizadeh et al. 2019), can influence belowground processes through its effects on productivity as well as on chemical diversity (Meier and Bowman 2008). Belowground diversity may be intrinsically linked to aboveground diversity because high plant diversity may provide a high diversity of litter quality and quantity to belowground systems that subsequently result in a high diversity of decomposers (Hooper et al. 2000). The specific relationship between aboveground plant communities and belowground microbial communities is context, system, and scale dependent (De Deyn and van der Putten 2005; Wu et al. 2011; Cline et al. 2018). For instance, Chen et al. (2018) found that plant diversity is coupled with soil beta diversity but not soil alpha diversity in grassland systems. Nonetheless, if aboveground diversity is indeed linked to belowground diversity, then aboveground estimates of plant diversity and plant traits could provide robust estimates of belowground processes.
Early work that focused on the influence of aboveground species diversity on litter decomposition yielded idiosyncratic results (Gartner and Cardon 2004; Hättenschwiler et al. 2005), with some studies reporting no effect of plant species diversity (e.g., Naeem et al. 1999; Wardle et al. 1999; Wardle et al. 2000; Knops et al. 2001), some reporting unpredictable results (Wardle and Nicholson 1996), and some reporting positive effects of plant species diversity on litter decomposition (Hector et al. 2000). Similar to aboveground processes, BEF studies that link aboveground diversity with belowground processes initially focused on aboveground species diversity (Scherer-Lorenzen et al. 2007; Ball et al. 2008; Gessner et al. 2010). The idiosyncratic relationship between species diversity and belowground processes led others to identify aboveground functional diversity and composition as more important to belowground processes than species diversity (Dawud et al. 2017).
Foliar chemistry is relevant to biodiversity and ecosystem functioning studies because plant chemistry varies widely among and within species and can influence belowground microbial communities and biogeochemical cycles (Cadisch and Giller 1997; Hättenschwiler and Vitousek 2000). It follows that variation in foliar traits important to decomposition (e.g., tannin concentration) will affect belowground microbial communities and the basic biogeochemical cycles that sustain forested ecosystems. Some studies have supported a chemical diversity approach toward elucidating the belowground effects of aboveground diversity (Hoorens et al. 2003; Smith and Bradford 2003). Epps et al. (2007) demonstrated that accounting for chemical variation was more informative regarding decomposition than was species diversity. While the usefulness of trait-based dissimilarity approaches remains somewhat equivocal (Frainer et al. 2015), there is increasing support for such trait-based approaches in explaining variation in leaf litter decomposition (Fortunel et al. 2009; Finerty et al. 2016; Jewell et al. 2017; Fujii et al. 2017). Handa et al. (2014) found that variation in leaf litter decomposition across widely different biomes was largely driven by commonly measured leaf traits such as N, lignin, and tannin content. At large scales, species traits rather than species diversity per se appears to at least partially drive variation in decomposition and belowground nutrient cycling.
In experimental systems, plant communities with high biodiversity result in high above- and belowground productivity (Tilman et al. 2001). The additional biomass that an ecosystem produces in diverse assemblages over what is expected from monocultures is called “overyielding” and has been documented in both grassland and forest experiments (Grossman et al. 2018; Weisser et al. 2017). The additional productivity results from several mechanisms acting simultaneously in more diverse communities, such as reduced pathogen attack, reduced seed limitation, and increased trait differences leading to “complementarity” in resource uptake (Weisser et al. 2017). Complementarity in resource use, particularly light harvesting, results in more efficient use of limiting resources and greater productivity (Williams et al. 2017). Similar patterns of greater productivity with higher diversity are observed in forest plots globally (Liang et al. 2016) although such patterns are scale dependent, and do not necessarily hold at large spatial extents (Chisholm et al. 2013). In naturally assembled grasslands, the relationship may not necessarily hold consistently (Adler et al. 2011). An open question, then, is the extent to which diversity and productivity are linked at large spatial scales in ecosystems globally. This is a question that can reasonably be addressed with remotely sensed measures of biodiversity and ecosystem productivity if scaling issues are appropriately considered (Gamon et al., Chap. 16). Plant diversity influences the quality of inputs and may allow for niche partitioning among functionally different microbes and may also influence productivity, the source of inputs of organic matter available to microbes, and microbial diversity. Through these linkages, foliar diversity has the potential to influence microbial diversity and function and hence belowground processes (Cline et al. 2018). The extent to which diversity and productivity, measured aboveground, can predict belowground microbial and soil processes is a question that is ready to be tackled at a range of scales across continents.
8.4 Case Studies
8.4.1 Remote Sensing of Belowground Processes via Canopy Chemistry Measurements
Plants act as aboveground signals for belowground systems. As such, RS of plant spectra can provide information about belowground systems. Plant spectra can provide a wealth of biological information important to plant physiology and community and ecosystem processes across multiple spatial scales (Cavender-Bares et al. 2017). Some researchers have used direct spectral measurements (e.g., NIRS) for direct measurements of soil characteristics (reviewed by Stenberg et al. 2010; Bellon-Maurel and McBratney 2011; Soriano-Disla et al. 2014), and there are limited examples of remotely sensed spectroscopic measurements of soils (reviewed by Ustin et al. 2004; Cecillon et al. 2009). Here we focus on remotely sensed spectral measurements of plant communities as a surrogate for belowground processes. The optical surrogacy hypothesis (sensu Gamon 2008) argues that plant spectra can serve as a surrogate for important belowground processes.
Direct spectral measurements have been used to assess belowground processes for decades. For instance, direct NIRS of leaf litter can be used to predict decomposition rates in a variety of systems (Gillon et al. 1993; Gillon et al. 1999; Shepherd et al. 2005; Fortunel et al. 2009; Parsons et al. 2011). RS of canopy traits to predict belowground processes is becoming increasingly useful. Spectroscopic measurement of δ15N is of particular interest for ecosystem processes (Serbin et al. 2014) because stable N isotopes can provide important information regarding ecosystem N cycling (Robinson 2001; Hobbie and Hobbie 2006). RS of forest disturbance (e.g., fire severity) and subsequent belowground processes is relatively common (e.g., Holden et al. 2016). Sabetta et al. (2006) used hyperspectral imaging to predict leaf litter decomposition across four forest communities. Fisher et al. (2016) were able to distinguish between arbuscular and ectomycorrhizal tree-mycorrhizal associations using spectral information gleaned from Landsat data. While the above examples focus on remotely sensed spectral information, remotely sensed forest structural information developed from lidar data can also provide information about belowground systems, as Thers et al. (2017) were able to use remotely sensed lidar data to estimate belowground fungal diversity. The growing number of examples that employ remotely sensed data to provide information about belowground systems points to the potential of plant spectra to be used as surrogates for ecosystem processes.
8.4.2 Forest Systems: Aspen Clones Example
An example of optical surrogacy in practice is illustrated by work completed in trembling aspen (Populus tremuloides) systems across the Western and Midwestern USA. Trembling aspen is the most widespread native tree species in North America (Mitton and Grant 1996) and is an ecologically important foundation species across its native range (Lindroth and St. Clair 2013). Aspen is facing large and rapid declines in intraspecific biodiversity because concentrated patches of aspen are currently experiencing high mortality rates in North America (Frey et al. 2004; Worrall et al. 2008). This phenomenon, commonly referred to as sudden aspen decline (SAD), leads to the death of apparently healthy aspen stands in 3–6 years (Shields and Bockheim 1981; Frey et al. 2004). These natural history traits, combined with the ecological and economic significance of the species, make trembling aspen an ideal system to employ RS techniques to estimate genetic diversity and the consequences thereof for belowground processes.
Aspen typically reproduces clonally, often creating a patchwork of clones with many ramets (Fig. 8.2). Aspen clones vary widely in canopy chemistry traits that are important to belowground processes such as litter decomposition (Madritch et al. 2006). Several studies have highlighted the importance of plant genetic diversity to ecosystem processes (Madritch and Hunter 2002, 2003; Schweitzer et al. 2005; Crutsinger et al. 2006; Madritch et al. 2006, 2007) and community composition (Wimp et al. 2004, 2005; Johnson and Agrawal 2005). These recent advances demonstrate that genetic diversity affects fundamental ecosystem processes by influencing both above- and belowground communities (Hughes et al. 2008). The natural history traits of aspen, its clonal nature, genetically mediated variation in canopy chemistry, and the concomitant wide range of variation in foliar traits make it an ideal model system for RS of biodiversity.
Aerial photo showing color differentiation of genetically distinct aspen clones. Genotypes can be detected rapidly via remote sensing techniques
Madritch et al. (2014) described how remotely sensed spectroscopic data from NASA’s AVIRIS platform can be used to describe aboveground genetic and chemical variation in aspen forests across subcontinental spatial scales. This work built upon past work that demonstrated the ability of imaging spectroscopy to detect both aboveground chemistry (Townsend et al. 2003) and biodiversity (Clark et al. 2005) and employed imaging spectroscopy to discriminate intraspecific, genetic variation in aboveground chemistry and diversity. Because of the tight linkages between aboveground and belowground systems and because of the large variation in secondary chemistries important to belowground processes in aspen, this project also demonstrated the ability to predict belowground process via RS of forest canopy chemistry. Figure 8.3 illustrates both the direct linkages between RS and canopy chemistry (A) and the subsequent indirect linkages to belowground function (B) and the microbial community (C). The indirect linkages represent the optical surrogacy hypothesis. Belowground attributes are not measured directly via RS, but rather RS of the forest canopy was able to provide detailed information regarding belowground process.
Imaging spectroscopy links to several layers of ecological processes in aspen forests. (a) Partial least squares (PLS) prediction (pred) for condensed tannin concentration from AVIRIS data compared to observed (obs) tannin. (b) PLS prediction (pred) for soil b-glucosidase activity compared to observed (obs) b-glucosidase. (c) PLS prediction from AVIRIS spectra for bacterial diversity compared to observed bacterial diversity, where bacterial diversity is the first axis of an NMDS ordination of amplicon sequencing of rDNA (525f and 806r primers). (Tannin and soil enzyme data are from Madritch et al. (2014))
8.4.3 Experiment Prairie Grassland System: Cedar Creek Example
Vegetation differences between prairie and forested ecosystems have important consequences for above- and belowground linkages. Detrital inputs in forests are dominated by leaf litter, whereas they are dominated by root exudates and turnover in prairie systems that are frequently burned. We employed a parallel application of spectroscopic imagery to assess above- and belowground diversity and functioning at the grassland biodiversity experiment located at Cedar Creek Ecosystem Science Reserve (Tilman et al. 2001). Rather than a monospecific forest canopy, the grassland experiment consisted of replicated diversity treatments ranging from 1 to 16 perennial grassland species in 9 m × 9 m plots. This work had more technical challenges associated with it compared to the aspen forest project due to the inherent complexity of a mixed species system and the small spatial scale of the experimental plots.
The relationship between plant diversity and aboveground biomass in the Cedar Creek BioDIV experiment is well documented (Tilman et al. 2001, 2006). Schweiger et al. (2018) further demonstrate that both plant diversity and function are measurable via remotely sensed spectra within the experiment and that spectral diversity predicted productivity. Wang et al. (2019) used AVIRIS imagery to map functional traits across the experiment. Remotely sensed productivity and functional trait composition can thus be tested for linkages with belowground processes. In this system, the quantity of inputs had a large impact on fungal composition and diversity (Cline et al. 2018). Productivity, measured as annual aboveground biomass, given that it is annually burned, can be accurately detected as remotely sensed vegetation cover (Fig. 8.4a; Wang et al. 2019, following the method of Serbin et al. 2015). Remotely sensed vegetation cover, in turn, predicted fungal diversity, measured as operational taxonomic unit (OTU) richness (Fig. 8.4b), and cumulative soil respiration (Fig. 8.4c). In addition to the total organic matter inputs to the soil, chemical composition also influenced belowground microbial communities. For example, remotely sensed %N (Wang et al. 2019) was positively correlated with soil microbial biomass (Cavender-Bares et al., unpublished manuscript).
Hyperspectral imagery links above- and belowground processes in a prairie ecosystem. (a) Remotely sensed vegetation cover significantly predicted aboveground plant productivity, R2 = 0.695 (a); cumulative soil respiration (mg CO2-C [g soil]−1), R2 = 0.63 (b); and fungal diversity, measured as OTU richness, R2 = 0.144 (c). (Soil respiration and fungal diversity data are from Cline et al. (2018))
8.4.4 Challenges and Future Directions
Employing plant spectra to predict belowground processes has both caveats and advantages over traditional belowground sampling. One important caveat is that any prediction of belowground processes requires a solid understanding of the linkages between above- and belowground processes in any given system. Examples in the literature that link remotely sensed attributes of aboveground systems with belowground systems remain scarce, in part, because of the historic separation of the two disciplines. It is unclear how well remotely sensed plant attributes will predict microbial and soil processes across ecological systems. In the above forest example, aspen forests were generally uniform in canopy coverage. It was also a single-species system where leaf structure remained consistent across the study area, despite the large spatial sampling scheme. Consequently, most of the variation in aspen spectral signal was likely due to variation in canopy chemistry and biomass rather than leaf structure. Lastly, in this temperate forest system, leaf litter accounts for a large fraction of inputs into belowground systems, compared to systems such as Cedar Creek that are burned frequently and where fine-root turnover dominates belowground inputs.
RS of aboveground properties poses further challenges that include separating the spectral signals important to canopy chemistry from those of physical properties of the forest canopy (Townsend et al. 2013). Larger challenges lie in the lack of accessibility of RS data and processing techniques to the broader ecological research community.
Several issues of scale present challenges to the application of RS to belowground systems. Large knowledge gaps remain in connecting the small spatial scale observations of traditional field studies with the large spatial scale observations of airborne or satellite RS platforms (Asner et al. 2015; Gamon et al., Chap. 16). In addition, there is a large mismatch in the spatial heterogeneity between above- and belowground systems, with belowground systems being notoriously heterogeneous across small spatial scales (Bardgett and van der Putten 2014). The majority of variation in belowground processes may be due to small, local-scale factors rather than large-scale factors such as climate (Bradford et al. 2016). In addition to issues associated with spatial scale, there are large spans in the scales of biodiversity and time. Speciose aboveground systems may contain upward of 600 species ha−1 (Lee et al. 2002), whereas soils contain many thousands of microbial “species” per gram of soil, with large numbers of endemics (Schloss and Handelsman 2006). Linking function to diversity remains a challenge in both systems and particularly in belowground systems where the functional role of the vast majority of species is unknown (Krause et al. 2014). Likewise, large differences in temporal scales exist between above- and belowground systems, with leaf responses to sunlight occurring on the order of seconds (Lambers et al. 1998), while the turnover of soil organic matter can take years to centuries (Bardgett and van der Putten 2014). Variation in temporal scales across systems is particularly important given that the importance of biodiversity to ecosystem processes increases with temporal scale (Cardinale et al. 2012; Reich et al. 2012).
Irrespective of RS, there are shortcomings associated with belowground measurement. For example, belowground measurements that use enzyme activity potentials as indicators of microbial function are widespread, but they are known to have numerous limitations (Nannipieri et al. 2018). Likewise, microbial diversity estimates based upon amplicon sequences of bacterial 16s rDNA have their own methodological and interpretive limitations (Schöler et al. 2017). Nonetheless, both enzyme activities and amplicon sequencing techniques provide useful information about belowground systems and are used widely enough to be compared across studies as long as protocols are consistent.
Advantages of using remotely sensed spectral properties of aboveground vegetation to predict belowground processes lie within the data-rich nature of imaging spectroscopy and the consequent ability to measure many more traits of the canopy than would otherwise be feasible with traditional benchtop methods. In Madritch et al. (2014), only four canopy traits were considered using traditional wet chemistry techniques (leaf tannin, N, C, lignin). These canopy foliar traits were expectedly well correlated with belowground processes. However, plant spectra themselves were better correlated with belowground processes than were plant leaf traits (Madritch et al. 2014). This strong relationship between plant spectra and belowground processing existed because the plant spectra provided quantitative information about many plant traits that were not measured via wet chemistry techniques. Potentially dozens of leaf traits important to belowground processes could be conveyed by plant spectra. The ability of plant spectra to capture many foliar attributes quickly and accurately is a large reason why plant spectra are useful for predicting belowground processes. In addition, identifying which regions of plant spectra are most variable and correlated with belowground process allows researchers to use spectra to identify plant traits important to soil processes. In short, the potential for RS products to link above- and belowground systems is promising but faces considerable obstacles.
References
Aber JD, Mellilo JM (1982) Nitrogen immobilization in decaying hardwood leaf litter as a function of initial nitrogen and lignin content. Can J Bot 60:2263–2269
Adler PB, Seabloom EW, Borer ET, Hillebrand H, Hautier Y, Hector A, Harpole WS, O’Halloran LR, Grace JB, Anderson TM, Bakker JD, Biederman LA, Brown CS, Buckley YM, Calabrese LB, Chu C-J, Cleland EE, Collins SL, Cottingham KL, Crawley MJ, Damschen EI, Davies KF, DeCrappeo NM, P a F, Firn J, Frater P, Gasarch EI, Gruner DS, Hagenah N, Hille Ris Lambers J, Humphries H, Jin VL, Kay AD, Kirkman KP, J a K, Knops JMH, La Pierre KJ, Lambrinos JG, Li W, MacDougall AS, McCulley RL, B a M, Mitchell CE, Moore JL, Morgan JW, Mortensen B, Orrock JL, Prober SM, D a P, Risch AC, Schuetz M, Smith MD, Stevens CJ, Sullivan LL, Wang G, Wragg PD, Wright JP, Yang LH (2011) Productivity is a poor predictor of plant species richness. Science 333:1750–1753
Allison SD (2005) Cheaters, diffusion, and nutrients constrain decomposition by microbial enzymes in spatially structured environments. Ecol Lett 8:626–635
Asner GP, Martin RE, Anderson CB, Knapp DE, (2015) Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sens Environ 158, 15–27
Asner GP, Martin RE, Carranza-Jiménez L, Sinca F, Tupayachi R, Anderson CB, Martinez P (2014) Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region. New Phytol 204:127–139
Ayres M, Clausen T, MacLean SF, Redman AM, Reichardt PB (1997) Diversity of structure and antiherbivore activity in condensed tannins. Ecology 78:1696–1712
Baldwin I (1994) Chemical changes rapidly induced by folivory. In: Bernays EA (ed) Insect-plant interactions. CRC Press, Tuscon, pp 1–23
Ball BA, Hunter MD, Kominoski JS, Swan CM, Bradford MA (2008) Consequences of non-random species loss for decomposition dynamics: experimental evidence for additive and non-additive effects. J Ecol 96:303–313
Bardgett RD (2017) Plant trait-based approaches for interrogating belowground function. Biol Environ Proc Royal Irish Acad 117:1–13
Bardgett RD, Van Der Putten WH (2014) Belowground biodiversity and ecosystem functioning. Nature 515:505–511
Bardgett RD, Mommer L, De Vries FT (2014) Going underground: root traits as drivers of ecosystem processes. Trends Ecol Evol 29:692–699
Bate-Smith E (1975) Phytochemistry of proanthocyanidins. Phytochemistry 14:1107–1113
Bellassen V, Delbart N, Le Maire G, Luyssaert S, Ciais P, Viovy N (2011) Potential knowledge gain in large-scale simulations of forest carbon fluxes from remotely sensed biomass and height. For Ecol Manag 261:515–530
Bellon-Maurel V, McBratney A (2011) Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - critical review and research perspectives. Soil Biol Biochem 43:1398–1410
Berg B (2014) Decomposition patterns for foliar litter – a theory for influencing factors. Soil Biol Biochem 78:222–232
Berryman E, Ryan MG, Bradford JB, Hawbaker TJ, Birdsey R (2016) Total belowground carbon flux in subalpine forests is related to leaf area index, soil nitrogen, and tree height. Ecosphere 7:e01418
Bossuyt H, Denef K, Six J, Frey SD, Merckx R, Paustian K (2001) Influence of microbial populations and residue quality on aggregate stability. Appl Soil Ecol 16:195–208
Bradford MA, Berg B, Maynard DS, Wieder WR, Wood SA (2016) Understanding the dominant controls on litter decomposition. J Ecol 104:229–238
Brinkmann K, Blaschke L, Polle A (2002) Comparison of different methods for lignin determination as a basis for calibration of near-infrared reflectance spectroscopy and implications of lignoproteins. J Chem Ecol 28:2483–2501
Burns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME, Wallenstein MD, Weintraub MN, Zoppini A (2013) Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol Biochem 58:216–234
Cadisch G, Giller K (1997) Driven by nature: plant litter quality and decomposition. CAB International, Wallingford
Cairns MA, Brown S, Helmer EH, Baumgardner GA (1997) Root biomass allocation in the world’s upland forests. Oecologia 111:1–11
Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Lariguaderie A, Srivasta DS, Naeem S (2012) Biodiversity loss and its impact on humanity. Nature 486:59–67
Cavender-Bares J, Gamon JA, Hobbie SE, Madritch MD, Meireles JE, Schweiger AK, Townsend PA (2017) Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am J Bot 104:966–969
Cecillon L, Barthes BG, Gomez C, Ertlen D, Genot V, Hedde M, Stevens A, Brun JJ (2009) Assessment and monitoring of soil quality using near infrared reflectance spectroscopy (NIRS). Eur J Soil Sci 60, 770–784
Chapin F, Matson P, Mooney H (2002) Principles of terrestrial ecosystem ecology. Springer, New York
Chen W, Xu R, Wu Y, Chen J, Zhang Y, Hu T, Yuan X, Zhou L, Tan T, Fan J (2018) Plant diversity is coupled with beta not alpha diversity of soil fungal communities following N enrichment in a semi-arid grassland. Soil Biol Biochem 116:388–398
Chisholm RA, Muller-Landau HC, Abdul Rahman K, Bebber DP, Bin Y, Bohlman SA, Bourg NA, Brinks J, Bunyavejchewin S, Butt N, Cao H, Cao M, Cárdenas D, Chang L-W, Chiang J-M, Chuyong G, Condit R, Dattaraja HS, Davies S, Duque A, Fletcher C, Gunatilleke N, Gunatilleke S, Hao Z, Harrison RD, Howe R, Hsieh C-F, Hubbell SP, Itoh A, Kenfack D, Kiratiprayoon S, Larson AJ, Lian J, Lin D, Liu H, Lutz JA, Ma K, Malhi Y, McMahon S, McShea W, Meegaskumbura M, Mohd. Razman S, Morecroft MD, Nytch CJ, Oliveira A, Parker GG, Pulla S, Punchi-Manage R, Romero-Saltos H, Sang W, Schurman J, Su S-H, Sukumar R, Sun I-F, Suresh HS, Tan S, Thomas D, Thomas S, Thompson J, Valencia R, Wolf A, Yap S, Ye W, Yuan Z, Zimmerman JK (2013) Scale-dependent relationships between tree species richness and ecosystem function in forests. J Ecol 101:1214–1224
Clark ML, Roberts DA, Clark DB (2005) Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens Environ 96:375–398
Cline LC, Hobbie SE, Madritch MD, Buyarski CR, Tilman D, Cavender-Bares JM (2018) Resource availability underlies the plant-fungal diversity relationship in a grassland ecosystem. Ecology 99:204–216
Coleman DC, Crossley DA (1996) Fundamentals of soil ecology. Academic Press, San Diego
Coq S, Souquet J-M, Meudec E, Cheynier V, Hättenschwiler S (2010) Interspecific variation in leaf litter tannins drives decomposition in a tropical rain forest of French Guiana. Ecology 91:2080–2091
Cornwell WK, Cornelissen JHC, Amatangelo K, Dorrepaal E, Eviner VT, Godoy O, Hobbie SEHB, Kurokawa H, Pérez-Harguindeguy N, Quested HM, Santiago LS, Wardle DA, Wright IJ, Aerts R, Allison SD, van Bodegom P, Brovkin V, Chatain A, Callaghan TV, Diaz S, Garnier E, Gurvich DE, Kazakou E, Klein JA, Read J, Reich RB, Soudzilovskaia JA, Vaieretti MW, Westoby M (2008) Plant species traits are the predominant control on litier decomposition rates within biomes worldwide. Ecol Lett 11:1065–1071
Crutsinger GM, Collins MD, Fordyce JA, Gompert Z, Nice CC, Sanders NJ (2006) Plant genotypic diversity predicts community structure and governs an ecosystem process. Science 313:966–968
Dawud SM, Raulund-Rasmussen K, Ratcliffe S, Domisch T, Finér L, Joly FX, Hättenschwiler S, Vesterdal L (2017) Tree species functional group is a more important driver of soil properties than tree species diversity across major European forest types. Funct Ecol 31:1153–1162
De Deyn GB, van der Putten WH (2005) Linking aboveground and belowground diversity. Trends Ecol Evol 20:625–633
De Vries FT, Manning P, Tallowin JRB, Mortimer SR, Pilgrim ES, Harrison KA, Hobbs PJ, Quirk H, Shipley B, Cornelissen JHC, Kattge J, Bardgett RD (2012) Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities. Ecol Lett 15:1230–1239
Díaz S, Hodgson JG, Thompson K, Cabido M, Cornelissen JHC, Jalili A, Montserrat-Martí G, Grime JP, Zarrinkamar F, Asri Y, Band SR, Basconcelo S, Castro- Díez P, Funes G, Hamzehee B, Khoshnevi M, Pérez-Harguindeguy N, Pérez-Rontomé MC, Shirvany FA, Vendramini F, Yazdani S, Abbas-Azimi R, Bogaard A, Boustani S, Charles M, Dehghan M, De Torres-Espuny L, Falczuk V, Guerrero-Campo J, Hynd A, Jones G, Kowsary E, Kazemi-Saeed F, Maestro-Martínez M, Romo-Díez A, Shaw S, Siavash B, Villar-Salvador P, Zak MR (2004) The plant traits that drive ecosystems: Evidence from three continents. Journal of Vegetation Science 15:295–304
Díaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Prentice IC, Garnier E, Bönisch G, Westoby M, Poorter H, Reich PB, Moles AT, Dickie J, Gillison AN, Zanne AE, Pierce S, Shipley B, Kirkup D, Casanoves F, Joswig JS, Günther A, Falczuk V (2016) The global spectrum of plant form and function. Nature 529:167–171
Eisenhauer N, Vogel A, Jensen B, Scheu S (2018) Decomposer diversity increases biomass production and shifts aboveground-belowground biomass allocation of common wheat. Sci Rep 8:17894
Epps KY, Comerford NB, Reeves JB III, Cropper WP Jr, Araujo QR (2007) Chemical diversity—highlighting a species richness and ecosystem function disconnect. Oikos 116:1831–1840
Fanin N, Bertrand I (2016) Aboveground litter quality is a better predictor than belowground microbial communities when estimating carbon mineralization along a land-use gradient. Soil Biol Biochem 94:48–60
Feeny P (1970) Seasonal changes in oak leaf tannins an nutrients as a cause of spring feeding by winter moth caterpillars. Ecology 51:565–579
Fierer N, Strickland MS, Liptzin D, Bradford MA, Cleveland CC (2009) Global patterns in belowground communities. Ecol Lett 12:1238–1249
Finerty GE, de Bello F, Bílá K, Berg MP, Dias ATC, Pezzatti GB, Moretti M (2016) Exotic or not, leaf trait dissimilarity modulates the effect of dominant species on mixed litter decomposition. J Ecol 104:1400–1409
Fisher JB, Sweeney S, Brzostek ER, Evans TP, Johnson DJ, Myers JA, Bourg NA, Wolf AT, Howe RW, Phillips RP (2016) Tree-mycorrhizal associations detected remotely from canopy spectral properties. Glob Chang Biol 22:2596–2607
Floudas D, Binder M, Riley R, Barry K, Blanchette RA, Henrissat B, Martínez AT, Otillar R, Spatafora JW, Yadav JS, Aerts A, Benoit I, Boyd A, Carlson A, Copeland A, Coutinho PM, de Vries RP, Ferreira P, Findley K, Foster B, Gaskell J, Glotzer D, Górecki P, Heitman J, Hesse C, Hori C, Igarashi K, Jurgens JA, Kallen N, Kersten P, Kohler A, Kües U, Kumar TKA, Kuo A, LaButti K, Larrondo LF, Lindquist E, Ling A, Lombard V, Lucas S, Lundell T, Martin R, McLaughlin DJ, Morgenstern I, Morin E, Murat C, Nagy LG, Nolan M, Ohm RA, Patyshakuliyeva A, Rokas A, Ruiz-Dueñas FJ, Sabat G, Salamov A, Samejima M, Schmutz J, Slot JC, St. John F, Stenlid J, Sun H, Sun S, Syed K, Tsang A, Wiebenga A, Young D, Pisabarro A, Eastwood DC, Martin F, Cullen D, Grigoriev IV, Hibbett DS (2012) The Paleozoic origin of enzymatic lignin decomposition reconstructed from 31 fungal genomes. Science 336:1715–1719
Fontaine S, Barot S, Barré P, Bdioui N, Mary B, Rumpel C (2007) Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature 450:277–280
Fortunel C, Garnier E, Joffre R, Kazakou E, Quested H, Grigulis K, Lavorel S, Ansquer P, Castro H, Cruz P, Doležal J, Eriksson O, Freitas H, Golodets C, Jouany C, Kigel J, Kleyer M, Lehsten V, Lepš J, Meier T, Pakeman R, Papadimitriou M, Papanastasis VP, Quétier F, Robson M, Sternberg M, Theau J-P, Thébault A, Zarovali M (2009) Leaf traits capture the effects of land use changes and climate on litter decomposability of grasslands across Europe. Ecology 90:598–611
Frainer A, Moretti MS, Xu W, Gessner MO (2015) No evidence for leaf-trait dissimilarity effects on litter decomposition, fungal decomposers, and nutrient dynamics. Ecology 96:550–561
Freschet GT, Aerts R, Cornelissen JHC (2012) A plant economics spectrum of litter decomposability. Funct Ecol 16:56–65
Frey BR, Lieffers VJ, Hogg EH, Landhausser SM (2004) Predicting landscape patterns of aspen dieback: mechanisms and knowledge gaps. Can J For Res 34:1379–1390
Fujii S, Mori AS, Koide D, Makoto K, Matsuoka S, Osono T, Isbell F (2017) Disentangling relationships between plant diversity and decomposition processes under forest restoration. J Appl Ecol 54:80–90
Funk JL, Larson JE, Ames GM, Butterfield BJ, Cavender-Bares J, Firn J, Laughlin DC, Sutton-Grier AE, Williams L, Wright J (2017) Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol Rev 92:1156–1173
Gamon J (2008) Tropical remote sensing: opportunities and challenges. In: Kalacska M, Sanchez-Azofeifa GA (eds) Hyperspectral remote sensing of tropical and subtropical forests. CRC Press, New York, pp 297–305
García-Palacios P, Prieto I, Ourcival JM, Hättenschwiler S (2016) Disentangling the litter quality and soil microbial contribution to leaf and fine root litter decomposition responses to reduced rainfall. Ecosystems 19:490–503
Gartner TB, Cardon ZG (2004) Decomposition dynamics in mixed-species leaf litter. Oikos 104:230–246
Gessner MO, Swan CM, Dang CK, McKie BG, Bardgett RD, Wall DH, Hättenschwiler S (2010) Diversity meets decomposition. Trends Ecol Evol 25:372–380
Gholizadeh H, Gamon JA, Townsend PA, Zygielbaum AI, Helzer CJ, Hmimina GY, Yu R, Moore RM, Schweiger AK, Cavender-Bares J (2019) Detecting prairie biodiversity with airborne remote sensing. Remote Sens Environ 221:38–49
Gillon D, Joffre R, Dardenne P (1993) Predicting the stage of decay of decomposing leaves by near-infrared reflectance spectroscopy. Can J For Res-Revue Canadienne de Recherche Forestiere 23:2552–2559
Gillon D, Houssard C, Joffre R (1999) Using near- infrared reflectance spectroscopy to predict carbon, nitrogen and phosphorus content in heterogeneous plant material. Oecologia 118:173–182
Gould IJ, Quinton JN, Weigelt A, De Deyn GB, Bardgett RD (2016) Plant diversity and root traits benefit physical properties key to soil function in grasslands. Ecol Lett 19:1140–1149
Grossman JJ, Vanhellemont M, Barsoum N, Bauhus J, Bruelheide H, Castagneyrol B, Cavender-bares J, Eisenhauer N, Ferlian O, Gravel D, Hector A, Jactel H, Kreft H, Mereu S, Messier C, Muys B, Nock C, Paquette A, Parker J, Perring MP, Ponette Q, Reich PB, Schuldt A, Staab M, Weih M, Clara D, Scherer-lorenzen M, Verheyen K (2018) Synthesis and future research directions linking tree diversity to growth, survival, and damage in a global network of tree diversity experiments ☆. Environ Exp Bot 152:68–89
Handa IT, Aerts R, Berendse F, Berg MP, Bruder A, Butenschoen O, Chauvet E, Gessner MO, Jabiol J, Makkonen M, McKie BG, Malmqvist B, Peeters ETHM, Scheu S, Schmid B, Van Ruijven J, Vos VCA, Hättenschwiler S (2014) Consequences of biodiversity loss for litter decomposition across biomes. Nature 509:218–221
Haslam E (1989) Plant polyphenols: vegetable tannins revisited. Cambridge University Press, Cambridge
Hatakka A, Hammel KE (2010) Fungal biodegradation of lignocelluloses. In: Osiewacz HD (ed) The Mycota: a comprehensive treatise on fungi as experimental systems for basic and applied research. Springer, New York, pp 319–334
Hättenschwiler S, Jørgensen HB (2010) Carbon quality rather than stoichiometry controls litter decomposition in a tropical rain forest. J Ecol 98:754–763
Hättenschwiler S, Vitousek P (2000) The role of polyphenols in terrestrial ecosystem nutrient cycling. TREE 15:238–243
Hättenschwiler S, Tiunov AV, Scheu S (2005) Biodiversity and litter decomposition in terrestrial ecosystems. Ann Rev Ecol Evol Syst 36:191–218
Hättenschwiler S, Aeschlimann B, Coûteaux M-M, Roy J, Bonal D (2008) High variation in foliage and leaf litter chemistry among 45 tree species of a neotropical rainforest community. New Phytol 179:165–75
Hättenschwiler S, Coq S, Barantal S, Handa IT (2011) Leaf traits and decomposition in tropical rainforests: revisiting some commonly held views and towards a new hypothesis. New Phytol 189:950–965
Hector A, Beale AJ, Minns A, Otway SJ, Lawton JH (2000) Consequences of the reduction of plant diversity for litter decomposition: effects through litter quality and microenvironment. Oikos 90:357–371
Herms DA, Mattson WJ (1992) The dilemma of plants: to grow or defend. Q Rev Biol 67:283–335
Higuchi T (2006) Look back over the studies of lignin biochemistry. J Wood Sci 52:2–8
Hobbie SE (1992) Effects of plant species on nutrient cycling. Trends Ecol Evol 7:336–339
Hobbie JE, Hobbie EA (2006) 15N in symbiotic fungi and plants estimates nitrogen and carbon flux rates in Arctic tundra. Ecology 87:816–822
Hobbie SE (2015) Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol Evol 30:357–63
Högberg P, Nordgren A, Buchmann N, Taylor AFS, Ekblad A, Högberg MN, Nyberg G, Ottosson-Löfvenius M, Read DJ (2001) Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature 411:789–792
Holden SR, Rogers BM, Treseder KK, Randerson JT (2016) Fire severity influences the response of soil microbes to a boreal forest fire. Environ Res Lett 11:035004
Hooper DU, Bignell DE, Brown VK, Brussard L, Dangerfield JM, Wall DH, Wardle DA, Coleman DC, Giller KE, Lavelle P, van der Putten WH, de Ruiter PC, Rusek J, Silver WL, Tiedje JM, Wolters V (2000) Interactions between aboveground and belowground biodiversity in terrestrial ecosystems: patterns, mechanisms, and feedbacks. Bioscience 50:1049–1061
Hoorens B, Aerts R, Stroetenga M (2003) Does initial litter chemistry explain litter mixture effects on decomposition? Oecologia 137:578–586
Horner JD, Gosz JR, Cates RG (1988) The role of carbon-based plant secondary metabolites in decomposition in terrestrial ecosystems. Am Nat 132:869–883
Huang N, Gu L, Black TA, Wang L, Niu Z (2015) Remote sensing-based estimation of annual soil respiration at two contrasting forest sites 2306–2325
Hughes AR, Inouye BD, Johnson MTJ, Underwood N, Vellend M (2008) Ecological consequences of genetic diversity. Ecol Lett 11:609–623
Jewell MD, Shipley B, Low-Décarie E, Tobner CM, Paquette A, Messier C, Reich PB (2017) Partitioning the effect of composition and diversity of tree communities on leaf litter decomposition and soil respiration. Oikos 126:959–971
Jo I, Fridley JD, Frank DA (2016) More of the same? In situ leaf and root decomposition rates do not vary between 80 native and nonnative deciduous forest species. New Phytol 209:115–122
Johnson MTJ, Agrawal AA (2005) Plant genotype and environment interact to shape a diverse arthropod community on evening primrose (Oenothera biennis). Ecology 86:874–885
Kardol P, Wardle DA (2010) How understanding aboveground-belowground linkages can assist restoration ecology. Trends Ecol Evol 25:670–679
Keiser AD, Knoepp JD, Bradford MA (2013) Microbial communities may modify how litter quality affects potential decomposition rates as tree species migrate. Plant Soil 372:167–176
Kirby R (2006) Actinomycetes and lignin degradation. Adv Appl Microbiol 58:125–168
Knops JMH, Wedin D, Tilman D (2001) Biodiversity and decomposition in experimental grassland ecosystems. Oecologia 126:429–433
Kokaly RF, Clark RN (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ 67:267–287
Kokaly RF, Asner GP, Ollinger SV, Martin ME, Wessman CA (2009) Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens Environ 113:S78–S91
Kraus TEC, Dahlgren RA, Zasoski RJ (2003) Tannins in nutrient dynamics of forest ecosystems—a review. Plant Soil 256:41–66
Krause S et al (2014) Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front Microbiol 5:251
Laliberté E (2017) Tansley insight - Below-ground frontiers in trait-based plant ecology. New Phytol 213:1597–1603
Lambers H, Chapin FS, Pons TL (1998) Plant physiological ecology. Springer-Verlag New York Inc., New York
Landsberg JJ, Waring RH (1997) A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For Ecol Manag 95:209–228
Lauber CL, Strickland MS, Bradford MA, Fierer N (2008) The influence of soil properties on the structure of bacterial and fungal communities across land-use types. Soil Biol Biochem 40:2407–2415
Lee HS, Davies SJ, Lafrankie J, Ashton PS (2002) Floristic and structural diversity of mixed dipterocarp forest in Lambir Hill National Park, Sarawak, Malaysia. J Trop For Sci 14:379–400
Liang J, Crowther TW, Picard N, Wiser S, Zhou M, Alberti G, Schulze E, Mcguire AD, Bozzato F, Pretzsch H, Paquette A, Hérault B, Scherer-lorenzen M, Barrett CB, Glick HB, Hengeveld GM, Nabuurs G, Pfautsch S, Viana H, Vibrans AC, Ammer C, Schall P, Verbyla D, Tchebakova N, Fischer M, Watson JV, Chen HYH, Lei X, Schelhaas M, Lu H, Gianelle D, Parfenova EI, Salas C, Lee E, Lee B, Kim HS, Bruelheide H, Coomes DA, Piotto D, Sunderland T, Schmid B, Gourlet-fleury S, Sonké B, Tavani R, Zhu J, Brandl S, Baraloto C, Frizzera L, Ba R, Oleksyn J, Peri PL, Gonmadje C, Marthy W, Brien TO, Martin EH, Marshall AR, Rovero F, Bitariho R, Niklaus PA, Alvarez-loayza P, Chamuya N, Valencia R, Mortier F, Wortel V, Engone-obiang NL, Ferreira LV (2016) Positive biodiversity-productivity relationship predominant in global forests. Science 354:aaf8957
Lindroth RL, St. Clair SB (2013) Adaptations of quaking aspen (Populus tremuloides Michx.) for defense against herbivores. For Ecol Manag 299:14–21
Lindroth RL, Osier TL, Barnhill HR, Wood SA (2002) Effects of genotype and nutrient availability on phytochemistry of trembling aspen (Populus tremuloides Michx.) during leaf senescence. Biochem Syst Ecol 30:297–307
Litton CM, Raich JW, Ryan MG (2007) Carbon allocation in forest ecosystems. Glob Chang Biol 13:2089–2109
Lohbeck M, Poorter L, Martínez-Ramos M, Bongers F (2015) Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology 96:1242–1252
López-Mondéjar R, Zühlke D, Becher D, Riedel K, Baldrian P (2016) Cellulose and hemicellulose decomposition by forest soil bacteria proceeds by the action of structurally variable enzymatic systems. Sci Rep 6:25279
Madritch MD, Hunter MD (2002) Phenotypic diversity influences ecosystem functioning in an oak sandhills community. Ecology 83:2084–2090
Madritch MD, Hunter MD. 2003. Intraspecific litter diversity and nitrogen deposition affect nutrient dynamics and soil respiration. Oecologia 136:124–8.
Madritch MD, Kingdon CC, Singh A, Mock KE, Lindroth RL, Townsend PA, B PTRS, Madritch MD, Kingdon CC, Singh A, Mock KE, Lindroth RL, Townsend PA (2014) Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales Imaging spectroscopy links aspen genotype with belowground processes at landscape scales
Madritch MD, Lindroth RL (2015) Condensed tannins increase nitrogen recovery by trees following insect defoliation. New Phytol 208:410–420
Madritch MD, Donaldson JR, Lindroth RL (2006) Genetic identity of Populus tremuloides litter influences decomposition and nutrient release in a mixed forest stand. Ecosystems 9:528–537
Madritch MD, Jordan LM, Lindroth RL (2007) Interactive effects of condensed tannin and cellulose additions on soil respiration. Can J For Res 37:2063–2067
Martin ME, Aber JD (1997) High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecol Appl 7:431–443
Martin ME, Newman SD, Aber JD, Congalton RG (1998) Determining forest species composition using high spectral remote sensing data. Remote Sens Environ 65:249–254
Martin ME, Plourde LC, Ollinger SV, Smith M-L, McNeil BE (2008) A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sens Environ 112:3511–3519
Meentemeyer V (1978) Macroclimate and lignin control of litter decomposition rates. Ecology 59(3):465–472
Meier CL, Bowman WD (2008) Links between plant litter chemistry, species diversity, and belowground ecosystem function. Proc Natl Acad Sci 105:19780–19785
Melillo JM, Aber JD, Muratone JF (1982) Nitrogen and lignin control of hardwood leaf litter decomposition dynamics. Ecology 63:621–626
Mitton JB, Grant MC (1996) Genetic variation and the natural history of quaking aspen. Bioscience 46:25–31
Mokany K, Raison RJ, Prokushkin AS (2006) Critical analysis of root:shoot ratios in terrestrial biomes. Glob Change Biol 12:84–96
Mueller KE, Hobbie SE, Chorover J, Reich PB, Eisenhauer N, Castellano MG, Chadwick OA, Dobies T, Hale CM, Jagodziński AM, Kalucka I, Kieliszewska-Rokicka B, Modrzyński J, Rożen A, Skorupski M, Sobczyk Ł, Stasińska M, Trocha LK, Weiner J, Wierzbicka A, Oleksyn J (2015) Effects of litter traits, soil biota, and soil chemistry on soil carbon stocks at a common garden with 14 tree species. Biogeochemistry 123:313–327
Naeem S, Tjossem SF, Byers D, Bristow C, Li S (1999) Plant neighborhood diversity and production. Ecoscience 6:355–365
Nannipieri P, Trasar-Cepeda C, Dick RP (2018) Soil enzyme activity: a brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol Fertil Soils. (2018 54:11–19
Ollinger SV, Smith ML, Martin ME, Hallett RA, Goodale CL, Aber JD (2002) Regional variation in foliar chemistry and N cycling among forests of diverse history and composition. Ecology 83:339–355
Ollinger SV, Reich PB, Frolking S, Lepine LC, Hollinger DY, Richardson AD (2013) Nitrogen cycling, forest canopy reflectance, and emergent properties of ecosystems. Proc Natl Acad Sci U S A 110:E2437
Osono T (2007) Ecology of ligninolytic fungi associated with leaf litter decomposition. Ecol Res 22:955–974
Parsons SA, Lawler IR, Congdon RA, Williams SE (2011) Rainforest litter quality and chemical controls on leaf decomposition with near-infrared spectrometry. J Plant Nutr Soil Sci 174:710–720
Phillips RP, Brzostek E, Midgley MG (2013) The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. New Phytol 199:41–51
Reich PB, Tilman D, Isbell F, Mueller K, Hobbie SE, Flynn DFB, Eisenhauer N (2012) Impacts of biodiversity loss escalate through time as redundancy fades. Science 336:589–592
Reich PB (2014) The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J Ecol 102:275–301
Robinson D (2001) δ15N as an integrator of the nitrogen cycle. Trends Ecol Evol 16:153–162
Rubert-Nason KF, Holeski LM, Couture JJ, Gusse A, Undersander DJ, Lindroth RL (2013) Rapid phytochemical analysis of birch (Betula) and poplar (Populus) foliage by near-infrared reflectance spectroscopy. Anal Bioanal Chem 405:1333–44
Sabetta L, Zaccarelli N, Mancinelli G, Mandrone S, Salvatori R, Costantini ML, Zurlini G, Rossi L (2006) Mapping litter decomposition by remote-detected indicators. Ann Geophys 49:219–226
Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ETA, Salas W (2011) Benchmark map of forest carbon stocks in tropical regions across three continents. 108
Scherer-Lorenzen M, Bonilla JL, Potvin C (2007) Tree species richness affects litter production and decomposition rates in a tropical bio-diversity experiment. Oikos 116:2108–2124
Schimel J, Van Cleve K, Cates R, Clausen T, Reichardt P (1996) Effects of balsam poplar (Populus balsamifera) tannins and low molecular weight phenolics on microbial activity in taiga floodplain soil: implications for changes in N cycling during succession. Can J Bot 74:84–90
Schloss PD, Handelsman J (2006) Toward a census of bacteria in soil. PLoS Comput Biol 2(7):e92
Schneider T, Keiblinger KM, Schmid E, Sterflinger-Gleixner K, Ellersdorfer G, Roschitzki B, Richter A, Eberl L, Zechmeister-Boltenstern S, Riedel K (2012) Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions. ISME J 6:1749–1762
Schnitzer M, Barr M, Hartenstein R (1984) Kinetics and characteristics of humic acids produced from simple phenols. Soil Biol Biochem 16:371–376
Schöler A, Jacquiod S, Vestergaard G, Schulz S, Schloter M (2017) Analysis of soil microbial communities based on amplicon sequencing of marker genes. 485–489
Schweiger AK, Cavender-Bares J, Townsend PA, Hobbie SE, Madritch MD, Wang R, Tilman D, Gamon JA (2018) Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat Ecol Evol 2:976. https://doi.org/10.1038/s41559-018-0551-1
Schweitzer JA, Bailey JK, Hart SC, Wimp GM, Chapman SK, Whitham TG (2005) The interaction of plant genotype and herbivory deccelerate leaf litter decomposition and alter nutrient dynamics. Oikos 110:133–145
Serbin SP, Singh A, McNeil BE, Kingdon CC, Townsend PA (2014) Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecol Appl 24:1651–1669
Serbin SP, Singh A, Desai AR, Dubois SG, Jablonski AD, Kingdon CC, Kruger EL, Townsend PA (2015) Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sens Environ 167, 78–87
Shepherd KD, Vanlauwe B, Gachengo CN, Palm CA (2005) Decomposition and mineralization of organic residues predicted using near infrared spectroscopy. Plant and Soil 277:315–333.
Shields WJ, Bockheim JG (1981) Deterioration of trembling aspen clones in the Great-Lakes region. Can J For Res 11:530–537
Smith VC, Bradford MA (2003) Do non-additive effects on decomposition in litter-mix experiments result from differences in resource quality between litters? Oikos 102:235–242
Soriano-Disla JM, Janik LJ, Viscarra Rossel RA, Macdonald LM, McLaughlin MJ (2014) The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl Spectrosc Rev 49:139–186
Srivastava DS, Cardinale BJ, Downing AL, Duffy JE, Jouseau C, Sankaran M, Wright JP (2009) Diversity has stronger top-down than bottom-up effects on decomposition. Ecology 90:1073–1083
Stenberg B, Rossel RAV, Mouazen AM, Wetterlind J (2010) Visible and near infrared spectroscopy in soil science. In: Sparks DL (ed) Advances in agronomy, vol 107, pp 163–215
Talbot JM, Yelle DJ, Nowick J, Treseder KK (2012) Litter decay rates are determined by lignin chemistry. Biogeochemistry 108:279–295
Tenney FG, Waksman SA (1929) Composition of natural organic materials and their decomposition in the soil. IV. The nature and rapidity of decomposition of the various organic complexes in different plant materials, under aerobic conditions. Soil Sci 28:55–84
Thers H, Brunbjerg AK, Læssøe T, Ejrnæs R, Bøcher PK, Svenning J (2017) Lidar-derived variables as a proxy for fungal species richness and composition in temperate Northern Europe. Remote Sens Environ 200:102–113
Thevenot M, Dignac MF, Rumpel C (2010) Fate of lignins in soils: a review. Soil Biol Biochem 42:1200–1211
Tilman D, Reich PB, Knops J, Wedin D, Mielke T, Lehman C (2001) Diversity and productivity in a long-term grassland experiment. Science 294:843–845
Tilman D, Reich PB, Knops J (2006) Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441:629–632
Townsend PA, Foster JR, Chastain RA, Currie WS (2003) Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE Trans Geosci Remote Sens 41:1347–1354
Townsend PA, Serbin SP, Kruger EL, Gamon JA (2013) Disentangling the contribution of biological and physical properties of leaves and canopies in imaging spectroscopy data. Proc Natl Acad Sci 110:E1074–E1074
Triebwasser DJ, Tharayil N, Preston CM, Gerard PD (2012) The susceptibility of soil enzymes to inhibition by leaf litter tannins is dependent on the tannin chemistry, enzyme class and vegetation history. New Phytol 196:1122–1132
Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54:523
Vitousek P, Asner GP, Chadwick OA, Hotchkiss S (2009) Landscape-level variation in forest structure and biogeochemistry across a substrate age gradient in Hawaii. Ecology 90:3074–3086
Wang Z, Townsend PA, Schweiger AK, Couture JJ, Singh A, Hobbie SE, Cavender-Bares J (2019) Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sens Environ 221:405–416
Wardle DA (1999) Is “sampling effect” a problem for experiments investigating biodiversity-ecosystem function relationships? Oikos 87:403–407
Wardle DA, Nicholson KS (1996) Synergistic effects of grassland plant species on soil microbial biomass and activity: implications for ecosystem-level effects of enriched plant diversity. Funct Ecol 10:410–416
Wardle DA, Bonner KI, Barker GM (2000) Stability of ecosystem properties in response to above-ground functional group richness and composition. Oikos 89:11–23
Wardle DA, Bardgett RD, Klironomos JN, Setälä H, van der Putten WH, Wall DH (2004) Ecological linkages between aboveground and belowground biota. Science 304:1629–1633
Weisser WW, Roscher C, Meyer ST, Ebeling A, Luo G, Allan E, Beßler H, Barnard RL, Buchmann N, Buscot F, Engels C, Fischer C, Fischer M, Gessler A, Gleixner G, Halle S, Hildebrandt A, Hillebrand H, de Kroon H, Lange M, Leimer S, Le Roux X, Milcu A, Mommer L, Niklaus PA, Oelmann Y, Proulx R, Roy J, Scherber C, Scherer-Lorenzen M, Scheu S, Tscharntke T, Wachendorf M, Wagg C, Weigelt A, Wilcke W, Wirth C, Schulze E-D, Schmid B, Eisenhauer N (2017) Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: patterns, mechanisms, and open questions. Basic Appl Ecol 23:1–73
Wessman CA, Aber JD, Peterson DL, Melillo JM (1988) Remote sensing of canopy chemistry and nitrogen cycling in temparate forest ecosystems. Nature 335:154–156
Williams LJ, Paquette A, Cavender-Bares J, Messier C, Reich PB (2017) Spatial complementarity in tree crowns explains overyielding in species mixtures. Nat Ecol Evol 1:0063
Wimp GM, Young WP, Woolbright SA, Martinsen GD, Keim P, Whitham TG (2004) Conserving plant genetic diversity for dependent animal communities. Ecol Lett 7:776–780
Wimp GM, Martinsen GD, Floate KD, Bangert RK, Whitham TG (2005) Plant genetic determinants of arthropod community structure and diversity. Evolution 59:61–69
Worrall JJ, Egeland L, Eager T, Mask RA, Johnson EW, Kemp PA, Shepperd WD (2008) Rapid mortality of Populus tremuloides in southwestern Colorado, USA. For Ecol Manag 255:686–696
Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender- Bares J, Chapin T, Cornelissen JHC, Diemer M, Flexas J, Garnier E, Groom PK, Gulias J, Hikosaka K, Lamont BB, Lee T, Lee W, Lusk C, Midgley JJ, Navas M-L, Niinemets Ü, Oleksyn J, Osada N, Poorter H, Poot P, Prior L, Pyankov VI, Roumet C, Thomas SC, Tjoelker MG, Veneklaas EJ, Villar R (2004) The worldwide leaf economics spectrum. Nature 428:821–827
Wu T, Ayres E, Bardgett RD, Wall DH, Garey JR (2011) Molecular study of worldwide distribution and diversity of soil animals. PNAS 108:17720–17725
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Madritch, M., Cavender-Bares, J., Hobbie, S.E., Townsend, P.A. (2020). Linking Foliar Traits to Belowground Processes. In: Cavender-Bares, J., Gamon, J.A., Townsend, P.A. (eds) Remote Sensing of Plant Biodiversity. Springer, Cham. https://doi.org/10.1007/978-3-030-33157-3_8
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