2.1.6 Foliar stoichiometry and nutrient resorption

Author: Soper FM1

Reviewer: Peñuelas J2,3, Estiarte M2,3


Measurement unit: mg g-1 or%; Measurement scale: plot; Equipment costs: €; Running costs: €-€€; Installation effort: low to medium; Maintenance effort: -; Knowledge need: low; Measurement mode: manual

Nitrogen (N) and phosphorus (P) are the most widely measured foliar nutrients because they are essential to leaf functions such as photosynthesis and because either or both may limit primary production in terrestrial ecosystems (Elser et al., 2007). Other elements (such as potassium and micronutrients) may also be of interest in specific situations where they are thought to be limiting (Fay et al., 2015; Sardans & Peñuelas, 2015; Kaspari & Powers, 2016). Controls over N and P concentrations and stoichiometric ratios in foliar tissue are complex, but may include environmental factors such as climate, soil type, and nutrient availability, as well as phylogeny, functional type, competition, or interactions with herbivores and parasites (Elser et al., 2010; Rivas-Ubach et al., 2012; Stock & Verboom, 2012; Borer et al., 2015). Stoichiometric ratios have been interpreted as one indicator of relative N or P limitation (Tessier & Raynal, 2003) although they should be interpreted cautiously as findings do not always agree with other limitation metrics such as fertilisation responses (Craine et al., 2008). Foliar stoichiometry and nutrient resorption are of interest in climate-change experiments or observations because they integrate plant metabolic responses to stress. Drought and warming, for example, have been shown to decrease N resorption in temperate trees, while elevated CO2 typically increases foliar C:N and C:P ratios (Sardans et al., 2012; Suseela & Tharayil, 2017). These metrics can also capture changes in ecosystem nutrient supply that result from climate-driven shifts in microbial nutrient cycling or from other other global-change impacts such as N deposition (which can decrease C:N ratios) (Elser et al., 2010; Suseela & Tharayil, 2017).

Perennial plants typically resorb N, P, and other nutrients from leaves prior to abscission, in order to store and recycle nutrients for the construction of future tissues. Thus, the degree of resorption is an important determinant of total plant nutrient availability (and therefore growth, photosynthesis, etc.), nutrient-use efficiency, and fluxes of nutrients between live biomass and necromass (Reed et al., 2012; Brant & Chen, 2015). These resorption values have been identified as an important parameter in ecosystem and biogeochemical models (Vergutz et al., 2012). Because resorption affects the quality of plant litter (e.g. C:N, C:P, or N:P ratios), it may also influence rates of litter decomposition and thus ecosystem nutrient cycling more broadly (Manzoni et al., 2008; Mooshammer et al., 2012; Suseela & Tharayil, 2017).

Globally, P-resorption efficiency has been shown to be higher than N-resorption efficiency (global leaf N:P ratio = 28:1, global litter N:P ratio = 45:1) but can be highly variable (Zechmeister-Bolstenstern et al., 2015). The degree of resorption can be influenced by factors that also drive stoichiometry such as species, functional type, climate, and external nutrient availability, as well as by initial nutrient status and stresses including early frost or storms that cause leaf damage or premature abscission (Killingbeck, 1988; Reed et al., 2012; Xu et al., 2017). Additionally, the degree of resorption may also be cautiously interpreted as an indicator of relative nutrient limitation (Killingbeck, 1996; Reed et al., 2012; Vergutz et al., 2012, Zechmeister-Bolstenstern et al., 2015), although care should be taken to account for potential interactions between elements (See et al., 2015). What and how to measure?

Sampling of live foliage (nutrient concentrations) or litter (resorption) is the most common measurement approach, although remote sensing may also be appropriate for estimating some foliar nutrients at broader scales. Manual sampling is straightforward and relatively low cost (depending on sampling intensity, canopy accessibility, and analysis method). Although destructive, this method requires relatively little tissue (in the order of a few grams dry material), most of which is used to generate a homogenous sample, rather than required for analysis.

The difficulty of sampling foliage is dependent on plant growth form. For vegetation up to several metres in height, full sun leaves can generally be reached directly or with the aid of a pole pruner. Beyond that, tall trees can often be sampled by shotgun or slingshot, but otherwise may require technical tree climbing skills and equipment.

Sampling requires collection of fully expanded, healthy, green, sun-lit and shade leaves. When sun and shade leaves are analysed separately, leaf nutrient concentration is taken as the weighted mean considering the proportion of each leaf type. Because inter-specific variation can be large (Townsend et al., 2007; Reed et al., 2012), individual species should be considered separately rather than pooled. In seasonal ecosystems, the best time to sample is at the height of the growing season, when foliar N has stabilised (but see Zhang et al., 2013) but before any signs of senescence have begun. It is preferable to sample multiple leaves from each plant (e.g. in each cardinal direction) and either combine them to form a homogenous sample or to analyse them separately to quantify variability. As foliar N and P can vary with leaf age in evergreens (Mediavilla & Escudero, 2003), care should be taken to collect a representative sample of the whole plant if individual leaf cohorts cannot be distinguished. The decision of whether to include petioles with leaves in analysis is discussed in Pérez-Harguindeguy et al. (2013).

Resorption measurements necessitate returning to resample the same individuals (or the same species within the same area) by collecting recently dropped, fully senesced leaves. To avoid bias, it is preferable that the same individual repeats sampling. Fallen leaves can be collected with litter traps or tarps, or by shaking from the plant, and should preferably be collected soon after abscission to avoid leaching of nutrients and decomposition that can begin quickly, especially when wet. In mixed species stands, it may be necessary to identify and sort litter by species.

Leaves should preferably be sampled into paper, rather than plastic, bags unless they will be dried immediately. Samples can be air dried if necessary, and ultimately placed in a drying oven at a maximum of 70 °C for 72h until they have reached constant weight (Pérez-Harguindeguy et al., 2013). The temperature and time for drying depend on the study question, how many samples are dried, the size, thickness and type of the plant material (e.g. large, fleshy or succulent leaves need more time) see protocol 2.1.1 Aboveground plant biomass for more details on the drying. Tissue is then ground to a fine powder using a ball or Wiley Mill, a mortar and pestle and liquid N, or an electric coffee grinder. The latter is an economical solution that can work well for moderately tough leaves.

To generate stoichiometric ratios (e.g. C:N or N:P) it may be necessary to employ more than one analytical method. Sample nutrient concentrations can be measured in a variety of ways, an overview of which can be found in van Heerwaarden et al. (2003). Generally, colorimetric methods have a lower accuracy and a higher detection limit than elemental analysers, but are more cost effective and require less specialised analytical equipment. Elemental analysis services are commonly provided by commercial or university service lab facilities. If isotopic analysis is performed on foliar samples, C and N content can be derived from these.

Foliar nutrient concentrations can be expressed either as a percent value (%) on a dry mass basis or in units of mg g-1 dry mass. For needle leaves, nutrient concentrations are commonly expressed per unit area rather than mass (Smith et al., 1981). Resorption is usually calculated as efficiency (percent of initial nutrients resorbed; high value = more efficient resorption), but reference can also be made to proficiency (concentration of nutrients left in sensed tissue; low value = more proficient resorption; Killingbeck, 1996).

Resorption efficiency, calculated as percent C or N content in leaves (Killingbeck, 1996):

[live] – [senesced] / [live] * 100

Resorption values can also be expressed per unit leaf area, or can be corrected to account for mass loss during senescence (van Heerwaarden et al., 2003; Vergutz et al., 2012). To correct for leaf mass loss the shrinkage in LMA between green leaves and leaf litter must be known.

There is a large body of literature on both foliar nutrient concentrations and resorption ratios available to contextualise results across many ecosystem types (e.g. Killingbeck, 1996; Townsend et al., 2007; Reed et al., 2012; Vergutz et al., 2012; Xu et al., 2017).


Where to start

Brant & Chen (2015), Elser et al. (2010), Killingbeck (1996), Vergutz et al. (2012), Zechmeister-Boltenstern et al. (2015) Special cases, emerging issues, and challenges

Loss of tissue prior to senescence

Foliar tissue can be lost from plants prior to the completion of normal senescence and nutrient resorption, often as a result of stresses such as frost or hurricane damage (Killingbeck, 1988) or drought (Estiarte & Peñuelas, 2015). This process would tend to reduce nutrient resorption and can be quantified as described above if suitable samples can be obtained.


As perennial grasses might not obviously drop senesced leaves, resorption can be measured by tagging fully expanded live leaves and monitoring them until they are considered thoroughly senesced by turning yellow or brown (e.g. Lü et al., 2011).

Nutrients in other plant tissues

Nutrient concentrations and resorption may also be of interest in other tissues with high turnover rates, particularly fine roots. While the protocol is essentially the same, designing root sampling is more challenging and requires additional considerations including the definition of fine roots, potential mass loss, the ability to separate roots of co-occurring species, and the ability to distinguish senescence (Kunkle et al., 2009; McCormack et al., 2015).

Remotely-sensed foliar nutrients

Airborne imaging spectroscopy by high spatial resolution (< 5 m) platforms mounted on drones or airplanes is increasingly used to measure foliar nutrients, most notably N (Asner & Martin, 2008; Lepine et al., 2016; also see protocol 5.12 Reflectance assessment of plant physiological status). Although these measures are generally applied at the plot or landscape scale, individual species and crowns can be identified from remotely sensed data with increasing accuracy (e.g. Chadwick & Asner, 2016). Platforms that measure foliar N can often concurrently provide some information on a suite of other chemical traits such as lignin or cellulose content (Martin & Aber, 1997; Asner et al., 2015). References

Theory, significance, and large datasets

Brant & Chen (2015), Reed et al. (2012), Townsend et al. (2007), Vergutz et al. (2012), Xu et al. (2017)


More on methods and existing protocols

Pérez-Harguindeguy et al. (2013), van Heerwaarden et al. (2003)


All references

Asner, G. P. & Martin, R.E.  (2008). Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels. Remote Sensing of Environment, 112(10), 3958-3970.

Asner, G. P., Martin, R. E., Anderson, C. B., & Knapp, D. E. (2015). Quantifying forest canopy traits: Imaging spectroscopy versus field survey. Remote Sensing of Environment, 158(C), 1-13.

Borer, E. T., Lind, E. M., Ogdahl, E. J., Seabloom, E. W., Tilman, D., Montgomery, R. A., & Kinkel, L. L. (2015). Food-web composition and plant diversity control foliar nutrient content and stoichiometry. Journal of Ecology, 103(6), 1432-1441.

Brant, A. N., & Chen, H. Y. H. (2015). Patterns and mechanisms of nutrient resorption in plants. Critical Reviews in Plant Sciences, 34(5), 471-486.

Chadwick, K., & Asner, G. (2016). Organismic-scale remote sensing of canopy foliar traits in lowland tropical forests. Remote Sensing, 8(2), 87.

Craine, J. M., Morrow, C., & Stock, W. D. (2008). Nutrient concentration ratios and co-limitation in South African grasslands. New Phytologist, 179(3), 829-836.

Elser, J. J., Bracken, M. E. S., Cleland, E. E., Gruner, D. S., Harpole, W. S., Hillebrand, H., … Smith, J. E. (2007). Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecology Letters, 10(12), 1135-1142.

Elser, J. J., Fagan, W. F., Kerkhoff, A. J., Swenson, N. G., & Enquist, B. J. (2010). Biological stoichiometry of plant production: metabolism, scaling and ecological response to global change. New Phytologist, 186(3), 593-608.

Estiarte, M. & Peñuelas, J. (2015). Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Global Change Biology, 21(3): 1005-1017.

Fay, P. A., Prober, S. M., Harpole, W. S., Knops, J. M. H., Bakker, J. D., Borer, E. T., … Yang, L. H. (2015). Grassland productivity limited by multiple nutrients. Nature Plants, 1(7), 15080.

Kaspari, M., & Powers, J. S. (2016). Biogeochemistry and geographical ecology: embracing all twenty-five elements required to build organisms. The American Naturalist, 188(S1), S62-S73.

Killingbeck, K. T. (1988). Hurricane-induced modification of nitrogen and phosphorus resorption in an aspen clone: an example of diffuse disturbance. Oecologia, 75(2), 213-215.

Killingbeck, K. T. (1996). Nutrients in senesced leaves: Keys to the search for potential resorption and resorption proficiency. Ecology, 77(6), 1716-1727.

Kunkle, J. M., Walters, M. B., & Kobe, R. K. (2009). Senescence-related changes in nitrogen in fine roots: mass loss affects estimation. Tree Physiology, 29(5), 715-723.

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Lü, X. T., Freschet, G. T., Flynn, D. F. B., & Han, X. G. (2011). Plasticity in leaf and stem nutrient resorption proficiency potentially reinforces plant–soil feedbacks and microscale heterogeneity in a semi‐arid grassland. Journal of Ecology, 100(1), 144-150.

Manzoni, S., Jackson, R. B., Trofymow, J. A., & Porporato, A. (2008). The global stoichiometry of litter nitrogen mineralization. Science, 321(5889), 684-686.

Martin, M. E., & Aber, J. D. (1997). High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecological Applications, 7(2), 431-443.

McCormack, M. L., Dickie, I. A., Eissenstat, D. M., Fahey, T. J., Fernandez, C. W., Guo, D., … Zadworny, M. (2015). Redefining fine roots improves understanding of below-ground contributions to terrestrial biosphere processes. New Phytologist, 207(3), 505-518.

Mediavilla, S., & Escudero, A. (2003). Leaf life span differs from retention time of biomass and nutrients in the crowns of evergreen species. Functional Ecology, 17(4), 541-548.

Mooshammer, M., Wanek, W., Schnecker, J., Wild, B., Leitner, S., Hofhansl, F., … Richter, A. (2012). Stoichiometric controls of nitrogen and phosphorus cycling in decomposing beech leaf litter. Ecology, 93(4), 770-782.

Pérez-Harguindeguy, N., Diaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., … Cornelissen, J. H. C. (2013). New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany, 61(3), 167-234.

Reed, S. C., Townsend, A. R., Davidson, E. A., & Cleveland, C. C. (2012). Stoichiometric patterns in foliar nutrient resorption across multiple scales. New Phytologist, 196(1), 173-180.

Rivas-Ubach, A., Sardans, J., Perez-Trujillo, M., Estiarte, M., & Peñuelas, J. (2012). Strong relationship between elemental stoichiometry and metabolome in plants. Proceedings of the National Academy of Sciences USA, 109(11), 4181-4186.

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Smith, R. B., Waring, R. H., & Perry, D. A. (1981). Interpreting foliar analyses from Douglas fir as weight per unit of leaf area. Canadian Journal of Forest Research, 11(3), 593-598.

Stock, W. D., & Verboom, G. A. (2012). Phylogenetic ecology of foliar N and P concentrations and N:P ratios across Mediterranean-type ecosystems. Global Ecology and Biogeography, 21(12), 1147-1156.

Suseela, V., & Tharayil, N. (2017). Decoupling the direct and indirect effects of climate ion plant litter decomposition: Accounting for stress-induced modifications in plant chemistry. Global Change Biology, 24, 1428-1451.

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Townsend, A. R., Cleveland, C. C., & Asner, G. P. (2007). Controls over foliar N: P ratios in tropical rain forests. Ecology, 88, 107-118.

Van Heerwaarden, L. M., Toet, S., & Aerts, R. (2003). Current measures of nutrient resorption efficiency lead to a substantial underestimation of real resorption efficiency: facts and solutions. Oikos, 101(3), 664-669.

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Zechmeister-Boltenstern, S., Keiblinger, K. M., Mooshammer, M., Peñuelas, J., Richter, A., Sardans, J., & Wanek, W. (2015). The application of ecological stoichiometry to plant-microbial-soil organic matter transformation. Ecological Monographs, 85(2), 133-155.

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Author: Soper FM1

Reviewer: Peñuelas J2,3, Estiarte M2,3



1 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, USA

2 CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

3 CREAF, Cerdanyola del Vallès, Spain