A Framework to Assess Biogeochemical Response to Ecosystem Disturbance Using Nutrient Partitioning Ratios

Ecosystems (2016) 19: 387–395 DOI: 10.1007/s10021-015-9934-1 Ó 2015 Her Majesty the Queen in Right of Canada

J. Marty Kranabetter,1* Kendra K. McLauchlan,2 Sara K. Enders,3 Jennifer M. Fraterrigo,4 Philip E. Higuera,5 Jesse L. Morris,6 Edward B. Rastetter,7 Rebecca Barnes,8 Brian Buma,9 Daniel G. Gavin,10 Laci M. Gerhart,2 Lindsey Gillson,11 Peter Hietz,12 Michelle C. Mack,13 Brenden McNeil,14 and Steven Perakis15

1Ministry of Forests, Lands, and Natural Resource Operations, PO Box 9536, Stn Prov Govt, Victoria, British Columbia V8W 9C4, Canada; 2Department of Geography, Kansas State University, 118 Seaton Hall, Manhattan, Kansas 66506, USA; 3Department of Land, Air, & Water Resources, University of California, One Shields Avenue, Davis, California 95616-8627, USA; 4Department of Natural Resources and Environmental Sciences, University of Illinois, 1102 South Goodwin Ave, Urbana, Illinois 61810, USA; 5Department of Forest Rangeland and Fire Sciences, University of Idaho, 875 Perimeter Drive, Moscow, Idaho 83844, USA; 6Department of Forest Rangeland, and Fire Sciences, University of Idaho, 875 Perimeter Drive, Moscow, Idaho 83844, USA; 7The Marine Biological Labo- ratory, The Ecosystem Center, 7 MBL Street, Woods Hole, Massachusetts 02543, USA; 8Environmental Program, Colorado College, 14 E Cache La Poudre, Colorado Springs, Colorado 80903, USA; 9University of Alaska Southeast, 11120 Glacier Hwy, Juneau, Alaska 99801, USA; 10Department of Geography, 1251 University of Oregon, Eugene, Oregon 97403-1251, USA; 11Plant Conservation Unit, Department of Biological Sciences, University of Cape Town, Private Bag X3 7701, Rondebosch, South Africa; 12Institute of Botany, University of Natural Resources and Life Sciences, Gregor Mendel-Str. 33, 1180 Vienna, Austria; 13Center for Ecosystem Science and Society, Northern Arizona University, PO Box 5620, Flagstaff, Arizona 86011, USA; 14Department of Geology and Geography, West Virginia University, P.O. Box 6300, Morgantown, West Virginia 26506, USA; 15US Geological Survey, Forest and Rangeland Ecosystem Science Center, 3200 SW Jefferson Way, Corvallis, Oregon 97331, USA

ABSTRACT Disturbances affect almost all terrestrial ecosystems, ratio of soil and plant nutrient stocks in mature but it has been difﬁcult to identify general princi- ecosystems represents a characteristic site property. ples regarding these inﬂuences. To improve our Focusing on nitrogen (N), we hypothesize that this understanding of the long-term consequences of partitioning ratio (soil N: plant N) will undergo a disturbance on terrestrial ecosystems, we present a predictable trajectory after disturbance. We inves- conceptual framework that analyzes disturbances tigate the nature of this partitioning ratio with by their biogeochemical impacts. We posit that the three approaches: (1) nutrient stock data from forested ecosystems in North America, (2) a process-based ecosystem model, and (3) conceptual shifts in site nutrient availability with altered dis- Received 27 April 2015; accepted 16 September 2015; turbance frequency. Partitioning ratios could be published online 18 November 2015 applied to a variety of ecosystems and successional Author Contributions The study was conceived and designed during a Novus-RCN workshop with the full participation of all the authors; states, allowing for improved temporal scaling of JMK, KKM, and JLM analyzed data; SKE, JMF, PEH, EBR contributed disturbance events. The generally short-term new models; JMK and KKM wrote manuscript with contributions from empirical evidence for recovery trajectories of all the co-authors. *Corresponding author; e-mail: [email protected] nutrient stocks and partitioning ratios suggests two

387 388 J.M. Kranabetter and others areas for future research. First, we need to recog- availability. Long-term studies, with repeated nize and quantify how disturbance effects can be sampling of soils and vegetation, will be essential in accreting or depleting, depending on whether their further developing this framework of biogeo- net effect is to increase or decrease ecosystem chemical response to disturbance. nutrient stocks. Second, we need to test how altered disturbance frequencies from the present Key words: Disturbance; Fire regime; Succession; state may be constructive or destructive in their Multiple element limitation (MEL) model; Nitrogen effects on biogeochemical cycling and nutrient stocks; Nutrient ratio.

INTRODUCTION and others 2013). Yet, key uncertainties remain in both the consequences of disturbance events and their An ecosystem disturbance is a ‘‘relatively discrete impacts at landscape scales. Thus it has been difficult, event in time that disrupts ecosystem, community for example, to quantify the direction and magnitude or population structure and changes resource, of biotic disturbances on forest C cycling in the United substrate availability, or the physical environment’’ States and Canada (that is, Hicke and others 2012). (Pickett and White 1985), which encompasses both Nonetheless, generalization of disturbance ecology a wide variety of natural disturbance types (fires, would benefit from a theoretical biogeochemical storms, landslides, flooding, volcanic eruptions) framework which would apply across single and and terrestrial biomes (forest, grassland, alpine, multiple events, in multiple biomes. deserts). The many additional types of anthro- For many terrestrial ecosystems, both the infre- pogenic influences (pollution, intensive land-use quent nature of disturbance events and slow post- practices, climate change, and invasive species) disturbance processes complicate efforts to empiri- which interact with and underscore global envi- cally study biogeochemical responses on long time- ronmental change highlight the need for a deeper scales. Although a single disturbance event has the understanding of disturbance ecology (Peters and potential to create a significant biogeochemical im- others 2011). Yet disturbances are complex, indi- pact (Pare´ and others 2002; Foster and others 2003; vidual events. Characterizing a disturbance from a Romme and others 2011), over the long term a shift in biogeochemical perspective aids in generalizations the disturbance regime itself and its associated suc- about its outcomes because of the potential to cessional pathways may combine to affect ecosystems integrate ecosystem processes over space and time more profoundly (for example, directional change, into metrics that ultimately control post-distur- Reiners 1983). Detecting shifts in disturbance regimes bance ecosystem trajectories (Turner 2010). The necessitates extending the temporal scale of distur- challenge is building an adequate understanding of bance history beyond the time period of direct or processes in key abiotic and biotic parameters historical observations (Marlon and others 2012). which more accurately predict the biogeochemical Recent studies have provided increasingly clear impacts of single or multiple disturbance events. characterizations of disturbance regimes (at least for Biogeochemical cycles of carbon (C) and nutrients fire and storm regimes) on millennial timescales exhibit a range of responses to disturbance over eco- (Donnelly and Woodruff 2007; Higuera and others logically meaningful time scales (Running 2008). In 2014), including return intervals, spatial extent, and turn, biogeochemical cycles and their interactions can intensity (Baker 2009). Several lines of evidence influence the pattern and pace of ecosystem recovery indicate incipient or ongoing shifts in disturbance from disturbance; both disturbance legacies and regimes during the past few decades, with larger and feedbacks between plant regrowth and soil element more frequent fire events (Dennison and others stores are particularly influential in shaping recovery 2014) and seemingly unprecedented bark beetle (Gough and others 2007; Pearce and others 2015). outbreaks (Raffa and others 2008). Projecting the Feedbacks among C accumulation, nutrient recycling, future biogeochemical trajectory of disturbed systems and other ecosystem processes may interact with requires the ability to identify how profoundly a dis- whole-ecosystem constraints on element supply and turbance regime has departed from the present state loss (Belyea and Baird 2006). These constraints on the (Walker and Wardle 2014). various individual and feedback processes can result Here, we introduce a conceptual framework in surprisingly consistent patterns in biogeochemical using the ratio of plant and soil nutrient stocks to cycling after disturbance across a wide range of characterize the relative stasis of mature ecosys- ecosystem types (Davidson and others 2007; Rastetter A Framework to Assess Biogeochemical Response to Ecosystem… 389 tems and to hypothesize the consequences of a moist in British Columbia to subtropical dry in single disturbance event on nutrient loss and Texas exhibit wide variability in soil N: vegetation recovery. We suggest this framework will improve N, spanning 1.3–40.9 across total soil N pools ecosystem comparisons of biogeochemical response ranging from 724 to 6929 kg ha-1 (Figure 1a) to multiple disturbances over time. This framework (Ponder and others 2012). However, for the four is designed for temporal scaling and it is flexible forest types with sufficient sample size, there are with regard to disturbance mechanism, ecosystem separate, unique values of soil N: vegetation N type, and spatial extent. We develop this frame- (Figure 1b). These results suggest that boreal and work with three different approaches: (1) nutrient temperate forested biomes as a whole may stock data from forested ecosystems in North America, (2) a process-based ecosystem model, and (3) conceptual shifts in site nutrient availability with altered disturbance frequency. We expect that this framework will be sufficiently robust to improve our understanding of biogeochemical outcomes across new disturbance types, regimes, and interactions.

CHARACTERIZING TERRESTRIAL ECOSYSTEMS BY PARTITIONING RATIOS In a mature terrestrial ecosystem (that is, when aboveground biomass accumulation has pla- teaued), nutrients are entrained into ecosystem cycles and accumulated predominantly in soils and vegetation. For this accumulation to occur, a balance has to be maintained between soil and plant processes; soil organic matter (SOM) cannot accumulate without the litter produced by vegetation and the vegetation cannot grow and continue to produce organic matter without the nutrients mineralized from the SOM. In addition, the accumulation of nutrients has to be synchronized; nitrogen (N) cannot accumulate in the ecosystem unless P and other vital nutrients also accumulate and vice versa. We posit that the ratio between soil and plant nutrient stocks represents a characteristic property of an ecosystem that can be quantified empirically, both to better allow cross-system comparisons and to provide insight into the fluxes between pools (given that internal cycling fluxes are often much larger than input and output fluxes) (Rastetter and others 2013). We call this value the partitioning ratio. There is empirical support for characteristic dif- Figure 1. A Ratios of soil N and vegetation N pools in 41 ferences in the partitioning ratio among ecosystems forests in North America that are part of the long-term at the biome scale. For illustrative purposes we soil productivity network (Ponder and others 2012). B focus our discussion on N, the most commonly Mean values for four Holdridge life zones with one standard error for both soil and vegetation N. Vegetation limiting nutrient in terrestrial ecosystems, although N includes both tree bole and tree crown N, whereas soil it should be recognized this element has unique N includes both O horizon and mineral soil N measured characteristics such as significant organic forms, to 20 cm depth. Sites represent a variety of successional multiple loss pathways, and multiple biotically stages, climate types, and dominant species. Locations: mediated transformations. As an example, six types British Columbia, Ontario, Louisiana, Mississippi, Texas, of North American forests ranging from boreal Washington, Missouri, North Carolina, and Idaho. 390 J.M. Kranabetter and others demonstrate consistent and predictable partitioning ratios, at least in late successional stages. This partitioning ratio concept may extend to other biomes beyond temperate forests. For example, grasslands contain large belowground N stocks in the soil pool relative to aboveground biomass. A prairie in Oklahoma (U.S.A.) had a partitioning ratio of 232 over a three-year period in the 1970s (Risser and others 1981). Deserts of the southwestern U.S. generally have both low aboveground biomass and low SOM stocks. Their N partitioning ratios range from 4 to 203 depending on the dominant vegetation type [for example, mesquite, sagebrush, creosote, and paloverde (West and Skujins 1978)]. Tropical forests with relatively organic-poor soils have low soil nutrient stocks relative to vegetation nutrient stocks and Figure 2. Plant nitrogen (N) and soil N plot illustrating exhibit partitioning ratios of 1.6 to 4.8 (Jordan theoretical trajectories of nutrient recovery after distur- 1985). Thus, the quantification of soil and plant bance to re-establish characteristic partitioning ratios of nutrient stocks via a partitioning ratio appears to be an ecosystem. The trajectory of succession in the plot a useful characteristic descriptor of biomes. proceeds up and toward the right as the ecosystem accumulates nutrient. The exact trajectory will depend on the local environmental conditions (climate, parent SINGLE DISTURBANCE EVENTS AND material, topography, potential biota), but because of the RECOVERY OF NUTRIENT STOCKS slow rate of nutrient accumulation, the plant and soil process will remain in balance. Isopleths of total nutrient Disturbance events can immediately and dramati- in the ecosystem (vegetation and soil, assuming negligi- cally alter the ratio of soil and vegetation N stocks, ble nutrient content in other ecosystem components) are shifting the system away from the partitioning ra- diagonal lines in this plot. tio. For example, a wildfire on Alaskan tundra in 2007 changed the N partitioning ratio from 16.8 to be below and to the left of the isopleths of total nearly infinity due to the complete combustion of ecosystem nutrient. This redistribution trajectory aboveground biomass (Mack and others 2011). It is should approach the predisturbance baseline as our contention that ecosystems undergo a pre- plant and soil processes come back into balance. dictable trajectory in recovery of nutrient stocks Once the balance is re-established, the ecosystem and partitioning ratios during a return to the pre- will be more effective at entraining and retaining disturbance condition over secondary successional nutrients in the ecosystem cycle and from that timescales (Jordan and others 1972; Vitousek and point on, the recovery trajectory should coincide Reiners 1975). The ratio between soil and plant with characteristic partitioning ratio of the ecosys- nutrient stocks represents a characteristic property tem. DeAngelis (1980) hypothesized that this of an ecosystem such that over time, the internal recovery time reflected system energetics, in par- and external factors affecting a given ecosystem ticular the mean transit time of essential and cause this ratio to be ‘‘attracted’’ (sensu Haeussler nonessential nutrients recycled between soils and 2011) to a characteristic value. vegetation during succession. To illustrate, after a disturbance event we pos- Several post-disturbance sampling sequences tulate a redistribution phase where there is a net indicate some degree of predictable temporal release of nutrients from soil and net accumulation change in soil N: vegetation N over time (Johnson by plants (Figure 2). If the nutrient is limiting to and Turner 2014). In lodgepole pine forests of the growth during the recovery, the recovery trajectory Greater Yellowstone ecosystem, soil N and vegeta- will parallel the isopleths of total ecosystem nutrition N stocks recovered at different rates during ent or be slightly above and to the right of those 331 years after stand-replacing fires, converging on isopleths if nutrient is also accumulated in the a characteristic partitioning ratio over time ecosystem as a whole. If the nutrient is not limiting (Smithwick and others 2009). In tropical rain- during recovery, there will tend to be a net loss forests of the Andean foothills in western Amazo- from the ecosystem and the recovery trajectory will nia, the partitioning ratio changed from 39 at three A Framework to Assess Biogeochemical Response to Ecosystem… 391 years post-disturbance, to 7.25 at 25–30 years post- disturbance event, we used the multiple element disturbance, relative to a ratio of 2.2 in primary limitation (MEL) model with parameters as de- forest (Scott 1978). Although we emphasize ratios scribed by Rastetter and others (2013) (Figure 3). because of the balance struck between plant:soil The MEL model was used to simulate three broadly nutrient cycles in the conceptual model, the abso- contrasting ecosystems in the U.S.A.: (1) the mixed lute amounts of ecosystem nutrient capital (iso- deciduous and coniferous forest of Hubbard Brook pleths in Figure 2) could also be a vital measure of Experimental Forest, located in New Hampshire biogeochemical recovery under many applications. (Bormann and Likens 1979), (2) temperate wet The initial effect of a disturbance on the parti- coniferous forest of the H. J. Andrews Experimental tioning ratio and nutrient stocks depends on the Forest in Oregon (Harmon 1992), and (3) Arctic specific mechanism because the biogeochemical tundra at the Toolik Lake Long Term Ecological consequences of disturbance events are not simply Research site in Alaska (Hobbie and Kling 2014). loss of nutrients. Disturbances can also increase We calculated the partitioning ratio for N (soil N: ecosystem nutrient stocks or change their distri- vegetation N) at steady state for each ecosystem. butions among pools. This argues for the need to The partitioning ratios at Hubbard Brook, H. J. expand current definitions of disturbance. Exam- Andrews, and Toolik Lake were 10.1, 5.5, and 52.7, ples of disturbance increasing nutrient stocks (ei- respectively. A disturbance was simulated in the ther concurrent with the disturbance event or model by removing 90% of the aboveground bio- displaced over time) include deposition of calcium- mass. To simulate a range of successional pathways, rich volcanic ash (Ayris and Delmelle 2012), post- 80 or 100% of the predisturbance biomass was fire N fixation by early successional plants such as added to the soil and coarse woody debris pools alder (Perakis and others 2011), sediment deposi- following the disturbance event. tion during floodplain disturbances (Appling 2012), In all three modeled ecosystems there was a thermokarst delivery of phosphorus (P) to tundra, temporal trajectory of return to the original parti- or anthropogenic N additions (Block and others tioning ratio after the disturbance event but at 2012). These types of accreting disturbance events greatly different rates because the mechanisms of are an important counterexample to the traditional recovery differed among the three ecosystems view of disturbance as reducing terrestrial ecosys- (Figure 3). The quantity of biomass returned to the tem nutrient stocks. system has a slight effect on the rate of recovery, but recovery pathways are quite similar. Symbiotic MODELING NUTRIENT STOCK RECOVERY TO N fixation is not thought to be a major component of the recovery at Hubbard Brook or Toolik Lake, A DISTURBANCE EVENT ACROSS but it is a major contributor to the N budget during CONTRASTING ECOSYSTEMS the recovery at H.J. Andrews. Hence, the N tra- To illustrate how ecosystems with differing parti- jectory for H.J. Andrews indicates a net gain of N by tioning ratios may recover N stocks following a the ecosystem soon after the disturbance, which is

Hubbard Brook HJ Andrews Toolik Lake primary succession 120 80 20 80% retained ) 100 -2 100% retained 60 15 80

60 40 10

40 20 5

Plant N (g m (g N N Plant 20

0 0 0 300 500 700 900 100 200 300 400 500 600 810 820 830 840 850 Soil N (g N m-2) Soil N (g N m-2) Soil N (g N m-2)

Figure 3. Trajectory of N recovery in vegetation versus soil following disturbance at the Hubbard Brook, H.J. Andrews, and Toolik Lake LTER sites as predicted by the multiple element limitation (MEL) model. The filled circles are the assumed steady state values for each ecosystem (to which the model was calibrated). The thin black lines are isopleths of constant total ecosystem N (soil + plant N). The solid lines represent the trajectory of recovery following a 90% removal of vegetation biomass with either 80% or 100% of that removed biomass added to the soil and coarse woody debris pools. Points above and to the right of the line have more N than the steady state and points below and to the left have less. 392 J.M. Kranabetter and others then lost during later stages of succession. Note that when regenerating forests place greater demands the recovery trajectories following disturbance on soil nutrients (Thiffault and others 2011), but it converge at a lower point in succession rather than is also possible that losses in nutrient stocks will returning directly to predisturbance levels. This result in only temporary, rather than permanent, convergence results from the loss of nutrients in reductions in growth capacity as soil processes re- the disturbance and in the early recovery phase. cover (Egnell 2011). For example, recent studies in These nutrients need to be recovered before the the Brazilian Cerrado forest/savanna ecotone sug- relative stasis of a mature ecosystem can be gest that less fertile sites are more sensitive to reached. There is potentially a high capacity for changes in fire frequency, likely because fire-in- predicting post-disturbance trajectories with this duced losses of nutrients greatly exceed the supply framework. In addition to measuring the immedi- of nutrients (de Dantas and others 2013; Pellegrini ate effects of a disturbance (accreting or depleting and others 2014). Alternatively, there is evidence in terms of any given soil nutrient stock, such as a that tropical savannas are well adapted to fire reduction in soil N), the relative ratio change (Bond 2008; Staver and others 2011), and the long- through time could be predicted based on known term nutrient balance of these biomes seems to not successional ratio development. be altered, as N supply can keep pace with losses from periodic, low intensity disturbance. ASSESSING BIOGEOCHEMICAL VULNERABILITY TO DISTURBANCES STABILITY OF NUTRIENT STOCKS UNDER HIFTING ISTURBANCE REQUENCIES The partitioning ratio could theoretically be used to S D F predict biogeochemical vulnerability to distur- It might be argued that, as illustrated by the mod- bances. Forest scientists have proposed analogous eling exercise, a single disturbance event may be ‘‘stability ratios’’ for ranking the sensitivity of sites less a question of ‘if’ nutrient stocks recover but to nutrient loss through disturbance and corre- rather a question of ‘when’. We suggest more sponding vegetation removal (Himes and others fundamental alterations in nutrient stocks and 2014). Conceptually, sites with proportionally partitioning ratios may require changes in distur- greater allocation of nutrients in vegetation are at bance frequency or intensity over multiple gener- risk of diminished productivity due to direct losses ations of a plant community (for example, many through disturbance. Himes and others (2014) decades for grasslands, many centuries for forests). postulated that forested sites with stability ratios A number of mechanisms have been identified or less than 0.1 have low risk, those with 0.1–0.3 have hypothesized as drivers in the biogeochemical re- minor risk, those with 0.3–0.5 have significant risk, sponse of ecosystems to disturbance frequency and and those with greater than 0.5 have an immediate intensity (Gorham and others 1979), such as and high risk of productivity declines. In their bryophyte- and lichen-associated N fixation, water analysis of forests in the Pacific Northwest, the table fluctuations, mineral weathering rates, forest areas with the highest concentrations of at-risk sites floor accumulations, and inputs of ericaceous plant were those with young, glacially derived soils. roots (Antoine 2004; Zackrisson and others 2004; Stability ratios have in many cases not been Simard and others 2007; Hazlett and others 2011; empirically confirmed, but this is an area of active Clemmensen and others 2013). Quantification of research through programs such as the Long-term these processes further develops the concept of soil productivity study (LTSP) (Powers 2006) and accreting or depleting disturbances by identifying the Centre for International Forestry Research mechanisms of biogeochemical change. Both pro- (Saint-Andre´ and others 2008). Over the first cesses might even occur simultaneously, as in the 10 years of the LTSP study, the complete site-level positive and negative aspects of forest floor accu- organic matter removal treatment has not yet re- mulation and loss (Prescott and others 2000). The sulted in significant overall declines in regenerating strength of these constructive and destructive pro- stand productivity (Ponder and others 2012), but cesses may mean that shifts in disturbance fre- early trends indicate some differing sensitivities to quency could push biogeochemical cycles to nutrient loss by forest soil type (for example, deeply support either an altered productive capacity of the weathered subtropical soils versus less developed ecosystem, or perhaps an entirely new, alternative glacial soils) that may corroborate the partitioning stable state (Reiners 1983). ratio concept. More significant effects of nutrient We have conceptually portrayed these possible capital removal may appear after canopy closure, interactions of site properties with disturbance A Framework to Assess Biogeochemical Response to Ecosystem… 393 frequency to illustrate how ecosystems may re- organic accumulations), low rates of symbiotic and spond with increased, decreased, or no change in asymbiotic N fixation, highly weathered soils, and a long-term nutrient supply rates (Figure 4). high allocation of site nutrients in aboveground Ecosystems exhibiting low sensitivity to excessive pools. Ecosystems with low sensitivity to reduced disturbance (that is, showing small changes in total disturbance frequency (point C) would have effi- nutrient capital, point A in Figure 4) would theo- cient and sustainable nutrient cycling between soils retically have rapid vegetation recovery with a and vegetation (low loss rates), adequate ongoing significant component of N-fixing plants, soils with replenishment of sequestered nutrients (via min- high buffering capacity, weatherable minerals for P eral weathering and N fixation), and balanced or- and base cation replenishment, and a high alloca- ganic matter inputs (for example, wood, litter, tion of site nutrients in belowground pools. roots, bryophytes) and outputs (decomposition). Ecosystems exhibiting high sensitivity to excessive Mechanisms leading to a high sensitivity to infre- disturbance (point B) would be relatively slow to quent disturbances (point D) would be excessive revegetate, highly prone to nutrient leaching (high nutrient immobilization through biomass seques- precipitation regime, low retention capacity of the tration, detrimental changes over time in soil soil) and nutrient volatilization (deep, dry surface thermal properties or drainage (cooling via forest floor accumulations, paludification), reduced rhi- zodeposition, and high inputs of low-quality litter such as peat or ericaceous plants. These characterizations of ecosystem properties and responses to multiple disturbances should generate several testable hypotheses suitable for cross-biome synthesis.

CONCLUSIONS

The partitioning ratio (soil N: vegetation N) could be a useful and fundamental characterization of terrestrial ecosystems, as well as a simple predic- tor of ecosystem resilience to disturbance. More experimental evidence to identify ecosystems that may be sensitive or insensitive to nutrient losses from aboveground disturbance should be Figure 4. Conceptual figure of how disturbance fre- collected from a variety of ecosystems and quency can balance the potentially accreting and successional states. depleting processes affecting ecosystem nutrient capital. Projected response ranges from complete loss (lower por- We suggest that classifying disturbances by their tion of the converse black curve) to no effect (top, flat black biogeochemical impacts may improve under- line) of disturbance frequency on nutrient supply. Points standing of their long-term consequences on A through D represent four contrasting ecosystem re- ecosystems. Particularly, disturbances can be sponses as described in the text; for example, a coniferous considered accreting or depleting depending on forest that requires a sufficient return interval of fire to whether they increase or decrease nutrient sustain productivity because nutrients immobilized in stocks. Standardization of accretion or depletion stand biomass and forest floors of old-growth are released will, in some cases, require consideration of for a new cycle of growth (left side of the black curve return intervals, establishment of common time- moving toward the center, marked D). Higher fire fre- frames for disturbance events, and assessment of quency, however, could be destructive (moving from typical spatial patterns to facilitate cross-biome center to the right side of the black curve, marked B) because nutrients are depleted faster than they can be re- comparisons. placed, thereby lowering ecosystem productivity. Every Sustainable management of forest, grassland, ecosystem and site type could occupy a unique spot in and other terrestrial ecosystems over long time the conceptual figure, helping researchers formulate periods can be conceptualized and tested by specific hypotheses on how disturbance frequency may considering how changes in disturbance fre- be constructive, destructive, or of no consequence to quency (for example, livestock grazing intensity, nutrient supply and ecosystem productivity. forest plantation rotation age) might balance the 394 J.M. Kranabetter and others

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