Dynamic Accumulators Part Two

Dynamic accumulators are plants that actively accumulate appreciable amounts of useful nutrients. These plants are potentially useful for those involved in soil remediation, composting operations, dietary planning, ecosystem studies and more. In Part One I describe how the dynamic accumulators (and excluders) may be qualified. I recommend reading this despite the fact it is quite dry, as it will make the information in this article easier to understand and apply.

This article presents plant-average nutrient concentrations (Table 1). It then uses the averages to identify plants that are accumulators and excluders of a range of plant nutrients (Tables 2 – 12).

The potential of any plant for accumulating a specific nutrient is limited by that nutrient’s availability in the plant’s environment and/or the symbionts that increase uptake for that nutrient. Active transport mechanisms (see Part One) are only effective where nutrients are present; and symbiosis is only beneficial where beneficial symbionts are present. A broad range of support species (the soil food web) are involved in plant nutrient acquisition and should be considered in any soil restoration project. Some brief discussion is included.

All data was originally sourced from Dr James A Duke’s Phytochemical and Ethnobotanical database and anonymously compiled in spreadsheets here. Plant-average concentrations have been derived using highest concentration data for each species wherever a range was provided in the data: this was around 5% of all data. This artificially inflates the average but better outlines the potential of a species found to be above average – the dynamic accumulators.

In Table 1 below are columns headed ‘99% Lower’ & ‘99% Upper’. These describe the lower and upper bounds of confidence intervals for the plant-average concentrations. Statistically, I have confidence that 99% of the time the plant-average concentration will be within these lower and upper bounds. The higher or lower you go from those bounds, the greater the odds are you are looking at accumulators or excluders respectively. In this manner we can identify these plants with reasonable certainty. Note that the 99% confidence interval gets proportionately smaller when the sample size increases; the more data we have the more accurate we get.

When you have a plant of interest, collect as much nutrient data as you can find, standardise the data’s units converting all to ppm, average the results and then compare these to the averages in Table 1. Assess if they are accumulators, excluders, or average (passive transport, see Part One).

Tables 2 – 12 below describe ten accumulators and excluders of each nutrient listed in Table 1. The column heading Bf is the biome-concentration factor (see Part One). This is a ratio you may derive from getting a plant species nutrient concentration and dividing by the plant-average concentration for that nutrient. The plant-average concentrations in table 1 can be used to assess the Bf of any plant you have concentration data for. Bf gives you a multiple of the average e.g. Bf = 2 = 2 times the plant-average concentration.

Nitrogen

Table 2. Biomeconcentration factors (Bf) of nitrogen dynamic accumulators and excluders.

Plant-average 16,801 ppm; 99% confidence interval 11395 – 22207 ppm; 52 samples.

Nitrogen is not originally sourced from soils, rather, from Rhizobium & other nitrogen-fixing bacteria; but the accumulators in Table 2 are not all associated with nitrogen-fixers. The overall trend of nutrients as observed here still applies: When considering the data without nitrogen-fixers, soil nitrogen appears to be captured more efficiently by some plants than others.

The excesses in this data do not seem excessive when compared to other nutrients in this study. However, the sample set is small (52), and may not have captured more extreme examples of nitrogen accumulation.

Phosphorus

Table 3. Biomeconcentration factors (Bf) of phosphorus dynamic accumulators and excluders.

Plant-average 3,706 ppm; 99% confidence interval 3244 – 4168 ppm; 438 samples.

The variance in phosphorus levels is high; from 10 x more to 50 x less than average. The large sample set and tight confidence interval qualify these plants well.

Phosphorus acquisition is significantly enhanced by mycorrhizal fungi; the accumulators of Table 3 are all endomycorrhizal associates.

Potassium

Table 4. Biomeconcentration factors (Bf) of potassium dynamic accumulators and excluders.

Plant-average 22,834 ppm; 99% confidence interval 20414 – 25254 ppm; 347 samples.

As with phosphorus so it is with potassium: mycorrhizae increase potassium uptake. Endomycorrhizal hosts dominate the accumulators and a mix of endo/ecto hosts are found in the excluders.

Sulphur

Table 5. Biomeconcentration factors (Bf) of sulphur dynamic accumulators and excluders.

Plant-average 2,337 ppm; 99% confidence interval 1680 – 2994 ppm; 90 samples.

A smaller sample set may have failed to capture the range of sulphur accumulators but high biodiversity is seen. As a general rule, fleshy fruits appear low in sulphur while leafy greens are higher.

Calcium

Table 6. Biomeconcentration factors (Bf) of calcium dynamic accumulators and excluders.

Plant-average 11,244 ppm; 99% confidence interval 9946 – 12542 ppm; 496 samples.

Like sulphur, calcium is lower in fruit than vegetative materials. Generally, there is wide diversity of plants found within the accumulators and excluders alike.

Various ‘hungry’ vegetables occur in two or more of the major nutrient tables (2-6). Cauliflower and other brassicas are notable accumulators. These plants are non-mycorrhizal but production can be enhanced with saprobic fungi e.g. Oyster mushrooms and Garden giants; and Trichoderma fungi.

Magnesium

Table 7. Biomeconcentration factors (Bf) of magnesium dynamic accumulators and excluders.

Plant-average 3488 ppm; 99% confidence interval 2983 – 3993 ppm; 287 samples.

Silicon

Table 8. Biomeconcentration factors (Bf) of silicon dynamic accumulators and excluders.

Plant-average 392 ppm; 99% confidence interval 107 – 677 ppm; 75 samples.

Iron

Table 9. Biomeconcentration factors (Bf) of iron dynamic accumulators and excluders.

Plant-average 374 ppm; 99% confidence interval 299 – 449 ppm; 427 samples.

Boron

Table 10. Biomeconcentration factors (Bf) of boron dynamic accumulators and excluders.

Plant-average 46 ppm; 99% confidence interval 35 – 57 ppm; 122 samples.

Copper

Table 11. Biomeconcentration factors (Bf) of copper dynamic accumulators and excluders.

Plant-average 21 ppm; 99% confidence interval 14 – 29 ppm; 213 samples.

Maganese

Table 12. Biomeconcentration factors (Bf) of manganese dynamic accumulators and excluders.

Plant-average 272 ppm; 99% confidence interval 170 – 374 ppm; 242 samples.

More trees appear among the accumulators of micro- compared to macro-nutrients. Access to sufficient quantities of some of the rarer elements in an ecosystem may require the increased depth and density of root-fungal exploration afforded by trees root systems. However, it could also be that, over the same period of time: higher Bf accumulator trees acquisition of a nutrient could be similar or even slower than that of lower Bf, but rapidly growing, herbal counterparts.

Where remediation of a specific pollutant (too much nutrient) is the goal: knowledge of the accumulator’s growth potential over time will help indicate a general time frame for remediation efforts.

Tree litter can be a good source of micronutrients as is shown in the data. By association ectomycorrhizal and lignicolous fungal communities, and litter communities have important roles in micronutrient cycling. Providing chip mulch and/or leaf litter for newly planted trees will help accelerate the restoration of the tree associated biology involved in these processes.

Through collecting nutrient concentration data for a plant species and getting the Bf value we can qualify dynamic accumulators with some certainty. Multiple points of data for singular species will enable better quantification. Much like various plants have different root profiles in the soil, various plants have differing nutrient profiles. If we have two dynamic accumulators (for the same nutrient/s) in close proximity we may create competition; whereas if we identify species with varying nutritional needs we might better identify plants that stack together to increase overall production.

Dynamic accumulators, like their hyperaccumulator counterparts (see Part One), will be useful to mop up specific excesses in soils; or provide specific richness to the diets of humans and animals. They have potential for improving silage, baleage, composting, mulching and other systems designed to capture and redistribute nutrients. Within permaculture systems they add to the palette of functions among the many plants we already employ. The accumulators might readily inhabit and mediate edges where excess nutrients are expected. Some will likely thrive in or proximal to stock enclosures, bird runs, effluent paddocks, ponds, wetlands, and composting and processing areas.