TrophLab (version of June 2000)

 

Introduction

Trophic levels (here abbreviated to ‘troph’, to avoid overlap with ‘TL’, used for total length), express where fish and other organisms tend to operate in their respective food webs. To estimate the trophs of fish, we must consider both their diet composition, and the trophs of their food item(s). The troph of a given group of fish (individuals, population, species) is then estimated from

 

 

Troph = 1 + mean troph of the food items                                                            …1)

 

 

where the mean is weighted by the contribution of the different food items.

 

This can be expressed more formally by

 

                                                                                   …2)

 

where Dcij is the fraction of prey (i) in the diet of consumer (i), troph j is the trophic level of j, and G is the number of groups in the diet of i.

 

Following a convention established in the 1960s by the International Biological Program, we attribute primary producers and detritus (including associated bacteria) a definitional troph of 1 (Matthews 1993).

 

Thus for example, an anchovy whose diet would consist of 50% phytoplankton (troph = 1) and 50% herbivorous zooplankton (troph = 2) would have a troph of 2.5. The last value is an estimated, fractional troph, differing conceptually and numerically from the integer values that are often assumed for higher trophs, and which we think are too imprecise and inaccurate to be useful in any kind of analyses.

 

An omnivore is a “species which feeds on more than one trophic level” (Pimm 1982). Thus, an omnivory index (O.I.) can be derived from the variance of the trophs of a consumer’s food groups i.e., from

 

                                                                           ... 3)

 

The O.I. takes values of zero when all feeding occurs at the same troph, and increases with the variety of food items’ trophs. Its square root is a standard error, i.e.,  (Christensen and Pauly 1992).

 

Routines for estimation of trophs and O.I. values are incorporated in the Ecopath software, which has been applied to a large number of ecosystems (see Pauly and Christensen 1995; Pauly et al. 1998). Troph estimates from Ecopath i.e., from diet compositions and food web studies have been found to correlate closely with troph estimates based on stable isotope ratios of Nitrogen (Kline and Pauly 1998; Pauly et al. 2000). This has led to numerous troph estimates for a wide range of taxa becoming available, notably for the invertebrates, fish, marine mammals and other groups covered by FAO statistics, and now included in FishBase. However, a tool was lacking so far which would allow estimation of trophs outside of Ecopath applications.

 

To remedy this situation, TrophLab was developed as a stand-alone application for estimating the troph of any consumer, given quantitative or qualitative information on the composition of its food.

 

TrophLab has two basic routines:

1)      Estimation of troph (and their s.e.) from quantitative diet composition data; and

2)      Estimation of approximate trophs (and their s.e.) from list of items known to occur in the diet of an animal, i.e., from qualitative information.

 

Quantitative data

The routine in (1) estimates trophs (and s.e.) from equation (1) given the percentage (in weight, volume or energy) of each item in the diet of the organisms in question, and the trophic level of their preys. In case such trophic levels are missing, default trophs (and s.e.) can be taken from the table below. [Note that this table was optimized for fish diets, and may not well cover other, e.g., terrestrial predators.]

 

The routine in (2) rests on the idea that it is possible to obtain a rough estimate of a species troph and its s.e. based on individual prey items (rather than a complete diet composition) granted that enough food groups or items are known for that species, and that one is willing to accept certain assumptions on the relative importance of these food items in the overall diet of the species.

 

Examination of diet compositions entered into FishBase until mid 1999 (n = >1,800) showed that, typically, the relative contribution of different food groups or items to the overall diet composition follows a pattern described by the equation

 

log10P = 2 – 1.9log10R – 0.16log10G                                                          …4)

 

where   P is the contribution of an item to the total diet in percent; R is the rank of the food item (in terms of its relative contribution to the total diet); and G is the number of food groups or items (Note that the equation is defined only for 1<G<10).

 

In the following, a description of the resampling routine is provided which is used in TrophLab to estimate trophs and their s.e. from individual food items. This routine involves three cases:

 

Case 1: all food items are plants or detritus

 

Then: troph = 2.0 and s.e. = 0;

 

Case 2: there is only one food item, and it is neither a plant nor detritus.

 

Then: troph = 1 + troph of food item & s.e. = s.e. of food item (see Food Items Table for trophic levels and s.e. of food items; use Food III if possible, else Food II, else Food I).

 

Case 3: There are several food items, and at least one is not a plant or detritus.

 

Then: run Subroutine A.

 

Subroutine A

 

1)      Count the food items, and call their number G;

2)      Select at random one of these food items, and give it the rank 1 (R = 1);

3)      Given G, and R, solve equation (1) for P;

4)      Select at random one of the remaining food items, give it a rank of 2 (R = 2) and again solve equation (1) for P;

5)      Repeat (2) – (4) until all items have been selected (R = 3, 4 . . . .  G);

6)      From the P values, and the trophs specific to each items, estimate a mean troph from:

 

                                                                 … 2)

 

7)  Compute s.e. of troph from Sachs (1984)

 

                     … 3)

 

7)      Save troph and s.e.; repeat (2) – (8), using different random numbers to select first, second, etc. item; stop after 100 loops.

 

8)      Take grand mean of computed trophs and of their standard, errors output these and stop.

 

The key point of this subroutine is that the grand mean s.e. that is estimated considers all possible permutations of the food items in term of the relative abundance they could have had in a ‘real’ diet composition. Note that the standard errors and corresponding troph estimates obtained from this routine are tentative, and should be replaced by estimates from quantitative diet compositions whenever possible.

 

Conclusion

Often, data exist that are intermediate in nature between the quantitative data required for the routine in (1) and the qualitative data analyzed in (2). Example is information that a given food items is “very abundant” or “dominant” in the diet, while others “occur occasionally” or are “infrequent”.

 

We recommend for such case to either attempt to turn such information into numbers (see Table 2) and then to use the quantitative routine in (1) or to enter the abundant or dominant food item several times into the file required for the qualitative routine in (2), such that it will be given more weight in the analysis. This process can be rendered less subjective by splitting the relatively more abundant groups in the diet (as indicated by qualitative statements) into its component taxa e.g., by splitting abundant ‘fishes’ into, say, 2 species of common prey fishes that are demersal, and one that is a small pelagic, or conversely, given the known habits of the predator.

 

 


Table 1. Default troph values used by TrophLab for various prey (arranged by 3 levels of aggregation). (Based on data in FishBase; Froese and Pauly 1999). Note that TrophLab allows this values to be overwritten when better estimates are available.

 

Food I

Troph

s.e.

Food II

Troph

s.e.

Food III

Troph

s.e.

detritus

1.00

0.00

detritus

1.00

0.00

debris; carcasses

1.00

0.00

plants

1.00

0.00

phytoplankton

1.00

0.00

blue-green algae; diatoms; etc.

1.00

0.00

 

 

 

other plants

1.00

0.00

benthic algae/weeds; periphyton; terrestrial plants

1.00

0.00

zoobenthos

2.50

0.50

sponges/tunicates

2.00

0.00

sponges; ascidians

2.00

0.00

 

 

 

cnidarians

2.50

0.52

hard corals; n.a./other polyps

2.34

0.61

 

 

 

worms

2.06

0.26

polychaetes; n.a./other annelids; non-annelids

2.06

0.26

 

 

 

mollusks

2.80

0.46

chitons

bivalves

gastropods

octopi

n.a./other mollusks

2.38

2.10

2.37

3.50

2.60

0.51

0.3

0.58

0.51

0.5

 

 

 

benthic crustaceans

2.50

0.50

ostracods

benthic copepods

isopods

amphipods

stomatopods

2.50

2.00

2.29

2.29

3.09

0.61

0.00

0.53

0.53

0.53

 

 

 

 

 

 

shrimps/prawns

lobsters

crabs

n.a./other benthic crustaceans

2.60

3.20

2.50

2.50

0.59

0.41

0.60

0.50

 

 

 

insects

2.10

0.40

insects

2.20

0.40

 

 

 

echinoderms

2.40

0.35

sea stars/brittle stars

sea urchins

sea cucumbers

3.10

2.00

2.00

0.60

0.00

0.00

 

 

 

 

 

 

n.a./other echinoderms

2.40

0.35

 

 

 

other benthic invertebrates

2.50

0.43

n.a./other benthic invertebrates

2.50

0.37

zooplankt.

2.10

0.28

jelly fish/hydroids

3

0.28

jellyfish/hydroids

3.00

0.28

 

 

 

planktonic crustaceans

2.1

0.3

planktonic copepods

cladocerans

mysids

euphausiids

2.00

2.00

2.20

2.20

0.00

0.00

0.40

0.40

 

 

 

 

 

 

n.a./other planktonic crustaceans

2.10

0.30

 

 

 

other planktonic

2.2

0.17

n.a./other planktonic invertebrates

2.40

0.45

 

 

 

 invertebrates

2.2

0.4

 

 

 

 

 

 

fish (early stages)

3.5

0.8

fish eggs/larvae

3.50

0.80

nekton

3.50

0.60

cephalopods

3.5

0.37

squids/cuttlefish

3.50

0.37

 

 

 

finfish

3.5

0.8

bony fish

3.50

0.80

 

 

 

 

 

 

n.a./other finfish

3.50

0.80

others

2.40

0.50

herps

2.6

0.52

salamanders/newts

toads/frogs

turtles

n.a./other reptiles

2.60

2.58

2.10

3.00

0.68

0.68

0.30

0.30

 

 

 

birds

3.6

0.62

sea birds

shore birds

n.a./other birds

3.77

3.36

3.56

0.34

0.81

0.58

 

 

 

mammals

4.1

0.03

dolphins

pinnipeds

n.a./other mammals

4.18

3.97

4.00

0.02

0.04

0.50

 

 

 

others

2.4

0.44

n.a./others

1.50

0.50


 

Table 2. Suggested percentage corresponding to words used for describing relative contribution to diet composition.

Word(s)

Approx. %

Rationale

“Almost completely”

>95

As in parametric statistics

“Dominant”

80

Based on the 80/20 rule

“Mostly”

50-60

---

“Fairly Common”

50

As for bird sightings

“Frequent”

20-50

---

“Occasional”

5-20

---

“Rare”

<5

As in parametric statistics

 

 

References

Christensen, V. and D. Pauly. 1992. ECOPATH II – a software for balancing steady-state ecosystem models and calculating network characteristics. ICLARM, Manila.

Kline, T. and D. Pauly. 1998. Cross-validation of trophic level estimates from a mass-balance model of Prince William Sound using 15N/14N data. In Fishery stock assessment models. Alaska Sea Grant College Program. AK-SG-98-01.

Mathews, C.P. 1993. Productivity and energy flows at all trophic levels in the River Thames, England: Mark 2, p. 161-171. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26. 390 p.

Pauly, D and V. Christensen. 1995. Primary production required to sustain global fisheries. Nature 374:255-257.

Pauly, D., V. Christensen, J. Dalsgaard, R. Froese and F. Torres, Jr. 1998. Fishing down the food webs. Science 279:860-863.

Pauly, D., V. Christensen, R. Froese and M.L. Palomares. 2000. Fishing down aquatic food webs. American Scientist 88(Jan.-Feb.):46-51.

Pimm, S. 1982. Food webs. Chapman and Hall, London and New York. 219 p.

Sachs, L. 1984. Angewandte Statistik. [Applied statistics, 6th Edition]. Springer-Verlag, Berlin. 552 p.

 

Daniel Pauly, Rainer Froese, Pascualita Sa-a, Maria Lourdes Palomares, Villy Christensen and Josephine Rius