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Predictions of vegetation change following fire in a great salt lake marsh
Aquatic Botany, 21 (1985) 43--51 Elsevier Science Publishers B.V., Amsterdam -- Printed in The Netherlands
43
PR EDI C TI ONS O F V E G E T A T I O N CHANGE F O L L O W I N G F I R E IN A G R E A T S A L T L A K E MARSH
LOREN M. SMITH* and JOHN A. KADLEC Department of Fisheries and Wildlife, Utah State University, Logan, UT 84322 (U.S.A.) (Accepted for publication 11 September 1984)
ABSTRACT Smith, L-M. and Kadlec, J.A., 1985. Predictions of vegetation change following fire in a Great Salt Lake marsh. Aquat. Sot., 21 : 43--51. A Gleasonian model of succession (van der Valk, 1981) was used to predict the species composition of a freshwater marsh following fire. Seed bank samples from 5 vegetation types (Distichlis spicata (L.) Greene, Seirpus lacustris L., S. maritimus L., Typha spp., open water) were used in conjunction with plant life history characteristics to make predictions of post-fire species composition for each vegetation type and the entire study area. In general, predictions for specific vegetation types were not good, but were more accurate for the entire area. Stronger correlations for specific vegetation types may have been prevented by considering marsh vegetation types as discrete units, statistical sampling problems, competitive interactions, or more specific germination requirements of some species. INTRODUCTION Van der Valk ( 1981) presented a Gleasonian m o d e l o f succession which was p r o p o s e d as a m o d e l to predict changes in species c o m p o s i t i o n o f marsh vegetation. F r o m a w e t l a nd management st andpoi nt this could help predi ct the o u t c o m e o f m a n a g e m e n t practices. Observations o f standing vegetation following m a n a g e m e n t practices are needed to test m o d e l predictions and t h e r e f o r e th eir utility in marsh management. Van der Valk (1981) gave several examples illustrating the prediction o f species c o m p o s i t i o n o f marsh vegetation following water-level fluctuation. In this s t u d y van der Valk's model is used t o predict potential vegetation changes within 5 vegetation t yp es and f o r the t o t a l s t udy area following a fire in a G r e a t Salt L ake marsh. METHODS Seed bank and field vegetation d a t a were col l ect ed f r o m O gden Bay Waterfowl Management-Area, a marsh directly adjacent t o t he east shore o f *Present address: Box 4169, Department of Range and Wildlife Management, Texas Tech University, Lubbock, TX 79409, U.S.A. 0304-3770/85]$03.30
© 1985 Elsevier Science Publishers B.V.
44
the Great Salt Lake in Utah, U.S.A. (Nelson, 1954). The marsh was drained at the beginning of April 1981 and areas were burned in September 1981. One week after the fire the area was reflooded and remained so through the following growing season of 1982. Additional details on the study area and fire can be found in Smith et al. (1984). Ten substrate samples (20 X 20 X 4 cm) in the burned area were collected from each of the following 5 vegetation types: Distichlis spicata; Scirpus lacustris;S. maritimus; Typha spp., open water sites. Open water siteswere dominated by Potamogeton crispus, P. pectinatus, and Zannichellia palusiris. Each vegetation type was easily discerned after the fire by examining residual stubble. Substrate samples were processed in the greenhouse according to procedures outlined by Smith and Kadlec (1983). Each was thoroughly mixed and all detritus, tubers, and rhizomes were removed. One half of each sample was placed in submersed (4--5 c m depth), and the other in moist soil (no standing water, but sprinkled daily) conditions. Samples were randomly arranged in plastic trays on benches in the greenhouse. Seedlings were identified as soon as possible. Species authorities are presented in Table I. TABLE I Classification of marsh plant species from Ogden Bay Waterfowl Management Area, Utah according t o van der Valk ( 1 9 8 1 ) Species
M o d e l classification a
Distichlis spicata (L.) Greene Eleocharis palustris (L.) Roemer & Schultes Leersia oryzoides (L.) Swartz
VS-I VS-II' VI-I,II VD-II VS-II VS-II' VS-I,II VS-I,IY VS-I,II' AS-II
Potamogeton crispus L. Potamogeton pectinatus L. Sagittaria cuneata Sheldon Scirpus lacustris L. Scirpus maritimus L. Typha spp. L. Zannichellia palustris L.
a A ffi annual; P = perennial w i t h limited lifespan; V ffi perennial w i t h o u t l i m i t e d lifespan; D ffi dispersal d e p e n d e n t species, short lived propagules; S = s e e d s a l w a y s present in the s e e d bank ( l o n g lived propagutes); I ffi g e r m i n a t i o n in c o n d i t i o n s w i t h n o standing water; II ffi g e r m i n a t i o n in standing water; ' = shade tolerant.
T o m a k e p r e d i c t i o n s o n p r e s e n c e o r a b s e n c e , species w e r e classified acc o r d i n g to life history characteristics (van der Valk, 1981) using seed bank
data, field composition data and existing information (e.g.,van der Valk and Davis, 1978; Smith, 1983). Species were first assigned life-span characteristics: "A-species" are annuals, 'T-species" are perennials without vegetative reproductive capacities, and "V-species" are perennials that can reproduce
45
vegetatively. Next, they were classified on propagule longevity: "S-species" have long-lived propagules that are always present in the seed bank while "D-species" are dispersal dependent with short-lived propagules. Finally, they were classified according to their establishment requirements; "Type I" species establish only when there is no standing water (moist soil) and "Type II" species when standing water is present. A prime, I', mark indicates that a species is shade tolerant (e.g., those species which can germinate where there is existing vegetation). Model predictions made on this basis were then compared to the actual species composition of the vegetation in the field (determined by sampling ten 1.0 X 0.5 m quadrats in each emergent vegetation type once in late May and once in late July). Species were predicted as being present (+) if their characteristics (Table I) matched the environmental conditions. For example, a species which germinates only under moist conditions would not be predicted for a marsh that has standing water unless it was already present in the standing vegetation. Similarly, a species can be predicted as present if it has vegetative structures or seed present in the marsh (given proper environmental conditions). The species would be predicted as absent (-) if it does not have vegetative structures or seed present unless it was classified as a dispersal dependent species (D). A nominal correlation test was used to test similarities (Ives and Gibbons, 1967; Zar, 1974). Since the model can be used only to predict species presence or absence, and not relative abundance, this nominal correlation was used in preference to Kendall's rank correlation test (Hollander and Wolfe, 1973). In addition, sediment conductivity (Kadlec, 1982) and moisture were determined at the beginning of the growing season in late March 1982 and towards the middle of the growing season in late July within each vegetation type. Soil conductivity and moisture measurements were also taken from seed bank samples for each treatment in the greenhouse. For each vegetation type, treatment and time-period combination, 8 samples were analyzed. RESULTS Species which occurred within the 5 vegetation types at Ogden Bay were classified according to their life history characteristics(van der Valk, 1981) in Table I. Species occurring in the field were compared to the predictions based on life-history data and submersed seed bank samples (Table II). Comparisons were made for both M a y 1982 and July 1982, since some species only occurred in the standing vegetation during M a y or July (Table I). Vegetative regrowth was rapid in Typha spp., Scirpus lacustrisand 8. maritimus sites,but very limited in Distichlissites(Smith and Kadlec, 1985) and, therefore, shade tolerance was an important factor in predictions. For the Typha spp. vegetation type (Table II), model predictions of species composition for the July sampling were fair (rn = 0.71, P = 0.063). Potamogeton peetinatus was found in the field, but not in the seed bank;
+
+
+
+
+
+
Zannicheiliapalustris
aPredicted = P, actual = A.
+
Typhaspp. +
+
+ +
8cirpusmaritimus +
+
+ +
-
+
-
-
+
-
+ +
+
+
+
+
+
cuneata
8agittaria
+
+
P A
May
Scirpus
Scirpuslacustris
ton pectinatus
crispus
Po tamoge
Potamogeton
oryzoides
+
Leersia
+
P A
paA
spicata
Distichlis
July
May
Eleocharispalustris
app.
Typha
Vegetation type
-
+
-
+
-
+ +
+
+
+
+
P A
July
lacustris
+
+
-
+
+
+ -
+
+
+
P A
May
+
+
+
-
+
-
+ +
+
+
-
+
P A
July
maritimus
Scirpus
÷
÷
_ ÷
•~
÷
÷
÷
÷
÷
÷
-
÷
÷
÷
÷
÷ ÷
÷
P A
July
P A
May
spicata
Distichlis
÷
÷
÷
÷
-
÷
÷
-
-
-
÷
÷
P A
Open water (July)
÷ ÷
÷ ÷ ÷
÷
÷ ÷
÷
÷
÷
÷
÷
-
÷
÷
÷
-
A
P
Total study mite
Marsh plant species (mature and seedling) predicted (see Table I) as being present (+) or absent (--) within 5 vegetation types following a prescribed burn at Ogden Bay Waterfowl Management Area, Utah
TABLE II
O~
47 thus, preventing a stronger correlation. However, for the May period, agreem e n t between model predictions and field data was perfect (rn = 1.00,
P < 0.05). F o r the Scirpus lacustris sites agreement between model predictions and field data was not close (rn = - 0 . 3 3 , P > 0.10). Zannichellia palustris, Leersia oryzoides, Eleocharis spp. and S. maritirnus were predicted, b u t not found in the field. These species were considered as shade tolerant (Table I). Even if Leersia is considered as rare and omitted from the prediction, the correlation is still p o o r (rn = - 0 . 2 0 , P ~> 0.10). If Sagittaria cuneata is considered as rare, species predicted using the model and those occurring in the field within the Scirpus maritimus vegetation t y p e were not similar in either May or July (rn = 0.10 in b o t h cases, P ~> 0.10). The correlation coefficient for July w o u l d be even lower if Sagittaria was considered as a c o m m o n species (Table II). The combined May and J u l y observations (again with Sagittaria as a rare species), gave a stronger correlation (r n = 0.60, P ~> 0.10) with only 1 species in disagreement. Eleocharis spp. was shade tolerant and was found in the seed bank, but not in the field. The correlation in May for the Distichlis spicata vegetation was rn = 0.33 (P ~> 0.10) with no Typha spp. or Scirpus maritimus found in the field. Typha germinated subsequently and in July only S. maritimus was absent from the quadrats (rn = 0.67, P ~> 0.10). DistichUs, although not occurring in the seed bank, was still predicted in the field following fire because it can exist vegetatively. Potamogeton crispus was classified as a dispersal species in open water sites, even though it m a y have been present in the soil. P. crispus o f t e n does not p r o d u c e seed, and germination is p o o r (Teltscherova and Hejn:~, 1973; Rogers and Breen, 1980). Predictions of vegetation response in the open water sites were not good (rn = - 0 . 3 3 , P > 0.10). Typha spp., Scirpus lacustris and S. maritimus were found in the seed bank, b u t not in the field. In addition, P. pectinatus was found in the field, b u t n o t predicted b y the model because of its absence (determined b y germination) from the seed bank. A more liberal test of model predictions would consider all the species from all vegetation types in the submersed seed bank and in the field quadrats in the test. When Leersia oryzoides and Sagittaria cuneata were treated as rare species and excluded, relatively accurate predictions were attained (rn = 0.78, P = 0.02). Again, Potarnogeton pectinatus was n o t predicted from the model, because o f the reasons outlined above. Including Sagittaria and Leersia in the test p r o d u c e d a poorer correlation (rn = 0.46, P ~> 0.10). Sediment conductivity (salinity) increased (Table III) in the field from April to July within all vegetation t y p e s (P < 0.05) except Distichlis spicata (P > 0.05). Salinity m a y have increased due to water level fluctuation. Soil conductivity o f submersed seed bank samples in July was also lower (P <~ 0.05) than in field samples from all sites, b u t the difference in Distichlis was
48 TABLE HI Mean sediment ~onductivities (n~mhos • cm -j @ 25°C) of seed bank and field samples from 5 vegetation types at Ogden Bay Waterfowl Management Area, Utah 1982 Vegetation type
Distichlis spicata Scirpus lacustris Scirpus maritimus Typha spp. Open water
Field samples
Greenhouse seed bank experiment
April
Moist
5.7 3.8 2.6 5.2 2.5
July
Submersed
S.D. ~
S.D.
~"
S.D. ~
S.D.
3.4 1.4 0.6 3.8 0.4
5.6 3.4 5.7 10.6 8.2
6,0 6.1 6.0 8.2 4.1
2.8 2.8 2,3 4.2 1.2
1.9 1.9 1.0 2.9 0.9
8.5 8.6 16.3 16.6 12.8
5.1 5.2 4.8 6,2 3.6
n o t significant (P ~> 0.05). Soil f r om Scirpus lacustris, Typha spp. and Distichlis moist seed bank samples was n o t di fferent (P ~ 0.05) f r o m field sediments in July; however, ope n water and S. maritimus conductivities were less (P <~ 0.05) t han J ul y field samples. Soil moisture decreased (P <~ 0.05: Table IV) April- Jul y, within all vegetation types. Soil f r om submersed seed bank samples was o f higher (P <~ 0.05) moisture c o n t e n t than J ul y field samples. Soil moistures in Distichlis spicata and o p e n w a t e r field sites were n o t different (P ~> 0.05) f r o m moist seed-bank samples. However, within the S. maritimus and Typha spp. vegetation t ypes moist seed bank samples contained a greater (P <~ 0.05) moisture percentage t h a n field samples. TABLE IV Mean soil moisture (%, are-sin transformed) of seed bank and field samples from 5 vegetation types at Ogden Bay Waterfowl Management Area, Utah, 1982 Vegetation type
April
Distichlisspicata $¢irpuslacustris $cirpusmaritimus Typhaspp. Open water
Greenhouse seed bank experiment
Field samples
37.39 44.05 36.53 45.59 42.40
July
Moist
Submersed
S.D.
~
S.D.
~
S.D.
~
S.D.
5.06 5.94 3.35 6.93 2.04
31.45 34.74 33.10 82.76 34.45
1.71 3.45 1.67 1.66 3.64
32.44 38.60 36.48 46.50 35.31
3.92 4.85 1.98 5.34 3.85
47.15 49.36 46.79 52.37 44.01
4.40 6.33 4.82 6.36 6.58
DISCUSSION Model predictions (van der Valk, 1981) o f marsh plant species composition following the fire were n o t good f or the specific vegetation t ypes o f
49
8cirpus lacustris, 8. maritimus, Distichlis spicata and open water. Within these vegetations, plant species, responding after the fire, may do so on the basis of responses to several environmental factors (Smith and Kadlec, 1983) which could modify van der Valk's (1981) model. Gradations in requirements for light (Cresswell and Grime, 1981), soil moisture, temperature (Thompson et al., 1977), and salinity (Lesko and Walker, 1969; Christiansen and Low, 1970) may all have had an influence on germination and, therefore, predictions for a particular vegetation type. Salinity and moisture of greenhouse soil samples were often different to those of field soils especially in July (Tables III and IV), giving some unreliability to predictions based on seed bank experiments. Also, much of the lack of agreement between predictions and field data may have been due to shade classifications (Table I). Shade may directly reflect substrate temperature, If seedlings of a species were found growing within 1 vegetation type, it was classified as shade tolerant for all vegetation types. However, sediment temperature and possibly shade have been shown to vary considerably among vegetation types (Smith, 1983). Sediment temperature is known to influence germination of wetland plants. Grime et al. (1981) found that most wetland species had a high temperature requirement for germination, and Sifton (1959) found that 30°C was the o p t i m u m for Typha spp. Higher sediment temperatures did not increase germination within the 8cirpus lacustris, 8. maritimus, or Distichlis spicata vegetation types to the point of improving nominal correlations. Shade classifications are directly related to competition. Van der Valk (1981) stated, "The model also ignores interactions among the plants (competition, allelopathy) that may play an important role in determining the presence or absence, abundance, and location of species in wetlands." Competition obviously plays an important role in the vegetation responding after fire and therefore competition is yet another factor contributing to the disparity between predicted and actual vegetation response. In addition, competition in the seed bank experiment itself (greenhouse) may prevent some species from being recorded as present. For example, absence of Potamogeton pectinatus in the seed bank experiment was one of the reasons better correlations were not obtained for some vegetation types. P. pectinatus was rare in the greenhouse, but was conspicuous in the field. In contrast, van der Valk and Davis (1978) found high numbers of P. pectinatus seeds in the seed bank and P. pectinatus was the dominant submergent macrophyte in their studies. Zannichellia palustris grows best in shallow water (van Vierssen, 1982) and is found in such areas at Ogden Bay. P. pectinatus and P. crispus occupy the deeper channels and borrow pits. Correlations between predictions and field data were partially dependent on the date when species composition was sampled. Moreover, substrate samples taken into the greenhouse were mixed while sediments in the field were undisturbed. Disturbing the soil surface often stimulates germination.
50
Burning did not expose new sediments (Smith, 1983) and, therefore, different seed banks. The differences between model predictions and field data could also have resulted from a statistical problem of sampling sparse, clumped populations. The presence of only a few species in both the field and seed bank for some vegetation types (e.g., Disttchlis) could also cause poor correlations for relatively small differences. Finally, van der Valk's (1981) model was designed for freshwater ecosystems. Ogden Bay had a "fresh" water supply, but sediments were saline at times (Table III), possibly making application of the model to Ogden Bay, under certain circumstances, difficult. However, van der Valk (1981) applied the model to conditions where salinities (Delta Marsh, Manitoba, Canada) were similar to those found at Ogden Bay. There was good agreement between prediction and actual species occurrence for Typha spp. vegetation and for the total study area. Salinity alone did not explain why Typha sites show good correlation since salinity in these areas was as high or higher than in vegetation types that did not show significant correlations (Table III). By considering the entire burn unit and all 50 quadrats a large sample size was attained and a strong correlation was apparent when 2 rare species i(see p. 47) were omitted. MacMahon (1980) stated: "On a regional basis we can predict with fair accuracy the average change in the biota under certain conditions, but it is difficult, nearly impossible, to predict the exact behavior of any one very small plot for a short time period, and even harder for a longer time period..." The model predictions for Ogden Bay support this statement. Problems also arise, as van der Valk (1981) noted, for dispersal species: "The establishment of D-species in a wetland is, however, impossible to predict from the model per se because a source of propagules is also necessary." These data support Smith and Kadlec (1983) who noted that seed banks could be used in a general sense to make important management decisions. Given the current form of the predictive model, we recommend that wetland managers merely identify species in the seed bank and in the field as an index to species composition following fire. Incorporation of additional variables (i.e., salinity), competitive interactions, and either more discrete classifications (e.g., shade intolerant, partially tolerant ... shade tolerant) or providing for a continuous variable (e.g., a continuum of light requirements) would greatly improve model utility for wetland managers. ACKNOWLEDGEMENTS M. Hadfield, J. Smith, K. Reese, D. Irving and B. Weber provided field assistance. M. Barkworth, M. Wolfe, F. Lindzey and D. Sisson offered helpful comments on the project. Thanks to J. Brown for comments on the manuscript. D. Sisson gave statistical advice and M. Barkworth verified plant identifications. Logistic support was provided by the Utah Cooperative Wildlife Research Unit, and the Utah State University Forest Resources
51
Department. Texas Tech University, the Utah Department of Wildlife Resources and the Federal Aid to Wildlife Restoration Project provided financial support.
REFERENCES Christiansen, J.E. and Low, J.B., 1970. Water requirements of waterfowl marshlands in northern Utah. Utah Div. Fish Game Publ., 69--123. Cresewell, E.G. and Grime, J.P., 1981. Induction of a light requirement during seed development and its ecological consequences. Nature (London), 291: 583--585. Grime, J.P., Mason, G., Curtis, A.V., Rodman, J., Band, S.R., Mowforth, M.A.G., Neal, A.M. and Shaw, S., 1981. A comparative study of germination characteristics in a local flora. J. Ecol., 69: 1017--1059. Hollander, M. and Wolfe, D.A., 1973. Nonparametric Statistical Methods. Wiley, New York, 503 pp. Ives, K.H. and Gibbons, J.D., 1967. A correlation measure for nominal data. Am. Stat., 21: 16--17.
Kadlec, J.A., 1982. Mechanisms affecting salinity of Great Salt Lake marshes. Am. Midl. Nat., 107: 82--94. Lesko, G.L. and Walker, R.B., 1969. Effects of sea water on seed germination in two Pacific atoll beach species. Ecology, 50: 730--734. MacMahon, J.A., 1980. Ecosystems over time: succession and other types of change. In: R. Waring (Editor), Forests: Fresh Perspectives from Ecosystems Analysis. Proc. Oregon Biol. Colloq., Corvallis, pp. 27--58. Nelson, N.F., 1954. Factors in the development and restoration of waterfowl habitat at Ogden Bay Refuge, Weber County, Utah. Utah Dep. Fish Game Publ., 6. Rogers, K.H. and Breen, C.M., 1980. Growth and reproduction of Potamogeton crispus in a South African lake. J. Ecol., 68: 561--571. Sifton, H.B., 1959. The germination of light sensitive seeds of Typha latifolia. Can. J. Bot., 37: 719--739. Smith, L.M., 1983. The effects of prescribed burning on the ecology of a Utah marsh. Ph.D. Dissertation. Utah State University, Logan, UT, 171 pp. Smith, LM. and Kadlec, J.A., 1983. Seed banks and their role during drawdown of a North American marsh. J. Appl. Ecol., 20: 673--684. Smith, L.M. and Kadlec, J.A., 1985. Fire and herbivory in a Great Salt Lake marsh. Ecology, in press. Smith, L.M., Kadlec, J.A. and Fonnesbeck, P.V., 1984. Effects of prescribed burning on nutritive quality of marsh plants in Utah. J. Wildl. Manage., 48: 285--288. Teltscherova, L. and Hejn~, S., 1973. The germination of some Potamogeton species from South Bohemian fish ponds. Folia Geobot. Phytotaxon., 8: 231--239. Thompson, K., Grime, J.P. and Mason, G., 1977. Seed germination in response to diurnal fluctuations of temperature. Nature (London), 267: 147--149. Van der Valk, A.G., 1981. Succession in wetlands: a Gleasonian approach. Ecology, 62: 688--696. Van der Valk, A.G. and Davis, C.B., 1978. The role of seed banks in vegetation dynamics of prairie glacial marshes. Ecology, 59: 322--335. Van Vierssen, W., 1982. The ecology of communities dominated by Zannichellia taxa in western Europe. II. Distribution, synecology and productivity aspects in relation to environmental factors. Aquat. Bot., 13: 385--483. Zar, J.H., 1974. Biostatisticat Analysis. Prentice-Hall, Englewood Cliffs, NJ, 620 pp.
43
PR EDI C TI ONS O F V E G E T A T I O N CHANGE F O L L O W I N G F I R E IN A G R E A T S A L T L A K E MARSH
LOREN M. SMITH* and JOHN A. KADLEC Department of Fisheries and Wildlife, Utah State University, Logan, UT 84322 (U.S.A.) (Accepted for publication 11 September 1984)
ABSTRACT Smith, L-M. and Kadlec, J.A., 1985. Predictions of vegetation change following fire in a Great Salt Lake marsh. Aquat. Sot., 21 : 43--51. A Gleasonian model of succession (van der Valk, 1981) was used to predict the species composition of a freshwater marsh following fire. Seed bank samples from 5 vegetation types (Distichlis spicata (L.) Greene, Seirpus lacustris L., S. maritimus L., Typha spp., open water) were used in conjunction with plant life history characteristics to make predictions of post-fire species composition for each vegetation type and the entire study area. In general, predictions for specific vegetation types were not good, but were more accurate for the entire area. Stronger correlations for specific vegetation types may have been prevented by considering marsh vegetation types as discrete units, statistical sampling problems, competitive interactions, or more specific germination requirements of some species. INTRODUCTION Van der Valk ( 1981) presented a Gleasonian m o d e l o f succession which was p r o p o s e d as a m o d e l to predict changes in species c o m p o s i t i o n o f marsh vegetation. F r o m a w e t l a nd management st andpoi nt this could help predi ct the o u t c o m e o f m a n a g e m e n t practices. Observations o f standing vegetation following m a n a g e m e n t practices are needed to test m o d e l predictions and t h e r e f o r e th eir utility in marsh management. Van der Valk (1981) gave several examples illustrating the prediction o f species c o m p o s i t i o n o f marsh vegetation following water-level fluctuation. In this s t u d y van der Valk's model is used t o predict potential vegetation changes within 5 vegetation t yp es and f o r the t o t a l s t udy area following a fire in a G r e a t Salt L ake marsh. METHODS Seed bank and field vegetation d a t a were col l ect ed f r o m O gden Bay Waterfowl Management-Area, a marsh directly adjacent t o t he east shore o f *Present address: Box 4169, Department of Range and Wildlife Management, Texas Tech University, Lubbock, TX 79409, U.S.A. 0304-3770/85]$03.30
© 1985 Elsevier Science Publishers B.V.
44
the Great Salt Lake in Utah, U.S.A. (Nelson, 1954). The marsh was drained at the beginning of April 1981 and areas were burned in September 1981. One week after the fire the area was reflooded and remained so through the following growing season of 1982. Additional details on the study area and fire can be found in Smith et al. (1984). Ten substrate samples (20 X 20 X 4 cm) in the burned area were collected from each of the following 5 vegetation types: Distichlis spicata; Scirpus lacustris;S. maritimus; Typha spp., open water sites. Open water siteswere dominated by Potamogeton crispus, P. pectinatus, and Zannichellia palusiris. Each vegetation type was easily discerned after the fire by examining residual stubble. Substrate samples were processed in the greenhouse according to procedures outlined by Smith and Kadlec (1983). Each was thoroughly mixed and all detritus, tubers, and rhizomes were removed. One half of each sample was placed in submersed (4--5 c m depth), and the other in moist soil (no standing water, but sprinkled daily) conditions. Samples were randomly arranged in plastic trays on benches in the greenhouse. Seedlings were identified as soon as possible. Species authorities are presented in Table I. TABLE I Classification of marsh plant species from Ogden Bay Waterfowl Management Area, Utah according t o van der Valk ( 1 9 8 1 ) Species
M o d e l classification a
Distichlis spicata (L.) Greene Eleocharis palustris (L.) Roemer & Schultes Leersia oryzoides (L.) Swartz
VS-I VS-II' VI-I,II VD-II VS-II VS-II' VS-I,II VS-I,IY VS-I,II' AS-II
Potamogeton crispus L. Potamogeton pectinatus L. Sagittaria cuneata Sheldon Scirpus lacustris L. Scirpus maritimus L. Typha spp. L. Zannichellia palustris L.
a A ffi annual; P = perennial w i t h limited lifespan; V ffi perennial w i t h o u t l i m i t e d lifespan; D ffi dispersal d e p e n d e n t species, short lived propagules; S = s e e d s a l w a y s present in the s e e d bank ( l o n g lived propagutes); I ffi g e r m i n a t i o n in c o n d i t i o n s w i t h n o standing water; II ffi g e r m i n a t i o n in standing water; ' = shade tolerant.
T o m a k e p r e d i c t i o n s o n p r e s e n c e o r a b s e n c e , species w e r e classified acc o r d i n g to life history characteristics (van der Valk, 1981) using seed bank
data, field composition data and existing information (e.g.,van der Valk and Davis, 1978; Smith, 1983). Species were first assigned life-span characteristics: "A-species" are annuals, 'T-species" are perennials without vegetative reproductive capacities, and "V-species" are perennials that can reproduce
45
vegetatively. Next, they were classified on propagule longevity: "S-species" have long-lived propagules that are always present in the seed bank while "D-species" are dispersal dependent with short-lived propagules. Finally, they were classified according to their establishment requirements; "Type I" species establish only when there is no standing water (moist soil) and "Type II" species when standing water is present. A prime, I', mark indicates that a species is shade tolerant (e.g., those species which can germinate where there is existing vegetation). Model predictions made on this basis were then compared to the actual species composition of the vegetation in the field (determined by sampling ten 1.0 X 0.5 m quadrats in each emergent vegetation type once in late May and once in late July). Species were predicted as being present (+) if their characteristics (Table I) matched the environmental conditions. For example, a species which germinates only under moist conditions would not be predicted for a marsh that has standing water unless it was already present in the standing vegetation. Similarly, a species can be predicted as present if it has vegetative structures or seed present in the marsh (given proper environmental conditions). The species would be predicted as absent (-) if it does not have vegetative structures or seed present unless it was classified as a dispersal dependent species (D). A nominal correlation test was used to test similarities (Ives and Gibbons, 1967; Zar, 1974). Since the model can be used only to predict species presence or absence, and not relative abundance, this nominal correlation was used in preference to Kendall's rank correlation test (Hollander and Wolfe, 1973). In addition, sediment conductivity (Kadlec, 1982) and moisture were determined at the beginning of the growing season in late March 1982 and towards the middle of the growing season in late July within each vegetation type. Soil conductivity and moisture measurements were also taken from seed bank samples for each treatment in the greenhouse. For each vegetation type, treatment and time-period combination, 8 samples were analyzed. RESULTS Species which occurred within the 5 vegetation types at Ogden Bay were classified according to their life history characteristics(van der Valk, 1981) in Table I. Species occurring in the field were compared to the predictions based on life-history data and submersed seed bank samples (Table II). Comparisons were made for both M a y 1982 and July 1982, since some species only occurred in the standing vegetation during M a y or July (Table I). Vegetative regrowth was rapid in Typha spp., Scirpus lacustrisand 8. maritimus sites,but very limited in Distichlissites(Smith and Kadlec, 1985) and, therefore, shade tolerance was an important factor in predictions. For the Typha spp. vegetation type (Table II), model predictions of species composition for the July sampling were fair (rn = 0.71, P = 0.063). Potamogeton peetinatus was found in the field, but not in the seed bank;
+
+
+
+
+
+
Zannicheiliapalustris
aPredicted = P, actual = A.
+
Typhaspp. +
+
+ +
8cirpusmaritimus +
+
+ +
-
+
-
-
+
-
+ +
+
+
+
+
+
cuneata
8agittaria
+
+
P A
May
Scirpus
Scirpuslacustris
ton pectinatus
crispus
Po tamoge
Potamogeton
oryzoides
+
Leersia
+
P A
paA
spicata
Distichlis
July
May
Eleocharispalustris
app.
Typha
Vegetation type
-
+
-
+
-
+ +
+
+
+
+
P A
July
lacustris
+
+
-
+
+
+ -
+
+
+
P A
May
+
+
+
-
+
-
+ +
+
+
-
+
P A
July
maritimus
Scirpus
÷
÷
_ ÷
•~
÷
÷
÷
÷
÷
÷
-
÷
÷
÷
÷
÷ ÷
÷
P A
July
P A
May
spicata
Distichlis
÷
÷
÷
÷
-
÷
÷
-
-
-
÷
÷
P A
Open water (July)
÷ ÷
÷ ÷ ÷
÷
÷ ÷
÷
÷
÷
÷
÷
-
÷
÷
÷
-
A
P
Total study mite
Marsh plant species (mature and seedling) predicted (see Table I) as being present (+) or absent (--) within 5 vegetation types following a prescribed burn at Ogden Bay Waterfowl Management Area, Utah
TABLE II
O~
47 thus, preventing a stronger correlation. However, for the May period, agreem e n t between model predictions and field data was perfect (rn = 1.00,
P < 0.05). F o r the Scirpus lacustris sites agreement between model predictions and field data was not close (rn = - 0 . 3 3 , P > 0.10). Zannichellia palustris, Leersia oryzoides, Eleocharis spp. and S. maritirnus were predicted, b u t not found in the field. These species were considered as shade tolerant (Table I). Even if Leersia is considered as rare and omitted from the prediction, the correlation is still p o o r (rn = - 0 . 2 0 , P ~> 0.10). If Sagittaria cuneata is considered as rare, species predicted using the model and those occurring in the field within the Scirpus maritimus vegetation t y p e were not similar in either May or July (rn = 0.10 in b o t h cases, P ~> 0.10). The correlation coefficient for July w o u l d be even lower if Sagittaria was considered as a c o m m o n species (Table II). The combined May and J u l y observations (again with Sagittaria as a rare species), gave a stronger correlation (r n = 0.60, P ~> 0.10) with only 1 species in disagreement. Eleocharis spp. was shade tolerant and was found in the seed bank, but not in the field. The correlation in May for the Distichlis spicata vegetation was rn = 0.33 (P ~> 0.10) with no Typha spp. or Scirpus maritimus found in the field. Typha germinated subsequently and in July only S. maritimus was absent from the quadrats (rn = 0.67, P ~> 0.10). DistichUs, although not occurring in the seed bank, was still predicted in the field following fire because it can exist vegetatively. Potamogeton crispus was classified as a dispersal species in open water sites, even though it m a y have been present in the soil. P. crispus o f t e n does not p r o d u c e seed, and germination is p o o r (Teltscherova and Hejn:~, 1973; Rogers and Breen, 1980). Predictions of vegetation response in the open water sites were not good (rn = - 0 . 3 3 , P > 0.10). Typha spp., Scirpus lacustris and S. maritimus were found in the seed bank, b u t not in the field. In addition, P. pectinatus was found in the field, b u t n o t predicted b y the model because of its absence (determined b y germination) from the seed bank. A more liberal test of model predictions would consider all the species from all vegetation types in the submersed seed bank and in the field quadrats in the test. When Leersia oryzoides and Sagittaria cuneata were treated as rare species and excluded, relatively accurate predictions were attained (rn = 0.78, P = 0.02). Again, Potarnogeton pectinatus was n o t predicted from the model, because o f the reasons outlined above. Including Sagittaria and Leersia in the test p r o d u c e d a poorer correlation (rn = 0.46, P ~> 0.10). Sediment conductivity (salinity) increased (Table III) in the field from April to July within all vegetation t y p e s (P < 0.05) except Distichlis spicata (P > 0.05). Salinity m a y have increased due to water level fluctuation. Soil conductivity o f submersed seed bank samples in July was also lower (P <~ 0.05) than in field samples from all sites, b u t the difference in Distichlis was
48 TABLE HI Mean sediment ~onductivities (n~mhos • cm -j @ 25°C) of seed bank and field samples from 5 vegetation types at Ogden Bay Waterfowl Management Area, Utah 1982 Vegetation type
Distichlis spicata Scirpus lacustris Scirpus maritimus Typha spp. Open water
Field samples
Greenhouse seed bank experiment
April
Moist
5.7 3.8 2.6 5.2 2.5
July
Submersed
S.D. ~
S.D.
~"
S.D. ~
S.D.
3.4 1.4 0.6 3.8 0.4
5.6 3.4 5.7 10.6 8.2
6,0 6.1 6.0 8.2 4.1
2.8 2.8 2,3 4.2 1.2
1.9 1.9 1.0 2.9 0.9
8.5 8.6 16.3 16.6 12.8
5.1 5.2 4.8 6,2 3.6
n o t significant (P ~> 0.05). Soil f r om Scirpus lacustris, Typha spp. and Distichlis moist seed bank samples was n o t di fferent (P ~ 0.05) f r o m field sediments in July; however, ope n water and S. maritimus conductivities were less (P <~ 0.05) t han J ul y field samples. Soil moisture decreased (P <~ 0.05: Table IV) April- Jul y, within all vegetation types. Soil f r om submersed seed bank samples was o f higher (P <~ 0.05) moisture c o n t e n t than J ul y field samples. Soil moistures in Distichlis spicata and o p e n w a t e r field sites were n o t different (P ~> 0.05) f r o m moist seed-bank samples. However, within the S. maritimus and Typha spp. vegetation t ypes moist seed bank samples contained a greater (P <~ 0.05) moisture percentage t h a n field samples. TABLE IV Mean soil moisture (%, are-sin transformed) of seed bank and field samples from 5 vegetation types at Ogden Bay Waterfowl Management Area, Utah, 1982 Vegetation type
April
Distichlisspicata $¢irpuslacustris $cirpusmaritimus Typhaspp. Open water
Greenhouse seed bank experiment
Field samples
37.39 44.05 36.53 45.59 42.40
July
Moist
Submersed
S.D.
~
S.D.
~
S.D.
~
S.D.
5.06 5.94 3.35 6.93 2.04
31.45 34.74 33.10 82.76 34.45
1.71 3.45 1.67 1.66 3.64
32.44 38.60 36.48 46.50 35.31
3.92 4.85 1.98 5.34 3.85
47.15 49.36 46.79 52.37 44.01
4.40 6.33 4.82 6.36 6.58
DISCUSSION Model predictions (van der Valk, 1981) o f marsh plant species composition following the fire were n o t good f or the specific vegetation t ypes o f
49
8cirpus lacustris, 8. maritimus, Distichlis spicata and open water. Within these vegetations, plant species, responding after the fire, may do so on the basis of responses to several environmental factors (Smith and Kadlec, 1983) which could modify van der Valk's (1981) model. Gradations in requirements for light (Cresswell and Grime, 1981), soil moisture, temperature (Thompson et al., 1977), and salinity (Lesko and Walker, 1969; Christiansen and Low, 1970) may all have had an influence on germination and, therefore, predictions for a particular vegetation type. Salinity and moisture of greenhouse soil samples were often different to those of field soils especially in July (Tables III and IV), giving some unreliability to predictions based on seed bank experiments. Also, much of the lack of agreement between predictions and field data may have been due to shade classifications (Table I). Shade may directly reflect substrate temperature, If seedlings of a species were found growing within 1 vegetation type, it was classified as shade tolerant for all vegetation types. However, sediment temperature and possibly shade have been shown to vary considerably among vegetation types (Smith, 1983). Sediment temperature is known to influence germination of wetland plants. Grime et al. (1981) found that most wetland species had a high temperature requirement for germination, and Sifton (1959) found that 30°C was the o p t i m u m for Typha spp. Higher sediment temperatures did not increase germination within the 8cirpus lacustris, 8. maritimus, or Distichlis spicata vegetation types to the point of improving nominal correlations. Shade classifications are directly related to competition. Van der Valk (1981) stated, "The model also ignores interactions among the plants (competition, allelopathy) that may play an important role in determining the presence or absence, abundance, and location of species in wetlands." Competition obviously plays an important role in the vegetation responding after fire and therefore competition is yet another factor contributing to the disparity between predicted and actual vegetation response. In addition, competition in the seed bank experiment itself (greenhouse) may prevent some species from being recorded as present. For example, absence of Potamogeton pectinatus in the seed bank experiment was one of the reasons better correlations were not obtained for some vegetation types. P. pectinatus was rare in the greenhouse, but was conspicuous in the field. In contrast, van der Valk and Davis (1978) found high numbers of P. pectinatus seeds in the seed bank and P. pectinatus was the dominant submergent macrophyte in their studies. Zannichellia palustris grows best in shallow water (van Vierssen, 1982) and is found in such areas at Ogden Bay. P. pectinatus and P. crispus occupy the deeper channels and borrow pits. Correlations between predictions and field data were partially dependent on the date when species composition was sampled. Moreover, substrate samples taken into the greenhouse were mixed while sediments in the field were undisturbed. Disturbing the soil surface often stimulates germination.
50
Burning did not expose new sediments (Smith, 1983) and, therefore, different seed banks. The differences between model predictions and field data could also have resulted from a statistical problem of sampling sparse, clumped populations. The presence of only a few species in both the field and seed bank for some vegetation types (e.g., Disttchlis) could also cause poor correlations for relatively small differences. Finally, van der Valk's (1981) model was designed for freshwater ecosystems. Ogden Bay had a "fresh" water supply, but sediments were saline at times (Table III), possibly making application of the model to Ogden Bay, under certain circumstances, difficult. However, van der Valk (1981) applied the model to conditions where salinities (Delta Marsh, Manitoba, Canada) were similar to those found at Ogden Bay. There was good agreement between prediction and actual species occurrence for Typha spp. vegetation and for the total study area. Salinity alone did not explain why Typha sites show good correlation since salinity in these areas was as high or higher than in vegetation types that did not show significant correlations (Table III). By considering the entire burn unit and all 50 quadrats a large sample size was attained and a strong correlation was apparent when 2 rare species i(see p. 47) were omitted. MacMahon (1980) stated: "On a regional basis we can predict with fair accuracy the average change in the biota under certain conditions, but it is difficult, nearly impossible, to predict the exact behavior of any one very small plot for a short time period, and even harder for a longer time period..." The model predictions for Ogden Bay support this statement. Problems also arise, as van der Valk (1981) noted, for dispersal species: "The establishment of D-species in a wetland is, however, impossible to predict from the model per se because a source of propagules is also necessary." These data support Smith and Kadlec (1983) who noted that seed banks could be used in a general sense to make important management decisions. Given the current form of the predictive model, we recommend that wetland managers merely identify species in the seed bank and in the field as an index to species composition following fire. Incorporation of additional variables (i.e., salinity), competitive interactions, and either more discrete classifications (e.g., shade intolerant, partially tolerant ... shade tolerant) or providing for a continuous variable (e.g., a continuum of light requirements) would greatly improve model utility for wetland managers. ACKNOWLEDGEMENTS M. Hadfield, J. Smith, K. Reese, D. Irving and B. Weber provided field assistance. M. Barkworth, M. Wolfe, F. Lindzey and D. Sisson offered helpful comments on the project. Thanks to J. Brown for comments on the manuscript. D. Sisson gave statistical advice and M. Barkworth verified plant identifications. Logistic support was provided by the Utah Cooperative Wildlife Research Unit, and the Utah State University Forest Resources
51
Department. Texas Tech University, the Utah Department of Wildlife Resources and the Federal Aid to Wildlife Restoration Project provided financial support.
REFERENCES Christiansen, J.E. and Low, J.B., 1970. Water requirements of waterfowl marshlands in northern Utah. Utah Div. Fish Game Publ., 69--123. Cresewell, E.G. and Grime, J.P., 1981. Induction of a light requirement during seed development and its ecological consequences. Nature (London), 291: 583--585. Grime, J.P., Mason, G., Curtis, A.V., Rodman, J., Band, S.R., Mowforth, M.A.G., Neal, A.M. and Shaw, S., 1981. A comparative study of germination characteristics in a local flora. J. Ecol., 69: 1017--1059. Hollander, M. and Wolfe, D.A., 1973. Nonparametric Statistical Methods. Wiley, New York, 503 pp. Ives, K.H. and Gibbons, J.D., 1967. A correlation measure for nominal data. Am. Stat., 21: 16--17.
Kadlec, J.A., 1982. Mechanisms affecting salinity of Great Salt Lake marshes. Am. Midl. Nat., 107: 82--94. Lesko, G.L. and Walker, R.B., 1969. Effects of sea water on seed germination in two Pacific atoll beach species. Ecology, 50: 730--734. MacMahon, J.A., 1980. Ecosystems over time: succession and other types of change. In: R. Waring (Editor), Forests: Fresh Perspectives from Ecosystems Analysis. Proc. Oregon Biol. Colloq., Corvallis, pp. 27--58. Nelson, N.F., 1954. Factors in the development and restoration of waterfowl habitat at Ogden Bay Refuge, Weber County, Utah. Utah Dep. Fish Game Publ., 6. Rogers, K.H. and Breen, C.M., 1980. Growth and reproduction of Potamogeton crispus in a South African lake. J. Ecol., 68: 561--571. Sifton, H.B., 1959. The germination of light sensitive seeds of Typha latifolia. Can. J. Bot., 37: 719--739. Smith, L.M., 1983. The effects of prescribed burning on the ecology of a Utah marsh. Ph.D. Dissertation. Utah State University, Logan, UT, 171 pp. Smith, LM. and Kadlec, J.A., 1983. Seed banks and their role during drawdown of a North American marsh. J. Appl. Ecol., 20: 673--684. Smith, L.M. and Kadlec, J.A., 1985. Fire and herbivory in a Great Salt Lake marsh. Ecology, in press. Smith, L.M., Kadlec, J.A. and Fonnesbeck, P.V., 1984. Effects of prescribed burning on nutritive quality of marsh plants in Utah. J. Wildl. Manage., 48: 285--288. Teltscherova, L. and Hejn~, S., 1973. The germination of some Potamogeton species from South Bohemian fish ponds. Folia Geobot. Phytotaxon., 8: 231--239. Thompson, K., Grime, J.P. and Mason, G., 1977. Seed germination in response to diurnal fluctuations of temperature. Nature (London), 267: 147--149. Van der Valk, A.G., 1981. Succession in wetlands: a Gleasonian approach. Ecology, 62: 688--696. Van der Valk, A.G. and Davis, C.B., 1978. The role of seed banks in vegetation dynamics of prairie glacial marshes. Ecology, 59: 322--335. Van Vierssen, W., 1982. The ecology of communities dominated by Zannichellia taxa in western Europe. II. Distribution, synecology and productivity aspects in relation to environmental factors. Aquat. Bot., 13: 385--483. Zar, J.H., 1974. Biostatisticat Analysis. Prentice-Hall, Englewood Cliffs, NJ, 620 pp.