OIKOS 114: 27 36, 2006
Species abundance models and patterns in dragonfly communities: effects of fish predators Frank Johansson, Go¨ran Englund, Tomas Brodin and Hans Gardfjell
Johansson, F., Englund, G., Brodin, T. and Gardfjell, H. 2006. Species abundance models and patterns in dragonfly communities: effects of fish predators. Oikos 114: 27 36. We investigated if dragonfly larvae community composition and species abundance curves are sensitive to variation in predation intensity, and whether the fit to a particular niche partitioning model could be used to make inferences about mechanisms structuring communities. The approach taken was to compare communities in lakes either having or lacking fish predation. Dragonfly species classified as active, strongly dominated the dragonfly communities in fishless lakes, and low active species dominated fishless lakes. As activity level is known to correlate with susceptibility to fish predation this indicates that these communities are structured by fish predation. Fitting relative abundance data to five niche partitioning models showed that the same model fitted data from both types of habitats (fish/no fish). This means that the observed differences in relative abundances were substitutive, i.e. the relative abundance of a rank stayed constant, even though the identity of the species having this rank changed. The best fit to data from both types of lakes was found for the random assortment model, which is usually interpreted as an indication that the community is not structured by within-guild interactions. This interpretation for fishless lakes did not seem to agree with other community measures (i.e. lowered diversity and evenness and no relationship between species richness and dragonfly biomass), which indicate that the community is structured by within-guild interactions. Moreover, a detail in the fitting procedure, the number of species included in the analysis, affected which model that fitted data best. Thus, we question if fitting niche partitioning models to data can provide mechanistic understanding of how resources are partitioned in natural communities. F. Johansson, G. Englund, T. Brodin and H. Gardfjell, Dept of Ecology and Environmental Ecology, SE-90187 Umea˚, Sweden (
[email protected]).
The study of relative abundance patterns, especially the distribution of species abundances in a community, has a long history in community ecology (reviewed by Tokeshi 1993, Hubbell 2001). The relative abundance of a species reflects its ability to survive and secure resources. Thus, one approach to explaining relative abundance patterns is to relate species’ traits to their abundances along environmental gradients (Reader 1998, Nash Suding et al. 2003). For example, the tradeoff between foraging activity and sensitivity to predation has been used to
explain the distribution of species in habitats varying in predation pressure (Sih 1987, Wellborn et al. 1996). A different approach is inspired by the finding that the pattern of species abundance distributions (SADs) observed in a wide range of communities can be described by a few statistical models, which suggests the existence of general underlying mechanisms (Hubbell 2001). In this spirit, models have been proposed that are based on the idea that SADs are robust patterns, insensitive to variation between species in traits such as competitive
Accepted 14 November 2005 Subject Editor: Dag Hessen Copyright # OIKOS 2006 ISSN 0030-1299 OIKOS 114:1 (2006)
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ability, and sensitivity to predation (MacArthur 1960, Tokeshi 1993, Hubbell 2001). Examples are the niche partitioning models proposed by Tokeshi (1990, 1996) and Sugihara (1980), which predict SADs based on rules on how niche space is divided as new species are added to the community. In these models niche partitioning is assumed to be sequential, stochastic, and following simple logical rules that make no assumptions about species traits. It is also assumed that the relative abundance of a species reflects how large a proportion of niche space it uses. This assumption was confirmed by Sugihara et al. (2003), who also demonstrated the correspondence between relative abundance patterns and the shape of nicheoverlap dendrograms. A second type of model that predicts SADs is the neutral theory of biodiversity (Hubbell 2001, Volkov et al. 2003). This model is dynamic and it assumes that all species have equal per capita rates of mortality, birth, and dispersal. The two different types of models both describe within-guild processes, where the total niche space is restricted and used up, implying resource competition. Since variation between species in mortality, caused by predation or other selective agents is not accounted for, it is implicitly assumed that such variation can be ignored when studying variation in SADs. Today there is evidence that predator densities, and the tradeoff between traits such as foraging activity and predation risk, can explain variation in relative abundances of species in many ecological systems (reviewed by Wellborn et al. 1996), but it is not known if such effects translate to differences in SADs. In this paper we ask how fish predation affects the relative abundance patterns in dragonfly (Odonata) larvae communities. We first examine if fish predation, and the tradeoff between foraging and predation risk, explain species relative abundances in lakes having or lacking fish. We then fit different niche partitioning models to the SADs patterns observed in the two types of lakes. This allows us to study whether SADs vary between habitats with different predation regimes. Because niche partitioning models make explicit assumptions about the rules governing how niche space is divided between species, it has been suggested that the fit to a particular model can be used to make inferences about general rules that structure communities (Tokeshi 1996, Fesl 2002, Mouillot et al. 2003). Thus we use the fit of different models to discuss the niche division rules in habitats with different predator regimes. As a background for this discussion we also estimate several other community descriptors in the two habitats.
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Activity and the tradeoff between growth and predation risk in odonates The relative abundance of dragonfly larvae is determined by a combination of predation from vertebrate predators, intraguild predation, and interference competition, while resource competition has been suggested to be of minor importance (Johnson and Crowley 1980a, Johnson et al. 1987, Wissinger and McGrady 1993). Dragonfly assemblages in fishless lakes are dominated by active, fast-growing species that are strong intraguild predators but sensitive to fish predation, whereas lakes with fish harbour cryptic dragonfly species with low activity, and low vulnerability to fish predation. Thus, in fishless lakes dragonflies are the top predators while in fish lakes fish are the top predators (Johnson and Crowley 1980b, McPeek 1990, 1998, Johansson 1993a, 2000, Johansson and Brodin 2003). Activity is thus believed to be an important trait that determines abundance of dragonfly larvae species. In lakes without fish we expect strong intraguild predator species to dominate the community, while in lakes with fish we expect fish predation to suppress these dominant species. Hence, we expect resource to be more evenly divided among species in lakes with fish since fish suppress the dominants. This should result in a higher evenness in lakes with fish.
Niche partitioning models Tokeshi (1990, 1993, 1996) proposed a number of different niche partitioning models to provide mechanistic explanations of species abundance patterns. The models are based on the assumption that total niche space is divided into smaller subunits as species are added to a community, but they differ in how the division of the total niche occurs. We focus on five commonly used niche partitioning models. In the dominance pre-emption model (DP) the unoccupied niche space is divided at random and the added species always occupies the larger of the two niches available after the division. In contrast in the dominance decay model (DD) the new species always occupy the smaller of two niches formed by random division. The MacArthur fraction model (MF) assumes that the total niche is divided at random and then chosen with a probability corresponding to the niche size. In the random fraction model (RF) the niche carved out by a species is randomly chosen after a random division of the niche. Finally, in the random assortment model (RA) it is assumed that species abundance vary independent of each other.
OIKOS 114:1 (2006)
Material and methods Data collection To estimate species abundance patterns we collected exuviae of odonates from 20 ponds and lakes (henceforth referred to as lakes). Eleven of those had fish and 9 lakes were fishless. The lakes with fish were dominated by one or several of the following species: perch (Perca fluviatils ), roach (Rutilus rutilus ), pike (Esox lucius ), crucian carp (Carassius carassius ) and brown trout (Salmo trutta ), which all include dragonflies and aquatic invertebrates in their diet (Frost and Smyly 1952, Frost 1954, Rask 1986, Penttinen and Holopainen 1992, Hjelm et al. 2003). Exuviae are the cast exoskeleton of insects that are left in conspicuous positions around the edges of lakes. They provide a measure of emergence rate and absolute population densities of newly emerged adults (Southwood 1978). Exuviae were collected on emergent macrophytes along the shorelines of the lakes during the whole emergence season in 1999 2001. Eleven lakes were sampled in 1999, four in 2000 and five in 2001. Lakes with and without fish were sampled each year. The sampled shoreline was 16 m and was chosen to be as similar as possible among lakes and to be representative of typical dragonfly larvae habitats. The identification of typical dragonfly larvae habitats was based on 20 years of collecting and sampling dragonflies in the area (Johansson 1993b). Each lake was sampled for one year only and we collected exuviae 2 3 times every week during the emergence period. Since species abundance models relates to division of resources, the number of individuals is an unsatisfactory estimate of abundance if species differ in size, and biomass reflects a better estimate of resource requirement than numbers (Tokeshi 1993). The dragonfly larva species in our study differed considerable in size (range 14 43 mm) and we therefore used biomass in our analyses on abundance curves. The biomasses of individuals were determined from exuviae length using length biomass regression equations in Benke et al. (1999).
Activity and relative abundance To evaluate the relationship between activity and abundance in fish and fishless lakes we classified dragonfly larvae into three categories according to their activity level (Appendix 1), and regressed activity against a fish impact index. The activity classification was based on data in Johansson (2000), and unpublished data collected by R. Stoks (Univ. of Leuven, Belgium) and F. Johansson. A categorical rather than a continuous measure was chosen because different studies used slightly different methods, and this discrepancy did not allow us to use a continuous estimate of activity. The fish impact index for each species was calculated OIKOS 114:1 (2006)
as ln (1/(relative abundance in fishless lakes/relative abundance in fish lakes)). A high index implies that a species is more common in lakes without fish.
Community descriptors In order to compare SADs between lakes with and without fish we estimated species richness using rarefaction (Krebs 1989), and an evenness index (J) based on the Shannon Wiener species diversity index (J /H/ Hmax). H denotes the Shannon Weiner function and Hmax denotes log S while S is number of species (Krebs 1989). We also estimated the relationship between richness and biomass of dragonfly larvae, for both fish and fishless lakes. This relationship may provide information on how much of the available niche space is filled in the two types of habitat (Lehman and Tilman 2000).
Fitting niche partitioning models The five different niche partitioning models described above were fitted to our data on the relative abundances of odonates, using the procedure proposed by Mouilott et al. (2003). The computer code used for this is given in Appendix 3 (O14495, which can be found at www. oikos.ekol.lu.se). The statistical test used by Mouilott et al. (2003) yields a likelihood value that can be used to compare the quantitative fit of each model, and a p-value that can be used to judge if model predictions, and data differ significantly. We used a process-oriented approach (Tokeshi 1993), which means that the SAD was calculated using the mean of the relative abundance for each rank across the lakes. A factor with large influence on the SADs predicted by several of the models is the number of species in the community. Most earlier studies have assumed that the number of species is equal to the maximum number observed in a single sample (Casey and King 2001, Fesl 2002, Mouillot et al. 2003). We will use an analysis based on this assumption as a benchmark and then examine the effect of two alternative assumptions. One alternative, proposed by Tokeshi (1990), is to restrict the analysis to dominant species. A similar analysis was performed using the minimum number of species found in a lake. Another alternative is using the observed number of species in each sample, when generating predictions for that sample. This approach was used by applying the procedure of Mouillot et al. (2003) to each lake. This, alternative analysis produced a goodness-offit value (a p-value) and a likelihood value for each lake. To quantify the fit of each model to the two groups of lakes investigated, we summed the likelihood values for the lakes in the group. Note that analyses of data for single lakes and analyses of mean values for groups of lakes have 29
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different interpretations. The single lake approach implies that each lake is a separate community and that the processes forming niche-use occur primarily within the lake. In contrast, the mean value approach is more relevant if all lakes of similar type can be treated as a community. For this case, the data from a single lake is viewed as a replicate observation of the patterns created by niche division rules, resulting from interactions occurring over the whole population of lakes.
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Fig. 1. Relationship between species richness and total biomass of dragonfly larvae. Triangles are for lakes without fish and filled dots for lakes with fish. The line shows the linear regression of the relationship between richness and biomass in lakes with fish. No relationship was found between richness and biomass in lakes without fish.
result reflects that species classified as having a high activity dominated in fishless lakes, whereas species with a low activity had their highest abundances in lakes with fish.
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A total of 1575 and 1654 exuvia were colleted in the lakes with and without fish, respectively (Appendix 1). The biomass of each species is given in Appendix 2. Total number of species was 17 in lakes with fish and 15 in fishless lakes. Observed mean number of species was 8.6 in lakes with fish and 7.4 in lakes without fish. A twoway ANOVA with fish (present/absent) and year as factors, showed no difference between fish and fishless lakes in species richness (F1,14 /0.59, P /0.45). The ANOVA showed no significant difference among years in species richness (F2,14 /0.76, P /0.49) and no significant interaction between year and fish (F2,14 /1.94, P /0.18). Rarefaction was used to calculate richness for a fixed number of exuviae. Using the lowest number observed in one lake (N /15) produced a mean species richness of 4.9 and 3.8 in lakes with and without fish, respectively, and this difference was significant (t /2.66, pB/0.05). Using the second smallest number of exuvia observed instead (N /34) gave the same conclusion (t /2.35, pB/0.05). Evenness was also analysed with a two-way ANOVA using fish (present/absent) and year as factors. Eveness was higher in lakes with fish (0.71) compared to lakes without fish (0.60) and this difference was significant (F1,14 /4.85, P /0.04: ANOVA). The ANOVA showed no significant difference among years in evenness (F2,14 /0.48, P /0.63) and no significant interaction between year and fish (F2,14 /2.82, P /0.14). A linear regression between biomass and species richness showed a significant positive relationship in lakes with fish (r2 /0.74, P /0.0007) and a weak and insignificant relationship in lakes without fish (r2 /0.18, P /0.26), (Fig. 1). The difference between the slopes of the two relationships was confirmed by a significant interaction between species richness and fish presence/absence in an ANCOVA, with fish as factor and species richness as covariate (F1,16 /11.52, p /0.004).
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Niche partitioning models Using our benchmark approach we found the best fit for the random assortment model, in both fishless lakes and lakes with fish (Fig. 3), although all models deviated significantly from data in lakes with fish (Table 1). For the other models, all goodness-of-fit tests showed significant deviations from the data. This test is based on the assumption that the number of species is equal to the maximum number of species found in a single lake, which was 12 species for both types of lakes. Altering the number of species used when generating predictions, gave different results. When restricting the analysis to the minimum number of species found (5 species in both types of lakes), we found the best fit for the MacArthur fraction model in lakes with fish (Table 1). In fishless lakes the random assortment model showed the best fit, but also the random fraction and the MacArthur fraction model showed non-significant deviations (p /0.05) from the data. Finally, when using the number of species observed in each lake to generate predictions, we found the best fit for the random fraction model in both types of lakes (Table 1).
Discussion We found large differences in the relative abundance of dragonfly species when comparing lakes with and without fish. Active species had highest abundance in fishless lakes, and species with a more passive foraging mode peaked in lakes with fish. Thus, we find support for the expectation that fish predation reduce the abundance of active species and favour species with low foraging activity. This interpretation is further supported by earlier studies showing that high activity and large body size carry a cost in terms of increased mortality due to fish predation, and that active species tend to be superior intraguild predators (Johnson and Crowley 1980a, 1980b, Morin 1984, Pierce 1988, Van Buskirk 1992, Johansson 1993a, McPeek 1998, Stoks and Johansson 2000, Stoks and McPeek 2003). Thus, we conclude that fish predation has strong effects on the structure of the studied odonate communities. Although the differences in the relative abundance of individual species was substantial, the net effects on SADs were rather small. Using the fitting procedure proposed by Mouillot et al. (2003), we found that the same model, the random assortment model, had the best fit in both types of lakes. This means that differences between habitats in relative abundance were largely substitutive; although the identity of the species having a particular rank varied between habitats, this had little effect on the relative abundance of that rank. When generating model predictions for the minimum number of species in a lake, the MacArthur OIKOS 114:1 (2006)
fraction model showed a good fit to both lake types, and when using the actual number of species observed in each lake, the random fraction model had the best fit in both types of lakes. Thus, overall we find support for the idea that SADs can be viewed as robust measures that are insensitive to variation in predation intensity and to variation in species’ traits that are correlated to their vulnerability to predation. This result is in agreement with the finding that the neutral theory proposed by Hubbel (2001) can predict species abundance patterns in many communities where there are ecologically important differences between species in competitive ability or vulnerability to predators (Hubbel 2001). Can the fit to a particular niche partitioning model be used to make inferences about the niche division process? Several authors have interpreted a fit to the random assortment model as an indication that the intensity of competition is low and that the community is organised in a random fashion (Tokeshi 1990, Fesl 2002, Mouillot et al. 2003). Applying the model fitting approach used by most authors we find that our data did fit the random assortment model. Three lines of arguments suggest that the interpretation proposed above is reasonable for lakes with fish. First, species richness and evenness were higher in these lakes. High species richness and evenness suggest weaker withinguild interactions in lakes with fish, possibly because fish predation reduces the abundance of dominant species and thereby prevents the exclusion of subdominant species (Paine 1966, Shurin and Allen 2001). Second, these communities had reduced dominance of active species that are strong intraguild interactors, which may reduce the intensity of interference competition and intraguild predation. Third, the finding that the total biomass increased with species richness in lakes with fish, but not in fishless lakes, indicates that the habitat is less saturated and competition weaker in lakes with fish (but see McPeek 1998). Obviously, the latter relationship can be affected by other mechanisms, such as variation in productivity between lakes, which is why this interpretation should be made with some caution. However, the same mechanisms and observations can be used to argue that communities in fishless lakes are saturated and controlled by intraguild interactions. This suggests that a model other than the random assortment model should be applied. Thus, using our benchmark approach, we found weak support for the interpretation that a fit to the random assortment model indicates that the community is unstructured. The support is even weaker when using the minimum number of species, or the number of species observed in each lake. The fit for the random assortment model was better for fishless lakes than for lakes with fish, which is 31
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Fig. 3. Observed species abundance patterns (dots connected with lines) in lakes without fish and in lakes with fish compared with model predictions from the dominance decay (DD), dominance pre-emption (DE), MacArthur fraction (MF), random fraction (RF) and random assortment model (RA) using a process oriented approach. Solid line is median values, dashed lines are 2.5 and 97.5 quantiles respectively, and dotted lines are min. and max. values for the simulation process.
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Table 1. Fit of means and variances predicted by different niche partitioning models to data on relative abundance of odonates in lakes with and without fish. The fit of each model is described by the likelihood value and, within parentheses, the p-value associated with a goodness-of-fit test. The number of species included in the analyses was a) the maximum number of species observed in a lake (N /12), b) the minimum number of species observed in a lake (N /5) and c) the actual number of species in each lake. In the latter analysis models were fitted to data from each lake and an overall goodness-of-fit value was not calculated. Figures in bold denotes the model with the best fit. DD a) Maximum number of species Fish lakes (mean) /91.6 71.3 (s2) Fishless lakes (mean) /94.8 2 (s ) /60.1 b) Minimum number of species Fish lakes (mean) /8.8 /7.5 (s2) Fishless lakes (mean) /19.1 2 (s ) /9.0 c) Observed number of species Fish lakes (mean) /262.8 Fishless lakes (mean) /194.4
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RF
RA
( B/0.001) /104.6 ( B/0.001) /101.4 ( B/0.001) ( B/0.001) /90.7 ( B/0.001) /78.6 ( B/0.001) ( B/0.001) /103.7 ( B/0.001) /101.9 ( B/0.001) ( B/0.001) /85.2 ( B/0.001) /66.9 ( B/0.001)
/45.0 (0.004) /26.64 (0.02) 61.5 (0.001) /31.6 (0.006)
/27.5 /9.6 /7.0 /8.3
(0.04) (0.59) (0.76) (0.72)
(0.09) (0.14) (0.003) (0.07)
/15.3 /4.2 /4.6 /4.7
/14.2 /4.4 /4.3 /5.8
(0.02) (0.50) (0.48) (0.30)
/40.1 /26.7 /39.2 /19.7
( B/0.001) ( B/0.001) ( B/0.001) ( B/0.001)
/612.7 /327.9
opposite to the pattern predicted from other community descriptors. The finding that assumptions about the number of species present influences which model that fit a particular community, is of course in itself a serious problem when making inferences about niche division rules in natural communities. Tokeshi (1990) noted that the procedure used to generate predictions assumes that all samples have the same species numbers, whereas empirical samples typically have different number of species. Tokeshi therefore restricted the analysis to species that were found in all samples, arguing that rare species use a negligible fraction of the total niche space, and therefore are unlikely to influence the process of niche division. However, Fesl (2002) showed that different models had the best fit when either all species, or only dominant species are used, which seems to indicates that rare species can not be ignored. Using the maximum number of species in a single sample is problematic, if the variation between samples reflects true variation between habitats, which is a reasonable assumption in our study. Obviously, most samples/lakes will have a lower number of species than the maximum number, which means that predictions will be generated for larger communities than those observed. Using instead only dominant species creates the opposite problem; predictions will be generated for communities having fewer species than the observed numbers. As the number of species in a community has a strong influence on the shape of the SAD predicted by most niche partition models, this will bias tests of these models towards not finding a good fit or finding the best fit for the wrong model. Thus, when samples can be considered as being representative of replicate communities of similar type, rather than random samples of the same community, it seems logical to generate predictions for each replicate using the actual number of species observed. OIKOS 114:1 (2006)
MF
/4.1 /2.7 /6.5 /4.2 /241.7 /164.7
(0.54) (0.82) (0.25) (0.57)
/71.5 /42.8
(0.02) (0.54) (0.41) (0.45)
/116.3 /55.4
Finally, we also question the interpretation of the random assortment model as a null model of no interaction for logical reasons. The interpretation seems to stem from the fact that the model does not make any restrictions about the total absolute abundance of organisms in the guild (Tokeshi 1990). However, the other models, that this model is compared to, make no assumption or prediction about how the total resource base used by the guild is changed when new species are added. Thus it is questionable if the models differ in the assumed importance of biotic interactions. In conclusion we find empirical, methodological and logical evidence that a mechanistic interpretation of the fit to different niche partitioning models may not be valid. We did find large differences in the relative abundances of dragonfly species when comparing lakes having or lacking fish. The differences were, however, largely substitutive, and SADs were similar in the two types of lakes. Hence, our data suggests that predation has little effects on SADs. It also lends support to the idea that SADs are insensitive to variation between species in traits that are related to competitive ability or vulnerability to predators. The finding that the differences in relative abundances were substitutive provides one possible explanation why neutral theory, that ignores differences in species traits, can predict SADs in communities where we expect a close relationship between trait values and relative abundances (Hubbel 2001). The generality of this explanation is an open question. Thus we encourage studies of SADs along clearly defined gradients in the strength of biotic interactions. Such studies are needed to determine if the substitutive changes in relative abundances that we found in this study is a special case or a general pattern. Acknowledgements We would like to thank Mark McPeek for valuable comments on a previous version of this article. FJ was supported by a grant from the Swedish Research Council and
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GE by a grant from the Swedish Research Council for Environment, Agricultural Sciences, and Spatial Planning.
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OIKOS 114:1 (2006)
OIKOS 114:1 (2006)
Appendix 1. Number of exuviae collected of each species along a 16 m stretch of the shoreline in lakes with (in bold) and without fish. Figures after species names denote activity where 1 represent a low active species and 3 a high active species.
1 0 0 1 0 1 0 169 0 0 0 2 47 0 43 0 0
0 0 9 0 0 1 0 86 0 0 0 31 0 7 14 0 0
/
Spelgrunden2
0 4 18 3 0 1 0 46 2 1 3 5 1 3 1 0 0
/
Spelgrunden1
0 1 13 5 0 3 0 162 25 0 0 22 0 0 0 0 0
/
L˚angviksskatan
0 0 10 0 0 0 0 34 51 5 1 104 0 0 0 0 0
/
H¨all¨angestj¨arnen
0 0 3 2 0 3 0 51 7 4 0 71 0 0 7 2 0
/
Grossj ¨ on ¨
0 1 1 0 0 0 0 90 0 0 0 64 0 4 0 0 0
/
Finkarsberget1
0 0 2 0 0 0 0 61 1 0 0 106 0 12 0 0 3
/
Vitsk¨arsudden
3 4 17 10 4 1 0 22 0 0 0 1 5 0 0 16 0
/
Jockestj¨arn2
1 0 0 0 0 0 0 0 1 0 12 2 0 0 1 0 17
/
Jockestj¨arn1
10 3 9 7 6 3 182 114 1 0 0 3 0 0 0 39 37
/
Finkarsberget2
42 1 17 0 14 31 0 39 10 0 0 5 0 0 0 10 1
/
Rovsundet
1 0 0 0 0 8 0 0 44 0 0 113 0 16 7 0 0
/
Mj¨osj¨on
0 0 4 18 1 14 0 1 0 3 0 5 3 1 0 21 0
/
Holmsj¨on
4 0 1 5 0 0 0 1 7 1 0 17 0 0 1 31 0
/
Thuresdamm
0 0 1 8 0 2 0 1 2 0 0 25 252 0 0 72 2
/
Sj¨alafj¨arden
5 1 0 57 5 1 15 0 2 0 0 3 5 0 0 12 0
/
Nydalasj¨on
0 0 0 6 4 12 0 0 0 0 2 30 0 1 0 1 0
/
Skravelsj¨on
0 0 0 2 0 4 0 0 0 0 0 1 0 0 3 5 0
/
Brunnsj¨on
/
Lassesdamm
Aeschna grandis (3) A. subarctica (3) A. juncea (3) Cordulia aenea (1) Somatochlora metallica (1) Libellula quadrimaculata (1) Leucorrhinia albifrons (3) L. dubia (3) L. rubicunda (3) Sympetrum danae (3) Coenagrion armatum (2) C. hastulatum (2) C. johanssoni (2) C. lunulatum (2) Lestes sponsa (3) Erythromma najas (1) Enallagma cyathigerum (2)
/
Bjensj¨on
Species/Lake
1 0 3 0 0 0 0 113 0 1 1 14 9 32 49 0 0
35
36 Appendix 2. Biomass (mg) of each species along a 16 m stretch of the shoreline in lakes with (in bold) and without fish. Figures after species names denote activity where 1 represent a low active species and 3 a high active species.
323 0 0 45.2 0 19.6 0 3312 0 0 0 19.2 373 0 1055 0 0
0 0 2540 0 0 19.6 0 1686 0 0 0 298 0 150 344 0 0
/
Spelgrunden2
0 1129 5080 136 0 19.6 0 902 44 11.2 28.9 48.1 7.93 64.3 24.5 0 0
/
Spelgrunden1
0 282 3669 226 0 58.8 0 3175 551 0 0 212 0 0 0 0 0
/
L˚angviksskatan
0 0 2822 0 0 0 0 666 1123 55.8 9.62 1000 0 0 0 0 0
/
H¨all¨angestj¨arnen
0 0 847 90.4 0 58.8 0 1000 154 44.6 0 683 0 0 172 79.8 0
/
Grossj ¨ on ¨
0 282 282 0 0 0 0 1764 0 0 0 616 0 85.7 0 0 0
/
Finkarsberget1
0 0 564 0 0 0 0 1196 22 0 0 1020 0 257 0 0 40.9
/
Vitsk¨arsudden
968 1129 4798 452 104 19.6 0 431 0 0 0 9.62 39.7 0 0 638 0
/
Jockestj¨arn2
323 0 0 0 0 0 0 0 22 0 115 19.2 0 0 24.5 0 232
/
Jockestj¨arn1
3227 847 2540 317 156 58.8 2419 2234 22 0 0 28.9 0 0 0 1556 504
/
Finkarsberget2
13552 282 4797 0 364 607 0 764 220 0 0 48 0 0 0 399 13.6
/
Rovsundet
323 0 0 0 0 157 0 0 969 0 0 1087 0 343 172 0 0
/
Mj¨osj¨on
0 0 1129 814 26 274 0 19.6 0 33.5 0 48.1 23.8 21.4 0 838 0
/
Holmsj¨on
1291 0 282 226 0 0 0 19.6 154 11.2 0 164 0 0 24.5 1237 0
/
Thuresdamm
0 0 282 362 0 39.2 0 19.6 44 0 0 241 1998 0 0 2872 27.3
/
Sj¨alafj¨arden
1613 282 0 2578 130 19.6 199 0 44 0 0 28.9 39.7 0 0 479 0
/
Nydalasj¨on
0 0 0 271 104 235 0 0 0 0 19 289 0 21.4 0 79.8 0
/
Skravelsj¨on
0 0 0 90.4 0 78.4 0 0 0 0 0 9.62 0 0 73.6 199 0
/
Brunnsj¨on
/
Lassesdamm
Aeschna grandis (3) A. subarctica (3) A. juncea (3) Cordulia aenea (1) Somatochlora metallica (1) Libellula quadrimaculata (1) Leucorrhinia albifrons (3) L. dubia (3) L. rubicunda (3) Sympetrum danae (3) Coenagrion armatum (2) C. hastulatum (2) C. johanssoni (2) C. lunulatum (2) Lestes sponsa (3) Erythromma najas (1) Enallagma cyathigerum (2)
/
Bjensj¨on
Species/Lake
323 0 847 0 0 0 0 2215 0 11.2 9.62 135 71.4 686 1202 0 0
OIKOS 114:1 (2006)
