The author has declared that no competing interests exist.
Conceived and designed the experiments: TAS. Performed the experiments: TAS. Analyzed the data: TAS. Contributed reagents/materials/analysis tools: TAS. Wrote the paper: TAS.
Current address: Department of Integrative Biology, Oregon State University, Corvallis, OR, United States of America.
The abiotic environment has strong influences on the growth, survival, behavior, and ecology of aquatic organisms. Biotic interactions and species life histories interact with abiotic factors to structure the food web. One measure of food-web structure is food-chain length. Several hypotheses predict a linear relationship between one environmental variable (e.g., disturbance or ecosystem size) and food-chain length. However, many abiotic and biotic variables interact in diverse ways to structure a community, and may affect other measures of food web structure besides food-chain length. This study took a multivariate approach to test the influence of several important environmental variables on four food-web characteristics measured in nine ponds along a hydroperiod gradient over two years. This approach allowed for testing the ecosystem size and dynamic constraints hypotheses while in context of other possibly interacting environmental variables. The relationship between amphibian and invertebrate communities and pond habitat variables was assessed to understand the underlying food-web structure. Hydroperiod and pond area had a strong influence on amphibian and invertebrate communities, trophic diversity and δ15N range. The range in δ13C values responded strongly to dissolved oxygen. Food-chain length responded to multiple environmental variables. Invertebrate and amphibian communities were structured by pond hydroperiod which in turn influenced the trophic diversity of the food web. The results of this study suggest food-chain length is influenced by environmental variation and species assemblage and that a multivariate approach may allow us to better understand the dynamics within and across aquatic food webs.
Temporary waters are abundant, diverse in physical, chemical, and biological characteristics, and are located throughout the world [
A current focus in food-web ecology is to understand how environmental variation influences food-web structure and function. Food webs depict consumer-resource (or predator-prey) interactions by characterizing trophic relationships among species or individuals in a particular habitat [
Food-chain length (FCL) is one common metric used to describe food-web structure. Several hypotheses have been proposed to explain the variability in, and the factors determining, food-chain length [
In this study, environmental variables in combination with food-web metrics were used to track how and if the structure of food webs changes along a hydroperiod gradient. To do so, FCL and three community-wide metrics developed by Layman et al. [
I characterized aquatic food webs, amphibian and invertebrate communities, and examined their relationships with environmental variables for nine natural ponds varying in hydroperiod on the Queen’s University Biological Station (QUBS) property north of Kingston, Ontario, Canada (301km [187miles] Northeast of Toronto). The hydroperiod gradient spanned from intermittent freshwater woodland ponds to near-permanent freshwater marshes (classification of temporary waters follows Williams [
The hydroperiod varied between years in the study ponds. All ponds had water with partial ice coverage on the 3rd of April of both years. Link, Blue2, and QUBS ponds had drastically reduced hydroperiods in 2009 compared to 2008 (
Category | pond | Hydroperiod (# of days) | CV Temperature | Dissolved oxygen (mg/L) | pH | CV water depth | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
2008 | 2009 | 2008 | 2009 | 2008 | 2009 | 2008 | 2009 | 2008 | 2009 | ||
short | Winter1 | 65 | 74 | 0.271 | 0.31 | 6.38 | 6.68 | 7.38 | 7.14 | NA | 2.65 |
short | Winter2 | 65 | 74 | 0.243 | 0.21 | 2.65 | 6.35 | 7.04 | 6.82 | NA | 1.91 |
short | Snake | NA | 102 | NA | 0.23 | NA | 3.61 | NA | 5.49 | NA | 1.06 |
intermediate | Link | 147 | 126 | 0.205 | 0.30 | 1.21 | 5.17 | 7.31 | 6.90 | NA | 1.20 |
intermediate | Qubs | 175 | 127 | 0.212 | 0.28 | 3.10 | 5.33 | 6.76 | 6.24 | NA | 0.85 |
intermediate | Blue2 | 175 | 125 | 0.302 | 0.19 | 7.11 | 5.80 | 7.73 | 7.18 | NA | 1.00 |
intermediate | Blue1 | NA | 140 | NA | 0.19 | NA | 5.48 | NA | 7.11 | NA | 0.94 |
long | Indian | 365 | 365 | 0.28 | 0.39 | 5.04 | 6.23 | 7.27 | 6.47 | NA | 0.74 |
long | P82 | 365 | 365 | 0.24 | 0.34 | 4.79 | 6.62 | 7.42 | 6.84 | NA | 0.52 |
2008 | 2009 | 2008 | 2009 | 2008 | 2009 | 2008 | 2009 | ||||
short | Winter1 | NA | 104.33 | NA | 0.83 | NA | 1.31 | NA | 50.26 | ||
short | Winter2 | NA | 358.83 | NA | 0.91 | NA | 1.35 | NA | 75.91 | ||
short | Snake | NA | 190 | NA | 0.44 | NA | 0.95 | NA | 64.62 | ||
intermediate | Link | NA | 188 | NA | 0.71 | NA | 1.19 | NA | 88.36 | ||
intermediate | Qubs | NA | 505.03 | NA | 0.93 | NA | 1.21 | NA | 47.58 | ||
intermediate | Blue2 | NA | 844.5 | NA | 0.24 | NA | 0.83 | NA | 28.02 | ||
intermediate | Blue1 | NA | 1258.15 | NA | 0.27 | NA | 0.8 | NA | 8.03 | ||
long | Indian | NA | 3488.84 | NA | 0.09 | NA | 0.22 | NA | 3.96 | ||
long | P82 | NA | 190.35 | NA | 0.38 | NA | 0.61 | NA | 25.54 |
Ponds Snake and Blue1 were not sampled in 2008. CV is the coefficient of variation. NA means data was not collected.
We collected invertebrates and amphibians by dip-netting from numerous areas within each pond every two weeks (April—September in 2008 and 2009) to comprehensively sample multiple trophic pathways leading to top consumers. Because of the relatively low species richness (four to nine species per pond) we could sample the entire amphibian community. I sorted major invertebrate groups in the field, placing samples in plastic jars, and holding them on ice until deposited in a freezer. Amphibians were euthanized by immersion in Tricaine Methanesulfonate (MS-222) buffered with an equal amount of sodium bicarbonate to a pH of ~7.0 in accordance with animal care guidelines approved by the University of Toronto and Queen's University Animal Care Committees (# 20007692), held on ice during field collection, and then frozen until further processing. Animals were collected under scientific collector’s permit (# 1051193) approved by the Ontario MNR.
Food-web resources were collected monthly from each pond for isotopic analysis during the spring, summer, and fall seasons (April to September) of 2008 and 2009. Basal organic resources consisted of detritus, aquatic macrophytes (submerged and emergent), algae, seston, and fine benthic organic matter (FBOM). Seston was acquired by collecting a 1000 mL water sample at mid-depth in the water column from each pond. FBOM was sampled by dredging a 53μm net across the bottom of the pond in several random locations. The net contents were emptied onto a sieve tower of decreasing mesh size. The benthic matter collected on the 53 μm sieve was rinsed with distilled water into a collection jar. Samples were held on ice during field collection and then frozen until processed.
Seston containing zooplankton, bacteria, and phytoplankton were obtained by filtering water samples onto pre-combusted Whatman glass microfiber GF/F filters. FBOM was poured into glass scintillation vials, allowed to settle, and the clear liquid was suctioned off using a pipette and discarded. The resultant concentrated FBOM was dried in scintillation vials. Aquatic macrophytes, algae, and coarse detritus samples were rinsed of attached periphyton or sediment and invertebrates (removal checked under a dissecting microscope). Tadpoles and salamander larvae were identified to species using specialist keys [
Realized food-chain length was estimated as the maximum trophic position obtained by a species from each pond [
The community-wide metrics, δ15N range, δ13C range, and total area encompassed by the consumer food web (TAfoodweb) were calculated for each pond using mean consumer species δ15N and δ13C values. Total area was calculated using the geometry [
Amphibian species and invertebrate family data sets were analyzed separately using correspondence analysis (CA) on presence-absence data. These communities were compared with the pond environmental data sets using canonical correspondence analysis (CCA). This direct gradient multivariate method summarizes the maximum amount of variation in the community data set while constraining it to axes associated with the environmental data [
A redundancy analysis (RDA) was performed to test the influence of environmental factors on food web components. RDA is a direct gradient ordination method that can be used to test if species composition is related to a set of measured variables. RDA is similar to principal components analysis because the Euclidean distances among the objects in ordination space are preserved [
Variation in food-web structure in response to changing environmental conditions was observed between sampling years. In both years the RDA models indicated that high amounts of variation in food-web structure could be explained by the environmental predictors (68% and 52%, respectively). Two important commonalities between years are that trophic diversity (TAfoodweb) positively covaried with hydroperiod and that FCL (MaxTP) was correlated to multiple environmental and food-web metrics. The largest pond (Indian pond) with the longest hydroperiod had relatively the same association with environmental variables (e.g., high temperature variability, high DO) and food-web structure (high trophic diversity and range in δ13C) between years. Generally ponds with a longer hydroperiod were associated with higher trophic diversity, higher species richness (Figs
Sampling sites are denoted by black outlined grey circles. MaxTP is the trophic position of the top predator or realized food-chain length, TAfoodweb is the total area encompassed by all species in δ13C–δ15N bi-plot space. Plot used scaling = 1 to create a distance biplot where objects approximate their Euclidean distances in the space of response variables. Length of the arrow represents the strength of the gradient. Arrows that are directed in opposite directions are negatively correlated. The angles between environmental and food-web variables reflect their correlations.
(A) Four environmental predictors in 2008 measured in seven ponds and (B) seven environmental variables from nine ponds in 2009. The direction of the arrow indicates direction of maximum change and the length is proportional to the rate of change of that variable. See
Pond name is placed adjacent to dot.
In the 2008 RDA, the first axis (RDA axis 1) explained 78% of the variability and described a gradient with ponds ranging from short hydroperiod, higher pH, low temperature variability, and low dissolved oxygen (negative side of axis 1) to longer hydroperiod ponds with high variation in temperature, high dissolved oxygen, and high trophic diversity (TAfoodweb) (
The first axis (RDA axis 1) of the corresponding RDA model using 2009 community-wide metrics and the four environmental variables explained 59% of the variation and was mostly summarized by pH and dissolved oxygen in one direction and hydroperiod in the other. The range δ13C was negatively related to hydroperiod, suggesting higher resource diversity in shorter hydroperiod ponds (
The RDA model on the expanded 2009 environmental data set (7 variables) explained 99% of the variation leaving only 1% unexplained by the environmental variables in the model, meaning the model was very close to over fitting the data. Examination of the VIF indicated hydroperiod was highly correlated to other variables. Given this, hydroperiod was removed from the dataset and the RDA was performed again using six environmental variables. The conclusions were similar between the analyses (with and without hydroperiod) because of the similar number of environmental variables to the number of sites. In 2009, 97% of the variation in food web structure was explained by the environmental variables in the expanded RDA model, indicating that food web variables could be explained mostly by the environmental variables even without hydroperiod in the model. All constrained canonical axes significantly summarized relationships between the food-web metrics and the environmental variables (permutation test
The CA of the invertebrate community in 2008 explained 49% and in 2009 46% of the total variance in each of the first two axes (
Year | Amphibian assemblage | Invertebrate assemblage | ||||||
---|---|---|---|---|---|---|---|---|
Eigenvalue | Eigenvalue | |||||||
CA total inertia | Axis 1 | Axis 2 | Variation explained by axes 1 and 2 (%) | CA total inertia | Axis 1 | Axis 2 | Variation explained by axes 1 and 2 (%) | |
2008 | 0.42 | 0.222 | 0.098 | 75 | 0.67 | 0.191 | 0.142 | 49 |
2009 | 0.61 | 0.255 | 0.190 | 73 | 0.80 | .220 | 0.145 | 46 |
CA reports total Inertia, which is a multivariate measure of the amount of variation in data set.
In 2008, CCA results estimated 70% of the total variation in the invertebrate community was explained by the environmental variables (
Eigenvalue | |||||||
---|---|---|---|---|---|---|---|
Total variation explained by environment (%) | Axis 1 | Axis 2 | Variation explained by axes 1 and 2 (%) | No. environmental variables | Pseudo-F all axes | ||
Amphibian assemblage | |||||||
2008 | 53 | 0.094 | 0.082 | 79 | 4 | 0.561 | 0.84 |
2009 | 49 | 0.25 | 0.17 | 77 | 5 | 1.768 | 0.17 |
Invertebrate assemblage | |||||||
2008 | 70 | 0.187 | 0.124 | 66 | 4 | 1.161 | 0.15 |
2009 | 75 | .202 | 0.129 | 52.5 | 7 | 1.008 | 0.40 |
Using CCA, 87% of the total variation in the 2009 invertebrate community was explained by the environmental data set (
Patterns of amphibian occurrence and environmental conditions across ponds can be seen between years. The CA of the amphibian communities in 2008 and 2009 summarized similar amounts of variance (
Species placement in biplot is marked with an open circle. MeanCanopy is the mean canopy cover, meanArea is the mean pond area (m2) measured over the ponds duration, CVdepth is the coefficient of variation of water depth, DO is dissolved oxygen, and CVtemp is the coefficient of variation of water temperature.
The CCA of amphibian species and environmental data in 2008 explained 53% of the total variability in the amphibian community (
No amphibians were found in Winter1 during 2009, so it was not included in the CA and CCA analyses. Using all seven environmental variables in the CCA caused the model to overfit the data. Examination of the VIF values indicated hydroperiod and CV water temperature were highly correlated with the other variables. Given this, we removed these variables from the dataset and re-ran the CCA using the remaining five environmental variables. The CCA of amphibian species and environmental data in 2009 explained approximately 82% of the total variation in the amphibian community. Most of the variation on CCA Axis 1 was associated with two opposing responses, varitaion in water depth and dissolved oxygen (
Invertebrate and amphibian communities were structured by the pond hydroperiod which in turn, especially for the invertebrates, influenced the trophic diversity of the food web. Trophic diversity responded strongly to hydroperiod, pond area, and variation in water temperature in both years. Larger ponds with longer hydroperiods conferred higher species richness and trophic diversity. This was best illustrated in 2009 when invertebrate richness ranged from 17 to 41, and amphibian species richness ranged from 3 to 6 from short to longer hydroperiod ponds. The range in δ13C also responded strongly to hydroperiod and variation in temperature in 2008, but was influenced by dissolved oxygen, temperature variation, pH, and canopy cover in 2009. Higher δ13C range was seen in Indian pond in 2008, this is a large pond with a long hydroperiod, mostly open canopy, emergent and non-emergent aquatic plants, a large detrital component from leaf fall and aquatic plant senescence, and a rich invertebrate community. The range in δ13C was positively correlated with dissolved oxygen indicating that ponds such as Link, which had high dissolved oxygen, also support diverse basal production sources (range δ13C). Range in δ13C was often positively correlated with total area of food web, indicating a strong association and a similar response to environmental variation. A wide δ13C range suggests organisms in the community have the opportunity to feed on a diverse set of resources [
The results presented clearly show that FCL (MaxTP) was influenced by multiple environmental variables and was not strongly responsive to any one particular variable thereby forcing it to the center of the ordinations. This finding that multiple controls may influence FCL has previously been acknowledged [
There are a few potential reasons for lack of support for the ecosystem size and dynamic constraints hypotheses. Community-wide metrics of food-web structure correlated weakly to moderately with FCL, which could decrease the reliability of predicting the relationship between FCL and other environmental predictors. Alternatively, perhaps the environmental gradients among ponds were not large enough to elicit a change in FCL or small sample size. However, the study ponds ranged in size from 78 m2 to almost 3500 m2 and differed in hydroperiod by 291 days between the shortest and longest hydroperiod pond. Additionally, despite disparity in environmental conditions the presence of the top predator of the system (usually
A consistent pattern was seen when pond communities were compared across years. Variation in water temperature and dissolved oxygen were positively correlated and associated with the variation in δ13C.
Most food-web studies test the effect of one environmental variable on FCL. By extending the effects on FCL to a multivariate approach, I was able to evaluate the relative influence of several abiotic variables on multiple food-web components including FCL. I found short hydroperiod ponds are characteristically different in their amphibian and invertebrate communities and differ markedly from longer hydroperiod ponds in terms of food-web responses and environmental predictors. I found the determinants of food-chain length in relatively small, isolated wetlands differed from the determinants in other habitat types (e.g., ecosystem size is a strong determinant of food-chain length in lakes). This reinforces the need for further investigation of the structure and function of temporary water systems. Small ponds are exceptionally useful to study because the scale of the system is directly relevant to the scale of the mechanisms driving ecosystem processes. In conclusion, I argue that to better understand diverse environmental influences on food-web dynamics, a multivariate approach including interacting factors should be adopted in theoretical and empirical research.
(DOCX)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
I thank the individuals who helped collect and process samples; J. Arblaster, S. Booth, M. Mahmood, G. Jegatheeswaran, R. Sambi, G. Gill, D. Bloom, S. Bloom, F. Munro, M. Conboy, and J. Cowper Szamosi. Comments and valuable feedback on this manuscript from D.D. Williams, J. Helson, M. Cadotte, D. Jackson, K. Winemiller and two anonymous reviewers were extremely helpful.