What does resilience of a clear-water state in lakes mean for the spatial heterogeneity of submersed macrophyte biovolume?

Aquatic Botany 87 (2007) 307–319 www.elsevier.com/locate/aquabot

What does resilience of a clear-water state in lakes mean for the spatial heterogeneity of submersed macrophyte biovolume? Ray D. Valley *, Melissa T. Drake Minnesota Department of Natural Resources, Division of Fish and Wildlife, 1200 Warner Road, St. Paul, MN 55106, USA Received 20 September 2006; received in revised form 29 June 2007; accepted 9 July 2007 Available online 12 July 2007

Abstract Short-term variability of spatial heterogeneity of submersed macrophyte biovolume (percent of water column occupied by vegetation) was evaluated over 3 years along a gradient of productivity in four north temperate glacial lakes in Minnesota, USA. We hypothesized we would observe the lowest among-year variability in spatial heterogeneity of biovolume in our undisturbed, moderately productive lake and high variability in our more locally disturbed productive lakes. Our analysis involved three major steps: first, we removed negative trends of biovolume across depth with non-parametric regression smoothers; second, we examined spatial pattern in residuals using variograms; finally, we compared spatial pattern of biovolume among lakes seasonally, over 3 years. Lake productivity negatively correlated with water clarity and the depth range of macrophyte growth, and positively correlated with the variability of spatial patterns. In the least disturbed moderately productive lake, vegetation grew over a large range of depths (up to 7.5 m), and spatial pattern across the littoral zone was similar for each survey. In contrast, in the more turbid, productive lakes, depth and spatial patterns of biovolume varied greatly from survey to survey. Factors that increase productivity and weaken resilience in lakes may lead to unstable spatial patterns of macrophyte biovolume. Published by Elsevier B.V. Keywords: Aquatic plant; Alternative stable state; Regime shift; Monitoring; Mapping; Geostatistics

1. Introduction Factors influencing lake ‘states’ or ‘regimes’ have been well studied (Scheffer, 1998; Carpenter, 2003). Scheffer et al. (2001) and Scheffer and Carpenter (2003) describe three lake regimes (often referred to as Alternative Stable States) as they relate to phosphorus loading: one resilient regime characterized by low phosphorus loading or recycling, clear water and abundant submersed macrophytes; one unstable regime characterized by moderate phosphorus recycling, variable water clarity and variable macrophyte abundance; and finally, one resilient regime characterized by high phosphorus recycling, turbid water and little or no macrophyte growth. Lake depth and baseline productivity affects where lakes naturally fall on the continuum between alternative regimes and their resiliency in either state (Genkai-Kato and Carpenter, 2005). Shallow, eutrophic lakes are probably less resilient and more susceptible

* Corresponding author. Tel.: +1 651 793 6539; fax: +1 651 772 7974. E-mail address: [email protected] (R.D. Valley). 0304-3770/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.aquabot.2007.07.003

to regime shifts than deep oligotrophic lakes (Genkai-Kato and Carpenter, 2005; Scheffer and van Nes, 2007). The resilience of lake habitats will affect how lakes respond to disturbance. Human disturbances threaten lake habitats globally (Bro¨nmark and Hansson, 2002). Studies show a long history of impacts at multiple spatial scales to littoral habitats in north temperate lakes. At local scales, macrophytes have long been physically removed or uprooted by lakeshore owners (Radomski, 2006). In addition, non-native plant and animal invaders have had long-term local impacts on macrophyte assemblages (Nichols, 1994; Wilson et al., 2004; Titus et al., 2004). At regional scales, phosphorus loading from agricultural or urban landscapes has promoted algal growth that has altered macrophyte abundance and species composition (Nichols and Lathrop, 1994; Egertson et al., 2004). At global scales, climate change is altering hydrologic and temperature regimes and is potentially exacerbating the effects of all previously mentioned disturbances (Schindler, 2001; Genkai-Kato and Carpenter, 2005; Dokulil et al., 2006). Disturbances occurring simultaneously at different scales challenge our ability to isolate and understand the primary driver of change in aquatic ecosystems. Therefore, efforts should be made to identify thresholds for

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regime shifts, identify unnatural disturbances that may be pushing the system towards thresholds, mitigate or remove the disturbance, then evaluate whether management actions were successful at sustaining the desired lake status. Janse (1997) published critical phosphorous loads of approximately 3 mg P/L/day for shallow lakes (mean depth less than 2 m) in The Netherlands. In simulations, macrophytes were absent from lakes when this threshold was exceeded. Genkai-Kato and Carpenter (2005) published much lower thresholds of approximately 0.1 mg P/L/day for deeper lakes in Wisconsin (mean depth = 12.7 m). Still, field patterns demonstrate the relationship between phosphorus loads and macrophyte abundance, distribution and composition is highly variable (Bachman et al., 2002; Scheffer and van Nes, 2007). Furthermore, although more recent work has focused on spatially explicit models of regime shifts (van Nes and Scheffer, 2005), very little is understood regarding the spatial characteristics of macrophyte habitats in different lake regimes. Because many littoral fish species respond to the spatial heterogeneity of macrophyte habitats (Chick and McIvor, 1994; Weaver et al., 1997; Jacobus and Webb, 2005), factors affecting macrophyte spatial heterogeneity will have appreciable effects on fish communities and food webs. In this paper, we evaluate the short-term variability of spatial pattern of macrophyte abundance in four north temperate glacial lakes that span a gradient of productivity and presumably, resilience to disturbance. Using the theoretical framework of regime shifts outlined by Scheffer et al. (2001) and Scheffer and Carpenter (2003), we predicted that our undisturbed, moderately productive study lake would display the lowest short-term variability of macrophyte spatial pattern among our study lakes. We further predicted that macrophyte spatial pattern in a locally disturbed, yet moderately productive lake would be more stable than spatial pattern in two locally disturbed, productive lakes. We hypothesized that a resilient clear-water regime in moderately productive lakes should translate to low variability in the overall spatial pattern of macrophytes. In contrast, we hypothesized that compromised resilience in the high productivity study lakes would lead to greater variability of macrophyte spatial pattern. None of our lakes occupied the third regime characterized by turbid water without macrophytes. We characterized patterns in percent macrophyte biovolume where biovolume is the percent of the water column occupied

by macrophytes (Canfield et al., 1984; Thomas et al., 1990). We focused on biovolume because these data can be rapidly collected (Valley et al., 2005), percent biovolume is visually intuitive (i.e., 0% = bare and 100% = vegetation growth to the surface) and biovolume describes the vertical habitat dimension for littoral fish (Werner et al., 1977; Schriver et al., 1995; Weaver et al., 1997). However, our method was meant to be a coarse, rapid snapshot of plant growth at individual sample points, and did not resolve the vertical partitioning of plant biomass within the water column. 2. Methods The spatial pattern of macrophyte biovolume within and among lakes was characterized using geostatistics (Isaaks and Srivastava, 1989; Rossi et al., 1992). We use the terms spatial pattern and spatial heterogeneity interchangeably to label a host of geostatistical metrics (Gustafson, 1998; Dent and Grimm, 1999). We used variograms of semivariance to quantify changes in local variability in macrophyte biovolume (nugget effect), overall variability in biovolume (sill variance), spatial structure of macrophyte biovolume (nugget:sill ratio) and the average patch size (variogram range; Rossi et al., 1992; Gustafson, 1998; Dent and Grimm, 1999). Together, these metrics provide a robust measure of spatial pattern, or spatial heterogeneity of macrophyte biovolume. 2.1. Study lakes We examined four small lakes in a metropolitan area of Minnesota, USA (Table 1 and Fig. 1). Square Lake was a moderately deep (mean depth = 9.5 m) seepage lake. Groundwater comprised 70% of Square Lake’s hydrological budget, and 14% of its 209 ha watershed was developed in 2000 (Table 1). Unlike most other Twin Cities metro lakes, total phosphorus (TP) levels have remained remarkably stable (TP = 12 mg/L) in Square Lake since the 1800s (Ramstack et al., 2004). Phosphorus loads estimated from loading models developed by Wilson and Walker (1989) were 0.02 mg/L/day, which may represent a coarse baseline level of natural phosphorus loading for lakes in the upper Midwestern US (Genkai-Kato and Carpenter, 2005). Christmas Lake was a relatively deep (mean depth = 12.9 m) seepage lake in a small watershed (344 ha) that was 33%

Table 1 Physical characteristics of the basin and watershed and phosphorus loads for each study lake Lake

Location

Square Christmas West Auburn Kohlman

458090 N 448540 N 448520 N 458010 N

a

– – – –

928480 W 938320 W 938410 W 938030 W

Size (ha)

Mean depth (m)

Lake watershed size (ha)

Total phosphorusa (mg/L)

Estimated phosphorus loadingb (mg/L/day)

Percent developedc

79 104 58 33

9.2 12.9 7.9 1.4

206 344 3285 3350

12 13 28 124

0.02 0.02 0.39 3.87

14 33 12 57

Total phosphorus was measured during multiple periods between 1994 and 2004 by the Minnesota Pollution Control Agency (MNPCA, 2007). Total phosphorus loading rates estimated using a lake nutrient model developed by Wilson and Walker (1989). c Based on classifications by Landsat satellite imagery acquired in 2000 (MN DNR GIS available from http://jmaps.dnr.state.mn.us/gis/ancillary/lulc_tchlcra3_methods.pdf). Developed includes all classifications of impervious surface. b

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Fig. 1. Morphometry of the study lakes. Depth contours are in meter increments.

developed in 2000 (Table 1). Like Square Lake, phosphorus loads in Christmas Lake were relatively low (TP = 13 mg/L; estimated loading rate = 0.02 mg/L/day). However, unlike Square Lake, Christmas Lake contained non-native carp (Cyprinus carpio; date of introduction unknown) and the non-native macrophytes, curly-leaf pondweed (Potamogeton crispus; date of introduction unknown) and Eurasian watermilfoil (Myriophyllum spicatum; discovered in 1996; Minnesota Dept. Natural Resources-MN DNR, unpublished survey data). West Auburn Lake was a moderately deep (mean depth = 7.9 m) drainage lake situated within a large watershed (3285 ha) with a TP concentration of 28 mg/L. Estimated phosphorus loading was greater than an order of magnitude higher in Auburn Lake (0.39 mg/L/day) compared with Christmas and Square. Urban and agricultural land uses comprised 12 and 20% of the watershed area, respectively (Table 1). West Auburn Lake contained common carp (date of introduction unknown) and was dominated by Eurasian watermilfoil (discovered in 1989; MN DNR, unpublished survey data). Kohlman Lake was a shallow (mean depth = 1.4 m) productive lake (TP = 124 mg/L) that drained a large

(3350 ha) urban watershed that was 57% developed in 2000 (Table 1). Phosphorus loads were an order of magnitude higher in Kohlman (3.87 mg/L/day) than in West Auburn and more than two orders of magnitude higher than in Square and Christmas Lakes. In addition, Kohlman Lake harbored common carp, curly-leaf pondweed (dates of introduction unknown) and Eurasian watermilfoil (discovered in 2000; MN DNR, unpublished survey data). 2.2. Equipment operation and survey design The design of our field surveys followed the criteria discussed by Dungan et al. (2002) that outlined steps necessary in the design of field surveys to detect spatial patterns. We surveyed submersed aquatic vegetation in each lake with hydroacoustics in June and August of each year over 3 years (2002–2004). We used a BioSonics Inc. (Seattle, WA, USA) DE-6000 echosounder equipped with a 430 kHz 68 split-beam transducer mounted on a 16 ft flat-bottom boat. We mapped biovolume using methods described by Valley et al. (2005). This entailed collecting hydroacoustic vegetation data (pulse

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rate = 5 pings/s; pulse width = 0.1 ms) over transects perpendicular to the longest shoreline and spaced 10 m apart. Boat speed was 1.5–2.5 m/s, separating differentially corrected GPS reports from a JRC DGPS212 receiver by 3–5 m (positional accuracy was 5 m; Japan Radio Co. Ltd., 2000). Each point in the GIS point coverage represented a 3–5 m cross-section of transect (i.e., the size of our sampling unit or our survey resolution), and included 10–11 pings. The location for each point was calculated as the mid point between DGPS reports (i.e., 5–6 pings on either side of the calculated location). Average transect location error from previous fixed-transects experiments in Square Lake was 1.1 m (R.D. Valley, unpublished data). The echosounder collected plant data in depths as shallow as 0.5 m. In most cases, we collected hydroacoustic samples to within 10 m of the shore. Large littoral areas where much vegetation was growing to the surface (i.e., topped-out) could not be sampled with hydroacoustics. Topped-out vegetation occupied up to 7% of the littoral area in Square Lake, 36% in Christmas Lake, 81% in West Auburn Lake and up to almost 100% in Kohlman Lake. In these areas, manual sampling procedures were used on a 30 m sampling grid to gather vegetation data. At each such grid point, we anchored each end of the boat and recorded latitude and longitude. Then, at seven points equally spaced along the length of the boat (approximately 0.7 m intervals), we recorded presence/absence of vegetation, vegetation height and water depth as measured with a survey rod. Our intent was to balance the need to maintain consistent levels of sample resolution (i.e., the length of the boat 4.9 m was similar to the length of hydroacoustic samples) with the need to acquire enough samples across the lake for representative statistical distributions and geostatistical analyses. Manually collected data were later appended to the hydroacoustic data for analyses. Submersed vegetation data were analyzed for the vegetated zone of each lake, which we define as the littoral zone. The outer boundary of the littoral zone was defined by the average maximum depth of contiguous bottom coverage of vegetation measured from echograms on each transect pass (i.e., every 10 m). Sparse plant stems growing at deeper depths were omitted from analysis. To assess the species composition of the macrophyte community, point-intercept macrophyte species surveys (Madsen, 1999) were conducted during late June through July 2003 in each study lake. This entailed recording species presence from a double-sided rake thrown and retrieved at numerous sampling locations. Percent frequency of occurrence of each species was determined as the proportion of occurrences to the total number of points surveyed. Number of survey points ranged from 89 points in Kohlman Lake to 147 points in West Auburn Lake. Survey sites were uniformly spaced approximately 60 m apart across the littoral zone of each lake. 2.3. Data processing Hydroacoustic data were processed with BioSonic’s EcoSAV1 Version 1.2.5.2 (BioSonics Inc., 2002). EcoSAV uses a multi-step algorithm to extract information on plant

attributes and depth by examining each echo signal. For each DGPS report, EcoSAV reports average plant height, a best bottom depth estimate, and percent frequency of pings signaling the presence of plants (i.e., percent cover; Sabol and Johnston, 2001; BioSonics Inc., 2002; Sabol et al., 2002). The percent of the water column occupied by vegetation, or observed biovolume, for each DGPS report was calculated using the following equation:   Plant height  Plant cover Biovolume ð%Þ ¼ Depth We detected a non-linear negative trend of biovolume with respect to depth in Square, Christmas and West Auburn Lakes during each sampling period. We used a non-parametric regression smoother to quantify the depth trend (Valley et al., 2005), as confounding trends must be removed prior to geostatistical analyses of spatial variation (Isaaks and Srivastava, 1989; Gustafson, 1998). The smoother was fit using Generalized Additive Models (GAM) with integrated smoothness estimation in the statistics software package R 1.8 (Hastie and Tibshirani, 1990; R Development Core Team, 2003). GAM’s fit by R use Multiple Generalized Cross Validation (MGCV) to maximize fit (R Development Core Team, 2003). We determined that smoothing with 8–10 degrees of freedom produced the best balance between smoothness and fit of biovolume to depth. Collectively, predicted values of biovolume plotted against depth resembled eighth order polynomials. Each regression fit was highly significant (Chisquare; p < 0.001). For each survey in Square, Christmas and West Auburn Lakes, we subtracted predicted biovolume from raw biovolume to remove the depth trend, and then examined remaining spatial pattern in the residuals with variograms of semivariance. We did not find a trend of biovolume versus depth in Kohlman Lake, where vegetation biovolume was variable at all depths within the range where vegetation occurred (up to 1.8 m). Therefore, all spatial modeling was performed on the raw observed biovolume data in Kohlman Lake. 2.4. Geostatistical analyses Sample-to-sample variance as a function of separation distance or lag interval (h) is typically referred to as semivariance and can be described by the following equation: 2 NðhÞ 1 X gðhÞ ¼ zðxi Þ  zðxi þ hÞ 2NðhÞ i¼1 where g(h) is the semivariance for lag interval h, z(xi) the measured sample value at point i, z(xi + h) the measured sample value at point i + h and N(h) is the total number of sample couples for h (Rossi et al., 1992). We fit spherical, exponential or Gaussian models of semivariance as a function of distance using GS+ 7.0 (Gamma Design Software, 2004). We selected the model that produced the best fit to the survey data. Because no clear patterns of directional anisotropy were detected at 08, 458, 908, 1358 directions from north using the default directional tolerance of 22.58, we used omni-directional

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variograms for all geostatistical analyses. Lag intervals were 10 m for all lakes. The number of pairs per lag used to compute mean values of semivariance was never less than 250 in any lake; hence, errors around these means were very small. The relative degree of spatial structure, or average degree of difference between adjacent patches of vegetation biovolume was estimated as one minus the nugget-to-sill ratio (Cambardella et al., 1994). Quantities less than 0.25 indicate weak spatial structure or indiscrete patches (traditionally described as a pure nugget effect), ratios between 0.25 and 0.75 indicate moderate spatial structure or a moderate difference between adjacent patches, and values higher than 0.75 indicate the presence of strong spatial structure or a great difference between adjacent patches (Cambardella et al., 1994). For example, spatial structure would be strong in littoral zones where patches of topped-out vegetation were interspersed with patches of bare sediment. To produce spatially unbiased statistical summaries of plant biovolume in each lake, we created biovolume maps with kriging and calculated the mean biovolume and associated standard deviation from the 5 m grid cells that were estimated by kriging (n > 8000 grid cell values pooled across the entire surface of each littoral zone). If significant depth trends were detected in a lake, we added the depth-predicted biovolume kriged surface to the detrended residual kriged surface for each survey (i.e., we removed the trend, modeled the residuals, then added the trend back at the end as suggested by Isaaks and Srivastava, 1989). In Kohlman Lake, where no depth trends were detected, ordinary kriging was performed on the raw biovolume data. We verified the accuracy of maps by subtracting the predicted biovolume grid cell values from overlying raw biovolume data points and computing the rootmean-squared error (Valley et al., 2005). Because such large data sets were used to model depth and spatial relationships, regressions were highly significant and error around mean values of semivariance at each lag distance were very small (e.g., differences among every survey were highly statistically significant). Therefore, to evaluate biological differences, we measured the standard deviation between modeled relationships among surveys in June and August of each year in each lake sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðxi  x¯ Þ2 s¼ n1

311

Fig. 2. Secchi depth and mean maximum depth of macrophyte colonization (standard deviation) from each survey (interpreted from echograms). Secchi data were collected bi-weekly from May to August by a variety of sources including volunteers, local environmental agencies and Minnesota Department of Natural Resources staff. For June surveys, Secchi data were averaged from the first measurement in May up to the June macrophyte survey date. In August, Secchi measurements were averaged between June and August macrophyte surveys.

data). Higher precipitation in 2002 may have been partially responsible for lower water clarity in West Auburn Lake (Fig. 2), which drains a large watershed. Nevertheless, precipitation did not correlate with Secchi patterns in our other drainage lake, Kohlman Lake. In general, variability in water clarity was greater within lakes than variability in the maximum depth that macrophytes colonized bottom. Furthermore, the mean maximum plant depth correlated strongly with water clarity among lakes (regression p < 0.01; R2 = 0.82). On average, plants covered most bottom areas to depths of 7.5 m in our clearest lake (Square Lake) and covered bottom areas up to only 1.8 m in our most turbid lake (Kohlman Lake; Fig. 2). Overall, maximum mean biovolume and seasonal variability of mean biovolume estimated from kriging models within lakes tended to increase with greater lake productivity (Fig. 3). Mean biovolume ranged from 21 to 25% in Square Lake, 39 to 52% in

where xi is the biovolume predicted from biovolume versus depth regressions at 0.5 m depth increment for each survey or xi is the semivariance g(h) at each 10 m lag interval for each survey. Sample size (n) for each comparison was equal to three (e.g., three June surveys and three August surveys were compared in each lake). 3. Results Environmental conditions varied among years, with the spring and summer of 2002 being warmer and wetter than 2003 and 2004 (MN DNR Climatology Working Group, unpublished

Fig. 3. Mean biovolume for June and August hydroacoustic surveys, in each lake, over 3 years derived from kriging models (see Section 2). Error bars represent the square root of the mean squared error (RMSE) calculated from predicted map values and corresponding observed biovolume values (n > 240 pairs).

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Christmas Lake, 54 to 75% in West Auburn Lake and 37 to 73% in Kohlman Lake (Fig. 3). Offshore (i.e., depth > 1 m) harvesting of Eurasian watermilfoil by a mechanical harvester in Christmas Lake, which constituted the majority of the 1.8 ha of plants that lakeshore owners were permitted to remove in that lake during the study period, occurred between June and August surveys of each year, producing lower biovolume in two of the three August surveys (Fig. 3). Offshore harvesting of vegetation did not occur in the other study lakes. Nevertheless, nearshore removal of macrophytes by lakeshore owners was common both in Christmas and Kohlman lakes. Recreational

boat activity was also heavy in these lakes (R.D. Valley, personal observation). 3.1. Effect of water depth on biovolume and species assemblages Water depth affected patterns in biovolume in Square, Christmas and West Auburn lakes (Fig. 4). In Square and Christmas Lakes, distributions somewhat resembled a boot in form and remained relatively consistent across surveys (Figs. 4A and B and 6C). At depths less than 2 m, biovolume was

Fig. 4. Percent vegetation biovolume vs. water depth in Square (A), Christmas (B) and West Auburn (C) lakes. The effect of depth on biovolume was modeled with a non-parametric regression smoother. R2 is the proportion of variability explained by the modeled relationship for each survey in each lake. All relationships were significant (Chi-square, p < 0.001). In Kohlman Lake, biovolume was variable at all vegetated depths in all surveys and thus no data are shown.

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highly variable. For depths greater than 2 m, vegetation biovolume declined with depth, presumably because of light limitation. We observed different macrophyte species and frequencies in shallow areas compared to deep littoral areas in Square and Christmas Lakes. Of the 19 submersed macrophyte species sampled in Square Lake, six macrophyte species were found in frequencies greater than 20% at depths less than 2 m. Low-growing Chara sp. and Nitella sp. were the only species greater than 20% frequency in deeper littoral depths. Despite harboring the typically aggressive non-native Eurasian watermilfoil, Christmas Lake was also species rich with 22 submersed species sampled. We found 11 species at frequencies

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greater than 20% in areas less than 2 m deep. We commonly found seven species in deeper littoral depths in Christmas Lake. In West Auburn Lake, unlike Square and Christmas Lakes, patterns of biovolume were variable with respect to depth (Figs. 4C and 6C). West Auburn Lake was dominated by coontail (Ceratophyllum demersum) and Eurasian watermilfoil, which co-occurred at frequencies greater than 85% all littoral depths. During both surveys in 2002, a high frequency of biovolume values of 100% (i.e., vegetation growing to the surface) was observed at depths less than 2 m. As depth increased beyond this depth, vegetation biovolume declined rapidly, as plants presumably approached light thresholds for growth. In 2003,

Fig. 4. (Continued ).

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Fig. 4. (Continued ).

biovolume declined appreciably at all depths. However, by August 2004, biovolume appeared to recover to levels similar to those observed in 2002. These differences resulted in seasonal and annual variability in the smoothed relationship between biovolume and depth (Figs. 4C and 6C). In Kohlman Lake, vegetation (mostly coontail—97% frequency of occurrence) was highly variable at all depths up to 1.8 m. At sample points in this lake, we often observed either 100% biovolume or 0% regardless of time of year. Still, in August of each year, we generally observed more 100% cases, than 0% cases. Vegetation was sparse to non-existent at depths beyond 1.8 m, and biovolume was not related to depth.

3.2. Spatial patterns of biovolume variance within lakes In Square Lake, across all surveys, variograms, which are overall summaries of the spatial pattern of macrophytes, were very similar (Table 2 and Figs. 5 and 6). Local variability (nugget effect) of macrophyte biovolume was similar and relatively low from survey to survey (Fig. 5). Spatial structure (1  nugget:sill) was always moderate in Square Lake, indicating that differences in biovolume between adjacent patches were moderate (Table 2 and Fig. 6A). Average patch size as indicated by the variogram range also remained relatively consistent from survey to survey (Table 2 and

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Table 2 Variogram model used to fit semivariance of biovolume residuals (subtracted from a modeled depth trend except in Kohlman Lake where raw biovolume semivariance is modeled), coefficient of determination (R2) of variogram fit, relative degree of spatial structure (1  nugget:sill; high values indicate large differences in biovolume between adjacent patches) and average patch size (variogram range) for each survey in each lake Lake

Month

Year

Model

R2

Relative degree of spatial structure (1  nugget:sill)

Square

June August

2002

Spherical Spherical

0.75 0.74

0.69 0.57

125 150

June August

2003

Exponential Exponential

0.65 0.59

0.70 0.58

180 195

June August

2004

Spherical Spherical

0.67 0.63

0.62 0.62

118 115

June August

2002

Spherical Exponential

0.84 0.59

0.55 0.50

107 78

June August

2003

Exponential Exponential

0.80 0.58

0.50 0.47

240 87

June August

2004

Exponential Exponential

0.70 0.57

0.55 0.35

153 60

June August

2002

Exponential Exponential

0.23 0.30

0.30 0.41

39 285

June August

2003

Spherical Exponential

0.91 0.90

0.43 0.63

43 1245

June August

2004

Exponential Exponential

0.77 0.99

0.43 0.80

60 1167

June August

2002

Exponential Gaussian

0.64 0.95

0.66 1.0

873 623

June August

2003

Exponential Gaussian

0.79 0.94

0.69 0.84

135 860

June August

2004

Gaussian Exponential

0.60 0.80

0.77 0.67

107 423

Christmas

West Auburn

Kohlman

Fig. 6B). Overall, semivariance also was low and showed consistent seasonal variation between June and August surveys (Figs. 5 and 6D). Compared with Square Lake, we observed greater variability in variogram metrics in Christmas Lake. Local variability of macrophyte biovolume (nugget) was relatively high and variable from survey to survey (Fig. 5). Overall variance (sill) was moderate and did not display any seasonal trends (Fig. 5). Spatial structure of macrophyte biovolume was always moderate (Table 2 and Fig. 6A). On average, macrophyte patches were larger in June surveys than in August surveys (Table 2). Still, the overall spatial pattern of macrophyte biovolume in Christmas Lake was relatively stable from survey to survey (Fig. 6). In productive West Auburn Lake, in most cases, we observed greater seasonal differences in variograms than in Square and Christmas Lakes (Table 2 and Figs. 5 and 6). Patterns of semivariance were similar in all June surveys, with relatively low local and overall variability in macrophyte biovolume, moderate spatial structure, and small patches. In August surveys, we observed greater local and overall variability in macrophyte biovolume, stronger spatial structure, and large patches (Table 2 and Fig. 5). In Kohlman Lake, overall, we observed large values of biovolume semivariance (Fig. 5). Biovolume semivariance

Average patch size (variogram range, m)

tended to be highest during August surveys. Biovolume in patches was either very low or very high. All other spatial characteristics of the vegetation did not appear to follow any seasonal patterns and were variable across all sample periods (Table 2 and Fig. 6). 4. Discussion 4.1. Patterns of biovolume across lakes Patterns in biovolume appeared linked to water clarity and the maximum colonization depth of macrophytes. As water clarity and colonization depth decreased among lakes, we observed increasingly variable spatial patterns of macrophyte biovolume. Due to its small, forested subwatershed, groundwater seeps, relatively deep depth and lack of non-native plant or fish species, Square Lake was relatively undisturbed. Consequently, water clarity was high, and vegetation grew over a large range of depths (7.5 m). Indeed, low phosphorus loads and high water clarity during the study placed Square Lake squarely within a resilient clear-water regime predicted by Genkai-Kato and Carpenter (2005). Biovolume was variable at shallow depths less than 2 m and decreased as depth increased. The changes with depth may be related to a combination of disproportionate

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Fig. 5. Mean semivariance g(h) of percent vegetation biovolume from each hydroacoustic survey in each lake. Note the greater range of the Y-axis in the Kohlman Lake graph.

sensitivity of percent biovolume to depth, and several natural factors such as: differences in macrophyte species communities with respect to depth, substrate heterogeneity at shallow depths, and attenuating light as depth increased (Chambers, 1987; Duarte and Kalff, 1990). After removal of the depth trend, variograms were flat with low nugget effects relative to the other study lakes. Apparently, species were arranged into mixed-species assemblages across the entire littoral zone of Square Lake (i.e., a homogeneous collection of mixed assemblage patches). If we could resolve differences among individual plants by using a finer sampling grain, we may have detected stronger

spatial structure. Or, if we chose to evaluate patterns at a very coarse scale (e.g., two 700 m grid cells) we would have observed higher biovolume in the northwest basin of Square Lake than the southeast basin. These alternative scenarios stress the importance of considering the scale at which organisms of interest perceive habitat heterogeneity when designing habitat surveys (Kolasa, 1989; Kotliar and Wiens, 1990). Our 5 m survey grain is appropriate for most fish species because they frequently move long distances within lakes. However, 5 m sampling units will not resolve differences between macrophyte habitats for much smaller organisms such as epiphytic macroinvertebrates (Kolasa, 1989). In Square Lake, as expected, we observed consistent and relatively small seasonal changes in biovolume over the 3-year study. The patch mosaic, as indicated by variograms was also relatively stable throughout the study period. Carpenter and Titus (1984) and Ramstack et al. (2004) document long-term stability in macrophyte communities and water clarity in forested, mesotrophic lake habitats. Despite geomorphological similarity to Square Lake, Christmas Lake experienced several local disturbances that may have affected macrophyte spatial pattern. Water clarity in Christmas Lake was slightly lower, and mean maximum colonization depth of vegetation was shallower than in Square Lake. Also, macrophyte biovolume was more variable and distributed into larger patches in June than in August. Disturbances that may have affected the spatial pattern of macrophyte biovolume included, common carp (Crivelli, 1983; Titus et al., 2004) and human removal of macrophytes (Radomski and Goeman, 2001). Approximately 102 yearround dwellings (approximately 1 dwelling per every 58 m) surrounded Christmas Lake during the study, and lakeshore residents had permits to remove 1.82 ha of aquatic plants using herbicides and mechanical harvesting each year of the study. In addition, most lakeshore owners around Christmas Lake removed a 230 m2 plot of vegetation in front of their lakeshore homes, as that amount was allowable without a permit. Removal of small plots of macrophytes in front of lakeshore homes was likely responsible for the relatively large nugget effects in this lake. We also observed a great amount of recreational boat traffic through surface growing macrophytes (Murphy and Eaton, 1983; Mosisch and Arthington, 1998). Still, the overall spatial structure of macrophytes and relationship between depth and biovolume remained consistent over the study duration in Christmas Lake. Consistency of macrophyte spatial pattern suggests resilience mechanisms, such as the deep, oxygenated hypolimnion in Christmas Lake buffer disturbances that may otherwise increase phosphorus recycling and decrease macrophyte stability (Genkai-Kato and Carpenter, 2005). Compared with Square and Christmas Lakes, spatial pattern of macrophyte biovolume was more variable in highly productive West Auburn and Kohlman lakes. In West Auburn Lake, we observed relatively low water clarity, and vegetation growth was limited to relatively shallow depths. We typically observed consistent spatial pattern in June surveys and then variable spatial pattern as the summer progressed. Overall,

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Fig. 6. Measures of variability of spatial pattern in the study lakes as they relate to phosphorus loads. (A) Spatial structure variability represents the range (maximum to minimum) of the nugget to sill ratio in Table 2 within each lake across all surveys. (B) Patch size variability represents the range of average macrophyte patch sizes in Table 2 within each lake across all surveys. (C) Vertical biovolume variability represents the average difference (mean standard deviation; units are biovolume percentages) between the modeled biovolume vs. depth relationships within each lake where a significant relationship existed across all surveys. This relationship was not significant in Kohlman Lake, and thus results for this lake are not shown. Note the different scale of the X-axis. (D) Horizontal biovolume variability represents the average difference in biovolume semivariance within each lake across all surveys.

vegetation was distributed in many small patches across the littoral zone in West Auburn Lake during June surveys. As summer progressed, spatial pattern changed from several small patches to fewer, larger ones, with distinct edges. This pattern is consistent with the aggressive behavior of non-native Eurasian watermilfoil in north temperate North American lakes (Lillie and Budd, 1992). Still, native coontail and Canada waterweed Elodea canadensis can grow in large canopies in similar lakes, including Kohlman Lake (coontail) in this study. In 2003, macrophyte biovolume was appreciably lower throughout the littoral zone than in 2002 or 2004. During the study, we observed several disturbances that may have impacted the growth and spatial pattern of macrophytes in West Auburn Lake. These disturbances included epiphytic algae growth on Eurasian watermilfoil and coontail plants (Weisner et al., 1997; Jones and Sayer, 2003), herbivory of Eurasian watermilfoil by the weevil Euhrychiopsis lecontei (R.M. Newman, University of Minnesota Dept. Fisheries, Wildlife and Conservation Biology, unpublished data), and bottom disturbances from carp (Crivelli, 1983; Titus et al., 2004). Also, because we did not take detailed measures of the underwater light climate, we cannot rule out that local variation in light penetration affected the variability of spatial patterns.

There was no lakeshore development on West Auburn Lake; consequently, we assumed that direct human disturbances to macrophytes were small. Still, estimated P loads in West Auburn Lake far exceeded the threshold for a turbid lake regime predicted by Genkai-Kato and Carpenter (2005) suggesting that even the continued presence of macrophytes in West Auburn are uncertain. We observed the greatest variability of spatial pattern of macrophyte biovolume in shallow, productive Kohlman Lake. In August of each year, biovolume variance was often much greater than variance in other lakes. Furthermore, the degree of spatial structure and patch size varied across surveys in Kohlman Lake. Like West Auburn Lake, we commonly observed local carp disturbances and epiphytic algae growing on macrophytes. Unlike West Auburn Lake, direct effects of lakeshore owners and recreational boaters on macrophytes were likely greater. Twenty-eight year-round dwellings surrounded Kohlman Lake’s shore (approximately 1 dwelling per 86 m) during the study. Lakeshore residents had permits to remove 0.70 ha of aquatic plants using herbicides and mechanical harvesting. In addition, most lakeshore owners around Kohlman Lake removed a 230 m2 plot in front of their lakeshore homes. Recreational boat traffic through surface

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growing macrophytes was also high during the study (Murphy and Eaton, 1983; Mosisch and Arthington, 1998). These findings support our original hypothesis that highly productive lakes are less resilient to disturbances and display greater variability in the spatial pattern of macrophyte biovolume. The high P loading in Kohlman Lake suggests this lake is unstable in the macrophyte-dominated regime and near the threshold for a turbid regime with no plant growth (Janse, 1997). Across all lakes, consistent with the theory that lakes with low P loads are relatively stable clear-water regimes, we documented that stability in the clear-water regime translated to short-term stability in macrophyte spatial pattern. As P loads and local disturbances increased, water clarity decreased, maximum seasonal biovolume increased, and macrophyte pattern became much more variable from survey to survey. Unstable spatial patterns of macrophyte biovolume in productive lakes may represent a prelude to a resilient turbid regime with further increased P loading or other local disturbances that reduce macrophyte biovolume. Acknowledgements Several people played important roles in the collection, analysis and interpretation of these data. W. Crowell, M. Goodnature, S. Skally, D. Wilfond assisted with fieldwork and data processing. BioSonics staff, D. Dahl, T. Loesch, P. Radomski and B. Richardson built software, GIS scripts and macros that expedited data processing. D. Mulla and B. Sabol provided helpful suggestions on project design and statistical analyses. Macrophyte species surveys were conducted by the Minnesota Dept. of Natural Resources Exotic Species Program. C. Anderson, T. Cross, B. Herwig, J. Vermaat, R. Virnstein and P. Wingate, and two anonymous reviewers provided helpful comments on earlier drafts of this manuscript. This work was funded in part by Federal Aid in Sport Fish Restoration Project F-26, Study 639 in Minnesota. Use of trade names does not imply endorsement of the product. References Bachman, R.W., Horsburgh, C.A., Hoyer, M.V., Mataraza, L.K., Canfield, D.E.J., 2002. Relations between trophic state indicators and plant biomass in Florida lakes. Hydrobiologia 470, 219–234. BioSonics Inc., 2002. EcoSAV1 Submersed Aquatic Vegetation Detection and Analysis. Seattle, Washington, USA. Bro¨nmark, C., Hansson, L., 2002. Environmental issues in lakes and ponds: current state and perspectives. Env. Conserv. 29, 290–307. Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F., Konopka, A.E., 1994. Field-scale variability of soil properties in Central Iowa Soils. Soil Sci. Soc. Am. J. 58, 1501–1511. Canfield, D.E., Shireman, J.V., Colle, D.E., Haller, W.T., Watkins, C.E.I., Maceina, M.J., 1984. Prediction of chlorophyll a concentrations in Florida lakes: importance of aquatic macrophytes. Can. J. Fish. Aquat. Sci. 41, 497– 501. Carpenter, S.R., 2003. Regime shifts in lake ecosystems: pattern and variation. In: Excellence in Ecology, vol. 15, Ecology Institute Oldendorf/Luhe, Germany. Carpenter, S.R., Titus, J.E., 1984. Composition and spatial heterogeneity of submersed vegetation in a softwater lake in Wisconsin. Vegetatio 57, 153–165.

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