Wetlands Ecol Manage (2018) 26:331–343 https://doi.org/10.1007/s11273-017-9576-5 ORIGINAL PAPER Arrowhead (Sagittaria cuneata) as a bioindicator of nitrogen and phosphorus for prairie streams and wetlands . . Katherine M. Standen Patricia A. Chambers Joseph M. Culp Received: 13 March 2017 / Accepted: 31 August 2017 / Published online: 27 September 2017 The Author(s) 2017. This article is an open access publication Abstract The emergent aquatic plant, Sagittaria 15 streams of varying nutrient concentrations. Plants cuneata, is an easily-identiﬁed and commonly-found grown in experimentally enriched sediment were more species in the Great Plains region of North America productive than those grown in enriched water or and has the potential to be a bioindicator of nitrogen control conditions: they produced more emergent (N) and phosphorus (P) because of its previously- leaves and tubers, had a larger ﬁnal biomass and identiﬁed leaf plasticity in response to nutrient con- height, and developed emergent leaves that showed a ditions. To identify associations between leaf mor- consistent increase in size and unique change in shape phology and soil and water nutrients, we conducted: over time. Emergent leaves collected from ﬁeld plants (1) a 10-week controlled experiment in which plants also showed signiﬁcant variability of leaf traits; were grown in nutrient-enriched sediment, nutrient- however, this variability occurred at all scales of enriched water, or unamended control trials, and (2) a replication (leaf, plant, quadrat, and site), with linear ﬁeld study where emergent leaves were collected from mixed effects modelling indicating that random chance was likely driving this variability. Although sediment nutrients were crucial to successful growth Electronic supplementary material The online version of of S. cuneata under controlled conditions, the high this article (doi:10.1007/s11273-017-9576-5) contains supple- variability in leaf morphology under ﬁeld conditions mentary material, which is available to authorized users. (likely due to large natural variability at the species, K. M. Standen (&) J. M. Culp population, and individual scale) make leaf plasticity Biology Department and Canadian Rivers Institute, of S. cuneata unsuitable as a bioindicator. Our results University of New Brunswick, PO Box 4400, Fredericton, emphasize the need to quantify within and among NB E3B 5A3, Canada e-mail: email@example.com plant variation in leaf morphology (and to clarify sampling methods) for the many taxa of aquatic J. M. Culp e-mail: firstname.lastname@example.org macrophytes that are phenotypically plastic and noto- riously difﬁcult to classify. P. A. Chambers Environment and Climate Change Canada, 867 Lakeshore Keywords Arrowhead Sagittaria Bioindicator Rd, PO Box 5050, Burlington, ON L7R 4A6, Canada e-mail: email@example.com Nitrogen Phosphorus Great Plains J. M. Culp Environment and Climate Change Canada, University of New Brunswick, PO Box 4400, Fredericton, NB E3B 5A3, Canada 123 332 Wetlands Ecol Manage (2018) 26:331–343 Introduction leaves, more leaves per plant, and were larger overall than plants grown in a low P solution (10 lM), Bioindicators are common species, or groups of suggesting that higher P concentrations increased species, with easily identiﬁable features that exhibit plant size and leaf width in S. lancifolia. Similarly, plastic responses along gradients in one or more Dorken and Barrett (2004) noted that emergent leaf environmental variables (Holt and Miller 2011). size of S. latifolia increased with fertilizer addition in Changes in species morphology, behavior, and phys- both di- and monoecious populations. Water depth has iology, presence or absence of taxa in a community, as also been found to inﬂuence leaf size, where increas- well as the structure of the entire biotic community ing depth was associated with decreased leaf size as a have all been used as indicators of stressors, both result of energy being directed to enhance petiole anthropogenic (pollution, land use changes) and length at the expense of leaf size (Wooten 1986). natural (drought, ﬂooding, etc.) (e.g., Richards and These predictable changes in leaf size with environ- Ivey 2004). In streams (e.g., Clements and Carlisle mental conditions suggest that the genus Sagittaria 2000) and wetlands (e.g., Sharma and Rawat 2009), may be a useful bioindicator of environmental bioindicators have often been used to detect changes in stressors. water quality, such as eutrophication caused by excess Sagittaria cuneata (Sheldon) is a widespread nutrient inputs. Although the benthic macroinverte- species across North America (Crow and Hellquist brate (BMI) community has been the most commonly 2000) and is particularly common in parts of the Great used taxa for assessment of water conditions (Hod- Plains region, such as the Red River Valley in kinson and Jackson 2005), aquatic macrophytes pos- Manitoba, Canada. Here, initial conversion of native sess many traits characteristic of bioindicators: they grassland to agricultural cropland or pasture, as well as are sessile, easily sampled and identiﬁed to genus (and present-day agricultural activities and urbanization, often species), and respond to environmental stressors has led to inputs of pollutants to rivers, lakes, and (e.g., nutrients, light, substrate texture, etc.) at the wetlands, particularly sediment-bound and dissolved individual, population, and assemblage level. Never- forms of nitrogen (N) and phosphorus (P) but also theless, compared to BMIs, relatively few studies have pesticides and heavy metals (Hall et al. 1999; Dodds explored the use of macrophytes as bioindicators of et al. 2004; Donald et al. 2007). The Great Plains is environmental condition (e.g., Carbiener et al. 1990; considered an endangered biome (Samson and Knopf Tremp and Kohler 1995; Robach et al. 1996; Demars 1994), with many waterbodies at risk as a result of and Harper 1998; Thie´baut and Muller 1999; Richards problems associated with agriculture and urbaniza- and Ivey 2004; Haury et al. 2006; Ceschin et al. 2010), tion, including pollution, hydrologic disturbance, especially in our region. modiﬁcations of riparian zone, and channelization The aquatic macrophyte genus Sagittaria (Alis- (Dodds et al. 2004). These changes are detrimental to mataceae) has the potential to be a useful bioindicator ecological functioning and have led to changes in in- of nutrient status because of its easily identiﬁable stream nutrient cycling that have impaired down- characteristics, known plastic leaf morphology, and stream water quality (Dodds et al. 2004). Within the occurrence in and tolerance of a range of environ- Great Plains region of North America, agriculturally- mental conditions. Sagittaria is a submersed or derived nutrient loading to the Canada-USA trans- emergent genus with extremely variable leaf size boundary Red River is particularly high (Environment and shape (Arber 1920; Fernald 1950; Sculthorpe Canada and Manitoba Water Stewardship 2011), 1967): the emergent leaf blades range from lanceolate prompting bioindicator research in order to quickly (lance-shaped) to sagittate (arrow-shaped) while the and accurately diagnose nutrient condition of streams submerged leaves may be subulate (awl-shaped) or and wetlands in this agriculturally-dominated ribbon-like. Environmental conditions have been watershed. shown to inﬂuence leaf size and shape in this genus The aim of this study was to determine the (e.g., Wooten 1986; Dorken and Barrett 2004; association between leaf morphology of Sagittaria Richards and Ivey 2004). For example, Richards and cuneata (Alismataceae) and nutrients found in both Ivey (2004) found that S. lancifolia plants grown in a water and sediment as a ﬁrst step towards development high P solution (1 mM) produced wider emergent of a bioindicator of ecosystem nutrient status. 123 Wetlands Ecol Manage (2018) 26:331–343 333 Sediments have been previously shown to provide the initial planting, 99% of tubers visibly germinated, and majority of nutrients to aquatic plants (Barko and plants were selected from this stock based on health, Smart 1980; Carignan and Kalff 1980; Huebert and and similarity in size. Ninety plants, remaining in Gorham 1983); however, the water column can also original pots, were transferred to an outdoor facility provide much of the required nutrients (Chambers and randomly placed in 20 L buckets evenly spaced on 1987; Robach et al. 1996; Pelton et al. 1998) especially a level deck and covered by a 50% shade cloth to when the ratio of sediment to water nutrient availabil- reduce heat exposure. Two air pumps continuously ity is low (Rattray et al. 1991). We therefore conducted bubbled air through each bucket to keep CO and two studies: (1) a controlled experiment in which oxygen levels consistent. plants were grown in either standardized low-nutrient Three trials were established: (1) nutrient-enriched water or sediment, each independently supplemented water (with four treatments of varying dosages); (2) with N or P and (2) a ﬁeld study to determine nutrient-enriched sediment (with four treatments of variability in leaf morphology in relation to in situ varying dosages), and (3) an unenriched control (SM water and sediment nutrient concentrations. These Table 1). All buckets received 16 L of de-chlorinated studies allowed us to establish, ﬁrst, whether S. municipal water. For the water trial, water was cuneata biomass and leaf morphology was primarily enriched with potassium phosphate and ammonium inﬂuenced by sediment or water nutrients in the nitrate. For the sediment trials, nutrients were added as absence of confounding effects (e.g., variability in slow-release fertilizer wrapped in two layers of incident light, sediment composition, water depth, landscaping fabric to allow for permeability and ease water clarity, and stream velocity) and, second, to of replacement, and buried below the sediment quantify patterns of variation in leaf characteristics surface. Water in all buckets was refreshed biweekly, within and among natural populations of S. cuneata in and buckets were scrubbed with 5% bleach solution relation to nutrient conditions of streams and wetlands and subsequently rinsed to reduce algal build up. in the Red River Valley of Manitoba, Canada, and Slow-release fertilizer packs were also changed more widely in the North American Great Plains. biweekly. Plants were monitored daily, and all new emergent leaves were traced and digitized using a CanoScan LiDe 110 scanner to determine if leaf size Methods and shape changed during the experiment. After a 10-week growing period, all plants were removed Experimental study from pots and sediment was rinsed from roots before being separated into parts (i.e., roots, stems, tubers, Tubers (i.e., nutrient-ﬁlled, clonal structures produced and leaves), and weighed to determine aboveground on underground rhizomes) were collected from two (AG) and belowground (BG) biomass, and AG:BG tributaries with populations of monoecious S. cuneata biomass ratio. Tubers produced by each plant were in the Red River Valley, Manitoba, in October 2013. counted and weighed. Tubers were cleaned with 5% bleach solution to Water samples were collected on three occasions remove harmful organisms (Hunter-Cario 2007), during the experiment and analyzed for total nitrogen rinsed with distilled water, and stored moist in Ziploc (TNw) and phosphorus (TPw) at Environment Cana- bags at 4 C to induce dormancy (adapted from da’s National Laboratory for Environmental Testing McIninch and Garbisch 1991). On May 24–25th, using standard methods (APHA 2005a, b). Sediment 2014, tubers were removed from cold storage, samples were collected at the end of the experiment, weighed, and planted 5–8 cm deep in individual, dried at 60 C for 5 days, ground and sieved to 1 mm, 12.7 9 12.7 cm square pots containing approximately stored frozen and later analyzed for Olsen-P (‘‘Sed P’’; 465 g of low-nutrient sediment composed of 1/3 loam Olsen et al. 1954), and nitrate and ammonium (‘‘Sed and 2/3 sand. Pots were placed in water-ﬁlled coolers N’’ for the sum of nitrate and ammonium; Keeney and in the University of New Brunswick research green- Nelson 1982 ammonium; Willis and Gentry 1987 house to break tuber dormancy. Three weeks after nitrate). 123 334 Wetlands Ecol Manage (2018) 26:331–343 Table 1 Sagittaria cuneata production among three nutrient trials at the termination of a 10-week nutrient enrichment experiment Nutrient trial Biomass (g) AG mass (g) BG mass (g) AG:BG Leaves per plant Tubers per plant Height (cm) Control 2.39a (0.37) 0.90a (0.13) 1.40a (0.21) 0.53a (0.12) 0.7a (0.5) 0.9a (0.2) 20.4a (0.6) Sediment 45.86b (5.66) 22.67b (2.89) 21.89b (2.99) 0.77b (0.07) 7.1b (0.7) 15.2b (1.5) 31.7b (1.4) Water 3.66a (0.27) 1.56a (0.11) 2.04a (0.17) 0.55a (0.05) 1.7a (0.3) 2.1c (0.2) 23.7a (0.6) Values presented as average (±SE), with different lower-case letters denoting signiﬁcance (p \ 0.05) among trials based on Tukey’s HSD test AG aboveground, BG belowground, AG:BG aboveground to belowground biomass ratio Field study and dried at 40 C for 5 days before being reweighed (note that four leaves were excluded due to improper Sampling was conducted along streams in the Red drying). Water depth and height were measured at River Valley, Manitoba, Canada, in mid-August 2014. each plant. To quantify nutrients, one 500 mL grab The Red River Valley spans about 13,000 km in water sample was collected at each site and sediment southern Manitoba, with approximately 76% used for samples were collected from each quadrat (n = 5 per agricultural activities (Red River Basin Board 2000). site) to a depth of 10 cm. Water sample were analyzed It is characterized by a continental climate with the for TPw (APHA 2012) and TNw (USEPA 1993) at the warmest and coldest months, on average, being July University of Alberta. Sediment nutrient concentra- (20 C) and January (-14.6 C), respectively, and an tions were analyzed as described previously. Light and average 427 mm of annual precipitation (1980–2010 air temperature loggers (HOBO Pendant Temperature records for Morden, MB; www.climate.weatherofﬁce. and Light Data Logger) were placed at 11 of the 15 gc.ca). We selected 15 sites that exhibited variability sites to determine average daylight (lux) and air in nutrient emitting activities in their catchments. Of temperature (C) over the summer (May–August these, 10 sites were ones previously described by 2014) (SM Table 2). Yates et al. (2012), who estimated the quantity of N and P produced by human activities (livestock, human Leaf Morphology population, cropland area) within subcatchments of the Red River using a principal component analysis To determine leaf size and shape, scanned leaf images (PCA). We selected an additional ﬁve study sites to ﬁll from both studies were analyzed using ImageJ (Fig. 1; gaps in the nutrient gradient. All 15 sites were on Rasband 1997–2014). Leaf size was denoted by six independent tributaries within the Red River Valley, measurements (Fig. 1a). Differences in leaf shape with stream orders between 2 and 4. were assessed using geometric morphometrics Sagittaria cuneata plants were sampled along a (Adams et al. 2004, 2012; Zelditch et al. 2004; 100 m reach using ﬁve, 900 cm quadrats distributed Mitteroecker and Gunz 2009) based on 12 landmarks across the reach (adapted from Downing and Ander- chosen to represent leaf shape (Fig. 1b). Euclidean son 1985). Placement of quadrats was intended to be coordinates were determined for each landmark on unbiased by blindly casting a quadrat downstream. every leaf using ImageJ (Rasband 1997–2014). Sampled plants were mature (i.e., ﬂowering and/or bearing fruit) to facilitate species identiﬁcation and Statistical analyses present in at least 5 cm of water to ensure a potential inﬂuence of water chemistry. Only the three newest (or All statistical analyses were conducted using the 2, if the plant had only 2 emergent leaves), fully statistical software, R (version 3.0.2, R Development formed emergent leaves were collected from each of Core Team, Vienna). One-way ﬁxed-effects ANOVAs three S. cuneata plants per quadrat (n = 42–45 per were used to assess differences in ﬁnal plant height, site; adapted from Cornelissen et al. 2003). Each leaf number of leaves, and tubers produced by each plant, was scanned, weighed, placed in a standard plant press biomass (ﬁnal, AG and BG), and AG:BG biomass 123 Wetlands Ecol Manage (2018) 26:331–343 335 Table 2 Results of three-way nested ANOVA testing the variable = l ? site ? quadrat (site) ? plant (quadrat signiﬁcant level of replication of leaf length and leaf shape (site)) ? e, where 2–3 leaves were collected from three plants (represented by scores of PC1) of emergent leaves of Sagittaria sampled in each of ﬁve quadrats at 15 sites (42–45 leaves per cuneata using the model: dependent site, 658 total) in August 2014 Dependent variable Source of variation df Mean square Fp Variance components Estimate % Hleaf length Site 14 3.83 58.50 <0.01 0.067 23 Quad(Site) 60 0.99 15.10 <0.01 0.076 26 QðSÞ Plant((Quad)Site) 150 0.30 4.59 \0.01 r 0.081 28 PðQðSÞÞ Residual 432 0.07 0.065 23 log (PC1 ? 0.5) Site 14 0.85 37.52 \0.01 0.017 30 10 r Quad(Site) 60 0.11 4.98 \0.01 r 0.007 13 QðSÞ Plant((Quad)Site) 150 0.05 2.21 \0.01 0.009 16 PðQðSÞÞ Residual 432 0.20 r 0.023 41 Variance components were included for all random effects. Signiﬁcance (p \ 0.05) is indicated by bold font and variables were transformed as indicated to meet the assumptions of ANOVA treatments. When a signiﬁcant main effect or interac- tion occurred, Tukey’s HSD tests were used to further assess trends in data. Differences in leaf shape within both studies were assessed using geometric morphometrics. In R pack- age geomorph v 1.1-6 (Adams and Otarola-Castillo 2013), a separate generalized Procrustes analysis (Gower 1975; Rohlf and Slice 1990) for each study was used to rotate and scale leaves thus removing size characteristics from data, resulting in 12 sets of scaled Euclidean coordinates (i.e., X and Y coordinates for landmarks 1, 2, 3, etc.) that represented the shape of individual leaves (henceforth called shape data). For each study, a principal components analysis (PCA) was conducted on shape data using R package Fig. 1 Leaf diagrams depicting A the six size measurements geomorph v 1.1-6 (Adams and Otarola-Castillo taken on each leaf: blade length (1), length (2), width (3), lobe length (4), distance between lobes (5), and the surface area of 2013) to assess and compare individual leaf shapes. each leaf (not shown), and B the 12 landmarks chosen on each Principal components (PC) 1 and 2 were examined to leaf to represent leaf shape, where landmarks 2 and 12 are ensure that each showed a meaningful shape gradient. exactly 50% of blade length and landmarks 4, 6, 8 and 10 are PC1 was extracted for the ﬁeld study PCA and used as exactly 50% of lobe length All other landmarks are on ﬁxed a response in mixed linear effects models. features of each leaf For the ﬁeld study, Pearson correlations were ratio among experimental nutrient treatments. These calculated to determine relationships among leaf size analyses showed no signiﬁcant differences (p[ 0.05) characteristics, and between plant height and leaf size of plant traits among treatments (i.e., dosages) within measurements, as well as water depth. To test for either the water-enriched or sediment-enriched trials. signiﬁcant variation in leaf size and shape within Therefore, results are presented for each trial (control, levels of replication [leaf (two or three replicates per water-enriched, and sediment-enriched) with pooled plant), plant (three replicates per quadrat), quadrat 123 336 Wetlands Ecol Manage (2018) 26:331–343 (ﬁve replicates per site), site (15 sites)], we conducted control trials (Table 1). Of plants in the sediment trial, a three-way nested ANOVAs (Underwood 1997)to 87 and 100% produced emergent leaves and tubers, test the model: dependent variable = l ? Site ? respectively. In contrast, 65 and 98% of water trial and Quadat(Site) ? Plant(Quadrat(Site)) ? e. Variance 20% and 70% of control trial plants produced emer- components were estimated for all random factors in gent leaves and tubers, respectively. Final biomass each model using R package lme4 (Bates et al. 2014) (F = 102.56), BG (F = 85.86) and AG bio- 2,77 2,77 to determine at which scale the majority of random mass (F = 101.71), AG:BG biomass ratio 2,77 variation of leaf traits occurred. (F = 5.92), ﬁnal plant height (F = 5.62), and 2,77 2,77 To determine inﬂuences of nutrients on leaf mor- both number of leaves (F = 25.64) and number of 2,77 phology, nine linear mixed effects (LME) models tubers (F = 101.96) per plant were signiﬁcantly 2,77 predicting leaf traits were developed a priori based on different (p \ 0.01 for all comparisons) among the known relationships of nutrients (water and sediment) three nutrient trials, with sediment trial plants having and water depth with macrophyte growth: positive higher biomass (ﬁnal, AG and BG), larger AG:BG inﬂuences of TPw and TNw, singly and in combina- biomass ratio, greater height, and more leaves and tion; positive inﬂuences of SedP and SedN, singly and tubers than water and control trials (p\ 0.05, Tukey’s in combination; positive inﬂuences of both water and HSD test; Table 1). In addition, water trial plants sediment nutrients; negative inﬂuence of water depth; produced 2.5X more emergent leaves and had signif- and a global model with a positive inﬂuence of icantly more tubers per plant than control trial plants. nutrients (water and sediment) and a negative inﬂu- Leaf length was chosen to represent leaf size ence of water depth. We included a random term (1| because it had strong (r [ 0.80), signiﬁcant (p\ 0.05) site/quadrat/plant) to account for variation at each correlations with all other size characteristics level of replication, and a null model to determine (Fig. 1a). Leaf length was signiﬁcantly inﬂuenced by whether random chance and/or variables outside of an interaction between nutrient trial and weeks this study were inﬂuencing leaf traits. After assess- (F = 9.03, p \ 0.01). Leaf length did not differ 8,172 ment of a priori models, post hoc models were (p[ 0.10, Tukey’s HSD test) among the 3 trials developed to explore the inﬂuence of additional during the ﬁrst week in which emergent leaves were variables (temperature, pH, latitude, longitude, Sed present (i.e., week 3); however, during subsequent NO ) on leaf size of S. cuneata. LME models were weeks, plants in the sediment trial produced larger calculated using R package lme4 (Bates et al. 2014). emergent leaves than those grown in either the water To determine which model was the ‘‘best’’ ﬁt, the or control trials (Fig. 2). In the case of the sediment corrected Akaike Information Criterion (AICc; trial, leaves were smallest during week 3, largest Sugiura 1978; Hurvich and Tsai 1991) was calculated during weeks 7 and 8, and intermediate and similar in for each model using R package AICcmodavg (Maze- size during weeks 4, 5, 6, and 10. For plants in the rolle 2015). The model with the lowest AICc value water and control trials, emergent leaves remained was deemed ‘‘best’’, and models with a change in small and similar in size throughout the experiment, AICc (i.e., D ) of less than 4 were considered plausible and were not produced in weeks 9 and 10. (i.e., competing models). Models with D of 4–7 were Similar to leaf length, shape of emergent S. cuneata considered less plausible, and values over seven were leaves varied among the three trials and over time determined as implausible (as suggested by Anderson (Fig. 3). About 57% of shape variation was explained 2008). by gradients associated with PC 1 and PC 2, which corresponded to the distance between lobes (variation between landmarks 5 and 9; Fig. 1b) and lobe length Results (variation between landmarks 5 and 7, or 9 and 7; Fig. 1b), respectively. Leaves produced in the control Experimental study trial were of a similar shape (Fig. 3), though the control trial had low replication of emergent leaves Comparison of plant traits among the three trials (n = 7). Similar to control plants, the average shape of showed that plants grown in nutrient-enriched sedi- water-trial leaves showed little variation over the ments were more productive than water-enriched and experiment. In contrast, average leaf shape of the 123 Wetlands Ecol Manage (2018) 26:331–343 337 Fig. 2 Average length (±SE; on a logarithmic scale) of emergent Sagittaria cuneata leaves (n = 2–40 for each trial and week, total = 272 leaves). Plants were grown in 1 of 3 different nutrient trials during a 10-week nutrient addition experiment. Different letters for a given week denote signif- Fig. 4 Bivariate plot of unaveraged (n = 42–45 leaves per site, icance (p \ 0.05, Tukey’s HSD test) among trials. Plants began 658 total leaves) principal components (PC) 1 and 2 scores producing emergent leaves in week 3; water-enriched and/or summarizing variation in 12 sets of Euclidean landmark control plants did not produce emergent leaves in weeks 5, 9 and coordinates (i.e., shape, see Fig. 3)of Sagittaria cuneata leaves collected from 15 tributary sites in the Red River Valley, Manitoba, Canada, in August 2014 Field study Principal components analysis (PCA) showed that leaf shape was variable within and among sites, with little distinct grouping (Fig. 4). Principal component (PC) 1 explained about 56% of leaf shape variation and corresponded to differences in the rotation of the lobes, whereas PC 2 explained 14% of shape variation and related to width of leaf blades. Three-way nested ANOVA of leaf size also showed that leaf length varied signiﬁcantly at site, quadrat, and plant replica- tion scales; variance components for all random Fig. 3 Bivariate plot of average (±SE) principal components effects indicated that variation was similar at all four (PC) 1 and 2 scores depicting variation in landmark coordinates scales (about 25%; Table 2). In contrast, nested (i.e., shape) of emergent Sagittaria cuneata leaves (n = 2–40 ANOVA of leaf shape (represented by scores of for each trial per week, total = 272 leaves) grown in three PC1) showed greatest variation at the leaf scale (41%), nutrient trials during a 10-week experiment Leaves began emerging in week 3 of the experiment. Line drawings illustrate followed by site (30%) and quadrat (13%) and plant the change in leaf shape depicted by each axis, with PC 1 (16%) scale. associated with a change in lobe distance (Fig. 1b, landmark 5 Comparison of a priori linear mixed effects models and 9) and PC 2 indicative of a change in lobe length (Fig. 1b, using AICc indicated that, for leaf length (Table 3) landmark 5 and 7, or 9 and 7) and leaf shape (Table 4), the null model was the sediment trial changed weekly. On average, leaves ‘‘best’’ model. For leaf shape, all other models were greater than 9 AICc units from the null model, produced by plants in the sediment trial also had longer lobes compared to those produced in the water suggesting that none of the variables, singly or in and control trials. combination, were inﬂuencing leaf shape of S. cuneata. For leaf length, however, TNw is considered a plausible model with a D of 2.55 units and a weight 123 338 Wetlands Ecol Manage (2018) 26:331–343 Table 3 Comparison of a priori and post hoc models for Valley, Manitoba, Canada in August 2014, using linear mixed predicting (a) leaf length and (b) leaf shape of emergent effects (LME) models and corrected Akaike Information Sagittaria cuneata leaves in 15 streams of the Red River Criterion (AICc) 2 2 Model # Model variables AIC D Likelihood x R R c i i M C A priori Null model 0 0.7654 541.95 0 1.000 0.710 2 TNw 0.1037 0.7712 544.50 2.55 0.279 0.198 1 TPw 0.0498 0.7738 548.04 6.09 0.048 0.034 7 TNw, TPw 0.1086 0.7745 548.49 6.54 0.038 0.027 4 HSed N 0.0134 0.7749 549.93 7.98 0.018 0.013 5 Hdepth 0.0074 0.7766 550.52 8.57 0.014 0.010 3 HSed P 0.0034 0.7721 551.11 9.16 0.010 0.007 6 HSed P, HSed N 0.0153 0.7769 556.00 14.05 0.001 0.001 8 HSed P, HSed N, TNw, TPw 0.1086 0.7810 560.59 18.64 0.000 0.000 9 Sed P, Sed N, TNw, TPw, depth 0.1170 0.7855 564.79 22.84 0.000 0.000 Post hoc Null model 0 0.7654 541.95 0 1 0.960 1 Sed NO , TNw 0.1084 0.7724 550.11 8.16 0.017 0.016 5 Sed NO 0.0069 0.7705 550.68 8.73 0.013 0.012 7 longitude, latitude 0.0548 0.7782 552.92 10.97 0.004 0.004 2 Sed NO , TNw, TPw 0.1135 0.7754 554.07 12.12 0.002 0.002 4 pH, Temp 0.1308 0.7804 555.70 13.75 0.001 0.001 8 Sed NO , Temp 0.0077 0.7757 555.94 13.99 0.001 0.001 3 w 3 HSed P, HSed N 0.0153 0.7769 556.00 14.05 0.001 0.001 6 Sed NO , pH 0.0064 0.7757 556.02 14.07 0.001 0.001 9 Sed NO , HSed P 0.0103 0.7730 556.60 14.65 0.001 0.001 17 Sed NO , TNw, TPw, latitude 0.1338 0.7763 557.89 15.94 0.000 0.000 16 Sed NO , TNw, TPw, latitude, Temp 0.1828 0.7758 559.03 17.08 0.000 0.000 3 w 10 Sed NO , TNw, latitude, Temp , pH 0.1872 0.7755 560.02 18.07 0.000 0.000 3 w 11 Sed NO , pH, Temp 0.0073 0.7814 561.17 19.22 0.000 0.000 3 w 14 Sed NO , TNw, TPw, latitude, longitude 0.1308 0.7804 563.12 21.17 0.000 0.000 12 Sed NO , TNw, TPw, latitude, HSed P 0.1338 0.7763 564.12 22.17 0.000 0.000 15 Sed NO , TNw, TPw, latitude, Temp , pH 0.1841 0.7788 564.68 22.73 0.000 0.000 3 w 13 Sed NO , TNw, latitude, Temp , pH, Hdepth 0.1848 0.7782 566.08 24.13 0.000 0.000 3 w Global model 0.1762 0.7892 581.70 39.75 0.000 0.000 2 2 Variables are transformed as indicated to meet assumptions of LME. Also presented are the marginal R (R ) and conditional R (R ) values, change in AIC (D ), relative model likelihood, and model weight (x ) c i i Sed P bioavailable phosphorus in sediment in the form of Olsen-P, Sed N nitrate and ammonia in sediment, Sed NO nitrate in sediment, TNw total nitrogen in water, TPw total phosphorus in water, Temp water temperature of 0.2, though the weight of the null model was 3.59 Similarly, post hoc comparisons showed that the null times greater (0.71). The R values show that the ﬁxed model was the best model for both leaf length (Table 3) factor of the model (TNw) explained about 10% of and leaf shape (Table 4), suggesting that variables not variation in leaf length, and the ﬁxed and random considered in this study or random variation are terms combined explained 77% of variation. inﬂuencing leaf traits. All other models were consid- ered not plausible, based on Di values greater than 7. 123 Wetlands Ecol Manage (2018) 26:331–343 339 Table 4 Comparison of a priori and post hoc models for August 2014, using linear mixed effects (LME) models and predicting leaf shape of emergent Sagittaria cuneata leaves in corrected Akaike Information Criterion (AICc) 15 streams of the Red River Valley, Manitoba, Canada in 2 2 Model # Model variables AIC D Likelihood x R R c i i M C A priori Null model 0.00 0.60 -1260.23 0.00 1.000 0.986 2 TNw 0.09 0.61 -1250.41 9.82 0.007 0.007 1 TPw 0.07 0.61 -1249.09 11.14 0.004 0.004 5 Hdepth 0.03 0.63 -1247.96 12.27 0.002 0.002 4 HSed N 0.00 0.61 -1244.51 15.72 0.000 0.000 3 HSed P 0.00 0.61 -1244.31 15.92 0.000 0.000 7 TNw, TPw 0.09 0.62 -1243.02 17.21 0.000 0.000 6 HSed P, HSed N 0.00 0.61 -1234.93 25.30 0.000 0.000 8 HSed P, HSed N, TNw, TPw 0.09 0.62 -1223.87 36.36 0.000 0.000 9 Sed P, Sed N, TNw, TPw, depth 0.11 0.64 -1218.12 42.11 0.000 0.000 Post hoc Null model 0.00 0.60 -1260.23 0.00 1.000 1.000 1 Sed NO , TNw 0.09 0.61 -1240.78 19.45 0.000 0.000 2 Sed NO , TNw, TPw 0.09 0.62 -1233.40 26.83 0.000 0.000 5 Sed NO 0.00 0.61 -1244.36 15.87 0.000 0.000 10 Sed NO , TNw, latitude, Temp , pH 0.09 0.64 -1216.23 44.00 0.000 0.000 3 w 7 longitude, latitude 0.01 0.63 -1237.82 22.41 0.000 0.000 16 Sed NO , TNw, TPw, latitude, Temp 0.09 0.64 -1217.59 42.64 0.000 0.000 3 w 17 Sed NO , TNw, TPw, latitude 0.09 0.63 -1225.09 35.14 0.000 0.000 4 pH, Temp 0.01 0.63 -1237.82 22.41 0.000 0.000 3 HSed P, HSed N 0.00 0.61 -1234.93 25.30 0.000 0.000 6 Sed NO , pH 0.01 0.62 -1236.30 23.93 0.000 0.000 8 Sed NO , Temp 0.01 0.62 -1236.35 23.88 0.000 0.000 3 w 9 Sed NO , HSed P 0.00 0.61 -1234.76 25.47 0.000 0.000 15 Sed NO , TNw, TPw, latitude, Temp , pH 0.09 0.65 -1209.43 50.80 0.000 0.000 3 w 13 Sed NO , TNw, latitude, Temp , pH, Hdepth 0.11 0.66 -1210.35 49.88 0.000 0.000 3 w 14 Sed NO , TNw, TPw, latitude, longitude 0.14 0.63 -1220.37 39.86 0.000 0.000 12 Sed NO , TNw, TPw, latitude, HSed P 0.09 0.63 -1215.49 44.74 0.000 0.000 11 Sed NO , pH, Temp 0.01 0.63 -1228.29 31.94 0.000 0.000 3 w Global model 0.19 0.64 -1193.00 67.23 0.000 0.000 2 2 Variables are transformed as indicated to meet assumptions of LME. Also presented are the marginal R (R ) and conditional R (R ) values, change in AIC (D ), relative model likelihood, and model weight (x ) c i i Sed P bioavailable phosphorus in sediment in the form of Olsen-P, Sed N nitrate and ammonia in sediment, Sed NO nitrate in sediment, TNw total nitrogen in water, TPw total phosphorus in water, Temp water temperature Discussion nutrient-enriched water or unamended control condi- tions. Plants grown in nutrient-enriched sediments When the emergent plant Sagittaria cuneata was were much more productive. In contrast, plants in grown under controlled conditions, extreme differ- nutrient-enriched water trial were indistinguishable ences were observed between plants growing in from those in the control in leaf size, plant height, and nutrient-enriched sediment and those propagated in biomass, differing only in the fact that water-enriched 123 340 Wetlands Ecol Manage (2018) 26:331–343 plants produced more tubers and leaves than control and leaf age, producing thinner leaves when grown in plants. Many submerged aquatic macrophytes prefer- high compared to low fertilizer treatments and with entially utilize sediment nutrients over water nutrients more pronounced differences later in the growth cycle (Nichols and Keeney 1976a, b; Best and Mantai 1978; (Dorken and Barrett 2004). Our experimental study Carignan and Kalff 1980; Barko and Smart also found differences in leaf shape in response to 1980, 1981, 1986; Chambers and Kalff 1985; Smith nutrient-enrichment and with plant maturation, with S. and Adams 1986; Madsen et al. 2001), although highly cuneata plants in the sediment trial producing leaves enriched water can sometimes offset the effects of low with lobes that were close together initially that later nutrient sediments (e.g., Rattray et al. 1991). Our changed to leaves with lobes that were further apart. observations that sediment trial plants were more Availability of nutrients for plant uptake has far- productive and had higher AG:BG ratio than either reaching implications: leaves of varying shape (i.e., water or control trial plants indicate that the emergent Nicotra et al. 2011) and larger size increase photo- S. cuneata was accessing and utilizing sediment synthetic area (Parkhurst and Loucks 1972; Jurik et al. nutrient sources. Plants in the control trial grew poorly 1982; Niinemets and Kull 1994) and, hence, energy and often remained in submerged form, indicating that accrual which, in turn, is manifest in greater clonal they did not have access to sufﬁcient nutrients to reach (i.e., tuber production) or sexual reproduction (i.e., maturity and thus produce emergent leaves and ﬂower and seed production) (Bazzaz et al. 1987; numerous reproductive propagules. The ﬁnding that Reekie and Bazzaz 1987). In the case of S. cuneata, AG:BG biomass ratios were greater for the sediment- leaf size began to decrease near the end of the growing enriched plants compared to both the water-enriched season (weeks 9 and 10), and leaf shape reverted to and control trials is consistent with our hypothesis of earlier shapes when plants were allocating nutrients to nutrient insufﬁciency in the latter two trials, as both production of over-wintering propagules. In fact, upon terrestrial and aquatic plants allocate resources to harvesting sediment trial plants were found to have 6.4 belowground production instead of aboveground and 15.2X more tubers than the water and control structures (Neill 1990; Hossain et al. 2004; Ket et al. trials, respectively, thus ensuring a greater likelihood 2011) when nutrient availability is limited (Darby and of survival and regeneration during the following year. Turner 2008). Leaf morphology from the ﬁeld study was mark- In addition to differences in productivity in edly different from that of the experimental study. response to nutrient enrichment, emergent leaf mor- Rather than the strong response to sediment nutrients phology varied among the three experimental trials. shown by experimental plants, ﬁeld collected plants Leaf size of other species of Sagittaria, as well as exhibited extreme variability in leaf morphology that various terrestrial plant species (Medina 1970; Atkin- was not explained by sediment nutrients or multiple son 1973; Gulmon and Chu 1981; Jurik et al. 1982), other abiotic variables. Instead, random chance (or has been shown to respond to nutrient availability. For variables such as water velocity and turbidity that were example, leaf size of S. latifolia and blade width of S. not included in our ﬁeld study but held constant in the lancifolia leaves were found to be larger in higher experimental study) may have strongly inﬂuenced leaf nutrient environments (Dorken and Barrett 2004, morphology of S. cuneata under natural conditions. Richards and Ivey 2004), consistent with our exper- Results of nested ANOVAs of leaf length and shape imental observations. Wooten (1986) reported a suggest that leaf morphology is governed by compli- decrease in leaf size with increasing water depth for cated processes operating at a variety of scales: leaf, several Sagittaria spp.; however, water depth was, on plant, quadrat and site scale, such that leaf length average, consistent among our nutrient treatments and exhibited similar variance at all four scales whereas thus unlikely to be inﬂuencing leaf size. Rather, leaf shape was most variable at extreme scales (leaf nutrients added to sediments were responsible for the and site). Site scale variation is likely related to the increased leaf size (and greater productivity) of genetics of the population since S. cuneata generally sediment trial plants. In addition, leaf shape of reproduces clonally, as do most macrophytes (Jones Sagittaria has been shown to vary with nutrient et al. 2012); thus, plants within a site are more availability: leaf shape of monoecious and dioecious S. genetically similar than plants from different sites. latifolia plants changed based on resource availability Variation in leaf traits undoubtedly also occurs 123 Wetlands Ecol Manage (2018) 26:331–343 341 because of environmental heterogeneity (quadrat Sampling methods that do not account for high scale); differences in plant age, resource availability, variation in leaf morphology may exacerbate the and the competitive interaction for resources amongst already notorious difﬁculty in classifying many taxa of plants of different age (plant scale); and within plant aquatic plants. variation of leaf age (leaf scale). To our knowledge, Acknowledgements This study beneﬁtted from suggestions there are no studies that have quantiﬁed patterns of from anonymous reviewers, J. Lento, M. Barbeau, M. Methven variation in leaf characteristics within and among and C. Tyrrell, assistance from J. Musetta-Lambert, C. Sagittaria cuneata populations. However, in the case Thompson, K. Doucet, R. Casey, K. Roach and M. Deschenes, of terrestrial plants, Bruschi et al. (2003) observed that and laboratory work from National Laboratory for Environmental Testing in Burlington, ON, M. Ma et al. at for the sessile oak Quercus petraea, morphological University of Alberta, C. Drury et al. at Agriculture and Agri traits such as leaf length and leaf width varied within a Food Canada in Harrow, ON, and P. Dube´ et al. at Institut de single tree, among trees in a single population, and Recherche et de De´veloppement en Agroenvironnement in among populations, with the variance in morpholog- Que´bec, QC. We also wish to acknowledge funding from Environment Canada’s Lake Winnipeg Basin Initiative, ical traits being almost equal (14–20%) among these Canadian Rivers Institute, and Canadian Water Network. three scales. Collectively, these ﬁndings emphasize KMS was supported by a Collaborative Research and Training the need to standardize sampling design when col- Experience (CREATE) grant, and Discovery grants to JMC lecting leaves for autecological and taxonomic studies. from the Natural Sciences and Engineering Research Council. This is particularly true for aquatic macrophytes as Author contributions KMS designed, executed, and collected many species (such as S. cuneata) exhibit highly and analysed data pertaining to this study. KMS also prepared variable leaf plasticity. this manuscript. PAC and JMC assisted in experimental design Collectively, the experimental and ﬁeld studies and execution, advised in statistical methods and interpretation, show that leaf development of Sagittaria cuneata is a and provided editorial comments on this manuscript. complicated process: under controlled settings, sedi- Open Access This article is distributed under the terms of the ment nutrients drive plant growth whereas under ﬁeld Creative Commons Attribution 4.0 International License (http:// conditions, leaf morphology is highly variable at all creativecommons.org/licenses/by/4.0/), which permits unre- hierarchical scales, with no over-arching environmen- stricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original tal drivers. Though results of both studies provide new author(s) and the source, provide a link to the Creative Com- information on the life history of S. cuneata and the mons license, and indicate if changes were made. relationship between leaf morphology and its sur- rounding environment, the variability of leaf mor- phology in the ﬁeld proves difﬁcult for use as a bioindicator. A good bioindicator should show great- References est variability at the site scale, with the trait or metric showing a dose response to the environmental stressor. Adams D, Otarola-Castillo E (2013) Geomorph: an R package However, the ﬁeld study demonstrated relatively equal for the collection and analysis of geometric shape data. Methods Ecol Evol 4:393–399 variability of leaf traits among the leaf, plant, quadrat Adams D, Rohlf F, Slice D (2004) Geometric morphometrics: and site scales, suggesting large natural variability ten years of progress following the ‘revolution’. 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