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Keywords:

  • early detection;
  • Egeria densa;
  • hyperspectral remote sensing;
  • imaging spectroscopy;
  • Myriophyllum spicatum native;
  • nonnative;
  • submersed aquatic plants (SAP)

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • Nonnative species may change ecosystem functionality at the expense of native species. Here, we examine the similarity of functional traits of native and nonnative submersed aquatic plants (SAP) in an aquatic ecosystem.
  • We used field and airborne imaging spectroscopy and isotope ratios of SAP species in the Sacramento–San Joaquin Delta, California (USA) to assess species identification, chlorophyll (Chl) concentration, and differences in photosynthetic efficiency.
  • Spectral separability between species occurs primarily in the visible and near-infrared spectral regions, which is associated with morphological and physiological differences. Nonnatives had significantly higher Chl, carotene, and anthocyanin concentrations than natives and had significantly higher photochemical reflectance index (PRI) and δ13C values.
  • Results show nonnative SAPs are functionally dissimilar to native SAPs, having wider leaf blades and greater leaf area, dense and evenly distributed vertical canopies, and higher pigment concentrations. Results suggest that nonnatives also use a facultative C4-like photosynthetic pathway, allowing efficient photosynthesis in high-light and low-light environments. Differences in plant functionality indicate that nonnative SAPs have a competitive advantage over native SAPs as a result of growth form and greater light-use efficiency that promotes growth under different light conditions, traits affecting system-wide species distributions and community composition.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Rapid environmental changes may cause plant functional traits to become mismatched with current environmental conditions. Current species distributions reflect both present ecological sorting and past selective pressures, and range expansion or survival in new geographic locations are dependent upon traits that are preadapted to the new environment. These preadaptations are likely to have competitive and evolutionary advantages, such as those observed in nonnative species with unconstrained growth and that become invasive (Mack et al., 2007) and replace native species. What is uncertain is whether the presence of nonnative species retains or changes the functionality of the previous ecosystem to the disadvantage of native species. In this paper we examine to what extent the functional traits of native and nonnative submersed aquatic plants (SAPs) are similar in an aquatic ecosystem.

One system where environmental conditions markedly constrain plant community functionality is the submersed aquatic ecosystem (Sculthorpe, 1965). Plant functional traits can be measured at metabolic, physiological and morphological levels. Functional metabolic traits include photosynthetic pathways, the substrates used for photosynthesis, and the ability to respond to varying light intensities. SAPs have constraints on photosynthesis that are imposed by carbon availability and light in the water column (Dennison et al., 1993). To cope with the low-CO2 environment in the water column, most SAPs have a carbon concentration mechanism (CCM; Maberly & Madsen, 2002) that allows them to store carbon for photosynthesis, either from CO2 or HCO3 substrates (Van et al., 1976; Sand-Jensen, 1983). The main photosynthetic pathways for SAPs are C3, often coupled with CCMs (Maberly & Madsen, 2002), and many use other photosynthetic pathways, including C4, CAM (Crassulean acid metabolism), and C3–C4 intermediates (Keeley, 1999; Ueno, 2001; Bowes et al., 2002; Keeley & Rundel, 2003). Most SAPs are restricted by light partitioning in the water column, with excess light at the surface and low light in deeper water (Dennison et al., 1993). Some SAPs are known to switch from C3 photosynthesis in the light-limited environment at depth to a C4-like photosynthesis and also in the high-light environment of the surface (Ueno, 2001). This facultative C4-like metabolism includes some of the properties of the C4 metabolic pathway, such as fixation of CO2 by phosphoenolpyruvate into malic acid (Ueno, 2001), and no activation of the xanthophyll cycle (Peñuelas et al., 1993, 1997). C4-like metabolism (Keeley, 1999; Keeley & Rundel, 2003) has been demonstrated for some SAP species, including Hydrilla verticillata (Salvucci & Bowes, 1981), Myriophyllum spicatum (Van et al., 1976), Egeria densa (Casati et al., 2000), and potentially Cabomba caroliniana (Salvucci & Bowes, 1981).

Functional physiological traits include the distribution, arrangement and composition of plant biochemical compounds (nutrients, pigments, Chl, etc.), and how these building blocks are combined to overcome environmental limitations such as submergence, high light, salinity, or temperature. Functional morphological traits include plant organs (leaves, stems, roots) and traits such as leaf :  root ratios, morphology (entire and dissected leaves, fine and tap roots, etc.), and architecture (three-dimensional distributions of leaves and stems, leaf angle distribution, ratio of leaves to stem, leaf to root, etc.). At the leaf level, morphological and physiological traits of SAP leaves are often similar to shade adaptations (Mommer et al., 2005), and their typically spherical leaf angle distributions allow them to absorb diffuse light from all directions. There are three main types of leaves in submersed plants: blade-shaped leaves (strap-shaped, elongated or ribbon-like, which are associated with lentic and lotic environments); dissected leaves (deeply cut or subdivided leaves); and whorled leaves (three or more blades at each node), which are associated with lentic environments (Luther, 1947; Sculthorpe, 1965). The metabolic, biochemical, and morphological plasticity and diversity of SAPs make them particularly well adapted to varying environmental conditions, and therefore have high potential to spread into new habitats. The functionality of ecosystem processes can be affected by changes in plant community composition depending on whether nonnative species replace or change the functions that native species performed.

While remote sensing is effective at monitoring invaded plant communities over large spatial extents (Lehmann & Lachavanne, 1997; Elmore et al., 2003; Kerr & Ostrovsky, 2003; Cohen & Goward, 2004; Coppin et al., 2004; Asner & Vitousek, 2005; Bradley & Mustard, 2006), imaging spectroscopy is well established for remote detection of plant biochemistry, photosynthetic efficiency, and leaf morphology and canopy structure (Ustin & Curtiss, 1990; Peñuelas et al., 1993; Jacquemoud et al., 1994; Gamon et al., 1997; Zhang et al., 1997; Asner, 1998; Kokaly, 2001; Ollinger & Smith, 2005). Imaging spectroscopy (also called hyperspectral remote sensing) measures hundreds of contiguous narrow bands spanning the solar reflective spectrum from 400 to 2500 nm. The result is a nearly continuous spectrum measured in each pixel that provide the information needed to detect SAPs in the water column (Zhang et al., 1997; Underwood et al., 2006; Hestir et al., 2008), despite interactions with the water column itself (Holden & LeDrew, 2001; Han, 2002; Bostater et al., 2003; Hall et al., 2004), which obscures the characteristic SAP spectral patterns. When plant species are spectrally distinct (Fyfe, 2003), imaging spectroscopy is a likely method for mapping SAPs down to species level (Ustin et al., 2009). Imaging spectrometers capture spectral differences resulting from species morphological (leaf and canopy), biochemical (pigment concentration) and metabolic (photosynthesis) traits. When the analysis framework includes additional biophysical information, such as stable isotope data (Farquhar et al., 1989; Raven et al., 2002; Carvalho et al., 2009), the combination provides multiple lines of evidence to compare and contrast functionality across species.

We applied this combined approach to the SAP species that co-occur in the Sacramento–San Joaquin River Delta (henceforth referred to as the Delta) in California, USA. The Delta is one of the major gateways for nonnative aquatic species in the United States (Cohen & Carlton, 1995, 1998). In the last 30 yr the aquatic plant community has dramatically changed: the number of species has increased (Atwater et al., 1979; Cohen & Carlton, 1998; Bossard et al., 2000; Light et al., 2005; Santos et al., 2009), with the plant community composition now c. 50% nonnative plant species (Fig. 1; Santos et al., 2009). The assemblage today includes five native and four nonnative SAPs (Table 1). All nonnative species in the Delta have both the C3 pathway and a facultative C4-like metabolism (Van et al., 1976; Salvucci & Bowes, 1981; Casati et al., 2000), as does the native Elodea canadensis (Nichols & Shaw, 1986). Varied types of leaf morphologies are found in the Delta SAP assemblage. All nonnative species have wider and longer leaves than native SAP and distinct growth forms, with blade-like and dissected leaves evenly distributed along the stem length through the water column (Sculthorpe, 1965).

image

Figure 1. Submersed aquatic plant species co-occurring in the Sacramento–San Joaquin River Delta. The top four species are natives and the bottom four species are nonnatives. Egeria densa and Myriophyllum spicatum are invasive. Note the difference in the leaf structure among the different species: wide leaves include Potamogeton nodosus, E. densa, and Potamogeton crispus; fine leaves include Elodea canadensis, Stuckenia pectinata, and Myriophyllum spicatum; and whorled leaves include Ceratophyllum demersum and Cabomba caroliniana.

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Table 1.   Submersed aquatic plant (SAP) species occurring in the Sacramento–San Joaquin River Delta, CA, USA
FamilyScientific namesCommon namesCodeRootsLeafStatusa
  1. aRefers to its current status in the Delta and not to its invasibility potential.

HydrocharitaceaeEgeria densaBrazilian waterweedEGDEYesEntire, wide bladeNonnative
HydrocharitaceaeElodea canadensisWaterweedELCAYesEntire, narrow bladeNative
CeratophyllaceaeCeratophyllum demersumCoontailCEDENoDissected, whorlNative
PotamogetonaceaeStuckenia pectinataSago pondweedSTPEYesEntire, no bladeNative
PotamogetonaceaeStuckenia filiformisBroadleaf sago pondweedSTFIYesEntire, no bladeNative
PotamogetonaceaePotamogeton nodosusAmerican pondweedPONOYesEntire, floating bladeNative
PotamogetonaceaePotamogeton crispusCurlyleaf pondweedPOCRYesEntire, wide bladeNonnative
HaloragaceaeMyriophyllum spicatumEurasian watermilfoilMYSPYesDissected, whorlNonnative
CabombaceaeCabomba carolinianaCarolina fanwortCACAYesDissected, whorlNonnative

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Study area

Located in central California (38°19′N, 121°36′W), the Delta is formed from the confluence of the Sacramento and the San Joaquin Rivers, and drains into San Francisco Bay (CA, USA). Its wetlands were reclaimed in the early 20th century for agriculture through construction of nonnatural islands, hydrologically connected through a reticulate network of earthen levees and channels. To avoid saline intrusion of tidal waters, counter-circuit pumping of freshwater occurs system-wide to force freshwater to extraction pumps. This creates a relatively stable aquatic environment for drinking water and agriculture and conditions ideal for growth of SAP (Cohen & Carlton, 1995; Jassby & Cloern, 2000; Lucas et al., 2002).

Airborne imaging spectroscopy

We used airborne HyMap imaging spectroscopy data acquired in June 2007 by HyVista, Inc. (Sydney, NSW, Australia) at a nominal spatial resolution of 3 m. HyMap is delivered as radiometrically and geometrically corrected image measurements in 126 spectral bands in the range 400–2400 nm with a bandwidth of 15 nm in the visible-near-infrared (VIS-NIR) and 15–20 nm in the shortwave-infrared (SWIR, 1500–2500 nm) domain.

Field measurements

Over 2000 SAP patches distributed over the 2100 km2 of Delta channels were visited concurrent with acquisition of imaging spectroscopy data. For each site we documented species composition, species percentage cover, and patch dimensions. SAPs frequently co-occur in the study area at scales smaller than the ground pixel size of 9 m2 (Santos et al., 2011). Therefore, to ensure that each pixel spectrum represents a single species rather than a mixture, we chose patches with > 60% area occupied by a single species, which resulted in 1151 field samples. No pure patches were found for E. canadensis and Stuckenia filiformis, and these species were excluded from subsequent analysis. Spectra of pure patches of each of the SAP species and of turbid and clear water (assessed with a Secchi disk) were extracted using STARSPAN (Rueda et al., 2005; http://code.google.com/p/starspan/) for all bands for each of the 1151 field samples, one pixel per sample.

Handheld spectrometer measurements

Ten specimens of each SAP species were collected in the field and grown in aquatic tanks in a glasshouse for handheld spectrometer measurements (Table 1). An ASD Field Spec Pro FR (ASD Inc. Boulder, CO, USA) spectrometer with a 0.45 rad instantaneous field-of-view (IFOV; 0.01 m diameter) was used to collect 30 reflectance measurements of monospecific dense canopy mats for each SAP species at 20 cm above the plant surface in full intensity midday (11:00–13:00 h) sunlight. To correct for background and water influence in the spectral signature, we collected 30 measurements of tank water and 30 of tap water. Because the phenological stages of the submersed species were different, the Potamogeton nodosus sample was in advanced senescence when the other species were ready for measurement, and consequently, it was excluded from the glasshouse study. This species is unusual, as it acts as an emergent dominant with most of its canopy at the water surface rather than within the water column (Santos et al., 2011), making it functionally more similar to emergent aquatic species like water hyacinth (Eichhornia crassipes), and dissimilar to other SAP species.

We used the Spectral Analysis and Management System (SAMS; http://sams.casil.ucdavis.edu/) software to extract reflectance for all spectrometer bands, and screened the data to exclude bands at wavelengths < 420 nm and > 1200 nm because of noise. We resampled the glasshouse spectrometer data to the wavelength resolution of the airborne sensor to facilitate comparison using a standard Gaussian resampling model with 15 nm band spacing in ENVI 4.4 (ITT, Boulder, CO, USA).

Mapping SAPs, species differences and separability analysis

We used hierarchical Boolean classification schemes (decision trees), utilizing several spectral analysis methods (Giardino & Zilioli, 2001; Holden & LeDrew, 2001; Han, 2002) at different nodes to identify SAP communities. Spectral methods included the Spectral Mixture Analysis (Ustin et al., 1993; Roberts et al., 1997, 1998; Elmore et al., 2000; Dennison et al., 2004; Lu et al., 2004) and Spectral Angle Mapper (Kruse et al., 1993; Dennison et al., 2004) coupled with reflectance thresholds for specific bands, vegetation red-edge detection – which describes the long-wavelength edge of the Chl absorption, from low reflectance near the maximum absorption to high reflectance at a wavelength where Chl does not absorb energy (Han & Rundquist, 1997; Han, 2002; Bostater et al., 2003) – and the absorption feature of Chla at 680 nm (Han & Rundquist, 1997; Han, 2002). This resulted in a map of SAP throughout the Delta, with a classification accuracy of 80% (for details on the classification method see Hestir et al., 2008).

We then investigated whether there were significant spectral differences between the SAP species in glasshouse spectrometer data and the airborne imaging spectrometer. We used the spectra collected in the glasshouse and, from the imagery, we collected spectra of ‘pure’ SAP end members (> 90% pixel cover of each individual SAP species) based on locations of each species from field measurements. We used ANOVA at each band and principal component analysis (PCA) of all bands to test whether species were separable (Jongman et al., 1995). ANOVA identifies which species are most different and which bands are most important to assess these differences. PCA identifies whether the spectral information locates each SAP species in different regions of the hyperellipsoid created in the data space, and the loadings of the PCA axis indicate the contribution of each of the spectral bands to the final result.

We used discriminant function analysis (DFA) (Im et al., 2008) to predict species identity using both resampled glasshouse spectra and airborne data. Species identity and native status were used as the categories, and reflectance spectra were used as covariates. The class discrimination quality was assessed as correct classification rates for the available field data points: for the native vs nonnative classification, the species conditional correct classification rates, and the overall rate. Where DFA supported high separability of the species, we applied its function to a subset of imaging spectroscopy data to create a map of the distribution of each SAP species where there was high co-occurrence of native and nonnative species after masking the area previously classified as SAP (Hestir et al., 2008).

Pigment concentration

Species differences in Chl concentration often manifest themselves as low reflectance at 440, 480, 650 and 680 nm wavelengths. Unfortunately, utilizing these bands in a radiative transfer model, such as PROSPECT (Jacquemoud & Baret, 1990; Jacquemoud, 1993; Feret et al., 2008), would not result in usable Chl concentration estimates, because of the extremely low reflectance of submerged plants at these wavelengths (J.B. Feret, pers. comm.). Therefore, the interspecies differences in Chl, carotenes and anthocyanin concentrations were tested indirectly using the three band indexes described in Gitelson et al. (2006), using the spectrometer reflectance data (not resampled to HyMap wavelengths and bandwidths). For Chl the best approximation using the green reflectance (550 nm) is estimated as:

  • image(Eqn 1)

where ρ is the reflectance at the specified wavelengths. We truncated the near infrared (NIR) to be between 760 and 800 nm to match the optimizations described in Gitelson et al. (2006). We also estimated Chl concentration using the red edge, using the following equation:

  • image(Eqn 2)

For carotenes, we followed the same procedures as for Chl, estimating carotene concentration with both the green- and red-edge reflectance values, using the following equations:

  • image(Eqn 3)
  • image(Eqn 4)

Finally, we estimated anthocyanin concentration using the following equation:

  • image(Eqn 5)

We tested whether Chl, carotene and anthocyanin concentrations were significantly different between species and between native and nonnative species using ANOVA and Tukey’s honestly significant difference tests (Zar, 1999).

Photosynthetic efficiency

Photosynthesis is limited in high-light environments to avoid damage to the photosynthetic reaction center (Grace et al., 2007). During high-light periods, plants are able to activate the xanthophyll cycle to divert excess energy. This cycle consists of two states: at high light de-epoxidation allows conversion of violaxanthin to zeaxanthin via antheraxanthin; when light intensities are reduced, epoxidation reverts zeaxanthin to violaxanthin, via the same intermediary products (Grace et al., 2007). This mechanism is not activated in plants using the C4 pathway, allowing them to maximize photosynthesis in high-light environments (Keeley & Rundel, 2003). In the context of SAPs within a tidal system such as the Delta, having a heterogeneous metabolic functionality for high-light environments (e.g. regular exposure to surface light at low tide) and low-light environments (e.g. regular submersion at high tide in turbid water) would increase fitness. For each species we computed the photochemical reflectance index (PRI; Gamon et al., 1997), which represents a measure of light-use efficiency, and changes in metabolic pathways (Grace et al., 2007). Gamon et al. (1992) showed that PRI values were correlated with the epoxidation state of the xanthophyll cycle in sunflower. PRI is a normalized ratio between reflectance at 531 and 570 nm (Gamon et al., 1997) given by:

  • image(Eqn 6)

Negative values of larger magnitudes indicate activation of the defense mechanism – photosynthetic inhibition at high-light conditions – while less negative or positive values indicate photosynthesis in high-light conditions (Grace et al., 2007). We used the nonresampled ASD reflectance to test whether PRI values were significantly different between species and between native and nonnative species using ANOVA and Tukey’s honestly significant difference tests (Zar, 1999).

In addition, 10 samples of each of the SAP species to estimate the values of δ13C as a measure of internal carbon concentration were analyzed by the UC Davis stable isotope facility (for details on their standard protocols, see http://stableisotopefacility.ucdavis.edu/13cand15n.html). Several confounding factors, however, can affect the measurements of δ13C for aquatic plants, such as the degree to which atmospheric CO2 is in equilibrium with the water mass, input of CO2 from the decomposition of 13C-depleted terrestrial detritus in the water, contribution from the dissolution of 13C-enriched carbonate rock, and seasonal rates of photosynthesis and respiration (Boutton, 1991). The Delta waters are slightly depleted in δ13C (Cloern et al., 2002) compared with terrestrial environments, so the δ13C values are expected to be slightly lower to reflect this depletion. The protocol we used is based on the δ13C of CO2 in the atmosphere. Aquatic plants use two sources of carbon for photosynthesis, uptake of carbonic anhydrase-mediated HCO3 or CO2, but the resulting δ13C signal from either source is likely indistinguishable (Riebesell & Wolf-Gladrow, 1995). All submerged aquatic plants in this study have a CCM, but only a few have a C4-like photosynthetic pathway (Maberly & Madsen, 2002). Thus, we expect that if there are differences between species in the δ13C value, and other factors cannot account for them, these would likely be a result of the photosynthetic pathway (Bowes et al., 2002). Plant species that undergo C4-like photosynthesis are likely to have less negative values of δ13C because they discriminate less against 13C from the photosynthetic substrate as a result of the bicarbonate fixed by phosphoenolpyruvate carboxylation (PEPC) rather than carbon dioxide fixed by Rubisco (Farquhar et al., 1989). For C4 photosynthesis, δ13C values c.−30‰ are expected (Farquhar et al., 1989). However, differences in the range of δ13C values are expected for the aquatic environment, where values from −3 to −50‰ have been observed for aquatic plants (Farquhar et al., 1989; Boutton, 1991; Cloern et al., 2002; Raven et al., 2002).

To assess the consistency between the remote sensing estimates (PRI) and the stable isotopes data, we regressed the estimated PRI values against the δ13C values, using the coefficient of determination (R2) to evaluate how well the two metrics are related and an F-test to assess model fit.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Species differences and separability analysis

Submersed aquatic species had significantly different reflectance in certain regions of the electromagnetic spectrum (< 0.0001; Fig. 2; Table 2) that allowed their identification (PCA and DFA; Fig. 3; Table 3). Both the glasshouse spectrometer data and the airborne imaging spectroscopy data discriminated between natives and nonnatives with 80% certainty, and the individual species’ correct classification rate amounted to 60% (Fig. 4; Supporting Information, Tables S1–S4).

image

Figure 2. Average spectral signature for each of the native and nonnative species, and water with the handheld spectrometer (ASD) and the airborne sensor (HyMap). Handheld spectrometer measurements were resampled to the airborne sensor spectral bands. For some species only one measurement was possible. Reflectance is measured in %.

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Table 2.   Regions of the electromagnetic spectrum where species were most distinct based on the ANOVA of each of the measured bands by the handheld spectrometer (ASD) and the airborne imaging spectrometer (HyMap).
Scientific namesANOVA
ASD (nm)HyMap (nm)
Egeria densa570–580; 950–1000; 1140–1150550–650
Elodea canadensis700
Ceratophyllum demersum660–670; 900–1050550–650
Stuckenia pectinata600–750; 800–1150Nondifferentiable
Stuckenia filiformisNondifferentiable
Potamogeton nodosus750–1000
Potamogeton crispusNondifferentiableNondifferentiable
Myriophyllum spicatum1030–1050; 1090–1120590–640
Cabomba caroliniana520–560560
Water520–1200750–1000
image

Figure 3. Principal component analysis of handheld spectrometer (ASD) and airborne spectrometer (HyMap) spectra for the submersed aquatic plant species occurring in the Sacramento–San Joaquin River Delta, CA, USA. (a) ASD measurements; (b) HyMap measurements. Native species are represented in green, nonnative species in red, and water in blue. Note that ASD does not include Potamogeton nodosus and turbid water, and that HyMap does not include Stuckenia filiformis, and Elodea canadensis because we did not register pure patches in the field. PCA 1, PCA axis 1; PCA 2, PCA axis 2.

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Table 3.   Regions of the spectrum that contributed to the principal component analysis (PCA) and discriminant function analysis (DFA) axis, and the amount of variability explained by each analysis
 PCADFA
ASD (nm)HyMap (nm)ASD (nm)HyMap (nm)
Axis 1575–900; 1025–1115710–1200496–511; 619–679; 816505–665
Axis 2425–465452–695557–588; 634; 695–740545–635
Axis 3525–565695–820600–700465; 565
 96%96%98%86%
image

Figure 4. Area selected to apply the discriminant function scores to the HyMap imagery: (a) hyperspectral imagery overlaid with field data points of patches dominated by natives (green) and nonnatives (red); (b) distribution of Ceratophyllum demersum, Egeria densa, Cabomba carolinensis, Potamogeton crispus, P. nodosus, Myriophyllum spicatum.

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The univariate analysis revealed that the species are most separable in the visible (400–700 nm) spectral region (Table 2), as indicated by both PCA axis 2 (Fig. 3) and the discriminant functions (Table 3). Native and nonnative species have different reflectance spectra in the visible region (Fig. 2), with the exception of the invasive M. spicatum. The only SAP species with nonsubmersed leaves, P. nodosus, is spectrally distinct from all other submersed species (Fig. 2).

The HyMap remote sensing data also separate well the natural canopies of native and nonnative species (Fig. 3; Table S3). Many species are rather distinct, including most native species such as C. demersum and P. nodosus; however, nonnative species were the most frequently confounded, especially M. spicatum, Potamogeton crispus and E. densa (Table S2). These three species have distinct spectral signatures (Fig. 2), when acquired with a handheld spectrometer. However, these differences are nearly entirely lost in the airborne spectra (Fig. 2; Tables S1, S2). Fig. 4 represents a section of the Delta where most species co-occur and where we applied the discriminant function to the airborne data. It shows a robust discrimination between native (green) and nonnative (orange-red) species at the pixel level (Fig. 4b; Table S4).

Pigment concentration

Nonnatives had significantly higher concentrations of Chl (Chlgreen: = 24.82, df = 104, < 0.0001; Chlred edge: = 84.24, df = 104, < 0.0001), carotenes (carotenesgreen: = 71.74, df = 104, < 0.0001; carotenesred edge: = 60.61, df = 104, < 0.0001) and anthocyanin (= 6.91, df = 104, = 0.009) when compared with native species (Fig. 5). At the species level there were also significant differences (Chlgreen: = 123.69, df = 104, < 0.0001; Chlred edge: = 188.49, df = 104, < 0.0001; carotenesgreen: = 55.17, df = 104, < 0.0001; carotenesred edge: = 58.01, df = 104, < 0.0001; anthocyanin: = 138.14, df = 104, = 0.009), but similar pigment concentrations grouped native and nonnative species (significantly different groups as letters on top of the box plots in Fig. 5).

image

Figure 5. Pigment concentration: chlorophyll (a, b), carotene (c, d), and anthocyanin (e) values for native (CEDE, ELCA, PONO, STFI, and STPE; right panel) and nonnative (CACA, EGDE, MYSP, and POCR; left panel) submerged aquatic plant species in the Sacramento–San Joaquin River delta, CA, USA. Pigment concentrations were significantly different between native and nonnative species, and among species (significantly different species are represented by different letters on top of the box plots, at α = 0.05). The black solid line represents the mean and the box plots are the 95% quantiles. CACA, Cabomba caroliniana; CEDE, Ceratophyllum demersum; EGDE, Egeria densa; ELCA, Elodea canadensis; MYSP, Myriophyllum spicatum; POCR, Potamogeton crispus; PONO, P. nodosus; STFI, Stuckenia filiformis; STPE, S. pectinatus. These indexes are unitless but they were calibrated against pigment concentrations in mg m−2.

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Photosynthetic efficiency

We found significant differences in PRI values between species (= 281.64, df = 104, < 0.0001) but not between native and nonnative status (= 0.34, df = 104, = 0.56) with the ASD reflectance data (Fig. 6a). Significantly the highest PRI values (less negative) were observed for E. canadensis and C. caroliniana, followed by P. crispus and E. densa, then by S. filliformis, C. demersum and M. spicatum, and the lowest PRI values (more negative) were for S. pectinata.

image

Figure 6. Photochemical reflectance index (PRI; unitless) (a) and carbon isotope ratio (δ13C; in & fractions) (b) values for native (CEDE, ELCA, PONO, STFI and STPE; left panel) and nonnative species (CACA, EGDE, MYSP, and POCR; right panel). (c) PRI and δ13C regression analysis for native (closed circles) and nonnative (open circles) submerged aquatic plant species in the Sacramento–San Joaquin River delta. Values of PRI (unitless) and δ13C (in ‰ fractions) were significantly different among species (significantly different species are represented by different letters on top of the box plots, at α = 0.05). The black solid line represents the mean and the box plots are the 95% quantiles. CACA, Cabomba caroliniana; CEDE, Ceratophyllum demersum; EGDE, Egeria densa; ELCA, Elodea canadensis; MYSP, Myriophyllum spicatum; POCR, Potamogeton crispus; PONO, P. nodosus; STFI, Stuckenia filiformis; STPE, S. pectinatus.

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Stable isotope results were consistent with those of PRI, especially for the nonnatives E. densa, M. spicatum, P. crispus, and the native C. demersum. Nonnative species showed significantly less negative δ13C values than the natives (= 27.52, df = 89, < 0.0001), with the exception of the nonnative C. caroliniana (the most negative) and the native Stuckenia spp. (the least negative; Fig. 6b). C. demersum and E. canadensis showed the greatest range of variation in isotope ratio. We were unable to compare isotope ratios and PRI for P. nodosus because samples for this species were unavailable at the time of measurement. PRI and δ13C values were strongly related for both native and nonnative species (Fig. 6c). Regression analysis coefficient of determination (R2) was 0.41 for both native and nonnatives, and the model was significantly fitted to the data (natives: = 25.9, = 0.0001; nonnatives: = 26.2, = 0.0001; Fig. 6c).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Our results show that morphological differences in plant structure and biochemistry allow spectral differentiation between natives and nonnatives. While many previous studies succeeded in discriminating terrestrial species (Cochrane, 2000; Lewis, 2000; Fyfe, 2003; Andrew & Ustin, 2006; Hutto et al., 2006; Atkinson et al., 2007), the aquatic environment presents a much greater challenge to spectral differentiation (Marshall & Lee, 1994; Hestir et al., 2008). So far, only a few submerged aquatic species have been successfully differentiated (Williams et al., 2003; Dogan et al., 2009). This differentiation challenge results from linear and nonlinear mixing with water, as well as the properties of the water column itself (Williams et al., 2003; Hestir et al., 2008; Dogan et al., 2009). Our data collection was designed to reduce the known challenges of optical remote sensing over aquatic systems, which include meteorological and illumination variability, in-water radiance, and water-leaving radiance (Giardino & Zilioli, 2001; Holden & LeDrew, 2001; Bostater et al., 2003; Vis et al., 2003; Williams et al., 2003; Dogan et al., 2009). To account for meteorological and illumination variability, we specified flight times that minimized clouds and specular reflectance and we controlled for wind velocities and time of the day during data acquisition (Hestir et al., 2008). In-water radiance varies with the ratio of SAP to water in the water column, as different mixtures have different results in the amount of radiance. In the Delta there are freshwater outflows with high turbidity gradients, which mix with tidal water inflows. These dynamics make the in-water radiance highly variable during the daily tidal cycles. To reduce the effects of in-water radiance variability we restricted our analysis to pure pixels, where individual SAP species were the dominant cover in the pixel rather than water (Hestir et al., 2008). Finally, SAP contribution to water-leaving radiance is likely affected by the depth of the water column above the SAP cover (Han & Rundquist, 1997; Han, 2002). To reduce this effect we restricted our imagery collection to low-tide conditions (Hestir et al., 2008). In a parallel study testing the impact of the water column, we observed no significant deterioration of the submerged plant spectra with depth, and the water column overlying the canopy did not limit the plant detectability (Hestir, 2010).

We have shown that native and nonnative species have systematic differences in their spectral properties related to biochemistry, light use, and morphological, and structural traits, in both ‘controlled’ and natural canopies. At the leaf level, reflectance is affected by the structure of the leaf tissue and biochemistry, and the size, shape, and orientation of the leaf. Submerged leaves have a poorly differentiated mesophyll and a high frequency of epidermal chloroplasts that are likely to be under selective pressure by the reduced diffusion coefficient of carbon dioxide in water (MacFarlane & Raven, 1990), which is not offset by the use of bicarbonate in photosynthesis (Raven et al., 2005). The presence of epidermal chloroplasts that maximize light absorption by the submersed leaf (Sculthorpe, 1965) results in minimal spectral differences between species. Hence, we believe that the structure of leaf tissue may be more important to differentiate between nonsubmersed and submersed plants rather than between co-occurring submersed plants. Nonsubmersed aquatic plants have a greatly differentiated mesophyll with palisade and spongy layers, and internal anatomy similar to land plants. In fact, our results show that the only SAP species with nonsubmersed leaves, P. nodosus, is spectrally distinct from all other submersed species (Fig. 2). The leaf structure of S. pectinata is also substantially different from the other species – stems without leaf blades, allowing S. pectinata to be spectrally separated from other SAPs in the glasshouse spectrometer data (Table 2). In the landscape, however, S. pectinata occurs in very sparse canopies, which often form patches smaller than the ground pixel size of 9 m2, producing low biomass per pixel area. As a result, S. pectinata is indistinguishable from other species in the airborne data (Table 2), despite its very characteristic laboratory spectrum (Fig. 2).

Plant leaf biochemistry greatly influences reflectance (Ustin et al., 1991, 2009; Ustin et al., 2004). This factor is not totally independent of the internal leaf arrangement, as leaf optical properties often correspond to pigment concentrations (Blackburn, 2007; Ustin et al., 2009). Chlorophylls, carotenoids, and other pigments have absorption peaks at overlapping but different wavelengths (Ustin et al., 2009), between 400 and 700 nm, which were used to separate species (Table 3). Several plant pigments are instrumental in plant photosynthetic activity (Ustin et al., 2009), and differences in leaf biochemistry affect photosynthetic efficiency in moles of carbon assimilated per mole of photons absorbed.

Different leaf widths, shapes, and colors of these species may also contribute to the measured reflectance. With the exception of P. nodosus, most native species have no leaf blades, dissected leaf blades (e.g. C. demersum) or narrow blades (e.g. E. canadensis), which are distinctly different leaf morphologies from the wide blades and large dissected leaf whorls of nonnative species. These growth forms may influence the amount of light intercepted, and thus the light reflected. Our analysis showed that native species are spectrally distinct from nonnatives, which is likely a result of three convergent characteristics of nonnative species: wider ribbon-like leaves, greater leaf area per plant with higher Chl concentration, resulting in lower visible reflectance. Native and nonnative species have different reflectance in the visible region and most of the nonnative species have darker green leaves while native species are a brownish color, indicating different pigment compositions (Ustin et al., 2009). Our results corroborate this prediction as we found significantly higher pigment concentrations (Chl, carotenes and anthocyanins) in nonnative than in native species. Larger differences were found for pigments that harvest photons for photosynthesis (Chl and carotenes) than for anthocyanins. Our spectral profiles show significant differences in reflectance that match these differences in pigment concentrations. The PRI results show that potentially different pigment concentrations are present in native and nonnative species (see paragraph on 13C and PRI results). We conclude that the observed reflectance patterns are related to the interaction of shape, width and color of the leaves, which explains the separability of these species.

When scaling up from the leaf to the canopy, factors such as leaf density and canopy closure come into play. Many species are quite distinct, including most native species, such as C. demersum and P. nodosus; however, the ones most frequently confounded were the nonnative species, especially M. spicatum, P. crispus and E. densa. The canopy of the nonnative species found in the Delta tend to have higher leaf density (Fig. 1), resulting in high reflectance in the NIR (Fig. 2), and the spectral signatures are less impacted by the surrounding water. Thus, the effect of water absorption in this part of the spectrum should be less evident than measurements of canopies of native species. Furthermore, the effect of the water column as a potential confounding factor to the discriminant analysis was minimal because the imagery was acquired to avoid specular reflectance and at low tide, when most of the submersed plant canopies are at or near the surface.

Myriophyllum spicatum is overclassified (Fig. 4) and is often confused with E. densa and P. crispus, contrasting with their current Delta-wide distribution. E. densa is ubiquitous (Hestir et al., 2008), and tends to exist in most channels, in areas of moderate- and low-velocity water, shallow and deep waters, turbid and clear waters, and water with variable salinity (Santos et al., 2011). M. spicatum tends to be more restricted to somewhat deeper and more turbid waters with higher salinity (Grace et al., 2007), in the western part of the Delta. The great range of environmental conditions and species assemblages required to develop a classified map for the Delta may have washed out site-specific spectral differences between the species in the area represented in Fig. 4. While training the classifier was done to encompass the variability at the larger scale, it may have led to misclassification of the species at this finer scale. Additionally, in this region E. densa was frequently associated with epiphytic algae that could have contributed to its misclassification. In fact, the patches classified as pure E. densa did not have algal growth, while those with algal growth were misclassified.

Our results show distinct δ13C and PRI values between native and nonnative species which were less negative for nonnative than for native species, and the two metrics were strongly correlated. Our results are within the ranges of other published δ13C (Cloern et al., 2002) and PRI data (Peñuelas et al., 1993). Several confounding factors could affect the interpretation of δ13C values, especially if native and nonnative species experienced different aquatic environments. In the Delta, natives and nonnatives co-occur throughout their distribution range (Cohen & Carlton, 1995; Jassby & Cloern, 2000; Lucas et al., 2002; Santos et al., 2011), all of which occupy slower water channels, where submerged plants are associated with a Delta-wide decrease in turbidity (Hestir, 2010), through their effects on sedimentation processes. All but one native species have roots, as do all the nonnatives (Table 1), and all experience δ13C from the same water sources. Thus we believe that there is a low probability that natives and nonnatives are experiencing the δ13C environment differentially, suggesting that the observed differences are the result of physiological differences. One possible explanation for these patterns is that different CCMs result in different δ13C signals. Since most native and nonnative SAPs have CCMs (Maberly & Madsen, 2002) and the resulting δ13C signal from either HCO3 or CO2 uptake is likely indistinguishable (Riebesell & Wolf-Gladrow, 1995), we can discard this possibility as explaining the observed differences. Alternatively, nonnative species have both C3 and C4-like photosynthetic pathways (Van et al., 1976; Salvucci & Bowes, 1981; Casati et al., 2000; Maberly & Madsen, 2002), which can overcome the high-light and -temperature limitations of C3-only plants. This may give nonnatives (mostly E. densa, and M. spicatum) the ability to maintain photosynthesis under high light and high temperature, which is supported by previous studies that have demonstrated that E. densa maintains continuous growth throughout the year (Pennington & Sytsma, 2005; Pennington, 2007; Santos et al., 2011). Our results also showed less negative PRI and δ13C values for P. crispus, which may indicate the presence of a C4-like mechanism, as suggested in previous research (Sand-Jensen, 1983; Nichols & Shaw, 1986). Exceptions, however, occur for E. canadensis and C. caroliniana, which have the highest PRI values and among the lowest δ13C values, suggesting both C3 and C4-like mechanisms as shown in previous research (Salvucci & Bowes, 1981; Sand-Jensen, 1983), or in the case of C. caroliniana that high-light photosynthesis can be activated with CO2 as a substrate (Smith, 1937). Finally, Stuckenia spp. showed the lowest PRI and the highest δ13C values, potentially because these species are heterophyllous, and their canopies are formed by submerged, emergent and terrestrial leaves that have high heterogeneity in photosynthetic traits (Iida et al., 2009).

The adaptive value of having a facultative C4-like photosynthesis allows plants to colonize environments that C3-only plants cannot utilize or utilize less efficiently, such as the high-light and -temperature conditions of shallower waters, and tidal sites of increased salinity (Nichols & Shaw, 1986). These conditions are common throughout the Delta, with the exception of water with carbon (and nutrient) limitations (Jassby & Cloern, 2000; Lucas et al., 2002). This plasticity in traits may allow the nonnatives to persist and succeed in the new environment (Simberloff & Holle, 1999; Simberloff, 2001). The nonnative species can occupy the same environments in which the native species occur, but also environments where natives are not competitive, giving nonnatives an advantage by occupying a wider range of environments of the Delta. Even if remote sensing identification is limited to identifying native and nonnative submersed species, this work advances systematic measurements of specific traits that may contribute to understanding invasibility and invasion success. This is a new approach to study invasiveness and elucidate why some species are more competitive, and we hope this information will be incorporated into systems for early detection and monitoring of new and recently introduced species.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Funding for this research was provided by the California Department of Boating and Waterways Agreement 03-105-114, and the California Department of Water Resources Contract #4600008137 T4. We would like to acknowledge D. Kratville, J.R.C. Leavitt, P. Akers, and the field crews of the California Department of Boating & Waterways; and J. Greenberg, M. Andrew, P. Haverkamp, M. Whiting, and A. Kultonov for useful discussion and sharing of ideas on environmental conditions in the Delta. We also thank R. McIlvaine and G. Scheer for administrative support.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • Andrew ME, Ustin SL. 2006. Spectral and physiological uniqueness of perennial pepperweed (Lepidium latifolium). Weed Science 54: 10511062.
  • Asner GP. 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment 64: 234253.
  • Asner GP, Vitousek PM. 2005. Remote analysis of biological invasion and biogeochemical change. Proceedings of the National Academy of Sciences, USA 102: 43834386.
  • Atkinson PM, Foody GM, Gething PW, Mathur A, Kelly CK. 2007. Investigation spatial structure in specific tree species in ancient semi-natural woodland using remote sensing and marked point pattern analysis. Ecography 30: 88104.
  • Atwater BF, Conard SG, Dowden JN, Hedel CW, Macdonald RL, Savage W. 1979. History, landforms and vegetation of the estuary’s tidal marshes. In: Conomos TJ, ed. San Francisco Bay the urbanized estuary. San Francisco, CA, USA: Pacific Division of the American Association for the Advancement of Science, 347388.
  • Blackburn GA. 2007. Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany 58: 855867.
  • Bossard CC, Randall JM, Hoshovksy MC. 2000. Invasive plants of California’s wildlands. Berkeley, CA, USA: University of California Press.
  • Bostater CR, Ghir T, Bassetti L, Hall C, Reyier E, Lowers R, Holloway-Adkins K, Virnstein R. 2003. Hyperspectral remote sensing protocol development for submerged aquatic vegetation in shallow water. In: Bostater CR, Santoleri R, eds. Remote sensing of the ocean and sea ice 2003: Proceedings of SPIE , 199215.
  • Boutton TW. 1991. Stable carbon isotope ratios of natural materials. In: Coleman DC, Fry B, eds. Carbon isotope techniques. San Diego, CA, USA: Academic Press, 173186.
  • Bowes G, Rao SK, Estavillo GM, Reiskind JB. 2002. C4 mechanisms in aquatic angiosperms: comparisons with terrestrial C4 systems. Functional Plant Biology 29: 379392.
  • Bradley BA, Mustard JF. 2006. Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing. Ecological Applications 16: 11321147.
  • Carvalho MC, Hayashizaki K-I, Ogawa H. 2009. Short-term measurement of carbon stable isotope discrimination in photosynthesis and respiration by aquatic macrophytes with marine macroalgal examples. Journal of Phycology 45: 761770.
  • Casati P, Lara MV, Andreo CS. 2000. Induction of a C4-Like mechanism of CO2 fixation in Egeria densa, a submersed aquatic species. Plant Physiology 123: 16111621.
  • Cloern JE, Canuel EA, Harris D. 2002. Stable carbon and nitrogen isotope composition of aquatic and terrestrial plants of the San Francisco Bay Estuarine System. Limnology and Oceanography 47: 713729.
  • Cochrane MA. 2000. Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing 21: 20752087.
  • Cohen AN, Carlton JT. 1995. Non-indigenous aquatic species in a United States estuary: a case study of the biological invasions of the San Francisco Bay and Delta. Washington, DC, USA: United States Fish and Wildlife Services.
  • Cohen AN, Carlton JT. 1998. Accelerating invasion rate in a highly invaded estuary. Science 279: 555558.
  • Cohen WB, Goward SN. 2004. Landsat’s role in ecological applications of remote sensing. BioScience 54: 535545.
  • Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E. 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25: 15651596.
  • Dennison PE, Halligan KQ, Roberts DA. 2004. A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sensing of Environment 93: 359367.
  • Dennison WC, Orth RJ, Moore KA, Stevenson JC, Carter V, Bergstrom PW, Batiuk RA. 1993. Assessing water quality with submersed aquatic vegetation. BioScience 43: 8694.
  • Dogan OK, Akyurek Z, Beklioglu M. 2009. Identification and mapping of submerged plants in a shallow lake using Quickbird satellite data. Journal of Environmental Management 90: 21382143.
  • Elmore AJ, Mustard JF, Manning SJ. 2003. Regional patterns of plant community response to changes in water: Owens Valley California. Ecological Applications 13: 443460.
  • Elmore AJ, Mustard JF, Manning SJ, Lobell DB. 2000. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sensing of Environment 73: 87102.
  • Farquhar GD, Ehleringer JR, Hubick KT. 1989. Carbon isotope discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology 40: 503537.
  • Feret J-B, François C, Asner GP, Gitelson AA, Marin RE, Bidel LPR, Ustin SL, Maire Gl, Jacquemoud S. 2008. PROSPECT-4 and 5: advances in leaf optical properties model separating photsynthetic pigments. Remote Sensing of Environment 112: 30303043.
  • Fyfe SK. 2003. Spatial and temporal variation in spectral reflectance: are seagrass species spectrally distinct? Limnology and Oceanography 48: 464479.
  • Gamon JA, Peñuelas J, Field CB. 1992. A narrow-waveband spectral reflectance index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41: 3544.
  • Gamon JA, Serrano L, Surfus JS. 1997. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types and nutrition levels. Oecologia 112: 492501.
  • Giardino C, Zilioli E. 2001. Imaging spectrometry for submerged vegetation mapping in Lake Garda. IEEE-IGARS 6: 27492751.
  • Gitelson AA, Keydan GP, Merzlyak MN. 2006. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters 33: 15.
  • Grace J, Nichol C, Disney M, Lewis P, Quaife T, Bowyer P. 2007. Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? Global Change Biology 13: 14841497.
  • Hall CR, Bostater CR, Virnstein R. 2004. Plant pigment types, distributions, and influences on shallow water submerged aquatic vegetation mapping. In: Remote sensing of the ocean and sea ice: Proceedings of SPIE, 183193.
  • Han L. 2002. Spectral reflectance of Thalassia testudinum with varying depths. In: IEEE-IGARS 4: 21232125.
  • Han L, Rundquist DC. 1997. Comparison of NIR/RED ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: a case study in a turbid reservoir. Remote Sensing of Environment 62: 253261.
  • Hestir EL. 2010. Trends in estuarine water quality and submerged aquatic vegetation invasion. Davis, CA, USA: University of California Davis.
  • Hestir EL, Khanna S, Andrew ME, Santos MJ, Viers JH, Greenberg JA, Rajapakse SS, Ustin SL. 2008. Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sensing of Environment 112: 40344047.
  • Holden H, LeDrew E. 2001. Effects of the water column on hyperspectral reflectance of submerged coral reef features. Bulletin of Marine Science 69: 685699.
  • Hutto KC, Shaw DR, Byrd JD, King RL. 2006. Differentiation of turfgrass and common weed species using hyperspectral radiometry. Weed Science 54: 335339.
  • Iida S, Miyagi A, Aoki S, Ito M, Kadono Y, Kosuge K. 2009. Molecular adaptation of rbcL in the heterophyllous aquatic plant Potamogeton. PLoS One 4: 17.
  • Im J, Jensen JR, Hodgson ME. 2008. Optimizing the binary discriminant function in change detection applications. Remote Sensing of Environment 112: 27612776.
  • Jacquemoud S. 1993. Inversion of the PROSPECT+SAIL canopy reflectance model from AVIRIS equivalent spectra: theoretical study. Remote Sensing of Environment 44: 281292.
  • Jacquemoud S, Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment 34: 7591.
  • Jacquemoud S, Verdebout J, Schmuck G, Andreoli G, Hosgood B, Hornig SE. 1994. Investigation of leaf biochemistry by statistics. In: Stein TI, ed. IGARSS 94: Proceedings of the International Geosciences and Remote Sensing Symposium. Pasadena, CA, USA: The Institute of Electrical and Electronics Engineers, Inc. Paper no. 940669.
  • Jassby AD, Cloern JE. 2000. Organic matter sources and rehabilitation of the Sacramento – San Joaquin Delta (California, USA). Aquatic Conservation 10: 323352.
  • Jongman RHG, TerBraak CJF, vanTongeren OFR. 1995. Data analysis in community and landscape ecology: Cambridge, UK: Cambridge University Press.
  • Keeley JE. 1999. Photosynthetic pathway diversity in a seasonal pool community. Functional Ecology 13: 106118.
  • Keeley JE, Rundel PW. 2003. Evolution of CAM and C4 Carbon-concentrating mechanisms. International Journal of Plant Science 164: 5577.
  • Kerr JT, Ostrovsky M. 2003. From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution 18: 299305.
  • Kokaly RF. 2001. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment 75: 153161.
  • Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH. 1993. The Spectral Image-Processing System (Sips)-interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment 44: 145163.
  • Lehmann A, Lachavanne JB. 1997. Geographic information systems and remote sensing in aquatic botany. Aquatic Botany 58: 195207.
  • Lewis M. 2000. Discrimination of arid vegetation composition with high resolution CASI imagery. Rangeland Journal 22: 141167.
  • Light T, Grosholz T, Moyle P. 2005. Delta Ecological Survey (Phase I): nonindigenous aquatic species in the Sacramento-San Joaquin Delta, a literature review. In: Final Report to the U.S. Fish and Wildlife Service. Stockton, CA, USA. 36pp.
  • Lu DS, Batistella M, Moran E. 2004. Multitemporal spectral mixture analysis for Amazonian land-cover change detection. Canadian Journal of Remote Sensing 30: 87100.
  • Lucas LV, Cloern JE, Thompson JK, Monsen NE. 2002. Functional variability of habitats within the Sacramento-San Joaquin Delta: restoration implications. Ecological Applications 12: 15281547.
  • Luther H. 1947. Morphologische und systematische Beobach-tungen an Wasserphanerogamen. Acta Botanica Fennica 40: 128.
  • Maberly SC, Madsen TV. 2002. Freshwater angiosperm carbon concentrating mechanisms: processes and patterns. Functional Plant Biology 29: 393405.
  • MacFarlane JJ, Raven JA. 1990. C, N and P nutrition of Lemanea mamillosa Kutz. (Batrachospermales, Rhodophyta) in Dighty Burn Angus U.K. Plant, Cell & Environment 13: 113.
  • Mack RN, Holle BV, Meyerson LA. 2007. Assessing invasive alien species across multiple spatial scales: working globally and locally. Frontiers in Ecology and the Environment 5: 217220.
  • Marshall TR, Lee PF. 1994. Mapping aquatic macrophytes through digital image analysis of aerial photographs: an assessment. Journal of Aquatic Plant Management 32: 6166.
  • Mommer L, Kroon Hd, Pierik R, Bögemann GM, Visser EJW. 2005. A functional comparison of acclimation to shade and submergence in two terrestrial plant species. New Phytologist 167: 197206.
  • Nichols SA, Shaw BH. 1986. Ecological life histories of the three aquatic nuisance plants, Myriophyllum spicatum, Potamogeton crispus and Elodea canadensis. Hydrobiologia 131: 321.
  • Ollinger SV, Smith M-L. 2005. Net primary production and canopy nitrogen in a temperate forest landscape: an analysis using imaging spectroscopy, modeling and field data. Ecosystems 8: 760778.
  • Pennington TG. 2007. Seasonal changes in allocation, growth, and photosynthetic responses of the submerged macrophytes Egeria densa Planch (Hydrocharitaceae) from Oregon and California. PhD dissertation thesis, Portland State University, Portland, OR, USA.
  • Pennington TG, Sytsma MD. 2005. Production and growth rates of Egeria densa in the Sacramento – San Joaquin Delta, California. San Antonio, TX, USA: American Plant Management Society.
  • Peñuelas J, Filella I, Gamon JA, Field C. 1997. Assessing photosynthetic radiation-use efficiency of emergent aquatic vegetation from spectral reflectance. Aquatic Botany 58: 307315.
  • Peñuelas J, Gamon JA, Griffin KL, Field CB. 1993. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment 46: 110118.
  • Raven JA, Ball LA, Beardall J, Giordano M, Maberly SC. 2005. Algae lacking carbon concentrating mechanisms. Canadian Journal of Botany-Revue Canadienne De Botanique 83: 879890.
  • Raven JA, Johnston AM, Kübler JE, Korb R, McInroy SG, Handley LL, Scrimgeour CM, Walker DI, Beardall J, Vanderklift M et al. 2002. Mechanistic interpretation of carbon isotope discrimination by marine macroalgae and seagrasses. Functional Plant Biology 29: 355378.
  • Riebesell U, Wolf-Gladrow D. 1995. Growth limits on phytoplankton. Nature 373: 28.
  • Roberts DA, Gardner M, Church R, Ustin SL, Green RO. 1997. Optimum strategies for mapping vegetation using multiple endmember spectral mixture models. In: Imaging spectrometry III: presented at the 42nd annual SPIE meeting. San Diego, CA, USA: SPIE. 108119.
  • Roberts DA, Gardner M, Church R, Ustin S, Scheer G, Green RO. 1998. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sensing of Environment 65: 267279.
  • Rueda CA, Greenberg JA, Ustin SL. 2005. StarSpan: a tool for fast selective pixel extraction from remotely sensed data. In: Center for Spatial Technologies and Remote Sensing (CSTARS). Davis, CA, USA: University of California at Davis.
  • Salvucci ME, Bowes G. 1981. Induction of reduced photorespiratory activity in submersed and amphibious aquatic macrophytes. Plant Physiology 67: 335340.
  • Sand-Jensen J. 1983. Photosynthetic carbon sources of stream macrophytes. Journal of Experimental Botany 34: 198210.
  • Santos MJ, Anderson LWJ, Ustin SL. 2011. Effects of invasive species on plant communities: an example using submersed aquatic plants at the regional scale. Biological Invasions 13: 443457.
  • Santos MJ, Khanna S, Hestir EL, Andrew ME, Rajapakse SS, Greenberg JA, Anderson LWJ, Ustin SL. 2009. Use of hyperspectral remote sensing to evaluate efficacy of aquatic plant management in the Sacramento-San Joaquin River Delta, California. Invasive Plant Science and Management 2: 216229.
  • Sculthorpe CD. 1965. The biology of aquatic vascular plants. Köenigstein, West Germany: Koeltz Scientific Books.
  • Simberloff D. 2001. Introduced Species, Effect and Distribution. Encyclopedia of Biodiversity. New York, USA: Academic Press.
  • Simberloff D, Holle BV. 1999. Positive interactions of nonindigenous species: invasional meltdown? Biological Invasions 1: 2132.
  • Smith EL. 1937. The induction period in photosynthesis. The Journal of General Physiology 21: 151163.
  • Ueno O. 2001. Environmental regulation of C3 and C4 differentiation in the amphibious sedge Eleocharis vivipara. Plant Physiology 127: 15241532.
  • Underwood EC, Mulitsch MJ, Greenberg JA, Whiting ML, Ustin SL, Kefauver SC. 2006. Mapping of invasive aquatic vegetation in the Sacramento-San Joaquin Delta using hyperspectral imagery. Environmental Monitoring and Assessment 121: 4764.
  • Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO. 2004. Using Imaging Spectroscopy to Study Ecosystem Processes and Properties. Bioscience 54: 523534.
  • Ustin SL, Curtiss B. 1990. Spectral characteristics of ozone treated conifer species. Environmental and Experimental Botany 30: 293308.
  • Ustin SL, Wessman CA, Curtiss B, Kasischke E, Way J, Vanderbilt VC. 1991. Opportunities for using the EOS imaging spectrometers and synthetic aperture radar in ecological models. Ecology 72: 19341945.
  • Ustin SL, Gitelson AA, Jacquemoud S, Schaepman M, Asner GP, Gamon JA, Zarco-Tejada P. 2009. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing of Environment 113: S67S77.
  • Ustin SL, Smith MO, Adams JB. 1993. Remote sensing of ecological rrocesses: a strategy for developing and resting ecological models using Spectral Mixture Analysis. In: Ehelringer JR, Field CB, eds. Scaling physiological processes: leaf to globe. San Diego, CA, USA: Academic Press, Inc, 339357.
  • Van TK, Haller WT, Bowes G. 1976. Comparison of photosynthetic characteristics of three submersed aquatic plants. Plant Physiology 58: 761768.
  • Vis C, Hudon C, Carignan R. 2003. An evaluation of approaches used to determine the distribution and biomass of emergent and submerged aquatic macrophytes over large spatial scales. Aquatic Botany 77: 187201.
  • Williams DJ, Rybicki NB, Lombana AV, O’Brien TM, Gomez RB. 2003. Preliminary investigatiin of submerged aquatic vegetation mapping using hyperspectral remote sensing. Environmental Monitoring and Assessment 81: 383392.
  • Zar JH. 1999. Biostatistical analysis. New Jersey, USA: Prentice Hall.
  • Zhang M, Ustin SL, Rejmankova E, Sanderson EW. 1997. Monitoring Pacific coast salt marshes using remote sensing. Ecological Applications 7: 10391053.

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Table S1 Training correct classification rates for discriminant functions of resampled handheld spectrometer reflectance data using as classes the SAP species co-occurring in the Sacramento–San Joaquin River Delta

Table S2 Training correct classification rates for discriminant functions of airborne spectrometer reflectance data using as classes the SAP species co-occurring in the Sacramento–San Joaquin River Delta

Table S3 Training correct classification rates for discriminant functions of resampled handheld spectrometer data and, in parenthesis, for the airborne sensor reflectance data, considering as classes native and nonnative species co-occurring in the Sacramento–San Joaquin River Delta

Table S4 Testing correct classification rates for the discriminant function predictions for the area described in Fig. 4

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