Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

Wetland vegetation is a core component of wetland ecosystems. Wetland vegetation structural parameters, such as height and leaf area index (LAI) are important variables required by earth-system and ecosystem models. Therefore, rapid, accurate, objective and quantitative estimations of wetland vegetation structural parameters are essential. The airborne laser scanning (also called LiDAR) is an active remote sensing technology and can provide accurate vertical vegetation structural parameters, but its accuracy is limited by short, dense vegetation canopies that are typical of wetland environments. The objective of this research is to explore the potential of estimating height and LAI for short wetland vegetation using airborne discrete-return LiDAR data.The accuracies of raw laser points and LiDAR-derived digital elevation models (DEM) data were assessed using field GPS measured ground elevations. The results demonstrated very high accuracy of 0.09 m in raw laser points and the root mean squared error (RMSE) of the LiDAR-derived DEM was 0.15 m.Vegetation canopy height was estimated from LiDAR data using a canopy height model (CHM) and regression analysis between field-measured vegetation heights and the standard deviation (σ) of detrended LiDAR heights. The results showed that the actual height of short wetland vegetation could not be accurately estimated using the raster CHM vegetation height. However, a strong relationship was observed between the σ and the field-measured height of short wetland vegetation and the highest correlation occurred (R² = 0.85, RMSE = 0.14 m) when sample radius was 1.50 m. The accuracy assessment of the best-constructed vegetation height prediction model was conducted using 25 samples that were not used in the regression analysis and the results indicated that the model was reliable and accurate (R² = 0.84, RMSE = 0.14 m).Wetland vegetation LAI was estimated using laser penetration index (LPI) and LiDAR-predicted vegetation height. The results showed that the vegetation height-based predictive model (R² = 0.79) was more accurate than the LPI-based model (the highest R² was 0.70). Moreover, the LAI predictive model based on vegetation height was validated using the leave-one-out cross-validation method and the results showed that the LAI predictive model had a good generalization capability. Overall, the results from this study indicate that LiDAR has a great potential to estimate plant height and LAI for short wetland vegetation.     

Improved modelling of the impacts of sea level rise on coastal wetland plant communities

This study presents an enhanced methodology for modelling the impacts of sea level rise on coastal wetlands. The tool integrates dGPS-calibrated LiDAR data, isostatic uplift and sediment accretion rates to predict the location and extent of plant communities at three non-contiguous micro-topographical coastal wetlands in Estonia by 2100 in response to global sea level rise. Scenarios were run including sediment accretion, elevated sediment accretion and then discounting sediment accretion and dGPS calibration for comparison. Results showed an increase in surface elevation (related to sediment accretion and isostatic uplift) resulting in a decrease in local sea level in the majority of sites and scenarios in the north of the country, although a rise in local sea level is predicted in sites with limited allochthonous sediment supply, predominantly impacting higher elevation plant communities. Wetlands situated on the west coast are likely to maintain equilibrium with sea level as a result of lower sedimentation and isostatic uplift than more northerly sites. This study shows that dGPS-calibrated LiDAR data and sediment accretion are essential to maintain model validity in Baltic coastal wetlands due to their low relief and could considerably improve current sea level rise impact models for other regions.     

How does dune morphology shape coastal EC habitats occurrence? A remote sensing approach using airborne LiDAR on the Mediterranean coast

We examined the relationship between coastal habitats (sensu European Union Habitats Directive) and local dune morphology along a Mediterranean coastal dune system by integrating field collected vegetation data and remotely sensed imagery. Specifically, we described the morphological profile of each EC habitat based on the morphological variables that are most likely to affect their occurrence, including elevation, slope, curvature, northness, eastness and sea distance. In addition, we assessed the role and strength of each morphological variable in determining the occurrence of EC habitats.We used 394 random vegetation plots representative of six EC habitats (Habitat 1210: “Annual vegetation of drift lines”; Habitat 2110: “Embryonic shifting dunes”; Habitat 2120: “Shifting dunes along the shoreline with Ammophila arenaria”; Habitat 2210 and 2230: “Crucianellion maritimae fixed beach dunes” and “Malcolmietalia dune grasslands”; Habitat 2250: “Coastal dunes with Juniperus spp.”; Habitat 2260: “Cisto-Lavanduletalia dune sclerophyllous scrubs”) found along the Tyrrhenian coast of central Italy. We derived each morphological variable from a DTM (Digital Terrain Model) obtained from 2-m resolution LiDAR (Light Detection And Range) images. The mean value of each variable was calculated at different spatial scales using buffer areas of increasing radius (2 m, 4 m, 8 m) around each vegetation plot. Mean morphological values for each EC habitat were compared using Kruskal-Wallis rank test. The role and strength of the relationship between habitat type and the morphological variables were assessed using Generalized Linear Models.EC habitats occur differentially across dune morphology, and the role and strength of each morphological variable define habitat specificity. Dune elevation and sea distance were determined to be the key factors in shaping EC habitat occurrence along this section of the Mediterranean coast. Identification of the close relationship between habitat type and morphological variables deriving from airborne LiDAR imagery points to the high potential of such remote sensing tool for analyzing and monitoring the integrity of coastal dune ecosystems. As airborne LiDAR enables the rapid collection of extremely accurate topographic data over large areas, it also offers useful information for the management of these threatened and fragile ecosystems.     

Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R² = 0.99 for streambed width, R² = 0.82 for riparian zone width, R² = 0.89 for PPC, R² = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia.     

Effects of plant diversity on microbial biomass and community metabolic profiles in a full-scale constructed wetland

There has been less understanding of relations of microbial community patterns with plant diversity in constructed wetlands. We conducted a single full-scale subsurface vertical flow constructed wetland (SVFCW, 1000 m²) study focusing on domestic wastewater processing. This study measured the size and structure of microbial community using fumigation extraction and BIOLOG Ecoplate? techniques, to examine the effects of macrophyte diversity on microbial communities that are critical in treatment efficiency of constructed wetlands. We also determined the relationship of plant diversity (species richness) with its biomass production under disturbance of the same wastewater supply. Linear regression analysis showed that plant biomass production strongly correlated with plant species richness (R = 0.407, P < 0.001). Increase in plant species richness increased microbial biomass carbon and nitrogen (R = 0.494, P < 0.001; R = 0.465, P < 0.001) and utilization of amino acids on Ecoplates (R = 0.235, P = 0.03), but limited the utilization of amine/amides (R = ?0.338, P = 0.013). Principal components analysis (PCA) showed that the diversity and community-level physiological profiles (CLPP) of microbial community at 168 h of incubation strongly depended on the presence or absence of plant species in the SVFCW system, but not on the species richness. This is the first step toward understanding relations of plant diversity with soil microbial community patterns in constructed wetlands, but the effect of species diversity on microbial community should be further studied.     

Depth distributions of Fucus vesiculosus L. and Zostera marina L. as classification parameters for implementing the European Water Framework Directive on the German Baltic coast

A classification approach was developed within the European Water Framework Directive for the outer coastal waters of the German Baltic Sea. We concentrated on the known recent presence and depth distribution of Zostera marina and Fucus vesiculosus along the German coast. According to the European Water Framework Directive the reference conditions were reconstructed based on historical data. The available databases indicate that both species formerly occurred down to 10 m depth along the whole German Baltic Sea coastline, independent on the salinity gradient. The recent depth distribution of Z. marina varied between 2.5 and 7.9 m along the German Baltic coast. Dense F. vesiculosus stands were observed only along the western part of this coast at a maximum depth of 4.7 m. Comparing the historical data sets with recent findings reveals a strong decline of depth limits for both species during the last century. Therefore, we used both species to describe the degradation of the Baltic Sea coastal waters using change in the depth distribution. The boundaries of the ecological status according to the Water Framework Directive were calculated based on modelling. Ecophysiological light demands of species and decrease of water transparency (light reduction in percent) were used to describe the degradations. This approach is robust against variation of macrophyte light requirements and is straightforward to classify recent long-term macrophyte monitoring. However, it is very sensitive against changes in the depth distribution and may result in erroneous estimations of ecological class boundaries when insufficient historical data are used as reference. This model allows the adaption of the boundaries calculations to new knowledge about historical data and ecophysiological light demand of plants. Actually, the boundaries of the classification were defined as follows: 1% light reduction represents the transition from high/pristine to good ecological status, and 5% indicates the transition from good to moderate status. At least 25% reduction corresponds to a poor status, more than 75% to a bad status.     

Soil bacterial community structure and physicochemical properties in mitigation wetlands created in the Piedmont region of Virginia (USA)

Wetland creation is a common practice for compensatory mitigation in the United States. Vegetation attributes have been used as a quick measure of mitigation success in most post-creation monitoring, while little attention has been paid to soils that provide the substrate for flora and fauna to establish and develop. Created wetland soils are often found not indicative of ‘hydric soil’ with a lack of development of physicochemical properties (i.e., bulk density, moisture content, and carbon and nitrogen contents) comparable to those in natural wetlands. Moreover, soil bacterial communities are rarely examined though they are integrally involved in biogeochemical functions that are critical for ecosystem development in created wetlands. We analyzed soil physicochemistry and profiled soil bacterial community structure using amplicon length heterogeneity polymerase chain reaction (LH-PCR) of 16S ribosomal DNA in three relatively young wetlands (<10 years old) created in the Piedmont region of Virginia. We examined the data by site and by specific conditions of each site (i.e., induced microtopography and hydrologic regime). Multidimensional scaling (MDS) and analysis of similarity (ANOSIM) showed clear clustering and significant differences both in soil physicochemistry (Global R = 0.70, p = 0.001) and in soil bacterial community profiles (Global R = 0. 77, p = 0.001) between sites. Soil physicochemistry (Global R = 1, p = 0.005) and bacterial community structure (Global R = 0.79, p = 0.005) of soils significantly differed by hydrologic regime within a wetland, but not by microtopography treatment. A significant association was found between physicochemistry and bacterial community structure in wetland soils, revealing a close link between two attributes (ρ = 0.39, p = 0.002). C/N (carbon to nitrogen) ratio was the best predictor of soil bacterial community patterns (ρ = 0.56, p = 0.001). The diversity of soil bacterial community (Shannon's H′) differed between sites with a slightly higher diversity observed in a relatively older created wetland, and seemed also fairly determined by hydrologic regime of a site, with a relatively dry site being more diverse.     

Micro-topography driven vegetation patterns in open mosaic landscapes

Elevation models based on remotely sensed data, especially high-resolution Digital Terrain Models (DTMs) generated using airborne laser scanner (ALS) data, are increasingly being used for the analysis of plant diversity patterns in open landscapes. The vegetation pattern of alkali landscapes shows a high correlation with the position of water table and salt accumulation, which are strongly correlated with topographic variations occurring at a small spatial scale of a few decimetres (micro-topography). In this study we classified eight grassland associations in an alkali landscape based on a DTM generated from ALS data at a pixel size of 0.25 m, and 30 variables derived from the DTM, using an ensemble learning method (Random Forest). Our aim was to identify the micro-topographic variables which could be indicators of vegetation pattern in alkali landscapes. The associations range from Cynodon pastures (short dry grasslands on soil with low salt content) occupying the highest elevations to Beckmannia meadows (wet grasslands on soils with moderate salt content composed of tall grass species) at the lowest elevations, with an elevation difference of approximately 1.2 m between the two. Apart from slope, aspect and curvature, we used Topographic Wetness Index (TWI), and Topographic Position Indices (TPI) at various kernel sizes ranging from 50 cm to 500 m for the classification. The eight associations were also grouped into four aggregated categories — loess grasslands, alkali steppes, open alkali swards and alkali meadows — for further analysis. Vegetation of the studied alkali landscape could be classified into the eight associations with an accuracy of κ: 0.56, and into the four aggregated categories with an accuracy of κ: 0.77 using all the variables. Sequential backward and forward selections of variables were implemented to reduce the number of variables while maximising the accuracies, resulting in increased accuracies of κ: 0.72 and κ: 0.83 for the associations and aggregated categories using six and three variables respectively. TPI at different kernel sizes, previously used to explain vegetation distribution in mountainous areas, was found to be a better indicator of vegetation types than absolute elevations in lowlands where the elevation differences are more subtle. Two characteristic features of the study area — erosional channels and alkali steps — could also be delineated using micro-topographic variables. The results point to the possibility of large-area mapping and monitoring of grasslands where micro-topography is an indicator of vegetation, using only the elevation data from ALS.     

Effects of plant diversity on biomass production and substrate nitrogen in a subsurface vertical flow constructed wetland

Most biodiversity experiments have been conducted in grassland ecosystems with nitrogen limitation, while little research has been conducted on relationships between plant biomass production, substrate nitrogen retention and plant diversity in wetlands with continuous nitrogen supply. We conducted a plant diversity experiment in a subsurface vertical flow constructed wetland for treating domestic wastewater in southeastern China. Plant aboveground biomass production ranged from 20 to 3121 g m^?2 yr^?1 across all plant communities. In general, plant biomass production was positively correlated with species richness (P = 0.001) and functional group richness (P = 0.001). Substrate nitrate concentration increased significantly with increasing plant species richness (P = 0.046), but not with functional group richness (P = 0.550). Furthermore, legumes did not affect biomass production (P = 0.255), retention of substrate nitrate (P = 0.280) and ammonium (P = 0.269). Compared to the most productive of the corresponding monocultures, transgressive overyielding of mixed plant communities did not occur in most polycultures. Because greater diversity of plant community led to higher biomass production and substrate nitrogen retention, thus we recommend that plant biodiversity should be incorporated in constructed wetlands to improve wastewater treatment efficiency.     

Potential of restoration and phytoremediation with Juncus roemerianus for diesel-contaminated coastal wetlands

Oil spills may considerably damage sensitive coastal wetlands. In this study, the tolerance limits of a dominant coastal salt marsh plant, Juncus roemerianus, to diesel oil and its phytoremediation effectiveness in wetland environments were investigated in the greenhouse. J. roemerianus was transplanted into salt marsh sediment contaminated with diesel fuel at concentrations of 0, 20, 40, 80, 160, 320, and 640 mg diesel g^?1 dry sediment. Plant stem density, shoot height, aboveground biomass and belowground biomass were detrimentally impacted at high oil dosages even 1 year after transplantation. Tolerance limits were estimated between 160 and 320 mg g^?1 based on various plant variables. Importantly, J. roemerianus enhanced oil degradation; at the 40 mg/g diesel dosage, concentrations of residual total petroleum hydrocarbons (TPH) in the sediment planted with J. roemerianus were significantly lower than those of unplanted sediments 1 year after treatment initiation. Furthermore, concentrations of total targeted polycyclic aromatic hydrocarbons (PAHs) and n-alkanes in the J. roemerianus planted treatment were, respectively, about 3% and 15% of the unplanted treatment. Concentration reduction in all categories of hydrocarbons suggested that J. roemerianus effectively phytoremediated the diesel-contaminated wetlands.     

Landscape hydrogeomorphic conditions determine structure and species composition of an ephemeral floodplain wetland

Wetlands are major sources of habitat heterogeneity, with certain environmental variables controlling wetland structure and composition. There is very little information on the heterogeneity of ephemeral floodplain wetland patch mosaics and how hydrogeomorphic circumstance affects composition and structure. Structure (wetland size) and composition (herbaceous species) are two attributes of an ephemeral wetland that are easily quantifiable using a moving window analysis. The moving window analysis is a statistical technique that identifies significant changes in parameters (i.e., structure and composition) along gradients. An analysis of changes in wetland structure and composition longitudinally identified two hydrogeomorphic types and laterally delineated wetland boundaries. The wide–flat type had a wide (131.2 ± 50.4 m) wetland patch and a mean lateral slope of 0.008 ± 0.003, in contrast the narrow–deep type had a significantly smaller (80 ± 40.2 m) and steeper (0.048 ± 0.06) wetland patch. Changes in hydrogeomorphology had distinct effects on the species composition of the wetland. Facultative wetland species such as Sporobolus pyrimidalis and Ischaemum afrum were associated with the wide–flat type, while, the narrow–deep type was characterized by more obligate, flood dependent species such as Phragmites australis, Mariscus congestus, and Eriochloa meyeriana. Internally, the structure and composition of ephemeral wetlands on the northern plains of Kruger National Park were spatially heterogeneous and correlated to hydrogeomorphic conditions, that are identifiable when examined at the wetland scale. Results add knowledge to wetlands as sources of landscape heterogeneity and highlight how environmental variation can result in increases in wetland heterogeneity.     

Congruence among vascular plants and butterflies in the evaluation of grassland restoration success

Using surrogate taxa in the monitoring of restoration success is strongly advocated but currently under debate. Are butterfly and vascular plant communities good surrogates for each other in monitoring the restoration of calcareous grassland grassland? The research was conducted in a small area in Tuscany (central Italy), which was the focus of a restoration project. We sampled vascular plants using 35 plots (1 m²) and butterflies using 20 transects (100 m in length) belonging to four treatment types: control scrubland, restored ex-arable, restored grassland and reference grassland. A mixed model nested ANOVA showed that reference grassland, 3 years after restoration, had the highest number of vascular plant species and the highest number of butterfly species. The different treatments were found to be distinct from a compositional point of view: pRDA showed that the “treatment” factor, independent of spatial variables, accounted for 22.1 and 21.9% of the total variance in the plant community and butterfly community, respectively (p < 0.01). The butterfly community was significantly correlated with the entire plant community data set and with the phanerophyte community (i.e. shrubs); larval host plants were significantly correlated with the composition of non-migratory butterflies. The results indicated that butterfly and vascular plant communities were congruent, suggesting the use of community composition of different taxa as a valuable tool for the evaluation of management success.     

Typha × glauca dominance and extended hydroperiod constrain restoration of wetland diversity

Urban wetlands typically have few plant species. In wetlands designed to improve water quality, nutrient-rich water and highly variable water levels often favor aggressive, flood-tolerant plants, such as Typha × glauca (hybrid cattail). At Des Plaines River Wetlands Demonstration Site (Lake Co., IL), we assessed T. × glauca dominance and plant community composition under varying hydroperiods in a complex of eight constructed wetlands. Plots flooded for more than 5 weeks during the growing season tended to be dominated by T. × glauca, while plots flooded fewer days did not. Plots with high cover of T. × glauca had low species richness (negative correlation, R² = 0.72, p < 0.001). However, overall species richness of the wetland complex was high (94 species), indicating that wetlands in urbanizing landscapes can support many plant species where T. × glauca is not dominant. T. × glauca-dominated areas resisted the establishment of a native plant community. Removing T. × glauca and introducing native species increased diversity initially, but did not prevent re-invasion. Although 12 of the 24 species we seeded became established in our cleared plots, T. × glauca rapidly re-invaded. In year 1, T. × glauca regained an average of 11 ramets m⁻², and its density doubled in year 2. The likelihood of planted species surviving decreased as duration of inundation increased, and in both seeded and planted plots, graminoids had greater survivorship through year 2 than forbs across a range of water levels. Within 4 years, however, T. × glauca was the most common plant, present in 92% of the cleared plots. Simply removing T. × glauca and adding propagules to an urban wetland is not sufficient to increase diversity.     

A review of indicators of estuarine tidal wetland condition

This review critically evaluates indicators of tidal wetland condition based on 36 indicator development studies and indicators developed as part of U.S. state tidal wetland monitoring programs. Individual metrics were evaluated based on relative scores on two sets of evaluation factors. A rigor score evaluated metric development based on conceptual relevance, indicator development method, degree of independent validation, and temporal and spatial extent tested. An applicability score evaluated metrics based on cost of data collection, probable spatial extent of applicability, technical complexity, and indicator responsiveness. The majority of indicators could be classified as biotic condition indicators (81%), with vegetation (37%) and macroinvertebrate (28%) metrics composing the largest proportion. Most metrics provided a conceptual model or scientific justification (97%), were developed by correlation to environmental gradients (46%), were tested over multiple seasons or years (49%) and at multiple sites (88%). Few were independently validated (18%). Average rigor score was 10 (on a scale of 0–25) and ranged between 1 and 21. Highest rigor scores were for trematode community metrics (community similarity index, species richness) and metrics of grass shrimp (Palaemonetes pugio) individuals (gene expression, relative fecundity, embryo hatching success, larval survival). Most metrics had a high cost of data collection (63%), required field and laboratory processing (84%), would be applicable across the U.S. (72%), and were responsive to the variable of interest (44%). Mean applicability score was 4.9 (range: 2–8). Highest scores were found for metrics that only required field collection of data using simple or no instrumentation. Lowest scoring metrics required expensive equipment, specialized taxonomic knowledge, complex laboratory analysis, and/or culturing of organisms. Scores for individual metrics were grouped by indicator, then averaged and rescaled between 0 and 100 to provide a composite evaluation of the indicator they measured. Among major indicator types, biotic indicators had the highest rigor scores (mean = 44, range 20–79), followed by indicators of chemical/physical characteristics (mean = 36, range 16–56), landscape condition (mean = 31, range 24–37), and hydrology/geomorphology indicators (mean = 21, range 4–52). In contrast, biotic indicators scored lowest for applicability (mean = 58, range 25–100) and indicators of landscape condition scored highest. The results of this review suggest that the development and selection of tidal wetland indicators could be vastly improved by employing a standardized development methodology that provides uniform information about each indicator. In addition, tidal wetland indicator research should focus on the development of indicators of ecological processes and disturbance regimes.     

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

Bloom forming Alexandrium ostenfeldii (Dinophyceae) in shallow waters of the Åland Archipelago,Northern Baltic Sea

In the past years, late summer blooms of the bioluminescent dinoflagellate Alexandrium ostenfeldii have become a recurrent phenomenon in coastal waters of the central and Northern Baltic Sea. This paper reports exceptionally high cell concentrations (10⁵ to 10⁶ cells L^?1) of the species found during bioluminescent blooms in 2003 and 2004 in a shallow embayment of the Åland archipelago at the SW coast of Finland. Clonal cultures were established for morphological, molecular, toxicological and ecophysiological investigations to characterize the Finnish populations and compare them to other global A. ostenfeldii isolates. The Finnish isolates exhibited typical morphological features of A. ostenfeldii such as large size, a prominent ventral pore and an orthogonally bent first apical plate. However, unambiguous differentiation from closely related Alexandrium peruvianum was difficult due to considerable variation of sulcal anterior plate shapes. The Finnish strains were genetically distinct from other isolates of the species, but phylogenetic analyses revealed a close relationship to isolates from southern England and an A. peruvianum morphotype from the Spanish Mediterranean. Together these isolates formed a distinct clade which was separated from a clade containing other Northern European, North American and New Zealand populations. Toxin analyses confirmed the presence of the PSP toxins GTX2, GTX3 and STX in both Finnish isolates with GTX3 being the dominant toxin. Total relative PSP toxin contents were moderate, ranging from approximately 6 to 15 fmol cell^?1 at local salinities of 5 and 10 psu, respectively. Spirolides were not detected. Salinity tolerance experiments showed that the Finnish isolates were well adapted to grow at the low salinities of the Baltic Sea. With a salinity range of approximately 6 to 20–25 psu, Baltic populations are physiologically distinct from their marine relatives. Vigorous production of different cyst types in the cultures suggest that cysts may play a crucial role in the survival and retainment of A. ostenfeldii populations in the Baltic Sea.     

Biological control of invasive apple snails by two species of carp: Effects on non-target species matter

Molluscivorous fish, especially carp, have been adopted as bio-control agents of the invasive apple snail Pomacea canaliculata, but previous studies have focused on their effectiveness, with little attention paid to their undesirable effects on non-target plants and animals. We conducted an 8-week mesocosm study to compare the effectiveness of two indigenous fish, common carp (Cyprinus carpio) and black carp (Mylopharyngodon piceus), in removing P. canaliculata, and their potential side effects on macrophytes and non-target mollusks in a freshwater wetland. Three species of macrophytes and a community of mollusks in the wetland sediment were enclosed in 1 × 1 × 1 m enclosures either with apple snails (AS), with apple snails and common carp (AS + CC), with apple snails and black carp (AS + BC), or without apple snails and fish. Both species of carp were effective predators of P. canaliculata, removing most of the individuals in the enclosures except a few that were too big to fit into their mouth. By reducing apple snail population, black carp reduced grazing of apple snail on macrophytes. In contrast, although common carp controlled apple snail population, it did not reduce overall loss in plant biomass as the fish might also fed on macrophytes. Both species of carp preyed on non-target mollusks. Application of bio-control agents in invasive species management needs to consider their effects on both the pest and non-target plants and animals. Adoption of common and/or black carp to control apple snail populations thus depends on the weight given to their effectiveness and subtle different effects on non-target organisms by wetland management authority.     

Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set

Little information is available to assess the dynamic changes in wetland soil quality in coastal regions, though it is essential for wetland conservation and management. Soil samples were collected in Suaeda salsa wetlands (SWs), Tamarix chinensis wetlands (TWs), Suaeda salsa–Tamarix chinensis wetlands (STWs), freshwater Phragmites australis wetlands (FPWs) and saltwater Phragmites australis wetlands (SPWs) in three sampling periods (i.e., summer and autumn of 2007 and spring of 2008). According to the flooding characteristics of these wetlands, the study area could be grouped into three sub-regions: short-term flooding region (STFR), seasonal flooding region (SFR) and tidal flooding region (TFR). Soil quality was evaluated using the soil quality index (SQI), which was calculated using the selected minimum data set (MDS) based on principal components analysis (PCA). Our results showed that soil salt content (SSC), total carbon (TC), magnesium (Mg), nitrate nitrogen (NO₃⁻-N) and total sulfur (TS) consisted of a MDS among 13 soil properties. The SQI values varied from 0.18 to 0.66 for all soil samples, of which the highest and lowest SQI values were observed in TFR. The average SQI values were significantly higher in summer (0.50 ± 0.13) than in spring (0.37 ± 0.13) and autumn (0.36 ± 0.11) in the whole study area (p < 0.05). The average SQI values followed the order STFR (0.44 ± 0.12) > TFR (0.41 ± 0.15) > SFR (0.35 ± 0.09) although no significant differences were observed among the three regions (p > 0.05). SPWs and SWs soils showed higher SQI values (0.50 ± 0.10 and 0.47 ± 0.15, respectively) than TWs (0.30 ± 0.08) soils (p < 0.05). The SSC was the dominant factor of soil quality with its proportion of 34.1% contributing to the SQI values, followed by TC (24.5%) and Mg (24.1%). Correlation analysis also showed that SQI values were significantly negatively correlated with SSC. SSC might be a characteristic indicator of wetland soil quality assessment in coastal regions. The findings of this study showed that the SQI based on MDS is a powerful tool for wetland soil quality assessment.     

Plants as bioindicator for temperature interpolation purposes: Analyzing spatial correlation between botany based index of thermophily and integrated temperature characteristics

The success of interpolation techniques relies heavily on the density and regularity of field reference data points. For instance temperature interpolations in the Arctic are hampered by few and scattered meteorological stations. The major objective of this study is to analyze the spatial relationship between plants, defined in terms of an index of thermophily (I_t) and temperature distribution. The study area is located in Kongsfjorden, northwest Spitsbergen (Svalbard). A systematic recording of floristic data covering the study area was made within quadrates of 1 km × 1 km (93 units). For each of them, the I_t was calculated. I_t provides a synthetic measure by which plants are taken as temperature indicators at a long time scale. Temperature values were recorded by means of 39 temperature loggers during the summer 2000. The model for spatial interpolation of temperature was developed using multiple regression of remote sensed data (Landsat TM) and topographical features derived from a digital elevation model (DEM). Continuous temperature layers were calculated at a spatial resolution of 50 m × 50 m, and aggregated to a resolution of 1 km × 1 km in order to correspond with the observed botanical units. Different maps were produced showing spatial distribution of the modelled temperature and I_t. Correlations between the I_t and temperature values derived from the modelled temperature layers were systematically explored. Correlation between the I_t and temperatures works well as standard deviation of residues is 0.7 °C only. Highest correlations (r) of I_t and the spatial distribution of temperature were obtained for: (a) maximum average temperature for August, excluding all areas higher than 100 m above sea level (0.75), (b) average daily maximum temperature for July–October (0.67), (c) average temperature for July and August (0.64, 0.65), and (d) when temperature range is >8 °C (0.55). Areas with low correlations between I_t and temperature were mainly attributed to the fact that these measurements represent (a) different time scales and (b) different spatial scales. However, results from this study have shown that calculating I_t provide a mean for restoring selected temperature parameters and thus can contribute to fill in and extend the network of field data points for temperature interpolation purposes.     

Improved retrieval of Secchi depth for optically-complex waters using remote sensing data

Water transparency is one of the ecological indicators for describing water quality and the underwater light field which determines its productivity. In the European Water Framework Directive (WFD) as well as in the European Marine Strategy Framework Directive (MSFD) water transparency is used for ecological status classification of inland, coastal and open sea waters and it is regarded as an indicator for eutrophication in Baltic Sea management (HELCOM, 2007). We developed and compared different empirical and semi-analytical algorithms for lakes and coastal Nordic waters to retrieve Secchi depth (Z_SD) from remote sensing data (MERIS, 300 m resolution). The algorithms were developed in water bodies with high coloured dissolved organic matter absorption (a_CDOM(442) ranging 1.7–4.0 m⁻¹), Chl a concentration (0.5–73 mg m⁻³) and total suspended matter (0.7–37.5 g m⁻³) and validated against an independent data set over inland and coastal waters (0.6 m < Z_SD < 14.8 m). The results indicate that for empirical algorithms, using longer wavelengths in the visible spectrum as a reference band decreases the RMSE and increases the coefficient of determination (R²). The accuracy increased (R² = 0.75, RMSE = 1.33 m, n = 134) when Z_SD was retrieved via an empirical relationship between Z_SD and K_d(490). The best agreement with in situ data was attained when Z_SD was calculated via both the diffuse and the beam attenuation coefficient (R² = 0.89, RMSE = 0.77 m, n = 89). The results demonstrate that transparency can be retrieved with high accuracy over various optical water types by the means of ocean color remote sensing, improving both the spatial and temporal coverage. The satellite derived Z_SD product could be therefore used as an additional source of information for WFD and MSFD reporting purposes.