The pigment phycocyanin (PC) is a marker for cyanobacterial presence in eutrophic inland water. We present a reflectance band-ratio algorithm for retrieval of cyanobacterial PC. The model conforms to the band settings of the Medium Resolution Imaging Spectrometer. The parameters of the algorithm were optimized using reflectance and absorption data from two highly eutrophic lakes. Using measured specific absorption coefficients for PC [a (620)] * pc for every sample, the error in the predicted PC concentrations was 19.7% (r 2 ϭ 0.94, n ϭ 34) for measured PC concentrations up to 80 mg m Ϫ3 . Applying a fixed value of a (620) caused an overestimation of the PC content * pc that increased toward lower PC concentrations. The PC prediction best matched observed values during periods of high relative abundance of cyanobacteria in the plankton community. The results suggest strong seasonal variation in a (620). The presence of pigments other than PC and chlorophyll a (Chl a) and a variable influence of Chl a * pc on retrieved absorption at 620 nm are potential causes of errors in PC retrieval. The algorithm in its current form is considered to be suitable for detection of the PC concentration in turbid, cyanobacteria-dominated waters.Eutrophic inland waters often exhibit blooms of cyanobacteria. Notorious for their negative impact on water quality, cyanobacterial blooms have been increasingly subject of water management and scientific studies. The hazards of toxic cyanobacterial blooms call for frequent and rapid monitoring of water bodies. Remote sensing provides insights into the distribution of blooms for a large number of lakes or reservoirs simultaneously. The concentration of chlorophyll a (Chl a) as a general indicator for plankton biomass can be assessed using imagery from a wide range of air-and spaceborn sensors (Vos et al. 2003). Recent advances in spaceborn remote sensing technology broaden the perspectives of monitoring toward the identification and quantification of plankton groups. Algorithms for the retrieval of Chl a from turbid water reflectance were already being developed (Gons et al. 2002). Now, the retrieval of the pigment phycocyanin (PC), which is characteristic of the presence of cyanobacterial, is being attempted. It is known that the presence of PC can be detected from spectral reflectance (Dekker et al.

Climate change will alter freshwater ecosystems but specific effects will vary among regions and the type of water body. Here, we give an integrative review of the observed and predicted impacts of climate change on shallow lakes in the Netherlands and put these impacts in an international perspective. Most of these lakes are man-made and have preset water levels and poorly developed littoral zones. Relevant climatic factors for these ecosystems are temperature, ice-cover and wind. Secondary factors affected by climate include nutrient loading, residence time and water levels. We reviewed the relevant literature in order to assess the impact of climate change on these lakes. We focussed on six management objectives as bioindicators for the functioning of these ecosystems: target species, nuisance species, invading species, transparency, carrying capacity and biodiversity. We conclude that climate change will likely (i) reduce the numbers of several target species of birds; (ii) favour and stabilize cyanobacterial dominance in phytoplankton communities; (iii) cause more serious incidents of botulism among waterfowl and enhance the spreading of mosquito borne diseases; (iv) benefit invaders originating from the Ponto-Caspian region; (v) stabilize turbid, phytoplankton-dominated systems, thus counteracting restoration measures; (vi) destabilize macrophyte-dominated clear-water lakes; (vii) increase the carrying capacity of primary producers, especially phytoplankton, thus mimicking eutrophication; (viii) affect higher trophic levels as a result of enhanced primary production; (ix) have a negative impact on biodiversity which is linked to the clear water state; (x) affect biodiversity by changing the disturbance regime. Water managers can counteract these developments by reduction of nutrient loading, development of the littoral zone, compartmentalization of lakes and fisheries management

Determination of subsurface spectral irradiance reflectance by use of a hand-held spectroradiometer and then estimation of chlorophyll a (Chl-a) concentration facilitate assessment of ecological change in turbid lakes, rivers, and estuaries. The method was applied under widely varying solar elevation and weather. Noise due to reflected sky light was evaluated by theory on surface waves and by field data. A strong relationship was established for the shallow and eutrophic freshwater IJssel Lagoon including the River IJssel (The Netherlands) between corrected Chl-a concentrations (3-185 mg m -3 ) and the reflectance ratio for 704 and 672 nm with backscattering derived from the reflectance at 776 nm (R 2 > 0.95; N ) 114; standard error of estimate <9 mg m -3 ). This relationship adequately predicted Chl-a concentrations in well-mixed and optically deep water in lakes in The Netherlands, the Scheldt Estuary in Belgium and The Netherlands, and Lake Tai in China (N ) 40; standard error of estimate <9 mg m -3 ). Floating layers of the cyanobacterium Microcystis in Lake Chao in China were distinguished from homogeneously distributed phytoplankton by reflectance in the violet, blue, and nearinfrared wavebands.

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Although phenotypic plasticity can be advantageous in fluctuating environments, it may come too late if the environment changes fast. Complementary chromatic adaptation is a colorful form of phenotypic plasticity, where cyanobacteria tune their pigmentation to the prevailing light spectrum. Here, we study the timescale of chromatic adaptation and its impact on competition among phytoplankton species exposed to fluctuating light colors. We parameterized a resource competition model using monoculture experiments with green and red picocyanobacteria and the cyanobacterium Pseudanabaena, which can change its color within ∼7 days by chromatic adaptation. The model predictions were tested in competition experiments, where the incident light color switched between red and green at different frequencies (slow, intermediate, and fast). Pseudanabaena (the flexible phenotype) competitively excluded the green and red picocyanobacteria in all competition experiments. Strikingly, the rate of competitive exclusion was much faster when the flexible phenotype had sufficient time to fully adjust its pigmentation. Thus, the flexible phenotype benefited from its phenotypic plasticity if fluctuations in light color were relatively slow, corresponding to slow mixing processes or infrequent storms in their natural habitat. This shows that the timescale of phenotypic plasticity plays a key role during species interactions in fluctuating environments.Keywords: adaptive dynamics, cyanobacteria, phycocyanin, phycoerythrin, resource competition theory, Synechococcus.Fluctuations in environmental conditions pose serious challenges to organisms. Many organisms respond to environmental changes by physiological and morphological adaptations. This flexible strategy, known as phenotypic plasticity, may improve their fitness in the new environments (Agrawal 2001). For example, plants increase their leaf area during periods of reduced light (Sultan and Bazzaz 1993), cladocerans develop armored helmets in the presence of predators (Woltereck 1909;Laforsch and Tollrian 2004), and some green algae aggregate into colonies to reduce their edibility for grazers (Hessen and Van Donk 1993;Lampert et al. 1994).Intuitively, phenotypic plasticity seems a suitable strategy to cope with environmental fluctuations. However, adaptation takes time. If adaptation is too slow, organisms will not be able to keep up with changes in their environment, resulting in a permanent mismatch between the physiology of the organisms and their environmental conditions. Indeed, theory shows that adaptation can even be disadvantageous when it has a strong time delay (Padilla and Adolph 1996;Gabriel 2005). Yet, although many studies have investigated phenotypic plasticity in fluctuating environments (e.g., Chesson et al. 2004;Egas et al. 2004;Abrams 2006aAbrams , 2006bGélinas et al. 2007; Van der Stap et al. 2007), the timescale of phenotypic adaptation has received surprisingly little attention (Miner et al. 2005).The colorful process of complementary chromatic ad-E170 ...