Merli Pärnoja

Predicting Species Cover of Marine Macrophyte and Invertebrate Species Combining Hyperspectral Remote Sensing, Machine Learning and Regression Techniques

In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.

Realized niche width of a brackish water submerged aquatic vegetation under current environmental conditions and projected influences of climate change

Little is known about how organisms might respond to multiple climate stressors and this lack of knowledge limits our ability to manage coastal ecosystems under contemporary climate change. Ecological models provide managers and decision makers with greater certainty that the systems affected by their decisions are accurately represented. In this study Boosted Regression Trees modelling was used to relate the cover of submerged aquatic vegetation to the abiotic environment in the brackish Baltic Sea. The analyses showed that the majority of the studied submerged aquatic species are most sensitive to changes in water temperature, current velocity and winter ice scour. Surprisingly, water salinity, turbidity and eutrophication have little impact on the distributional pattern of the studied biota. Both small and large scale environmental variability contributes to the variability of submerged aquatic vegetation. When modelling species distribution under the projected influences of climate change, all of the studied submerged aquatic species appear to be very resilient to a broad range of environmental perturbation and biomass gains are expected when seawater temperature increases. This is mainly because vegetation develops faster in spring and has a longer growing season under the projected climate change scenario.

Detecting patterns and changes in a complex benthic environment of the Baltic Sea

Today, the knowledge on the distribution of marine habitats is very fragmented and temporal changes in such patterns are even less known. In this study we assessed spatial variability and temporal dynamics of benthic habitat types in a relatively turbid northeastern Baltic Sea coastal environment using the space-borne multispectral sensor QuickBird. Seven broad habitat classes were defined for the study area representing the most typical habitats of the coastal environment. The studied classes were bare sand, the brown alga Fucus vesiculosus, hard bottom with ephemeral algae, higher-order plants and/or charophytes on soft bright bottom, dense higher-order plant habitats, and drifting algal mats and deep water (>3 m). Two QuickBird images acquired over a 3 year interval (2005 to 2008) of Western-Estonian archipelago were processed and change detection analysis applied. Although there was a relatively large scatter in reflectance variability within each habitat type, the analyses allowed a clear differentiation of most habitat types. Exceptions were the lack of statistical differences among deep water, drifting algae, and dense higher-order plant communities, as well as among low density higher-order plant and algal communities. Major changes in the spatial patterns of benthic habitats occurred in hydrodynamically active areas. Differences in water properties caused some confusion in classification and therefore resulted in inaccuracies in maps of change. Thus, the used broad habitat classes represent the limit of the method and the multispectral sensors do not allow finer elements of habitats to be captured.

Mapping Baltic Sea shallow water environments with airborne remote sensing

It is known that the structure of benthic macrophyte and invertebrate habitats indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats makes it possible to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes, and describe the processes behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view. Our previous results clearly demonstrated that remote sensing methods can be used to map water depth and distribution of taxonomic groups of benthic algae (e.g., red, green, and brown algae) in the optically complex coastal waters of the Baltic Sea. We have as well shown that benthic habitat mapping should be done at high spatial resolution owing to the small-scale heterogeneity of such habitats in Estonian coastal waters. Here we tested the capability of high spatial resolution hyperspectral airborne image in its application for mapping benthic habitats.A big challenge is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study two benthic habitat classification schemes—broader level and finer level—were defined for the study area. The broader level classes were relatively well classified, but discrimination among the units of the finer classification scheme posed a considerable challenge and required a careful approach. Benthic habitat classification provided the highest accuracy in the case of the Spectral Angle Mapper classification method applied to a radiometrically corrected image. Further processing levels, such as spatial filtering and glint correction, decreased the classification accuracy.

Functional traits of marine macrophytes predict primary production

Summary 1.The relationship between community structure and the functioning of ecosystems is the subject of ongoing debate. Biological or functional trait-based approaches that capture life strategy, morphology and behavioural characteristics have received far less attention than taxonomic diversity in this context, despite their more intuitive link to ecosystem functioning. 2.Macrophyte primary production underpins aquatic food webs, regulates benthic and pelagic ecosystems and is a key aspect of the global carbon cycle. This study spans a range of aquatic biomes across Europe and aims to examine potential for predicting primary production of macrophyte communities based on the functional traits of species and identify the traits that are the most informative indicators of macrophyte production. 3.Macrophyte primary production was assessed based on the oxygen production of the whole community, linked to biomasses of selected biological traits derived of its component species and analysed using the novel boosted regression trees (BRT) modelling technique. 4.Results showed that functional traits derived from macrophyte community data explained most of the variation in primary production of macrophyte communities without the need to incorporate environmental data on the habitats. Macrophyte primary production was influenced by a combination of tolerance, morphology and life habit traits; however tolerance traits contributed most of variability in macrophyte primary production when all traits were analysed jointly. 5.The study also showed the existence of trait clustering as the studied trait categories were not fully independent; strong interlinkages between and within trait categories emerged. 6.Our study suggests that functional trait analysis captures different aspects of ecosystem functioning and thereby enables assessing primary production of macrophyte communities over geographically distinct areas without extensive taxonomic and environmental data. This could result in a novel framework through which a simplification of the general procedure of production estimations and comparisons across environmental gradients can be achieved. This article is protected by copyright. All rights reserved.

Establishing functional relationships between abiotic environment, macrophyte coverage, resource gradients and the distribution of Mytilus trossulus in a brackish non-tidal environment

Benthic suspension feeding mussels are an important functional guild in coastal and estuarine ecosystems. To date we lack information on how various environmental gradients and biotic interactions separately and interactively shape the distribution patterns of mussels in non-tidal environments. Opposing to tidal environments, mussels inhabit solely subtidal zone in non-tidal waterbodies and, thereby, driving factors for mussel populations are expected to differ from the tidal areas. In the present study, we used the boosted regression tree modelling (BRT), an ensemble method for statistical techniques and machine learning, in order to explain the distribution and biomass of the suspension feeding mussel Mytilus trossulus in the non-tidal Baltic Sea. BRT models suggested that (1) distribution patterns of M. trossulus are largely driven by separate effects of direct environmental gradients and partly by interactive effects of resource gradients with direct environmental gradients. (2) Within its suitable habitat range, however, resource gradients had an important role in shaping the biomass distribution of M. trossulus. (3) Contrary to tidal areas, mussels were not competitively superior over macrophytes with patterns indicating either facilitative interactions between mussels and macrophytes or co-variance due to common stressor. To conclude, direct environmental gradients seem to define the distribution pattern of M. trossulus, and within the favourable distribution range, resource gradients in interaction with direct environmental gradients are expected to set the biomass level of mussels.

Novel crab predator causes marine ecosystem regime shift

The escalating spread of invasive species increases the risk of disrupting the pathways of energy flow through native ecosystems, modify the relative importance of resource (‘bottom-up’) and consumer (‘top-down’) control in food webs and thereby govern biomass production at different trophic levels. The current lack of understanding of interaction cascades triggered by non-indigenous species underscores the need for more basic exploratory research to assess the degree to which novel species regulate bottom-up and/or top down control. Novel predators are expected to produce the strongest effects by decimating consumers, and leading to the blooms of primary producers. Here we show how the arrival of the invasive crab Rhithropanopeus harrisii into the Baltic Sea – a bottom-up controlled ecosystem where no equivalent predators ever existed – appeared to trigger not only strong top-down control resulting in a decline in richness and biomass of benthic invertebrates, but also an increase in pelagic nutrients and phytoplankton biomass. Thus, the addition of a novel interaction – crab predation – to an ecosystem has a potential to reduce the relative importance of bottom-up regulation, relax benthic-pelagic coupling and reallocate large amounts of nutrients from benthic to pelagic processes, resulting in a regime shift to a degraded ecosystem state.

Predicting macroalgal pigments (chlorophyll a, chlorophyll b, chlorophyll a + b, carotenoids) in various environmental conditions using high-resolution hyperspectral spectroradiometers

ABSTRACT Photosynthetic pigments may indicate the health and productivity of vegetation and thereby are among the most important targets of the remote-sensing science. We studied the relationship between macroalgae pigment concentration measured in situ and spectral reflectance, to develop predictive remote-sensing methods for macroalgal pigments. The measurements of spectral reflectance of macroalgae were made using both a field portable spectrometer Ramses built by TriOS GmbH (Germany) and a laboratory hyperspectral imaging device HySpex built by Norsk Elektro Optikk (Norway). Our results showed that differences in total chlorophyll (Chl-a + b) concentrations resulted in the consistent change of spectral reflectance for studied brown (Fucus vesiculosus) and green (Cladophora glomerata, Ulva intestinalis) macroalgae species. Charophytes (Chara aspera, Chara horrida) were also studied, and the relationship was much weaker for this taxon. If spectral indices predicted relatively well the concentration of Chl-a + b (R2 = 0.64–0.73) and the carotenoid to total chlorophyll ratio (Car:Chl-a + b, R2 = 0.80) across the five studied macroalgae species, then the concentration of chlorophyll a (Chl-a), chlorophyll b (Chl-b), and carotenoids (Car) were more difficult to model (R2 = 0.004–0.51). The HySpex imaging system yielded systematically better results in predicting pigment concentrations compared to the Ramses spectroradiometer. By using traditional assessment of pigment concentration along with the Hyspex imaging device, we were able to build models with a capability to predict the spatial patterns of pigment concentration for Baltic Sea macroalgae.

Report on the nature and types of driver interactions including their potential future

The Baltic Sea is a dynamic environment responding to various drivers operating at different temporal and spatial scales. In response to climate change, the Baltic Sea is warming and the frequency of extreme climatic events is increasing (Lima & Wethey 2012, BACC 2008, Poloczanska et al. 2007). Coastal development, human population growth and globalization intensify stressors associated with human activities, such as nutrient loading, fisheries and proliferation of invasive and bloom-forming species. Such abrupt changes have unforeseen consequences for the biodiversity and the function of food webs and may result in loss of ecological key species, alteration and fragmentation of habitats. To mitigate undesired effects on the Baltic ecosystem, an efficient marine management will depend on the understanding of historical and current drivers, i.e. physical and chemical environmental conditions and human activities that precipitate pressures on the natural environment. This task examined a set of key interactions of selected natural and anthropogenic drivers in space and time, identified in Task 3.1 as well as WP1 and WP2 (e.g. physico-chemical features vs climate forcing; eutrophication vs oxygen deficiency vs bio-invasions; fisheries vs climate change impacts) by using overlay-mapping and sensitivity analyses. The benthic ecosystem models developed under Task 2.1 were used to investigate interactions between sea temperature and eutrophication for various depth strata in coastal (P9) and offshore areas (P1) of the Baltic Sea. This also included investigation on how the frequency and magnitude of deep-water inflow events determines volume and variance of salinity and temperature under the halocline, deep-water oxygen levels and sediment fluxes of nutrients, using observations and model results from 1850 to present (P1, P2, P6, P9, P12). The resulting synthesis on the nature and magnitude of different driver interactions will feed into all other tasks of this WP3 and WP2/WP4. Moreover, the results presented in this report improve the process-based and mechanistic understanding of environmental change in the Baltic Sea ecosystem, thereby fostering the implementation of the Marine Strategy Framework Directive.