UNIVERSITÀ DEGLI STUDI DIGENOVA
DIPARTIMENTODISCIENZE DELLA TERRA, DELL’AMBIENTE
E DELLA VITA
CORSO DI LAUREA IN BIOLOGIA ED ECOLOGIA MARINA
Characterization of subtidal rocky
substrates covered by macroalgae and
by Posidonia oceanica using the
Manta-2 multi-probe
Relatore: Monica Montefalcone
Correlatore: Cristina Misic
Summary
I. ABSTRACT ... 1
II. INTRODUCTION ... 3
III. GAPS OF KNOWLEDGE ... 5
3.1THE DIFFERENT HABITATS:PHYLLOPHORA CRISPA,POSIDONIA OCEANICA AND HARD-BOTTOM ... 5
3.2THE ROLE OF OXYGEN IN BOTH ENVIRONMENTS ... 7
3.3COMPARISON OF PHYLLOPHORA CRISPA AND CYSTOSEIRA SPP. AS ECOSYSTEM ENGINEERS ... 9
3.4THE MULTI-PROBE MANTA-2 ... 11
3.5PREVIOUS USES OF MANTA-2 ... 14
IV. MATERIALS AND METHODS ... 15
4.1STUDY AREA ... 15
4.2FIELD ACTIVITIES ... 16
4.3POINT INTERCEPT TRANSECT (PIT) FOR THE CHARACTERIZATION OF THE AREA ... 17
4.4DATA ACQUISITION WITH THE MULTI-PROBE MANTA-2 ... 19
4.5CURRENT DATA ACQUISITION ... 21
V. RESULTS ... 22
5.1TRANSECT RESULTS ... 22
5.3DATA ON CURRENTS TO DIFFERENTIATE HABITATS ... 29
5.4DISSOLVED OXYGEN IN P. CRISPA MATS AND IN P. OCEANICA MEADOW ... 31
5.5WEEKLY CYCLE OF DISSOLVED OXYGEN IN THE MATS OF PHYLLOPHORA CRISPA ... 36
5.6DAILY CYCLE OF DISSOLVED OXYGEN IN PHYLLOPHORA CRISPA MATS ... 39
5.7TEMPERATURE AND SALINITY PARAMETERS ... 41
VI. DISCUSSION ... 47
VII. CONCLUSIONS ... 51
III. BIBLIOGRAPHY ... 52
I. Abstract
The acquisition and comparison of chemical and physical parameters are pivotal for the interpretation of environmental gradients and allow the recognition of different types of marine environments. In this framework, the continuous monitoring of the environmental characteristics allows to record their variability on long as well as short time scales, to understand the driving processes that shape the gradient intensity. The aim of this study was to characterize, through the use of the Manta-2 multi-probes, three different marine habitats based on their chemical and physical properties and, through visual inspection, biological characteristics.
This thesis was carried out at the Giglio Island, between September and October 2019. The ‘Punta del Morto’ study area has three different habitats between 28 and 30 m depth: 1) red algal mats, dominated by the species Phyllophora crispa, an algae 10-15 cm high; 2) a meadow of Posidonia oceanica; 3) bare rocky substrates without vegetation. The bare rocky substrate without vegetation has been used as a control environment.
A total of 21 scuba dives were carried out to place four Manta-2 multi-probes from the Eureka Company in each of the three types of environment, allowing the simultaneous acquisition of the chemical parameters, such as dissolved oxygen, and the physical parameters, such as temperature and salinity. Manta-2 is equipped with probes at the bottom of the instrument, allowing data acquisition every minute at the substrate level and recording for two or three consecutive days. The multi-probe has been joined by the presence of Gypsum Balls, which are “clod cards” that have been placed in the same environments and used for the measurement of the current velocity on the bottom, a useful parameter to define a closed or an open environment. Visual and photographic surveys along horizontal transects were also carried out in the study area in order to characterize vegetated stands. The acquired data were processed through the Excel software, analysing the monthly, weekly and daily cycles in order to evaluate changes in the chemical-physical parameters over time in the different environments.
In the relatively closed environment of the P. oceanica rhizomes, changes in temperature, salinity and oxygen during the day and between the two months of investigation were minimal, while higher variations were identified in the more open environment
characterized by mats of P. crispa. dissolved oxygen concentration was the variable that mainly identified the characteristics of the P. crispa habitat. While the monthly variations were also influenced by the environmental temperature, the daily fluctuations were different from those of the control, namely the bare rocky substrate, indicating time lags when respiration was significantly consuming oxygen (afternoon) and, on the contrary, time lags when production was more efficient (morning). These trends suggest the existence of a complex community that can be sustained by P. crispa engineering properties.
The data of this thesis will be used for a future project aimed at confirming the hypothesis that P. crispa can be considered as a structuring species and an ecosystem engineer, such as the brown algae of the genus Cystoseira.
II. Introduction
Coastal marine ecosystems are essential components of the marine environment. They are hot spots for biodiversity and the main participants in food networks, material cycles and energy transfer, as well as sources of goods and ecosystem services for human populations (Berov et al., 2018).
The heterogeneity of hard bottom is greater than that of soft bottom, resulting in a wealth of different communities characterized by sessile organisms (White et al., 2007). In the coastal area, the vegetation is represented by marine phanerogams and macroalgae. While a variety of research has been carried out for marine phanerogams, information is poor and not detailed for macroalgae, in particular for the red algae Phyllophora crispa P.S. Dixon, 1964 (Kostylev et al., 2010). Marine macroalgae inhabit hard substrates in the euphotic zone providing some of the most diverse and productive marine ecosystems, supplying food and a favourable environment for different marine invertebrates (Christie
et al., 1998), fish species and mammals (Norderhaug et al., 2005).
The presence of macroalgae increases the complexity of the habitat and their regression can lead to the availability of the biodiversity of the environment. The genus Cystoseira C. Agardh 1820, a brown algae belonging to the order Fucales distributed along the Mediterranean coast, is ecologically relevant as an "ecosystem engineer" (Jones et al, 1984) and plays a functional role in controlling spatial habitat heterogeneity, productivity and nutrient cycle in temperate rock barriers (Mineur et al., 2015). Currently, some
Cystoseira populations are declining/lost throughout the Mediterranean (Boudouresque et al., 2004) largely due to multiple human impacts such as urbanization, overfishing and
climate change. As a result, many systems have evolved from complex and productive food webs to less productive habitats by affecting the provision of ecosystem services (Thibaut et al., 2005). Cystoseira species are listed as species of "community interest" under the Habitats Directive (92/43/EEC) (Directive, 1992) and are indicators of the quality of the environment in the Mediterranean coastal waters according to the Marine Water Framework Directive (2000/60/EC) (EEC, 2000). In addition, the order Fucales is the only marine habitat in the Mediterranean defined as endangered by the IUCN (International Union for Conservation of Nature) (IUCN, 2016). More studies on the
can be important and contribute to being an indicator of the quality of the environment such as Cystoseira spp.
The distribution and structure of the macroalgal community is modelled by an interaction of various environmental factors such as the latitude, which involves radiation gradients, length and temperature of the day and wave exposure (Rinde et al., 2005).
Along the coast of the Giglio Island, the Tyrrhenian Sea offers horizontal areas where the rocky substrate can take on significantly varied aspect and anfractuosities in which fish and other animals can find refuge. From the green algae, which can reach up to 20 m depth depending on the turbidity, begin to give way to brown and red algae. The cavities in the rocks are bioconstructed by calcareous algae, bryozoans, polychaetes, cnidarians and other organisms. Coralligenous structures require various factors to settle at the substrate including a substrate of reduced hydro-dynamism and a limited amount of light (Petrella et al., 2005).
The aim of the thesis is to characterize three different ecosystems trough the analysis of their main environmental features: 1) the habitat of marine phanerogams with P. oceanica (Linnaeus) Delile, 1813; 2) the habitat of red algal mats with P. crispa; and 3) the rocky substrate without any kind of vegetation.
The research took place between September and October 2019 at the Punta del Morto diving spot on the Giglio Island (Tuscany, Italy) and was carried out using four Manta-2 multi-probes, which were placed on the three different habitats in order to determine their physical and chemical characteristics. The chemical-physical data obtained by the multi-probes was integrated by data obtained during underwater visual surveys along horizontal point intercept transects (PIT), which have been used to characterize biological features of the habitats.
III. Gaps of knowledge
3.1 The different habitats: Phyllophora crispa, Posidonia oceanica and hard-bottom
The first habitat investigated is the macroalgal community dominated by P. crispa, a Rhodophyta, red calcareous algae with leafy thallus. The ability to trap sediments is considered a structural component of algae (Kendrick, 1991; Piazzi e Cinelli, 2001) and their persistence is related to stressful habitats (Hay, 1981; Airoldi, 1995; Piazzi et al., 200). The presence of P. crispa mats along a vertical cliff on the Giglio Island can be defined as monodominant (Petrella et al., 2005). It is a moderately common species of red algae that grows in schiaphilous habitats (Meichik et al., 2011). In the Tuscan archipelago, P. crispa has also been reported at 52 and 55 m depth (Piazzi et al., 2002).
P. crispa is characterized by flat and red fronds with an average heigh of 10 cm, rarely
15 cm, and branched irregularly foliar laminae and each new thallus is often connected to the old one by a short base (Newrot, 1971). Algal mats are the dominant forms that grow in shallow rocky habitats and are commonly found in intertidal, tropical and temperate rocky reefs (Hay, 1981; Hackney et al. 1989; Kendrick, 1991; Wallenstein et
al. 2009).
The species has an attached form which grows on hard substrates and an unattached shape which grows on sandy sediments (Kalaungina-Gutnik, 1975). The vegetative reproduction year-round and asexual from Dicember through March (Milchakova, 2011).
P. crispa reproduces predominantly sexually producing tetrasporangia and releasing the
spores into the water column in the cold season (Kalungina-Gutnik, 1975). Laboratory experiments with the unattached form of P.crispa have shown that it has the ability to form a thick protective cuticle and pause its development for up to two years, and then resume growth once it settles in water with optimal light and temperature (Kalugina-Gutnik, 1975). The dispersion ranges of red macroalgal species are not well studied (Kain et al., 1990; Lindstrom, 2001). The dispersion of macroalgal propagules and spores is rather limited and is strongly influenced by hydrographic conditions at the time of the release of propagules and their physical and morphological properties (Santelices, 1990).
considered of great importance as an ecosystem engineer and a key-species for biodiversity, evidenced in the Black Sea by Zaitsev (2008). It is the dominant red algae of these coasts and is able to develop algal complexes forming large mats in the rocky shore, as at the Giglio Island (Bavestrello et al., 2000). A research conducted at the Giglio Island in 2010, immediately after the Costa Concordia sinking, detected a 100% coverage of the bottom by P. crispa at 35 m, 80% and 90% at 25 m, 25% and 30% at 15 m and 15% at a depth of 10 m; the thallus were heavily encrusted with bryozoans and fleshy or calcified algae (Bonifazi et al., 2017).
Macro-vegetation habitats play important roles in the coastal areas as primary producers; they also strengthen the soils, provide food reserves, a place for laying eggs and shelter for fish and invertebrates (Meichik et al, 2011). Macroalgae typically develop in nutrient-poor waters, but when the availability of nutrients increases macroalgae and phytoplankton become dominant and develop large mats (Sand-Jersen et al., 1991). Such mats can greatly influence biotic interactions between oxygen and nutrient dynamics in shallow coastal areas (Sfriso et al., 1987; Valiela et al., 1992).
The second habitat investigated is covered by a marine plant, the P. oceanica phanerogam. It can produce leaves up to 1 m long and it develops large meadows on the sandy substrate, but can also grow on debris or rock to form an irregular and patched meadow due to reduced space. The P. oceanica meadows are able to create very complex habitats from a few centimeters below the sea level. The maximum depth reached by the
P. oceanica meadows depends on the transparency of the waters. As the marine plant is
a photophilic plant, the amount of light is an essential element for the extension of the meadows. It is also a stenohaline plant that needs almost constant salinity levels, which can range between 36%o-39%o and is able to produce a significant amount of oxygen
(Guido et al., 2003). P. oceanica differs from all the other marine phanerogams, above all in the size of the leaves and in the rhizomes characterized by the remains of the leaf bases, and because it is able to create a unique biogenic structure called "matte", which consists of the interweaving of rhizomes, roots and trapped sediment (Minelli et al., 2008). Within the meadows, which are not always homogeneous, other structures without plants such as the "intermatte" channels or the bare channel scan be recognized (Montefalcone, 2009), which are due to the hydrodynamic characteristics of the area. P.
oceanica produces flowers and fruits but, since this is a relatively rare event, asexual
reproduction by stolonization is the main process adopted to enlarge the meadow, through the division and growth of rhizomes (Minelli et al., 2008). The maximum leaf production is reached in spring and then, with the arrival of the autumn season, the old leaves fall. The youngest and shortest leaves are internal in the shoot, while the oldest leaves are external. After a life-cycle that varies from 5 to 12 months, leaves detach and are scattered on the seabed to increase the available detritus or the leaves are deposited on beaches forming the so-called "banquette" (Tortaro, 2014).
The last investigated habitat is made by a rocky substrate (hereafter referred to as hard-bottom), free from any kind of vegetation. This bare rocky substrate has been used as a control habitat since the chemical, physical and biological parameters are not affected by the presence of vegetation.
3.2 The role of oxygen in both environments
Rising seawater temperature affects the ability of organisms to obtain sufficient oxygen, as warmer temperatures increase the amount of oxygen required for metabolism. In addition, the solubility of oxygen in seawater decreases with increasing temperature. In quantitative terms, dissolved oxygen is a function of temperature and salinity (Nilsson et
al., 2010).
During the day, algae are able to assimilate the nutrients available at the top of the mats (where the leaves are placed) and effectively intercept the flow of benthic nutrients such as NO! and PO"!#(Krause-Jensen et al, 1999). Oxygen and nutrient concentration profiles
vary depending on algal activity and water turbulence. High surface irradiation stimulates photosynthesis and oxygen release in the surface layers of the algal mats and the turbulence of water introduces oxygen into the inner layers (Lavery et al., 1991).
The concentrations of oxygen and nutrients within dense macroalgal mats and the productivity change on a day scale. During the day, oxygen saturation can exceed 200% in the upper layers of the mats and decrease towards the lower layers due to the limitation of light and therefore of photosynthesis (Lavery et al., 1991). The concentrations of oxygen are significantly decreased during the night and the lower layers of mats can
become hypoxic. The vertical distribution of production and respiration within mats is also reflected in steep vertical gradients of nutrient concentrations (Krause-Jensen et al, 1999).
The oxygen content in coastal waters is determined by the current flow-intrusion of offshore oxygen-rich waters, environmental conditions and the production of oxygen from marine plants that, with photosynthesis, enrich the sea.
Nevertheless, an oxygen distribution inside the organism tissues is known. Oxygen produced in the leaves by photosynthesis is transported to the roots and then distributed to all the tissues, allowing the aerobic metabolism of underground structures. The presence of an aerenchyma is an adaptation to allow algae to grow into anoxic sediments typical of algal environments (Pedersen et al, 2005). During the night, oxygen is rapidly consumed in the plant tissue through respiration and lost by the outflow of radical oxygen from the roots (Borum et al., 2007). A low oxygen content in the water column can cause internal oxygen stress in the plant and the hypoxia of the water column will eventually lead to an anoxic tissue (Greve et al., 2003).
Laboratory experiments have shown that in the darkness the pO2 in the rhizomes of a marine phanerogam, Cymodocea nodosa (Ucria) Ascherson 1870, increases with the increase of water flow. The speed of flow is important for tissue aeration as rhizomes would become anoxic when the water column is at 20% air saturation in running water and in stagnant water at 30% air saturation (Binzer et al., 2005). Compared to leaves, roots and rhizomes may be subject to oxygen deprivation for shorter periods, but these underground tissues have a physiological adaptation that allow them to temporarily rely on anaerobic fermentative metabolism (Pregnall et al., 1984). In addition to the importance of internal oxygen in plant tissues, maintaining oxygenated conditions around the roots can provide effective protection against toxic compounds and metal ions from surrounding sediment (Armstrong et al., 1990). There are many benefits in maintaining a rich supply of oxygen to all tissues including roots and rhizomes (Pedersen et al., 1998). DO is necessary for all aquatic life in the water column, as well as for benthic organisms on the bottom. DO deficiency can lead to mortality of life forms, influencing the chemical and biological processes in the ecosystem. Studies have shown that sensitive invertebrate (and fish) species are adversely affected when DO levels drop below 5 mg/l (Xu, 2016).
DO is considered an environmental indicator currently adopted to classify the water quality, as requested by legislation and promoted by the Italian Ministry of the Environment and by the Ligurian Region Agency for Environmental Protection (Coppo, ARPAL). Many of the methods presented by the Marine Strategy Framework Directive (MSFD) include metrics assessing eutrophication indicators. Human-induced eutrophication creates negative effects in the environment, such as biodiversity losses, ecosystem degradation, harmful algae blooms and oxygen deficiency in bottom water. The concentration of DO is one of items of physical-chemical quality to be evaluated for the ecological classification of coastal waters. Some guidelines are provided for assessing the eutrophication status of a system (EC, 2009). OSPAR (Oslo/Paris Convention for the Protection of the Marine Environment of the North-East Atlantic) (2005;2009) has established a specific EcoQO (Ecological Quality Objective): oxygen concentrations, decreased as a direct effect of nutrient enrichment, must remain above area-specific oxygen assessment levels, ranging from 4 to 6 mg oxygen per liter (Ferreira et al., 2011).
3.3 Comparison of Phyllophora crispa and Cystoseira spp. as ecosystem engineers
Some of the key services provided by marine ecosystems, such as primary production, nutrient cycle and fishing, depend on the abundance, spatial distribution and the structure of algal communities (Loreau, 2000). Algae hide animals from predators, provide food supply, build complex 3D structures with increasing niche availability, and their presence is influenced by the physical characteristics of the environment, such as wave exposure and currents (Urypova et al., 2012). The ecosystem engineering concept focuses on how organisms physically change the abiotic environment. This concept depends on the spatial, temporal and organizational scale of the species. All species interact to some extent with other species through the physical environment, the extent of these interactions and its consequences vary from species to species and depend on the environmental context. The ecosystem engineering concept is particularly promising in the area of ecological applications, where influence over abiotic variables and their consequent effects on biotic communities may facilitate ecological restoration and counterbalance anthropogenic influences (Hastings et al., 2007).
The communities of P. crispa and Cystoseira spp. were widely studied in the Black Sea where the two algae have similar ecological activity. Both the macroalgae constitute a benthic autotrophic link in the ecosystem and take part in the production process. P.
crispa and Cystoseria spp. have been defined as bioindicator elements because, in the
event of intensification of processes such as eutrophication, they are the first component to disappear from algae communities as they are not able to compete with the small filamentous algal species (Minicheva et al., 2007).
The thalli of P. crispa, like those of Cystoseira spp., are inhabited by many species of epifauna and the decline of the algae causes the loss of sessile organisms that use the thallus to grow and live (Simakova et al., 2009). As a result of the eutrophication processes over the past 40 years in the Black Sea, communities of P. crispa have undergone similar changes to the communities of Cystoseira spp. in the 1970s and the 1980s (Minicheva et al., 2007). Other studies carried out in the Black Sea have found P.
crispa together with Cystoseira spp., forming a mixed community in the central area
(contribute to this community other dominant algae such as Corallina elongata, Codium
vermilaria, Cladostephus spongiosus) (Simakova et al., 2009).
As the depth increases the community structure changes regularly. The seabed features and relief influence the distribution of algae in the Black Sea; specifically, the hard-bottom on which P. crispa settles and the complexity of its epibionts were studied by Rybnikov. The non-uniformity of the distribution of algae is mainly caused by the uneven distribution of abiotic factors in the relief. The speed of the currents also varies between the ridges of P. crispa and the bases of the thalli (Rybnikov, 1997). The results of the project entitled "Influence of the Sea Bottom Relief on the Cystoseira Communities of the North Caucasian Coast of the Black Sea" show that depth is much less important to the structure of the algae communities than the location of the relief. At the same depth there are two different communities, the community dominated by Cystoseira crinita and
Cystoseira barbata, and a community with P. crispa which differ in both composition
3.4 The multi-probe Manta-2
Manta-2 is a multi-probe marketed by Eureka Water Probes, which since 2002 has been involved in the construction of multi-probes with the aim of helping to protect the world's water resources. Beyond date and time, the probe acquires several chemical and physical parameters including conductivity (μS/cm), temperature (°C), salinity (PSS), dissolved oxygen (DO) (mg/l and O2 %), chlorophyll (μg/l), pH, depth (m) and turbidity (NTU), or other parameters through the addition of other sensors. The kit provided by Eureka includes an underwater cable to connect the Manta-2 to a computer, a storage/calibration cup (that with the lid screwed on the cup has to contain a few ounces of tap water to keep the sensor moist), a Maintenance kit (which contains spare DO membranes and DO electrolyte), and Manta control Software (which allows you to connect the Manta directly to a computer through a USB port and view data, set up logging files and calibrate the instrument). The basic element is a sensor and each sensor detects one or more parameters. For instance, the measure of temperature can be recorded using a thermistor in °F and °C units of measure; conductivity sensor is one sensor with four parameters that can be read as specific conductance μS/cm, specific conductance mS/cm, total dissolves solids mg/l and salinity (Fig. 1).
The parameters considered in this thesis are salinity, temperature and DO expressed as mg/l and O2%.
Figure 1– 1) Manta-2 from the Eureka
association: it has been modified with the addition of weights to increase stability in the four spots. 2) The sensors.
Dissolved oxygen
Manta-2is used for detecting algae and seagrass respiration: the sensor can report exaggerated swings in diurnal oxygen pressure because mats of algae and seagrass meadows have their own micro-environment of oxygen pressure.
Temperature
Fig. 3 - Temperature's sensor.
The DO is measured with an optical sensor that comprises a blue light source and a red-light receiver. The sensing surface is an oxygen-active compound stabilized in an oxygen-permeable polymer using silicone. When the sensing surface is exposed to water (or any other means) oxygen diffuses into the sensing surface according to the amount (partial pressure) of oxygen in the water. The oxygen-active compound fluoresces absorbs energy in the form of blue light and then emits energy as red light. In each measurement cycle, the blue light is first turned on and then turned off. The red-light receiver measurers the time it takes after the blue light is turned off for the fluorescence to die off. This value is proportional to DO (Fig. 2).
Fig. 2 – DO’s sensor.
The temperature sensor is an electrical resistor whose resistance changes predictably with temperature. The sensor is protected by a stainless-steel tube. Thermistors are very stable and do not require calibration (Fig. 3).
Salinity
Fig. 4 - Conductivity's sensor.
In 2017, the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) carried out a laboratory and field assessment of the water quality probe. Tests were carried out in the project to confirm chemical standards and the accuracy of the probe was confirmed by additional instrumentation (Tillman et al., 1978). The USGS organization tested the specific conductivity sensors in a test chamber at four controlled temperatures of 1, 15, 25 and 40 °C and using a potassium chloride (KCL) solution as a test standard. The Manta-2 sensors were part of the NFM (National Field Manual) recommendations and manufacturer specifications in all four temperatures examined (Radke et al., 2005). The National Field Manual for the Collection of Water-Quality data provides documented methods and protocols for USGS field personnel who collect water-quality data. The NFM provides detailed and citable procedures for sampling water resources, processing samples for analysis of water quality, measuring field parameter sand specialized procedures.
The DO sensor was tested in water with multiple concentrations of DO corresponding to 100%, 24% and 0% of atmospheric saturation at varying temperatures. For 100% and 24% air saturation, it was created in a ministand tube, a test chamber at four different water temperatures using an aquarium pump and airstone to saturate the water with air. The 0% DO saturation solution was created by putting the probes in a ministand tube and seal it with parafilm. Before each measurement, an airstone was used to scatter water with
Manta-2 uses the four-electrode method to determine water conductivity. A constant voltage is applied to one of each electrode pair and the amount of current required to maintain that voltage is measured. As the conductivity of the water increases the current increases. Manta-2 reports specific conductance standardized to 25°C. Conductivity has several other forms like total dissolved solids (TDS) and salinity like Practical Salinity Scale (PSS) (Fig. 4).
dry ozone gas for at least one hour. The DO sensor was also part of the NFM recommendations at all concentrations examined.
3.5 Previous uses of Manta-2
This particular multi-probe is mainly used to test water qualities, for monitoring and it is designed for surface water and groundwater applications (Tillman et al., 1978).
The Manta-2 has been used for various scientific researches. For example, at Dongua University in Shanghai (China), the multi-probe was used to verify the influences of the seasons through the relationships of N/P and chemical compounds on phosphate removal performance in algal ponds. The Manta-2 multi-probe has been used in this research to capture daytime lighting intensity values using a StacSense-specific probe, an optical digital sensor that measures biological parameters (Zhimiao et al., 2016).
In another research project, conducted in the coastal waters of Sindh (Pakistan), the multi-probe was used for the characterization of macroalgal communities following the influence of reversal monsoons. After the cyclone “Nilofar” they observed a slowdown in the growth of Sargassum species and changes in the structure of the community. The text registered 36 total species (12 of which were Rhodophyta) and the Manta-2 was used for the acquisition of the physical-organic parameters (temperatures, pH, DO chlorophyll) of those seafloors. (Ali et al., 2017).
Due to water pollution and eutrophication in the East Lake in Wuhan, China, an Autonomous Underwater Robotic Fish (AURF) was developed in 2012 with the aim of monitoring water quality in entire lakes and in fixed locations. The multi-probe Manta 2.2.5 has been fixed in the lower part of the AURF which communicates through a specific connection to record the various parameters (Fig. 5).
Figure 5 - Prototype of the AURF and its skeleton.
The autonomous underwater robotic fish sends commands consisting of strings of alphanumeric characters for the operation of Manta-2 and receives the water quality parameters and information on the sensors installed (Yu et al., 2012).
At Shanghai Maritime University, in Shanghai, the multi-probe was used for the treatment of water, such as ponds with microalgae which possess the ability to grow in this type of water and have a high photosynthetic efficiency. They can increase pH and DO through photosynthesis. Manta-2 was used to record algae concentrations in the influent and effluent of the wastewater (Ding et al., 2018).
In this thesis the acquisition and analysis of the chemical-physical parameters detected by the multi-probe Manta-2 were used to define the three different habitats investigated.
IV. Materials and methods
4.1 Study area
The study was conducted at the Giglio Island, an Italian municipality located in the Tuscan Archipelago 18 km from the Argentario promontory. The east coast of the Giglio Island, where Porto Giglio is located, overlooks the mainland. The coast is protected from sea storms created by the southwest Libeccio winds. More frequent in the area of the Tuscan Archipelago are the sea storms of the southeast of the Scirocco winds, whose waves are attenuated by the effects of refraction and coastal drift (Brandini et al., 2017).Based on morphological and structural characteristics, the island can be divided into two distinct areas: the North area is characterized by a large and regular continental shelf crossed by some deep neogenic basins (Mariani et al., 1988), which connect the side of Tuscany with the deepest morphology of the Ligurian Sea, resulting from the rifting and oceanization processes of the western Mediterranean in the Miocene (Mauffret, 1981); the South area has a more articulated morphology dominated by the Corsican basin and the Dorsal Pianosa (Zitellini et al., 1986). The period between July and September is generally characterized by high water temperature that varies on average from 24 to 26
°C, while in October it is lowered by a few degrees remaining stable at 21 °C and then decreases in the winter months, when the water temperature is expected between 16 and 18 °C (data from www.Giglioinfo.it). Field activities were carried out at the Punta del Morto underwater spot, considered to be one of the best sites on the island for the quality of the seabed and for touristic interest. It is located in the northern part of the island, not far from the Punta di Fenaio, in a north-eastern direction (Fig. 6).
Figure 5 - Map of the Giglio Island with diving spots. Punta del Morto is showed in the corresponding aerial image.
4.2 Field activities
Field activities by scuba diving were carried out from 4 September to 22 October 2019, totalising 21 dives in about 8 weeks. The habitat of red algae is deep so that four deep stations were chosen in Punta del Morto. Three stations were located at 28 m depth: 1) the P. oceanica meadow; 2) the mats of P. crispa; and 3) the hard-bottom. Another station at 31 m depth has been located with mats of P. crispa. The Manta-2 multi-probe data has been integrated with the data collected during visual surveys through the use of the Point
Intercept Transects (PIT), and also with photographic samplings and the use of Gypsum balls, which are spheres used for the measurement of the current flow in the four stations. The technology used to acquire chemical and physical parameters was provided by the University of Bremen (Germany) and the Institute für Marine Biologie at the Giglio Island, which is directed by Dr Jenny Tuček (PhD).
4.3 Point Intercept Transect (PIT) for the characterization of the area
For the characterization of the environment, six PIT transects were carried out parallel to the coast at a constant depth (Fig. 8), minimizing environmental variability, which allowed the study of the quality-quantitative composition of the specific populations (Loya, 1978). Another transect was carried out in the hard-bottom station, which was laid perpendicularly to the coast (i.e., a depth transect) at depths between 28 m and 31 m (Fig. 8). To perform the Point Intercept Transect a fiberglass measuring tape, a depth gauge, a compass and a white slate were used (Fig. 7). This method allowed for the identification of the substrate at predetermined distance points along the tape (Bianchi et al., 1991; Bianchi et al., 2003).
The PIT was from 5 to 7 m long. At each meter of the tape the data was acquired based on the following parameters:
• Substrate typology: bare rock, sand, vegetated bottom
• Algae or seagrass: P. crispa, other red algae, green algae, brown algae, P. oceanica • Sessile organisms: Cnidarians, Bryozoans, Tunicates, others.
Figure 6 – Tools used for the PIT transects: a fiberglass measuring tape and a white slate.
In Figure 8 the sampling area of Punta del Morto is showed. The figure shows the four stations viewed from above and the related transects performed (PITs and depth transect). In the PIT transect n°3 it was possible to survey all the three habitats, as it is visible in Figure 9.
Figure 7 - View from above of the transects placed at Punta del Morto.
From the data recorded along the PITs, the frequency in percentage of each species and each substrate typology was computed in all the 7 transects.
The Fig. 10 shows the hard-bottom station. In this spot (transect n° 5) was made a deep transect, perpendicular to the coast.
Figure 8 – Transect n°3 at Punta del Morto where the three habitats are visible: 1° spot with hard-bottom, 2° spot with P. oceanica, 3° spot with P. crispa.
Figure 9–The deepest PIT transect n°5. The picture on the left shows the beginning of the transect at a depth of 28 m characterized by hard bottom. As the depth increases, the presence of algae, including P. crispa, increases. The picture on the right shows the end of the transect, which ends at 31 m depth with the presence of coarse sand.
4.4 Data acquisition with the multi-probe Manta-2
The four Manta-2 were modified with the addition of weights at the bottom of the instrument to increase stability on the under-investigated substrates. Once in the water they acquired the signal every minute for two to three consecutive days in the 4 selected stations.
Figure 11- Manta-2 in the P. crispa mats.
The Manta-2 has been placed on the mats of the red algae P.
crispa at 28 m and at 31 m depth
(Fig. 11). The sensors are positioned at the base of the
multi-probe and acquired
chemical and physical
parameters from the algae fronds.
Figure 12 - Manta-2 in the hard-bottom habitat.
Figure 13 - Manta-2 in the P. oceanica meadow.
The Manta-2 has been also positioned on the hard-bottom at 28 m depth (Fig. 12). This station was used as a control habitat.
The Manta-2 has been located also in the meadow of P. oceanica at 28 m depth (Fig. 13). As the sensors in the multi-probe are placed on the bottom, the acquired data refers to the rhizoid part of the plant, which extends for several cm, according to the growth of the plant (Minelli
Subsequently, the data recorded by the sensors was processed through the Excel program. The following graphs refer to the chemical-physical fluctuations that occur in the stations. To derive daily cycles, the values obtained were averaged at every minute of the day. Subsequently, the daily cycles were separated according today/night hours, divided into weeks and months. The values of the daily and monthly graphs the data were averaged every 6 hours for 24 hours, while the graphs of the weekly cycles were averaged every 10 minutes for 24 hours
4.5 Current data acquisition
Simultaneously with the physical and chemical parameters acquisition by the multi-probes, data on current intensity in correspondence of the red algal mats, the P. oceanica meadow and the hard-bottom was acquired in order to obtain information on the different types of environments in the three habitats. Current intensity data was obtained using Gypsum balls ("clod cards") within the habitats at regular distances from the bottom (Fig. 14). A structure was created to maintain predefined distances (Fig. 15): at the bottom, 2 cm from the bottom, 5 cm from the bottom and at a distance greater than the length of P.
crispa thallus, to record current intensity outside the mats. The Gypsum balls were placed
at regular distances within the stations and remained in the water for a week. The weight loss of the clod cards during deployment provides an estimate of the current intensity.
Figure 14 – Manta-2 and Gypsum balls placed in the P. crispa mats.
Figure 15 - Gypsum balls structure.
V. Results
5.1 Transect results Table1 - Descriptive analysis
Average (cm) 5.48
Maximum (cm) 7
Minimum (cm) 2
Standard Deviation 1.46
S. E. 0.48
Counting point (u) 25
The box-plot graph below (Fig. 16) shows the height of P. crispa at the beginning, in the middle portion and at the end of the seven transects performed. The height of this algae does not change in the three portions of the transects.
P. crispa is the most frequent species (Fig. 17) and occupies most of the substrate. The
occurrence of other green algae, such as Codium bursa and Caulerpa cylindracea, was also detected. These algae are usually located at shallower depths of about 20 m (Petrella
et al., 2005).
BEGINNING MIDDLE END
He ig ht o f Phyl lo pho ra cr is pa (c m ) 0 1 2 3 4 5 6 7
The mean height of Phyllophora crispa, in the seven visited transects, is 5.48±1.46 cm (± SD). The minimum height value is 2 cm and the maximum value is 7 cm, both values being found in the transect 7 (Tab. 1).
A standard error of 0.48 confirms the high variability of this parameter.
Figure 16 – Height of the thallus of P. crispa detected in all the 7 transects.
P. oceanica was detected only in the transect 3, which connected all sample areas at a
depth of 28 m.
For each transect, three-way graphs were realised showing on the left side the depth of the transect (m), on the upper side the length of the transect (m), and on the right of the graphs the height of the mats of P. crispa measured at the beginning of the transect, in the middle portion and at the end. The slope of the bottom is also represented (Fig. 18). The distribution of P. crispa is homogeneous in all the transects performed at Punta del Morto, the same is true also for the height of the thallus and for the slope of the bottom.
Fr eq uen cy (% ) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Hard Bottom P. crispa Codium bursa Caulerpa cylindracea P. oceanica
Figure 17 - Frequency % of all the species of
algae and
seagrass found in all the transects performed.
Figure 18 - Three-way graphs of the parameters measured in all the 7 transects. The black line represents the slope of the bottom.
5.2 Comparison of Phyllophora crispa mats at 28 m and 31 m of depth
Two Manta-2 out of the four available were placed on the P. crispa mats at 28 m and 31 m depth to check if the two stations had any differences in the parameters according to depth. The data were subsequently divided in September and October (Fig. 19).
Figure 109 - Graphs of the DO (mg/l) in the two months of sampling in the mats of P. crispa at 28 m depth. The mats of P. crispa at 28 m depth in September showed a mean DO of 8.19±0.16 mg/l (±SD). The minimum value of 7.78 mg/l was recorded at 1:08, while the maximum value of 8.56 mg/l was measured at 4:22. During the night the DO values are lower with a mean value of 8.07±0.10 mg/l (±SD), while during the day the mean value was 8.30±0.13 mg/l (±SD).
The month of October was characterized by lower DO values than in September. The P.
crispa mats at 28 m depth had a daily average of 7.49±0.22 mg/l (±DS). The minimum value measured was 7.00 mg/l recorded at 8:06, while the maximum value of 7.96 mg/l was measured at 00:46.
The graphs in Figure 20 and 21 show the trend of the DO throughout the day at 28 m depth in September and October.
As the DO value increases in the morning and decreases in the evening, scatter charts have been complemented by polynomial trendlines to verify the significance of the results. The trend of DO in the month of September shows a significant increase (r=0.95, p<0.05) in central hours, where the insolation is maximum at noon, and then decreases
September October Ox yg en (m g/ l) 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5
towards the evening. Oxygen values in the month of October are lower in conjunction with the solar intensity trend which results in lower intensity and distributed more evenly throughout the day (r=0.88, p<0.05).
Figure 20 - Daily cycle (24h) of the DO (mg/l) measured in the mats of P. crispa at 28 m depth in September.
Figure 21 - Daily cycle (24h) of the DO (mg/l) measured in the mats of P. crispa at 28 m depth in October. The mean value recorded by the Manta-2 multi-probe in the P. crispa mat at 31 m depth in September is 8.16±0.1 mg/l (±SD) (Fig. 22). The minimum value is 7.96 mg/l measured at 18:28 in the late afternoon, while the maximum value of 8.46 mg/l was recorded at 15:37. The mean value measured in October in the same station was lower
y = -1,5294x2+ 1,4286x + 7,9879 R² = 0,8972 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5 00:00 02:24 04:48 07:12 09:36 12:00 14:24 16:48 19:12 Ox yg en (m g/ l)
Daily time (every 6 hours for 24h)
Daily cycle of P. crispa at 28 m of depth in September
y = 2,0155x2- 1,5964x + 7,65 R² = 0,7889 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5 00:00 02:24 04:48 07:12 09:36 12:00 14:24 16:48 19:12 Ox yg en (m g/ l)
Day time (every 6 hours for 24 h )
than in September and was 7.26±0.44 mg/l (±SD). The minimum value was recorded at 19:34 and is 6.86 mg/l and the maximum value of 8.58 mg/l at 20:45.
Figure 22 - Graphs of the DO (mg/l) in the two months of sampling, in the mats of P. crispa at 31m depth. The graphs in Figure 23 and 24 show the trend of the DO throughout the day at 31 m depth in September and October. Some values were not recorded at the P. crispa station at 31 m deep in October. This missing data was due to technical problems found in the placement of the Manta logger, as it fell down due to the current intensity and/or did not record the data. The data recorded was less than half-day, 622 minutes of the total 1440 minutes in a day. The development of DO of P. crispa at a depth of 31 m in the two months of sampling is similar to the development at 28 m depth.
The trend of DO in the month of September (r=0.75, p<0.05) increases at noon and decreases in the evening hours, the insolation is slightly lower than at the P. crispa station at 28 m due to the greater depth of the station. The value from October (r=1, p<0.05) could depend on a smaller total number of acquired values than other stations.
September October Ox yg en (m g/ l) 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5
Figure 23 - Daily cycle (24h) of DO (mg/l) measured in the mats of P. crispa at 31 m depth in September.
Figure 24 - Daily cycle (24h) of DO (mg/l) measured in the mats of P. crispa at 31 m depth in October. In conclusion, no significant differences were found in the averages and trends of DO at the two depths in the P. crispa mats. Therefore, the 28 m depth values will be used for comparison with the other two studied habitats (the hard-bottom and the station P.
oceanica), given that their depths were both 28 m.
y = -0,912x2+ 0,7588x + 8,0741 R² = 0,5648 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5 00:00 02:24 04:48 07:12 09:36 12:00 14:24 16:48 19:12 Ox yg en (m g/ l)
Day time (every 6 hours for 24 h)
Daily cycle of P. crispa at 31 m of depth in September
y = 5,0784x2- 4,4034x + 8,0254 R² = 1 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5 00:00 02:24 04:48 07:12 09:36 12:00 14:24 16:48 19:12 Ox yg en (m g/ l)
Day time (every 6 hours for 24 h)
5.3 Data on currents to differentiate habitats
Data from the Gypsum balls placed in the mats of P. crispa, in the meadow of P. oceanica and in the hard-bottom stations are reported in Figure 25. The current intensity was related to the height of the clod card (0 cm, 2 cm, 5 cm from the bottom and outside the mats of
P. crispa and the meadow of P. oceanica) and the graphs shows the % of weight loss per
day.
To verify the hypothesis that the current speed is different at the three stations, a one-way ANOVA was performed with a significance level a=0.05 for each station and for each of the Gypsum balls heights from the bottom (Tab. 2).
The current intensity is
significantly lower inside the red algal mats than outside. In the P.
oceanica meadow the % of weight
loss does not change at the different heights above the bottom. This is due to the placement of the Gypsum balls within the rhizoidal portion of the plant, where there is less water exchange as typically happens in a closed environment. The last graph refers to the hard-bottom, which is an open environment and where no differences in the weight loss at 0 cm, 2 cm, 5 cm and 20 cm from the bottom have been recorded. Figure 25 - Graphs of the mean loss (±SD) of the weight of the
Gypsum balls at the P. crispa mats, P. oceanica meadow and hard-bottom.
Table 2 - One-way ANOVA
High of the Gypsum balls from the bottom
(cm) P. crispa vs P. oceanica P. crispa vs hard-bottom P. oceanica vs Hard-bottom 0 0.782 0.331 0.404 1 0.576 0.0981 0.290 5 0.194 0.489 0.383 20 0.000 0.299 0.303
One-way ANOVA results show that there are no significant differences in current speed at the three stations since all values have a p-value>a, so the current flow within the P.
crispa, P. oceanica, and hard-bottom is similar.
The only exception is visible in the comparison of P. crispa with P. oceanica with the clod cards height of 20 cm from the bottom: this difference is due to the placement of gypsum balls outside the mats of P. crispa (since the thallus is maximum high 10-15 cm) and therefore exposed to a greater current and inside the meadow of P. oceanica, where the current flow is reduced due to the presence of leaves (P. oceanica can reach a height of 1 m).
5.4 Dissolved oxygen in P. crispa mats and in P. oceanica meadow
The Manta-2 multi-probe acquired the DO data in both mg/l and O2%. The following
graphs (Fig. 26 and Fig. 27) analyse the differences detected by the multi-probe in two units of measurement.
The DO expressed in O2% has a mean value of 103.97±1.53 O2% (±SD) for the P. crispa
mat, while the mean value for the seagrass meadow is 102.86±1.52 O2% (±SD). The
values are supersaturated, there is no possibility of anoxia or hypoxia in the mats algae and also in the meadow of P. oceanica.
P. crispa P. oceanica Ox yg en (m g/ l) 6,70 6,90 7,10 7,30 7,50 7,70 7,90 8,10 8,30 8,50
Dissolved oxygen (mg/l)
P. crispa P. oceanica Ox yg en (% ) 100 101 102 103 104 105 106 107 108Dissolved oxygen (%)
Figure 26 - DO measured in mg/l in the stations of P. crispa and P. oceanica in both months of sampling. Figure 27 – DO measured in % in the stations of P. crispa and P. oceanica in both months of sampling.Since there are no significant differences between the trends of the two units of measurement detected by the multi-probe, in the following part of this thesis I will continue referring to the DO expressed as mg/l.
The differences of the DO in the two habitats are visible in the following graphs, which represent the daily cycles of September and October. The graphs were made by averaging data every six hours per day to represent DO trends.
While the DO trend detected in the P. crispa mat increases during daylight hours and decreases during the night hours (Fig. 20), in the P. oceanica meadow it is constant throughout the day (Fig. 28). This difference can be attributed to the placement of the Manta-2 in the rhizoidal portion of the meadow, which is the part of the plant that does not perform photosynthesis and it is also not exposed to intense water current.
Figure 28 - Daily cycle of DO mg/l in the P. oceanica meadow, detected in September.
The daily average of DO in P. oceanica meadow detected in September was 7.69±0.06
mg/l (±SD). The minimum value measured in September is 7.57 mg/l at 2:14, while the maximum value is 7.90 mg/l at 13:58. The trend of DO is significant (r=0.83, p<0.05), similar with the trend in the mats of P. crispa (Fig. 20).
The trend of the DO in mg/l in the P. oceanica meadow in October (r=0.73, p<0.05) is constant during the day , with a mean value of 7.46±0.06 mg/l (±SD). The minimum value
is 7.28 mg/l at 5:40 and the maximum value is 7.63 mg/l at 3:59 (Fig. 29).
y = -0,6497x2+ 0,482x + 7,6468 R² = 0,6939 7 7,2 7,4 7,6 7,8 8 8,2 8,4 00:00 02:24 04:48 07:12 09:36 12:00 14:24 16:48 19:12 Ox yg en (m g/ l)
Day time (every 6 hours for 24h)
Figure 29 - Daily cycle of DO (mg/l) in P. oceanica meadow detected in October.
The graph (Fig. 30) shows the DO measured in the three habitats during the two months of sampling. It can be noted that the values recorded in the P. oceanica meadow are lower than the that measured in the P. crispa mat and on the hard-bottom. The P. oceanica meadow has an average DO value of 7.69±0.06 mg/l (± SD). The P. crispa mat has an
y = 0,2169x2- 0,233x + 7,497 R² = 0,733 7 7,2 7,4 7,6 7,8 8 8,2 8,4 00:00 02:24 04:48 07:12 09:36 12:00 14:24 16:48 19:12 Ox yg en (m g/ l)
Day time (every 6 hours for 24 h)
Daily cycle of DO in P. oceanica meadow - October
P. crispa P. oceanica Hard bottom
Ox yg en (m g/ l) 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5
Dissolved oxygen
Figure 30 - Values of the DO (mg/l) in the twomonths of sampling in the three habitats.
average DO value of 7.81±0.11 mg/l (±SD). The hard-bottom has an average DO of 7.82±0.07 mg/l (±SD).
The graph in Figure 31, showing the daily DO cycles, helps to determine how the DO concentration changes in the three habitats from day to night.
Figure 31 – Average daily cycles of DO in P. crispa, P. oceanica and hard-bottom habitats in the two months of sampling.
To verify the hypothesis that DO values differ between September and October, a one-way ANOVA was performed with a significant level a= 0.05, at the stations of P. crispa,
P. oceanica and hard-bottom.
• Descriptive analysis and one-way ANOVA of P. crispa in September and October (Tab. 3).
Table 3 - Descriptive analysis and one-way ANOVA of P. crispa.
Groups Count Sum Average Variance
P. crispa September 1440 11792.20 8.19 0.03 P. crispa October 1440 10788.76 7.49 0.05 Source of Variation SS df MS F P-value F crit Between Groups 349.62 1 349.62 9158.46 0.000 3.84 Within Groups 109.87 2878 0.04 Total 459.49 2879 7,5 7,6 7,7 7,8 7,9 8 00:00 04:48 09:36 14:24 19:12 00:00 04:48 Ox yg en (m g/ l) Daytime (24 h) RA 28 m Posidonia oceanica HB P. crispa
DO concentrations in P. crispa differ significantly between September and October, with a p-value=0.000 (p-value<a).
• Descriptive analysis and one-way ANOVA of P. oceanica in September and October (Tab.4).
Table4 - Descriptive analysis and one-way ANOVA of P. oceanica.
Groups Count Sum Average Variance
P. oceanica S 1440 11066.991 7.685 0.004 P. oceanica O 1440 10738.175 7.457 0.003 Source of Variation SS df MS F P-value F crit Between Groups 37.54 1 37.54 10558.67 0.000 3.84 Within Groups 10.23 2878 0.004 Total 47.77 2879
DO concentrations in P. oceanica differ significantly between September and October, with a p-value=0.000 (p-value<a).
• Descriptive analysis and one-way ANOVA of hard-bottom in September and October (Tab.5).
Table5 - Descriptive analysis and one-way ANOVA of hard-bottom.
Groups Count Sum Average Variance
Hard-bottom S 1440 11264.87 7.82 0.01
Hard-bottom O 1440 10578.13 7.35 0.01
Source of
Variation SS df MS F P-value F crit
Between Groups 163.75 1 163.75 19609.19 0.000 3.84
Within Groups 24.03 2878 0.01
Total 187.79 2879
DO concentrations in hard-bottom station differ significantly between September and October, with a p-value=0.000 (p-value<a).
5.5 Weekly cycle of dissolved oxygen in the mats of Phyllophora crispa
The data acquired by the multi-probe in the P. crispa mats were divided into each week of September and October in order to verify changes in the DO in the mats during the months.
The data are related to the 1st, 2nd, 3rd week of September and the 2nd and the 3rd week of October. During the first week of September (Fig. 32) less data than in the following weeks were acquired. The mean DO value is 8.23±0.22 mg/l (±SD).
Figure 32 - DO (mg/l) detected in the P. crispa mats at the first week of September.
The mean DO value acquired during the second week of September (Fig. 33) is 8.16±0.01 mg/l (±SD). No significant trend was detected during the day.
6,7 7,2 7,7 8,2 8,7 00:00 04:48 09:36 14:24 19:12 00:00 04:48 Ox yg en (m g/ l) Daytime (24h)
1° week of September
Figure 33 - DO (mg/l) detected in the P. crispa mats at the second week of September.
The third week of September (Fig. 34) has a mean DO value of 8.16±0.07 mg/l (±D), and no significant trend was detected during the day.
Figure 34 - DO (mg/l) detected in the P. crispa mats at the third week of September.
The second week of October (Fig. 35) has a mean DO value of 7.61±0.01 mg/l (±SD), a pick between 19:00 and 21:00 but not a significant daily trend.
y = 0,1748x + 8,0766 R² = 0,1842 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5 00:00 04:48 09:36 14:24 19:12 00:00 04:48 Ox yg en (m g/ l) Daytime (24h)
2° week of September
y = -0,0246x + 8,1767 R² = 0,013 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5 00:00 04:48 09:36 14:24 19:12 00:00 04:48 Ox yg en (m g/ l) Daytime (24h)3° week of September
Figure 35 - DO (mg/l) detected in the P. crispa mat at the second week of October.
The mean value for the third week of October (Fig. 36) of DO is 6.89±0.01 mg/l (±SD),
and no significant trend was detected during the day.
Figure 36 - DO (mg/l) detected in the P. crispa mat at the third week of October.
The variations of DO visible in the mats of P. crispa in the second week of October probably depend on the wind-wave conditions of the Giglio Island. Thunderstorms occurred for a couple of days, while the equipment was positioned in Punta del Morto.
y = 0,3021x + 7,4623 R² = 0,0795 6,7 7,2 7,7 8,2 8,7 00:00 04:48 09:36 14:24 19:12 00:00 04:48 Oc yg en (m g/ l) Daytime (24h)
2° week of October
y = 0,2645x + 6,7133 R² = 0,1632 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5 00:00 04:48 09:36 14:24 19:12 00:00 04:48 Ox yg en (m g/ l) Daytime (mg/l)3° week of October
The trend of DO during the second week of September and the third of October do not vary throughout the day.
5.6 Daily cycle of dissolved oxygen in Phyllophora crispa mats
The following graphs show the average values of DO recorded during the night and the day in the two months of sampling. The DO measured in P. crispa was compared with the values measured in the control habitat, the hard-bottom, in order to determine any change in the DO trend due to photosynthetic activity.
The division between night and day was performed by averaging the sunrise and sunset times over the two months (Table 6).
Table 6 - Average of sunrise, sunset and total hours of light in September and October. Months Sunrise Sunset Total hours of light
September 06:53 19:30 12:36
October 07:26 19:39 11:12
According to Table 2, the DO values were divided between day and night of September and October in order to have an overall estimate of the trend of the parameter between the two phases of the day. In the month of September (Fig. 37) a mean DO value of 8.07±0.1 mg/l(±SD) was recorded during the night hours; daylight hours averaged 8.30±0.13 mg/l (±SD). The month of October has a mean DO value of 7.64±0.18 mg/l (±SD) at night and a mean value of 7.33±0.13 mg/l (±SD) recorded during daylight hours (see also data in table 3).
Figure 37 - Night and day DO detected in P. crispa in September (S) and October (O).
Table 7 - Descriptive analysis of night and day of September (S) and October (O) detected in the P. crispa mats.
Night/ Day Avarage Max Min Variance St. Dev. Units St. Error
NIGHT - S 8.07 8.30 7.78 0.01 0.1 682 0.004
DAY - S 8.30 8.56 7.97 0.02 0.13 758 0.005
NIGHT - O 7.64 7.96 7.18 0.03 0.18 767 0.006
DAY - O 7.33 7.67 7.00 0.02 0.13 673 0.005
In the hard-bottom habitat the average of DO between hours of darkness and hours of light in the two months of sampling have similar values (Fig. 38). The mean value of DO acquired in September is 7.79±0.07 mg/l (±SD) for night hours and 7.85±0.06 mg/l (±SD) for daylight hours. October has 7.36±0.01 mg/l (±SD) and 7.33±0.01 mg/l (±SD), respectively (see also table 8).
NIGHT - S DAY - S NIGHT- O DAY - O
Ox yg en (m g/ l) 6,5 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5
Figure 38 - Night and day DO detected in hard-bottom habitat in September (S) and October (O).
Table 8 - Descriptive analysis of DO at night and day of September (S) and October (O) detected in the hard-bottom habitat.
Night/ Day Avarage Max Min Variance St. Dev. Units St. Error
NIGHT - S 7.79 7.99 7.66 0.005 0.07 682 0.003
DAY - S 7.85 8.12 7.63 0.004 0.06 758 0.002
NIGHT - O 7.36 7.68 7.13 0.01 0.01 767 0.004
DAY - O 7.33 7.58 7.16 0.008 0.09 673 0.003
5.7 Temperature and salinity parameters
The Manta-2 multi-probe recorded simultaneously with the DO also other physical and chemical parameter (temperature and salinity).
The following graphs show the data acquired in the P. crispa mats, in P. oceanica meadow and in the hard-bottom habitat to compare if there is any difference of physical parameters between September and October in the stations. The water temperature in September is lower than in October. The mean value in the P. crispa habitat in September
NIGHT - S DAY - S NIGHT - O DAY - O
Ox yg en (m g/ l) 6,5 6,7 6,9 7,1 7,3 7,5 7,7 7,9 8,1 8,3 8,5
was 17.49±0.5 °C (±SD) (Fig. 39). October had a mean value of water temperature of 18.69±1.1 °C (±SD).
Figure 39 - Temperature parameter in the mats of P. crispa in September and October.
To confirm the hypothesis that the temperature is significantly different in the station of
P. crispa in September and October, a one-way ANOVA was performed with a level of
significance a=0.05 (Tab. 9).
Table 9 - Descriptive analysis and one-way ANOVA of temperature of P.crispa.
Groups Count Sum Average Variance
P. crispa S 1440 25188.77 17.49 0.25 P. crispa O 1440 26907.02 18.69 1.22 Source of Variation SS df MS F P-value F crit Between groups 1025,13 1 1025.13 1400.11 0.000 3.84 Within gropus 2107.21 2878 0.73 Totale 3132.34 2879
A p-value equal to 4.7277E-250 confirms that there are significant temperature differences between September and October.
The mean value in P. oceanica habitat (Fig. 40) in September was 18.31±0.36 °C (±SD). October had a mean value of 19.4±0.41 °C(±SD).
September October Tem per at ur e (° C) 16 16,5 17 17,5 18 18,5 19 19,5 20 20,5 21
Figure 40 - Temperature parameter in the mats of P. oceanica in September and October.
The one-way ANOVA test gave a p-value = 0.000 There are significant temperature differences between September and October in the P. oceanica station (Tab.10).
Table10 - Descriptive analysis of Temperature of P. oceanica.
Groups Count Sum Average Variance
P. oceanica S 1440 26360.26 18.31 0.16
P. oceanica O 1440 27933.92 19.40 0.13
Source of
Variation SS df MS F P-value F crit
Between Groups 859.86 1 859.86 5839.80 0.000 3.84
Within Groups 423.76 2878 0.15
Total 1283.62 2879
Since one of the four multi-probes used for the research did not have the temperature sensor, the data was obtained in the hard-bottom station only in September. The mean value of water temperature in the hard-bottom was 18.08±0.52 °C (±SD) (Fig. 41).
September October Tem per at ur e (° C) 15 16 17 18 19 20 21
Figure 41 - Temperature parameter in the hard-bottom station.
The Manta-2 in the P. crispa mats recorded a mean value of salinity of 38.3±0.05 PSU (±SD) (Fig. 42) in September; in October it recorded a mean value of 38.45±0.07 PSU(±SD).
Figure 42 - Salinity parameter in the mat of P. crispa.
The one-way ANOVA test obtained a p-value = 0.000 (p-value <a). there are significant salinity differences between September and October in the P. crispa station (Tab.11).
September Tem per at ur e (° C) 16 16,5 17 17,5 18 18,5 19 19,5 20 20,5 21
Temperature of Hard bottom (HB)
September October Sal in ity (PSU ) 37,5 38 38,5 39 39,5 40
Table 11 - Descriptive analysis and one-way ANOVA of salinity of P. crispa.
Groups Count Sum Average Variance
P. crispa S 1440 55147.605 38.297 0.003 P. crispa O 1433 55146.273 38.483 0.027 Source of Variation SS df MS F P-value F crit Between groups 24.89 1 24.89 1680.4 0.000 3.84 Within groups 42.52 2871 0.01 Total 67.41 2872
The mean value in P. oceanica habitat (Fig.43) in September was 38.3±0.02 (±SD). October had a mean value of 38.4±0.04 PSU (±SD).
Figure 43 - Salinity parameter in the mats of P. oceanica in September and October
The one-way ANOVA test gave a p-value = 0.000, there are significant salinity differences between September and October in the P. oceanica station (Tab.12).
Table12- Descriptive analysis and one-wat ANOVA of salinity in P. oceanica.
Groups Count Sum Average Variance
P. oceanica S 1440 55161.9 38.3 0.000 P. oceanica O 1440 55315.9 38.4 0.0018 September October Sal in ity (PSU ) 37,5 38 38,5 39 39,5 40
Source of Variation SS df MS F P-value F crit Between groups 8.24 1 8.24 7379.1 0.000 3.8 Within groups 3.22 2878 0.001 Total 11.46 2879
The mean value of salinity in the hard bottom (Fig. 44) in September is 38.28±0.04 PSU (±SD); in the month of October the recorded salinity has a mean value of 38.45±0.07 PSU (±SD).
Figure 44- Salinity parameter in the hard-bottom station.
The one-way ANOVA test gave a p-value = 0.000. There are significant salinity differences between September and October in the hard-bottom station (Tab.13).
Table13 - Descriptive analysis of salinity of hard-bottom.
Groups Count Sum Average Variance
Hard-bottom S 1440 55117.2 38.3 0.002
Hard-bottom O 1440 55361.8 38.4 0.004
Source of Variation SS df MS F P-value F crit
Between groups 20.78 1 20.78 6866.56 0.000 3.84 Within groups 8.71 2878 0.00 Total 29.48 2879 September October Sal in ity (PSU ) 37,5 38 38,5 39 39,5 40
