Ecological modeling based on benthic foraminifera Tupilipalem coast, southeast coast of India: Implications for ecological trajectory

Landsat 8 OLI/TIRS data of Tupilipalem coast demonstrations the periodic closure of lagoon mouth over a brief period affecting the ecological setting and the faunal diversity and distribution. The coastal ecosystem includes a significant metal pollutant due to anthropogenic activities. This study documents forty-nine numbers of benthic foraminiferal species and establishes its relationship with heavy metal concentrations (Fe, Mn, Cr, Cu, Ni, Pb, Zn and Cd). The factor loading matrix generated using factor analysis grades the positive and negative correlations among the heavy metals species. The results of geoaccumulation index, enrichment factor, and pollution load index, construes the seasonal differences in metal concentrations and prevalence of higher concentrations of metals in the sediments. Besides, this paper discusses the impact of coastal morphology on heavy metal concentrations coupled with ecology and distribution of benthic foraminifera.

The coastal ecosystem is under stress and cryptic degradation from pervasive exposure to various anthropogenic activities (Crain et al., 2009;Cochard, 2017). Over the last few decades, anthropogenic influx of heavy metals into the aquatic ecosystem has increased to alarming magnitude (Ansari et al., 2003;Gheorghe et al., 2017;Pandey & Singh, 2017;Cardoso et al., 2019;Reid et al., 2019). As a result, ecosystems have chronic inputs of metals thus contaminated sediments heavily (Qian et al., 2015;Soliman et al., 2015;Liu et al., 2020; 3 Tian et al., 2020).The consequence of the non-degradable state of heavy metals, causes for bioaccumulation in the food chains (Singh et al., 2011). This work aims to propose a model to understand the impact of coastal morphological changes on the lagoon mouth by affecting the concentrations of heavy metal in the sediments and taxonomic composition, quantitative distribution and test morphologies of benthic foraminifera. The ecological trajectory results are a reference of baseline datasets for further comparison of changes in costal morphodynamics, sediment biogeochemical properties and function as benchmark.

Study area
Tupilipalem is likely to be developed as a major sea port named as Dugarajapatnam Port by the Government of India (Fig. 1). The study area is geographically located on southeastern part of India, lying between latitude 14°0'10" -14°02'30" N and longitude 80°08'20"-80°19'00"E. Tupilipalem is over 120 km away from Pulicat Lake and the satellite launching center (SHAR) at Sriharikota. The Buckingham Canal is part of the lagoon on its western side. This lagoon (mouth) connects to the open Bay forms the study area. The subtropical climate prevails over the study area with an average annual rainfall of 1041 mm.
The average minimum and maximum temperatures are 20°C and 39.6°C respectively.
However, the currents are also affected by the tidal cycles, by the action of waves, the shore line geography and by the presence of different water masses, assuming predominantly a SW-NE direction (Sreenivasulu et al., 2017).

Coastal morphology
To detect the coastal morphodynamics, multi temporal satellite images of Landsat 8 OLI/TIRS data during monsoon and Pre-monsoon were downloaded from USGS, Earth Explorer (Sreenivasulu et al., 2017). Then each image was cropped using area of interest 4 cropping method. The cropped images were geometrically corrected by using the auto-sync tool in ERDAS Imagine 9.1 software by applying the UTM-WGS 84 projection and coordinate system (Chenthamilselvan et al., 2014). Wind components with a resolution of 0.125 o x 0.125 o were obtained from the European Centre Medium-range Weather Forecast (ECMWF). Wind speed and direction maps were plotted using Coastline+ and MATLAB (Satar et al., 2020).

Sample collection
Twenty six sediment samples were collected from 13 sampling stations during two seasons (monsoon and Pre-monsoon) by using a grab sampler. The position of the sampling stations was located using a Global Positioning System (GPS).

Geochemical Analysis
About 1g of dried sediment samples were digested (at 110°C for 90 min) with 14 ml of aquaregia solution (HNO3:HCl). After cooling, 14 ml of aqua-regia was added and heated again at 110°C for 30 minutes. The digested samples were filtered through 0.45 μm membrane and the concentrations of heavy metals (Fe, Mn, Cr, Cu, Ni, Pb, Zn and Cd) were measured by inductively coupled plasma-optical emission spectrometry (ICP-OES) (Trevizan & Nóbrega, 2007;He et al., 2017;Lai et al., 2018). To understand the existing environmental condition and the amount of heavy metal contamination with respect to the natural environment, other indices were applied like Geoaccumulation Index (Igeo), Enrichment Factor (EF) and Pollution Load Index (PLI) (Ahmed et al., 2016;Kowalska et al., 2018;Sreenivasulu et al., 2018;Shirani et al., 2020).
Geaccumulation Index (Igeo) is calculated using the following equation: Where Cn is the concentration of element 'n',Bn is the geochemical background value and 1.5 is a factor for potential variation in background data due to lithogenic effect.
The EF is calculated using the following equation

=
Where Mx is sediment sample concentration of the heavy metal, Fex is the Fe concentration in the sediment,Mb and Feb are their concentrations in a suitable background or baseline reference material.
The Pollution Load Index (PLI) is calculated as: Where n is the number of metals and contamination factor CF is given by CF = Cmetal/ Cbackground, (C metal is the corresponding metal concentration of the sample and C background metal concentration in the back ground)

Benthic foraminiferal analysis
For the benthic foraminiferal analysis, sediments were washed over a 0.625 mm mesh to obtain samples devoid of fine silt and clay. Then the residue was air dried and nearly 50 g of sample obtained by coning and quartering. All subsamples were examined under a Stereo Zoom Binocular Microscope and foraminifer tests from each sample were picked using a Windsor Newton Sable Hair brush ("000") and identified following the taxonomic classification of Brady (1884), Loeblich and Tappan (1987)  The Ammonia-Elphidium Index (AEI) described by Sen Gupta et al., (1996) was calculated as = + Where NA= Total number of Ammonia/ sample, NE= Total number of Elphidium/ sample.
The FORAM Index (FI) was calculated using the following formula: In which, FI = FORAM Index, Ps = Ns/ T, with "Ns" the number of symbiont-bearing foraminifers, Po = No/T, with "No" the number of opportunistic foraminifers and Ph = Nh/T, with "Nh" the number of other small, heterotrophic foraminifers, T= Total number of foraminifera.

Statistical analyses
Factor analysis (FA), which is the most common multivariate statistical method was used to reduce data and to extract a small number of latent factors for analyzing relationships among the observed variables by using XLSTAT 2018. Hierarchical cluster analysis (HCA) was used to estimate similarities in species composition among the samples. The data were logarithmically transformed to reduce the score and bias of higher values that may have otherwise masked the effect of lower values.

Coastal Morphology
Coastal morpho-dynamics are the marine, physical, meteorological and biological activities that interact with the sediments to produce a particular coastal environmental setting (Short & Jackson, 2013). The waves and currents play a significant role in controlling sediment migration and deposition (Sreenivasulu et al., 2016). Wind-generated wave energy input into the littoral zone and, together with wave-generated currents are responsible for the alteration of coastal morphology. Remote sensing multi-temporal Landsat 8 OLI/TIRS images acquired on October 27, 2014 (Monsoon) and August 27, 2015 (Pre-monsoon) revealed that sandbar across the lagoon mouth is vastly dynamic. In Fig. 2, during monsoon the lagoon mouth closure was noticed and the image during pre-monsoon has two opening points at the northern and the southern parts of the lagoon. Out of two, one is the natural opening at northern side and another is a non-natural at southern side. The non-natural one is 8 opened by the villagers for their fishing boats passage during lagoonal mouth closure.
Tupilipalem coast revealed that the rate of erosion and accretion reflects coastal dynamics and the loss or gain of sediments causes the formation of young beaches, berms, sand dunes and seacliffs depending on wave energy and littoral currents. Over the last five years (2011)(2012)(2013)(2014)(2015), accretion is geological process over the erosion, which deposits the sediments in the lagoon mouth, causing the closure of the lagoon (Sreenivasulu et al., 2017(Sreenivasulu et al., & 2018. In order to assess the factors influencing the morphological changes of lagoon mouth, wind speed and direction plots during the seven days prior to each satellite image were used. Thus, the wind strength is significant in large volume of sand transport. The prevailing northern wind causes an oblique wave approach to the shoreline, which generates a westward littoral transport ( Fig.   3). Strong winds blowing above 2-4 m/s are considered to be effective during monsoon and pre-monsoon. In conclusion, the strong onshore winds and large waves are the factors for the development of elevated water levels which allow the larger waves to transport sand to the shoreline and tend to deposit on the river mouth causing its closure. During pre-monsoon, the wind speed and direction has been changed ( Fig. 4) in order to facilitate the open of lagoon mouth.

Heavy Metals distribution
The variations of heavy metal concentrations (Fe, Mn, Cr, Cu, Ni, Pb, Zn and Cd) in different locations of Tupilipalem coast during monsoon and pre-monsoon are shown (Tables   1 and 2 Cd. These results exposed that greater variation was observed in Fe

Metal Enrichment Assessment
To understand the existing environmental condition and the amount of heavy metal contamination with respect to the natural environment, other approaches should also be applied. The anthropogenic contribution of the selected heavy metals in marine sediments can be estimated from the metal enrichment compare to the background levels. Various methods have been suggested for quantifying metal enrichment in surface sediments like Geoaccumulation Index (Igeo), Enrichment Factor (EF) and Pollution Load Index (PLI).
The enrichment factor (EF) is a suitable measure of geochemical trends and is used for making comparisons among areas. In the present study, iron was used as a conservative tracer to differentiate natural from anthropogenic components. According to Sutherland, and TP-9. Moreover, the remaining heavy metals (Fe and Mn) showed deficiency to minimal enrichment in all the stations (Fig. 6).
The values of the Pollution Load Index (PLI) varied from 0.04 to 2.83 during monsoon and 0.05 to 2.33 during premonsoon respectively (Fig. 7). The PLI values of the Cd showed higher (>1) values during both the seasons (monsoon and premonsoon) due to the influence of direct external sources like agricultural runoff, industrial activities, and other anthropogenic inputs. According to the GESAMP (1985), Cd comes from contaminated agricultural soils, mining waste, municipal sewage effluents and sludges and also derived from erosion of sulfide ores, phosporites, hydrothermally mineralized rocks and black shale deposits. The analyzed metals like Fe, Mn, Cr, Cu, Ni, Pb and Zn recorded baseline levels (<1). The difference in indices results due to the difference in sensitivity of these indices towards the sediment pollutants (Praveena et al., 2007).

Factor Analysis
The selected heavy metal data were subjected to factor analysis.  (Figs 8c and 8d).

Ecology and Distribution of benthic foraminifera
13 Spatial distribution of total benthic foraminifer tests in terms of absolute numbers is given in (Tables 4 & 5). A total of 49 species, 19 genera and 3 suborders have been recognized. In monsoon, total population ranges from 5 to 271 with an average of 57 counts and in premonsoon, from 5 to 744 with an average of 84 counts per 100 grams dry sediment.

Diversity Indices
The values of Species richness ( Fig. 9).

14
The AEI was formerly established as an indicator of hypoxic conditions (Sreenivasulu et al., 2019). Studies have shown that the AEI has strong correlation with sediment hypoxia (Sen Gupta & Platon, 2006;Minhat et al., 2013). The AEI values ranged between 83.464-100 during monsoon and 75-100 during pre-monsoon. During monsoon, the higher value of AEI (100) was recorded at lagoon side (Stations TP-5, TP-6, TP-7, TP-8, TP-9, TP-10, TP-11 and TP-M) indicating greater hypoxic conditions (Fig. 10). The lower value of AEI (83.464) was observed at station TP-4 which is located at the beach environment indicating a lower influence of hypoxia at this site. During pre-monsoon, the higher value of AEI (100)

Deformed tests as indicators of polluted environments
Mode of test deformation depends on the degree of pollution and type of pollutants.
Forms having corrosion, cavity development, broken peripheries and reduction in the overall growth are associated with high trace metal levels (Pati & Patra, 2012). Here are some of the abnormalities noted in the study area (Plates 1 & 2) i.e. corrosion, cavity development, broken peripheries and reduction in the overall growth. These abnormalities on the text morphology were taken as proxies of pollution signatures on the bio indicators in the present study caused by metal pollutants such as Cd, Zn, Cr, Cu, Fe, etc., released by external sources like agricultural runoff, industrial activities and other anthropogenic inputs.

15
Cluster analysis divided the study area into three biotopes in both the seasons. Each biotope is characterized by a distinct foraminiferal assemblage related to ecological settings.
During monsoon, three biotopes were formed by the cluster analysis (Fig. 11).
Biotope-I comprises only one station (TP-6). The higher value of Mn (11.029 ppm), Zn ( In pre-monsoon, the cluster analysis performed three clusters i.e Biotope-I, Biotope-II and Biotope-III (Fig. 12). Biotope-I comprised only one station i.e., TP-9 where the highest value of total individual foraminiferal species were reported. Ammonia beccarii, A.

Conclusions
• The heavy metals and benthic foraminiferal distribution infer that the Tupilipalem coast is ecologically stressed owing to lagoon mouth dynamics.
• The strong northern wind triggers an oblique wave approach to the shoreline, resulting the closing of the lagoon mouth.

18
• The heavy metals sequence as; Fe> Mn> Zn> Cr> Ni> Pb> Cu> Cd. The higher concentrations may be due to the effect of intensive anthropogenic stress as well as lagoon mouth closure in the area.
• The metal enrichment reveals that the Cd is extremely high and Pb enrichment is 70% in some stations. The PLI values of Cd is>1 during Pre-monsoon and monsoon seasons due to the influence of direct external sources like agricultural runoff and associated anthropogenic inputs.
• The Factor loadings matrix infers the heavy metal distribution and clusters of similar factors (Fe, Cu, Cd, Zn and Cr) during monsoon and pre-monsoon seasons.
• A total of 49 benthic foraminiferal species and cluster analysis results indicated that the taxonomic compositions, quantitative distributions were changed from monsoon to pre-monsoon (lagoon mouth closure to open).
• The coastal morphology and anthropogenic input as a source of heavy metals that affected the taxonomic composition and quantitative distributions of foraminiferal assemblages has been alarming.
• An AEI indicates that the hypoxic conditions from all the sampling stations except TP-12 from lagoon environment during the mouth closure. The FI values of monsoon and pre-monsoon shows <2, indicating environmental conditions that support substantial population of stress tolerant microbiota.
For the present study, the coastal morphology and anthropogenic input as a source of heavy metals and its impact on this area is affecting the taxonomic composition and quantitative distributions of foraminiferal assemblages. Moreover, this study area is considered to be the proposed site to construct a Major Sea-Port to be named as Dugarajapatnam Port soon. Therefore, sustainable development and protection of the coastal zone is need of the hour. The periodical monitoring and migration of coastal dynamics of this area is warranted.