2010 Physico-Chemical Indicators- Monitoring For

PHYSICO-CHEMICAL INDICATOR´S MONITORING FOR WATER QUALITY IN THE SALINAS OF NORTHWEST BONAIRE, N.A.

A BASE LINE STUDY

JANUARY 2010

PHYSICO-CHEMICAL INDICATOR´S MONITORING FOR WATER QUALITY IN THE SALINAS OF NORTHWEST BONAIRE, N.A.

A BASE LINE STUDY

Joaquín Buitrago Martín Rada María. Elizabeth Barroeta Eneida Fajardo Euclides Rada Jesús Narváez Fernando Simal José Monente Juan Capelo Jesús Narváez

JANUARY 2010 This document is formatted in PDF (Portable Data File) for copying, web posting and mailing convenience. However, some characters, especially those imported from GIS maps may be too small to read at the normal (100%) text viewing size. Fortunately, PDFs viewing text size can be easily increased.

For bibliographical purposes, authors suggest that this document should be cited as follows:

Buitrago, J., M. Rada, M. E. Barroeta, E. Fajardo, E. Rada, F. Simal, J. Monente, J. Capelo, J. Narváez. 2010. Physico-Chemical Indicator´s Monitoring for Water Quality in the Salinas of Northwest Bonaire, N.A.. A base line study. A technical report. Estación de Investigaciones Marinas de Margarita to STINAPA. 23 tables, 237 Figures. 214 p.

3. PREFACE

This project was born as the result of Fernando Simal, former Washington Slagbaai National Park (WSNP) worries about the effects of a deteriorating terrestrial landscape on the Bonaire National Marine Park. As it is well known the economy of Bonaire is strikingly undiversified. The economic mainstay for Bonaire is tourism, particularly that related to SCUBA diving, and this depends strongly on reef health. Theory indicates that salinas serve as a deposit and filter for rain runoff, before water discharges reach the ocean and may affect the reef. The idea was to create a baseline, taking into account the baseline syndrome, to allow park managers to monitor some key indicators of salinas water quality. Shifting Baseline Syndrome (SBS) identified by Daniel Pauly in 1995 for fisheries sciences (Campbell et al., 2009) and now recognized as a mayor problem in all ecological aspects. As a concept, SBS is simple to grasp and its logic is compelling. As ecosystems are continuously altered by human activities each generation of scientist accepts like “original” the status they first encountered. Ecological regime shifts are large, abrupt, long-lasting changes in ecosystems that often have considerable impacts on human economies and societies. Avoiding unintentional regime shifts is widely regarded as desirable, but prediction of ecological regime shifts is notoriously difficult (Biggs et al., 2009). So the salinas water quality baseline, probably does no has many similarities with the conditions existing, let’s say before the introduction of large mammals, wood cutting for charcoal and land clearances for Aloe cultivation. The idea was not to use the classical field sampling and lab processing, but instead to use as much as possible the available technology to collect continuous data. And when that was not possible, to use the most simple, but reliable technology available, to analyze samples in situ or at the park facilities. This project has encountered numerous obstacles in its way, equipment availability, chemical products transport, one of the most intensive rainy seasons of the last decades, so it has been very time consuming. However, in addition to the results specified in the study agreement between EDIMAR and STINAPA the team wanted to contribute with some tools that may be useful to WSNP staff in the future. The References section (10) not only includes those articles cited in the text but over a hundred references related with the study objectives. A WSNP GIS including the following maps is also offered in two different formats (MAPINFO tab and ESRI shp).

-GENERAL MAPS (based in N.A. Cadastral Survey Department Maps)

- LANDSCAPE; SOILS AND VEGETATION (FREITAS et al.) - SOILS SOILS IN CATCTHMENTS SOILS IN PARK - VEGETATION VEGETATION IN CATCTHMENTS VEGETATION IN PARK

Chapter 3. PREFACE 1 - FENCES-ROUTES-WALKING TRAILS. - - GENERAL SALINAS physic-chemical behavior.

- SALINAS CATCHMENTS DATA o BARTOL o FRANS o FUNCHI o GOTO o MATIJS o SLAGBAAI o TAM o WAYAKA

This project was possible thanks to the hard work and dedication of Fernando Simal. The valuable help and support of STINAPA staff, and the kindly friendship of local people.

Chapter 3. PREFACE 2 4. GENERAL INDEX

1. TITLE PAGE

2. SUGGESTED REFERENCE

3. PREFACE

4. GENERAL INDEX

5. INTRODUCTION

5.1. Theoretical aspects about tropical hypersaline coastal lagoons

5.2. Northwest Bonaire

5.2.1. General Description

5.2.1.1. Geology

5.2.1.2. Climate

5.2.1.3. Soils and Landtypes

5.2.1.4. Vegetation and landscapes

5.2.1.5. Physicochemical aspects of the water

5.2.1.6. Biology of the salinas.

6. OBJECTIVES AND SCOPE OF THIS REPORT

7. METHODOLOGY

8. RESULTS

8.1. SALINAS’ BASINS

8.2. A GENERAL VIEW OF THE SALINAS.

8.2.1. HISTORICAL BACKGROUND

Chapter 4. GENERAL INDEX 1

8.2.1.1 STORMS

8.2.1.2. “BAY BARRIER” MORPHODYNAMIC MONITORING

8.2.1.3. NONCONVECTIVE ESTRATIPHICATION

8.2.2 GENERAL MANAGEMENT RECOMMENDATIONS

8.3. INDIVIDUAL SALINAS

8. 3.1 MATJIS

8.3.1.1. MATJIS BASIN LAND COVER

8.3.1.2. SALINA MATJIS WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3.1.3. SALINA MATJIS HYDROGRAPHIC CONDITIONS

8.3.1.5. BEACH, DUNES, TABAKU THE PISKADO

8.3.1.6. MANAGEMENT RECOMENDATIONS

8. 3. 2 BARTOL

8.3.2.1. BARTOL BASIN LAND COVERS.

8.3.2.2.SALINA BARTOL WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3. 2. 3. SALINA BARTOL HYDROGRAPHIC CONDITIONS

8.3.2. 4. MANAGEMENT RECOMENDATIONS

8. 3. 3. FUNCHI

8.3.3.1. FUNCHI BASIN LAND COVER

8.3.3.2. SALINA FUNCHI WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3. 3.3. SALINA FUNCHI HYDROGRAPHIC CONDITIONS

8.3.3. 4. MANAGEMENT RECOMENDATIONS

Chapter 4. GENERAL INDEX 2

8. 3. 4. WAYAKA

8.3.4.1. WAYAKA BASIN LAND COVER

8.3.4.2. WAYAKA SALINA WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3. 4. 3. SALINA WAYAKA HYDROGRAPHIC CONDITIONS

8.3. 4. 4. MANAGEMENT RECOMENDATIONS

8. 3.5. SLAGBAAI

8.3.5.1. SLAGBAAI BASIN LAND COVER

8.3.5.2. SALINA SLAGBAAI WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3. 5.3. SALINA SLAGBAAI HYDROGRAPHIC CONDITIONS

8.3.5. 4. MANAGEMENT RECOMENDATIONS

8. 3.6. FRANZ

8.3.6.1. FRANS BASIN LAND COVER

8.3.6.2. SALINA FRANS WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3. 6.3. SALINA FRANS HYDROGRAPHIC CONDITIONS

8.3.6. 4. MANAGEMENT RECOMENDATIONS

8. 3.7. TAM

8.3.7.1. TAM BASIN LAND COVER

8.3.7.2. SALINA TAM WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3. 7.3. SALINA TAM HYDROGRAPHIC CONDITIONS

8. 3.8. GOTO

Chapter 4. GENERAL INDEX 3

8.3.8.1. GOTO BASIN LAND COVER

8.3.8.2. SALINA GOTO WATER BALANCE, TIDAL vs RAIN INFLUENCE.

8.3. 8.3. SALINA GOTO HYDROGRAPHIC CONDITIONS

9. REFERENCES

Chapter 4. GENERAL INDEX 4 5. INTRODUCTION

In the Caribbean, beaches, coastal lagoons and other typical environments as mangrove forests, etc., are highly valued by coastal residents not only for tourism, relaxation, sports and simple enjoyment, but also for representing an important part of islanders’ natural heritage as well as areas for fishing, and also because they play the role of flexible barriers protecting valuable coastal habitats land and infrastructure during storms and hurricanes, which are very common in this region. The quality of Bonaire's coral reef is declining in comparison with historical data (Bak et al., 2005), this is thought to be caused by the high loads of nutrients that reach the sea (van Kekem et al,. 2006).

5.1. Theoretical aspects about tropical hypersaline coastal lagoons

5.1.1. Physicochemical aspects

All coastal marine lagoons include highly complex components. The first of which are the physicochemical features. It is important to mention, that the key abiotic components (salinity, temperature, dissolved oxygen and ionic composition) are primarily influenced by the basic hydrologic balance of inputs (i.e. precipitation, groundwater inflow, ocean over wash or tidal underground input, sediment morphology, erosion rates, tides etc.). On the other hand are outputs (i.e. evaporation, groundwater seepage), as well as basin morphometry, surrounding geology, biologic activity and climate (Saenger et al, 2006; Gajardo et al., 2006). For example, the anchialine pools (a type of coastal lagoon) with lack of connections to the sea (Stephens and Daniel, 2006). They show a vertical salinity and dissolved oxygen zonation and a relative lack of food (Carey et al., 2001). In 1984, a panel of experts in the International Symposium on the Biology of Marine Caves held in Bermuda made a review of the concept of anchialine, leading to a more refined definition of it. Although they made an approach of the anchialine systems from a marine caves expert’s point of view, they exposed some important physicochemical characteristics. They defined the term anchialine as a habitat consisting of bodies of haline waters, usually with a restricted exposure to open air, always with more or less extensive subterranean connections to the sea, and showing noticeable marine as well as terrestrial influences. These waters are usually polyhaline or euhaline, but sometimes mesohaline or hyperhaline. Regarding to the connections with the sea these must be subterranean, otherwise the water body would classify as a lagoon, tidal pool, salt pan, etc. Also, the nature of the connection may vary from a large blue hole-like geomorphology, to a more horizontal submarine cave entrance, or smaller cracks and crevices, or macroporus connection through coral rubble, purnice, etc., provided that the water- filled interstices are large enough to allow animals to pass through (Stock et al., 1986).

Chapter 5. INTRODUCTION 1 According to this definition, the marine influence should be clear in two ways: a) the ionic composition is mainly derived from (diluted/concentrated) sea water; b) in areas with marked tidal difference, anchialine habitats reflect these tides. On the other hand, they remark that the terrestrial influence may be one of the following three phenomena, or any combination of them: a) dilution by rain (direct, by drip, flow, or subterranean streams); b) concentration by evaporation; c) supply of terrestrial nutrients (leaf litter, bat guano, land snails, etc.) (Stock et al. 1986). Some other authors also remark important geomorphological factors influencing the physical processes that occur in coastal lagoon systems. This are the inlet configuration and dimension, lagoon size and orientation with respect to prevailing winds, and water depth. Other factors are the hydrological mechanisms. It is known that advective transport dominates gains and losses by rainfall, evaporation, surface runoff and groundwater seepage. Since there is no freshwater inflow, the lagoon dynamics are driven entirely by the surface buoyancy flux (heating/cooling and evaporation) and the exchange of buoyancy between the lagoon and ocean (caused by differences in temperature and salinity). There are two types of mechanism for this exchange. The first is a convective cycle produced by a difference in density between the water of the lagoon and that of the ocean. That cycle can flow in either of two directions according to whether the lagoon is ‘classical’, i.e. the density of the lagoon is lower than the ocean, or ‘inverse’, i.e. its density is higher than the ocean (Hearn and Sidhu, 1999). In salt ponds seawater may enter over the surface or through the ground because the berms that separate salt ponds from the sea are composed of permeable sediments, mainly sand and coral rubble. Seawater input is predicted to be an important force in controlling pond hydrology and salinity. The easiest way to determine how directly these ponds are connected to the sea is to check their tidal range. Those with a large tidal range are more closely connected to the sea, have a higher flushing rate, and are typically more stable, with higher species diversity (Thomas, 1998). It is known that the major factor that affects these kinds of habitats is the salinity which limits aquatic communities in hypersaline water, and it influences both dissolved oxygen concentrations and temperature (Borowitzka,1981) (Greenwald and Hurlbert,1993). Salinity which fluctuates widely in shallow hypersaline water bodies because their high surface to volume ratio makes them especially sensitive to seasonal and shorter-term environmental changes (Garcia and Niell, 1993) (Jarecki and Walkey, 2006). All terms, however, show a distinct seasonality in respond to seasonally changing winds, which is controlled in part by responses to seasonally changing winds, wet and dry periods and higher evaporative losses during summer months. Weather patterns also control the concentration of salts by evaporation and dilution. In coastal ponds, seawater input and flushing can also influence salinity (Carpelan 1967; Thomas,1991; Smith, 1994; Jarecki and Walkey, 2006). Another important physicochemical indicator found at hypersaline environments is Nitrogen (N) which is a major element in all organisms. It accounts for approximately 6% of their dry mass on average and thus in nature its assimilation is a key process of the N-cycle carried out by higher plants (Hageman and

Chapter 5. INTRODUCTION 2 Redd,1980), algae (Solomonson and Vennesland,1972), yeast (Sengupta et al., 1996), and bacteria (Moreno-Vivián et al., 1999). It is important to notice that these wetlands provide important ecological services, including storm protection and flood mitigation, shoreline stabilization, erosion control, and retention of nutrients and sediments, (Marshal, 1994; Tam and Wong, 1999) as well as a critical habitat and food resources for resident and migratory and local birds in the Caribbean (Scott and Carbonell,1986). Present-day ponds should represent stages in a natural hydrological progression, from near-marine systems to near-terrestrial systems (McKee and Faulkner, 2000) (Jarecki and Walkey, 2006.)

5.1.2. Biological Data

Despite that harsh environmental conditions that made up the salinas, can cause a low species richness, there are typical inhabitants, migratory or local, of this type of coastal environment, and these species choose salinas as their restricted place for breed or feed because of these unique conditions. Microscopic life is abundant in most salinas, including groups as Haloarcheas (Papke eta al., 2007), among them some species as those from the genus Halorubrum. Halophilic archaea (haloarchaea) flourish in extremely saline environments and are exceptionally tolerant of many environmental stresses. They are typified by the well-studied model organism, Halobacterium sp. NRC-1, which grows fastest aerobically in amino acid-rich environments at moderate temperatures and nearly saturated brine (Coker et al., 2007). Other extremely halophilic Bacterium, as Salinibacter ruber, However, not yet reported from Bonaire, has an ample geographical distribution (Antón et al., 2008) is also a possible contributor to the reddish aspect of many Caribbean salinas and its report for the region is waiting for the relevant studies. Two of the most important and well known species that inhabits Salinas, are Brine Shrimps (Artemia sp.), and Caribbean Flamingo (Phoenicopterus ruber) which is one of the main cultural and natural symbols of Bonaire.

Chapter 5. INTRODUCTION 3

Fig 5.1: Salina Goto pink colored (Picture Fernando Simal).

Brine Shrimps (Artemia sp.)

The brine shrimp Artemia is widely distributed in salt lakes, coastal lagoons, and solar saltworks in all continents, except Antarctica (Castro, et al., 2006). One of the most remarkable features of the brine shrimp is its capacity to inhabit extreme salinity waters, such as freshwaters and hypersaline lakes, it has been proven to be restricted to areas of high salinity (106 ppt) (Trip and Collazo, 2003), that is translated into some unique physiological features, which can be considered very interesting regarding the osmotic regulation. More surprising than the influence of the salinity over the morphology of the brine shrimp, is the variable ionic composition of the brines where it lives, being able to be found in salty lakes where the dominant ion can be chloride, sulfate, carbonate, potasic, etc. Concerning the thermal range that the brine shrimp tolerates, it can be wide; being the optimal temperature around 25 to 27 °C, and the highest and lowest sills for its survival between 6 and 35 °C. However, some strains have proved to survive much higher temperatures. The existence of the brine shrimp in environments with high salinity is due to the absence of predators and competitors. Often these communities are similar in structure, which it had lead to an underestimation for the diversity of the physicochemical features in the habitat of brine shrimps. Besides brine shrimps shows little morphological variation, which suggest few differences between populations. Nevertheless, laboratory surveys indicates the opposite; that brine shrimps populations varies significantly with respect to the reproductive features, presumably as a result of the selective pressures in their original habitats. The diversity of the environmental conditions in which the brine shrimps inhabit varies as a function of the anionic composition, the weather conditions, and the altitude (Bowen et al., 1985; Bowen et al., 1988) (Sarabia, 2002).

Chapter 5. INTRODUCTION 4 Despite the limited range of ecological conditions of hypersaline lakes, Artemia is extremely successful in achieving high population sizes and as explained before tolerating large environmental variation that is why members of the genus are considered to be extremophiles organisms. For example, the encysted gastrula embryo (cyst) is the most resistant of all animal life history stages, while the motile stages (nauplii and adults) are considered as one of the best osmoregulators in the animal kingdom [Clegg JS, Trotman, 2002]. Females switch reproductive mode (ovoviviparity vs. oviparity) as an adaptive response to environmental pressure, and hence this trait reflects water condition. Organisms living in hypersaline lakes are expected to evolve relatively fast due to the mutagenic effect of the environment, for example, high UV radiation and salt concentration (Hebert et al., 2002), nevertheless species richness of the genus is low. On the contrary, at the species level it is exhibited a highly heterogeneous population genetic structure, a pattern generated and maintained by the environmental heterogeneity and patchy (island-like) distribution of hypersaline lakes (Gajardo et al., 2006).

Caribbean Flamingo (Phoenicopterus rubber)

Arengo and Baldassarre’s (1998), study of flamingo use of commercial salt impoundments found that low and high salinity ponds (4-87 ppt and 127- 218 ppt respectively), but not intermediate ponds (68-150 ppt), contained suitable food resources for flamingos.

Fig 5.2. Flamingos flying at Matijs.

Chapter 5. INTRODUCTION 5 They found that high salinity ponds contained feeding material primarily located in the water column and were dominated by brine shrimp while low salinity ponds did not contain brine and housed most potential food in the substrate layer. High salinity ponds also showed low fluctuations in the number of food items over the course of the study and therefore may be a more consistent source of food (Arengo and Baldassarre 1998). As flamingos concentrate in areas with the highest food density (Sutherland 1983; Arengo and Baldassarre 1995; Arengo and Baldassarre 1999), this study suggest that ponds with salinity at approximately 205 g/l may have ideal prey concentrations for flamingos. This is also supported by the finding that searching behavior was not occurring with high frequency in the high salinity ponds, suggesting that little searching was required due to the high abundance of prey. The jump in bird density between the 184 g/l pond and 205 g/l pond further suggests that prey density may be optimal in the pond with the highest salinity. Although not directly confirmed during this study, existing data about ponds with salinity higher than those measured show that these ponds generally host only a small number of feeding flamingos, supporting the Baldassarre’s (1998) study where bottom feeding dominated in low salinity ponds and feeding in the water column was seen with greatest frequency in high salinity ponds. In general, while each of the feeding behaviors did not dominate a different water depth, each behavior appeared to occur preferentially at specific depths. It is difficult to determine whether water depth truly determines prey availability or simply determines the choice of feeding behaviors because of flamingos’ anatomical constraints (Mawhinney, 2008).

5.2. Northwest Bonaire.

5.2.1. General Description

Bonaire lies on a conservative plate boundary, where the South American and Caribbean Tectonic Plates meet and slide past one another. Along with its sister island of Curacao and the oceanic islands off Venezuela’s north coast, it has been traveling eastward at a slow but steady rate (De Meyer and MacRae, 2006). WSNP is located at the northwest tip of Bonaire. The central portion of WSNP is hilly, a product of the submarine volcanic activity during the Cretaceous period. The highest elevation of the park and also of the island of Bonaire is a product of this volcanic activity, with a height of 241m above sea level and an estimated age of 90-100 million years. This hilly landscape is surrounded by terraces and limestone plateaus resulting from changes in the sea level and the formation of coral reefs during approximately the last 5 million years (Boikschoten, 1982). Depending on the location, one two or even three terraces are visible. The highest terrace of limestone reaches 50m above sea level and its age is estimated at 1 million years. Some of this terraces, especially + 2 m and +20 m are present all around Caribbean. The changes in sea level are also responsible for another important feature of the

Chapter 5. INTRODUCTION 6 landscape of Bonaire, the natural salt-pans. The northern area has nine salt-pans or “salinas” that are the main objective of this report. They vary both in size and the presence of water throughout the year. (Simal, 2005).

5.2.1.1. Geology

The geology of Bonaire is complex, with the core of the island consisting of strongly folded and faulted rocks of volcanic origin, silica rich sediments and turbidities (debris deposited from an underwater landslide) formed during the Cretaceous era some 120 million years before present (Beets, 1972a; Beets, 1972b)). Overlying this are later fossil reef and reef-generated calcareous (calcium rich) deposits. It is these limestone formations which make up the coastline in the form of coral-rubble beaches (coral shingle and calcareous sand) or iron shore, except in the north where low limestone cliffs are found (see Image 6) (Zonneveld, et al., 1972). Klein Bonaire consists entirely of limestone formations (Buisonje, 1974) which are the remains of emergent reefs. Substantial changes in sea level have left up to four stranded terraces above the present mean sea level on Bonaire, and one below. These terraces can generally be distinguished by undercutting caused by chemical erosion, physical erosion and in some cases biological erosion in the elevated seaward facing limestone cliffs. The other islands are separated from the mainland by the Bonaire Trench precluding such faunal exchanges (Hulsman et al., 2008). One of the main features of the coastal zone of Washington Slagbaai National Park are the Bay barriers at the mouth of each “salina”. However, despite its importance in the system in general, maybe it is one of the less studied characteristics of the park. As the “salinas” were formed as craved valleys in times were the sea level was lower, the Bay barriers were formed by effect of the sea action by accumulating sediments at these depositional places, and coral rubble during storms. In most Northwest Bonaire “salinas” this process seems to be still active. So, a very strong rain can open the Bay barrier and the sea will close it again in a few days, and a very strong storm can open it in the reverse way. Although these cycles are multi- year, they play a very important role in the ecology of the lagoons. The man-made infrastructure at Bay barriers supposes adverse effects for the balance. As these Bay barriers logically correspond with small inlets in the shoreline, they have been used since centuries ago as landing sites and in some of them ports and houses, docks and warehouses were constructed affecting the dynamic.

5.2.1.2. Climate

The climate of Bonaire is arid tropical characterized by low rainfall, high evaporation rates, year round high temperatures with little seasonal variation and almost constant easterly trade winds. Average monthly air temperatures range from 26.6°C (February) to 28.4°C (October). Bonaire is dry with an average annual rainfall of 450 mm falling mostly in the period October–January. Rainfall is

Chapter 5. INTRODUCTION 7 unequally distributed geographically, with approximately four times as much rain falling in the northern portion of the island as in the south and also important differences between eastern and western zones (De Meyer and MacRae, 2006; Wells and Debrot, 2008). These figures are higher than those reported for more eastern Caribbean Islands like Margarita, where in the southeast average over 50 years is of only 350 mm. Prevailing easterly trade winds provides a consistent 25 km/h breeze (van Kekem et al., 2006). On islands, approximately 50% of the annual rainfall (Bonaire 463 mm) occurs in October-December, while May, June and July are the driest months. (Hulsman et al., 2008). Some years, as 2000, had continued heavy rainfall during the first quarter of the year, and caused streams and reservoirs (dams), which normally would be dry by February, to carry water well into June of 2000 (Debrot 2003a), and thus, its results resembled wet season circumstances. (Hulsman et al., 2008).

Fig 5.3. Shows the mean number of rainy days for each month. Bonaire as the other ABC islands proudly offer their “hurricane free status” as a tourist attraction. Tropical storms occurrence in direct relation to WSNP are treated in 8.2.1.1 STORMS chapter.

Days ofRain 4

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DIC Month

Fig 5.3. Number of days of rain at Bonaire between the years 1951 and 1980 at the International Airport of Bonaire. (Simal, 2005).

Chapter 5. INTRODUCTION 8

5.2.1.3. Soils and Landtypes

Bonaire, soils and landtypes have been studied extensively by De De Freitas et al., (2005). In the catchment areas of WSNP and salinas Wr_Wi “Hilly land” cover near 45% of the area. This type includes both very high and high hills, partly in ridges, irregular and isolated medium high and low hills (Table 5.1).

Figure 5.4: Soils and Landtypes Map of Washington Slagbbai National Park, including nearby areas inside the Salinas basins (after De Freitas et al., 2005). Coordinate system: The grid is the local rectangular cadastral coordinate systems. Coordinates units are kilometers.

Chapter 5. INTRODUCTION 9

SOILS AND LANDTYPES IN WSNP BASINS cAB9 rooi bottom , IWu soils of the plains WASHIKEMBA , Tc CORAL BEACHS

TL LOWER TERRACE , Tm Rock land of depositional_erosional terrace , Tr Terrace remanents

, Tx PLATEAU LAND IP , Wr_Wi Hilly land , Ws_Wx Stonyland

Other important soil types include Ws_Wx Stony land covering 28.2 % and Cab9 Rooibottom and plain soils covering 11.6% in northern WSNP areas. The situation does not change significantly if only WSNP areas are considered (Table 5.2).

Table 5.1. Soil types, area cover and % of total area in the catchments areas of salinas and WSNP.

Area (sq Soil type m) % cAb9 6 350 789 11.6 ITI 54 146 0.1 IWu 1 311 115 2.4 Tc 157 746 0.3 TI 3 351 572 6.1 Tm 1 898 199 3.5 Tr 570 880 1.0 Tx 1 037 093 1.9 WrWi 24 555 471 44.9 WsWx 15 401 307 28.2

Table 5.2. Soil types, area cover and % of total WSNP area.

Soil type Area (sq m) % Tx 647 577 1.3 IWu 1 303 359 2.6 ITI 54 146 0.1 cAb9 6 350 789 12.5 Tc 136 922 0.3 TI 3 295 850 6.5 Tm 1 452 311 2.9 Tr 570 748 1.1 WrWi 22 466 841 44.2 WsWx 14 558 287 28.6

Chapter 5. INTRODUCTION 10

5.2.1.4. Vegetation and landscapes

In WSNP and the basins of its salinas are represented 19 out of 32 landscapes units identified by De De Freitas et al., (2005) for Bonaire.

Chapter 5. INTRODUCTION 11

Figure 5.5 : Vegetation and landscapes. Map of Washington Slagbbai National Park, including nearby areas inside the Salinas basins (after De Freitas et al., 2005). Coordinate system: The grid is the local rectangular cadastral coordinate systems. Coordinates units are kilometers. Landscape units in WSNP and their basins:

B1 Sesuvium-Li A Agrarian/Human Use D2 (Haematoxylon_ casearia) D1 Eragrostis_ D4 Prosopis_Su D3 Prosopis_Ca E1 Prosopis_Casearia Esc. D5 Prosopis_Op TH1 Haematoxylon Croton HT S2 Sesuvium sa TL6 Caesalpina Metopium LT TL1 Lithophila TL8 Prosopis_C TL7 Croton Prosopis LT TM6 Haematoxylon Croton MT TL9 Prosopis_S TM8 Haematoxylon_caesalpinia MT TM7 Acacia Croton MT

TM9 Prosopis_Euphorbia MT

In WSNP the main landscape unit is the Prosopis-Casearia characterized by species with wide geographical distribution in the American continent.

Chapter 5. INTRODUCTION 12

Table 5.3. Landscaspe units, area cover and % in WSNP.

Landscape Area (sq units m) % A 1 557 708 3.0 B1 147 808 0.3 D1 3 713 253 7.1 D2 11 418 800 21.8 D3 26 622 452 50.9 D4 1 736 704 3.3 D5 191 270 0.4 S2 1 610 057 3.1 TH1 461 272 0.9 TL1 1 332 093 2.5 TL6 726 794 1.4 TL7 957 431 1.8 TL8 190 097 0.4 TL9 356 848 0.7 TM1 233 410 0.4 TM6 493 841 0.9 TM7 70.0 TM8 230 402 0.4 TM9 339 766 0.6

De Freitas et al., (2005) consider some of the vegetation types as of High Conservation Value (HCV) based in criteria as structural complexity, development of the highest layer, diversity of plant species, relative scarcity and number of rare species found and presence of species characteristic for climax communities. They consider that only D3 is adequately represented in WSNP. In fact vegetation types considered as of High Conservation Value cover 53 % of WSNP, but 51 % is represented by only one vegetation type Prosopis_Ca (D3) according to (De Freitas et al., 2005). The other 3 HCV types present in the park cover less than 1% of park areas and are limited to specific localities in the south of the park and in drainage zones. The Figure 5.6 shows the distribution of these vegetation types.

Chapter 5. INTRODUCTION 13

Figure 5.6. Vegetation Types considered of High Conservation Value for Bonaire in WSNP.

D3 Prosopis_Ca

D5 Prosopis_Op

TM6 Haematoxylon Croton MT

TH1 Haematoxylon Croton HT

Chapter 5. INTRODUCTION 14 5.2.1.5. Water Physicochemical aspects

The different conditions of each lagoon in Northwest Bonaire represent diverse environments. So a revision of bibliographical data about marine coastal lagoons is needed. Nevertheless, each lagoon represents a very valuable economic and ecological asset that must be protected, and in order to do this, it is necessary a better understanding of their complex systems.

5.2.1.6. Salinas Biology

The biological aspects of the salinas of Northwest Bonaire are not included in objectives and scope of this report. So, for example, the myriad of waterfowl species that uses these habitats as migration stops, permanent, reproductive or wintering areas are not mentioned. However, some of the most common and evident species, are critical to the monitoring of these water bodies, both because their sudden increase or disappearance may act as water quality major changes indicators, and because they may play and important function in nutrient cycling. So, this brief description is included in the introduction section and not in the results section.

Chapter 5. INTRODUCTION 15

Figure 5.7. Some of the dozens of species, resident or migratory (or both) that frequent WSNP. From top to bottom Casmerodius albus. Ardea herodias, Pluvialis squatarola.

Chapter 5. INTRODUCTION 16 Some important species for WSNP salinas monitoring

Dunaliela Dunaliella sp.

Dunaliella salina and some of the other species of this genera undergo complex life cycles that encompass, in addition to division of motile vegetative cells, the possibility of sexual reproduction. The pigment responsible for the brightly red coloration displayed by D. salina, often designated in the older literature as "hematochrome", was recognized already very early as a carotenoid. One of the methods used in such biotechnological operations to induce massive carotenoid accumulation is reduction of the growth rate by deprivation of nutrients. That a high carotenoid content of the cells may be caused by nutrient limitation as well as by high light intensities was already reported by Lerche, (1937). Dunaliella blooms therefore occur in mainly when during unusually wet winters the upper water layers become sufficiently diluted to enable growth, and when phosphate, the limiting nutrient, is available. (Oren, 2005).

Artemia Artemia sp.

Figure 5.8. Artemia sp collected at Salina Slagbaai.

Chapter 5. INTRODUCTION 17

Caribbean Flamingo Phoenicopterus rubber

Figure 5.9 Flamingos at Matijs salina.

Chapter 5. INTRODUCTION 18

The Bonaire Flamingo birds are clearly important in the context of a global population estimated at 2,500–9,999 individuals. However, over 300 birds were illegally caught for the local pet trade between 1998 and 2002 which has presumably halted any potential population growth on the island. In 2008 several broods were poached from nests on Bonaire, some of which have reportedly been seen as pets in homes in Bonaire and neighboring Curaçao (Well and Debrot, 2008). Bonaire is of global importance for its waterbird populations including Caribbean Flamingo Phoenicopterus ruber whose numbers, over the last 10 years have fluctuated between c.1,500 and 7,000 breeding individuals (though most normally averaging c.5,000). The flamingos fly to mainland Venezuela to feed in lagoons along the coast of the state of Falcón where hundreds are regularly seen but are not known to breed. The movements of the flamingos within the island and to-and- from mainland Venezuela are poorly known and warrant further research. A regionally important concentration of 500 Caribbean Flamingo Phoenicopterus ruber occurs (Well and Debrot, 2008) The Caribbean flamingo (Phoenicopterus ruber ruber) population on Bonaire and in Venezuela is estimated at 20,000 individuals, with many birds moving between the two locations in the mornings and evenings to nest and feed (Ross and Scott 1997; Esté and Casler, 2000). Flamingos feed in large flocks in areas with high food concentrations and low numbers of predators. Their main prey items are gastropods, crustaceans and chironomids generally found in lagoons and salt water lakes and ponds (Arengo and Baldassarre, 1995). Brine shrimp and flies are an important prey item for Flamingos, and they tend to concentrate in salinas (Casler and Esté, 2000).The Leeward group of the Netherlands Antilles in the Caribbean because its aquatic biodiversity is well known as a result of a series of comprehensive studies (Stephensen, 1933; De Beaufort, 1940; Feltkamp and Kristensen, 1970; Debrot, 2003, Hulsman et al., 2008.)

Chapter 5. INTRODUCTION 19 Black Land Crab Gecarcinus ruricola (Linnaeus, 1758).

Figure 5.10. Black Land Crab Gecarcinus ruricola

The Black Land Crab has a dark purple to black dorsal surface with light orange to yellow claws and is frequently hunted for food. They are common up to 300 meters elevation. (Hoffman, 2001). The massive migrations occurring at the beginning of the rainy season in the large Antilles are very well known. Although the crab and their burrows are common in WSNP, especially near the southern salinas (Goto, Tam, Frans) no remarkable reproductive migrations have been reported. G. ruricola may reach high densities in natural habitats up to 2174 crabs ha−1 and became an important component of coastal ecosystems (Hartnoll et al., 2006).

Fiddler crab Uca sp

Chapter 5. INTRODUCTION 20

Figure 5.11. Fiddler crab Uca sp. collected at Salina Frans.

Fiddler crabs are reported south from Massachusetts (Barnwell and Thurman, 2008). Fiddler crabs are conspicuous and abundant in most coastal Caribbean areas. Existing paradigms suggest that mangrove leaf litter is processed primarily via the detrital pathway in the Caribbean. However, some reports provide support for the hypothesis that leaf litter is in fact processed in fundamentally different ways in different biogeographic realms (McIvor and Smith, 1995). In Bonaire WSNP, Uca crabs were observed mainly near the southern salinas (Goto, Tam, Frans) where they may play an important role in nutrient processes.

Blue land Crab Cardisoma guanhumi, Latreille, 1825

Land crabs of the genus Cardisoma, Family Gecarcinidae, are an immiportant ele ment of the fauna of many tropical coastal and estuarine areas. The genus is circum-equatorial, with different species on the east and west coasts of each continent. Cardisoma guanhumi. Its range includes the east coast of America, from Florida to Brazil, and the Caribbean Islands. Cardisoma occurs in or near many densely populated areas, and its spectacular colors, migrations, and swarming, it has been largely neglected by zoologists. In Bonaire and in WSNP Cardisoma crabs are not common, at least not as in many coastal areas nearby, where crabs are a common and popular food.

Chapter 5. INTRODUCTION 21

Figure 5.12. Blue land Crab Cardisoma guanhumi, at Salina Tam.

Chapter 5. INTRODUCTION 22 Barigonchi, Petotica. Cyprinodon dearborni Meek, 1909

Figure 5.13. Barigonchi (Cyprinodon dearborni) Colected at Salina Tam.

Cyprinodon dearborni is a common eurohalyne fish from the Caribbean basin. They are able to support abrupt sanility changes up to 100 ppt (Chung, 2001). This phenomenon is important for tropical aquatic organisms in shallow waters, where they can adapt to high salinity during the dry season and cannot lose their acclimation level at low salinity during abrupt rain. For saline adaptation of tropical organisms, this behavior will contribute to their proliferation and distribution in fluctuating salinity environments. In WSNP Barigonchi was observed at salinas Wayaka and Tam.

Tropical guppy Poecilia reticulata Peters, 1859

Figure 5.14. Tropical guppy Poecilia reticulate collected at Salina Wayaka.

Chapter 5. INTRODUCTION 23

Tropical guppies, occurring on all ABC islands (Hulsman et al., 2008), are one of the most common fish species. They have show tolerance to rapid temperature changes up to 42.86 °C (Chung, 2001 a). In WSNP they have been seen in water body’s in Pos Nobo and Pos Mangel and Dos Pos,. But it is believed that they may enter salinas during rainy seasons, and probably survive there for some time. Inland population of fishes in arid regions frequently disappear through the dry season only to restock their former distribution ranges in wetter times by populations that live in coastal, marine waters. These populations may need the inland trek to meet certain requirements of their amphidromous life cycle. Dwindling habitats will see a deterioration of that mechanism and bring down the species diversity in these arid regions. Processes, other than normal seasonal droughts, that can influence the ecological integrity of aquatic communities are the introduction of foreign species, habitat alteration, environmental pollution, erosion by logging, and climate change amongst others (Hulsman et al., 2008).

Widgeon Grass Ruppia maritime

Figure 5.15. Widgeon Grass Ruppia maritime collected at Tam salina.

Seagrasses of the Ruppia genus occur on all continents of the world and on many islands: the northern limit is about 69 degrees North, the southern limit is at least

Chapter 5. INTRODUCTION 24 55 degrees SoutAlthough widgeon grass is not a true seagrass, it grows in both fresh and brackish aquatic environments. It is widely distributed worldwide in temperate and subtropical regions. In North America, widgeon grass is found along the Atlantic coast from Newfoundland south to Texas. The blades are wider at the base of the stem, arising alternately from the sheath and tapering to long point tips. This grass is often confused with shoal grass in low salinity locations. Forming extensive meadows in subtidal areas with exposure to intense sun, widgeon grass is able to tolerate some desiccation. Since it does not occur in full-strength seawater, it is not considered a true seagrass. However, the widgeon grass does provide food and refuge for a variety of organisms, similar to the function of the true seagrasses. At WSNP Ruppia is common in salinas Tam and Matijs.

Chapter 5. INTRODUCTION 25 6. OBJECTIVES AND SCOPE OF THIS REPORT

The original ideas of the project generating this report were to build up a preliminary base-line of the physical and chemical characteristics of the salinas of North-west Bonaire, and defining the seasonal and circadian variability information needed to establish a monitoring plan. A supporting plan was to train park personal in the basic skills needed to perform the monitoring plan. In order to study the physical and chemical characteristics of the waters in the salinas and in consequence propose monitoring programs, three field campaigns were suggested, with a seasonal periodicity, based in the main physical force affecting the park; weather. The plan was to collect samples before and after of the tropical storm season and during the transition period. The location and number of sampling stations depended on the extension of each saline. During the fieldwork sensors were used for each variable: oxygen, nutrients CTD profiles and Secchi disk for turbidity. Fixed sensors were placed for temperature, depth and salinity, to register diurnal variability and determine the possible influx of tides. The benthic fauna of the salinas will be evaluated using a small beach seine and an eight inch corer and sieve. Benthic organisms will be counted and identified on field at the highest possible level and samples preserved to further identification. The final product is a base-line of the current state of the salinas, suggesting indicators and a monitoring scheme using the smallest possible quantity of laboratory analysis and alert levels for such indicators. Coastal lagoons make up about 13% of global coastal environments. Lagoons have a number of physiographic attributes which increase habitat heterogeneity not only spatially by the presence of a multitude of ecotones, which provide refugia for fauna, but also temporally with the seasonal hydrological cycle (Gordon, 2000). There is as yet no a globally accepted measure for assessing management effectiveness and the sheer number of protected areas means that a full assessment of management effectiveness for all sites worldwide remains unlikely in the short term. Develop and implement a global protected areas monitoring project to measure baseline and ongoing conservation effectiveness over a minimum 5–10 year period—potentially expressed through measures of ecological integrity. Ideally, such a project should include every country but would at least need to include a representative sample of protected areas in all biomes/ecoregions. (Chape et al., 2005). A baseline tries to assess the behavior of a system in the absence of some disturbing influence. As explained in other chapters this is often a problem, because the original baseline does not exist any more. It is impossible to know, for instance how was the water quality in the salinas in the absence of large mammals in their basins. This indicates that the baseline presented in this report for the salinas North-west Bonaire should be actualized year to year. Indicator is taken to mean a significant physical, chemical, biological, social or economic variable which can be measured in a defined way for management purposes. Indicator development and use must be 'plugged-in' to the environmental management plan. It must begin by addressing a question posed at some stage in the cycle and end by delivering answers back to the cycle. Questions from

Chapter 6. OBJECTIVES AND SCOPE 1 different stages of the cycle will motivate different types of indicators or ones that operate at different scales in time and space. A monitoring program is often needed for indicators to establish the facts and the trends. A trade-off may be necessary between the cost of monitoring and the quality of the information acquired. The most cost-effective indicators should be chosen and the cheapest options are not necessarily the most effective. Environmental indicators are measures of physical, chemical, biological or socio-economic factors which best represent key elements of complex ecosystems or environmental matters. To achieve their aim of accurately and relatively simply reflecting often complex realities, indicators need to be based on system knowledge and understanding and be embedded in a well-developed interpretive framework. A monitoring program of repeated measurements of the indicator, in various places and times and in a defined way, will give the basis for detecting environmental change, through comparison with a benchmark set or condition. The indicator must reflect the aspect of the system that is the objective of the monitoring. Where causal links between activities and effects are well understood, it may be possible to use indicators as early warning signals: In the initial phases of indicator use, the scientific community may need to help managers to build their understanding. As indicators become more routine, their interpretation by managers should become simpler, more confident. The indicator must reflect the aspect of the system that is the objective of the monitoring. A good indicator must be simple to measure and with clear interpretation. This is not an easy task because natural processes are usually non-linear and dependent of many independent variables. So the final decision, on the meaning of a result should be always based in the expertise and knowledge of the system. Often, indicator data will require sophisticated interpretation, such as through extensive statistical analyses, computer modeling, or expert assessment. At the case of STINAPA Bonaire the initial idea of the relationship between STINAPA and EDIMAR included further assistant for a number of years.

Chapter 6. OBJECTIVES AND SCOPE 2 7. METHODOLOGY

Methodological design was very eclectic. Each salina, each season and each location in a same saline was very unique, and was obvious that a sampling scheme was not going to work for all situations. Also to complicate the sampling planning, the second half of 2008 was one of the wettest periods in Bonaire’s recent years. As explained in Chapter 6. OBJECTIVES AND SCOPE OF THIS REPORT. The idea was to create a baseline, taking into account the baseline syndrome, to allow park managers to monitor some key indicators of the salinas’ water quality. The idea was not to use the classical field sampling and sophisticated lab processing, but instead to use as much as possible the available technology to collect continuous data, and when that was not possible, to use the most simple, but reliable technology available, to analyze samples in situ or at the park facilities. Obviously not all the desired water quality indicators could be used. Nutrients, perhaps the main concern for water quality, are difficult to measure in situ. Equipment recently developed for this purpose, is either, not reliable or too complicated to be used under field conditions as those found at WSNP. As a common sampling frame two types of strategies were used. Programmable recording sensors and discrete sampling data, both in-situ, using hand-held or even fixed position CTD’s, and for those variables needed to be analyzed at lab conditions, bottled samples keep under refrigeration or freeze if time lag between sampling and analysis was longer. To train park personal in the basic skills needed to perform the monitoring plan was another objective (Fig 7.14). Results are expressed in units as close as possible to Standard Units (Taylor and Thompson, 2008.

7.1. Sampling equipment.

- A CTD Sea Bird SBEa 37. - A YSI 556 MPS, multiparamtric; probe, - PC HP Pavilion dc 1500. - Sony DSCW7 digital camera. - Underwater case for the camera. - Aquatic measure device, Speedtech Instruments. - GPS Garmin, GPSmap 60CSx. - GPS Garmin III. - Six temperature Sensors HOBO Temp UA-002-64. - Three Water level loggers HOBO U20-001-01. - Five HOBO Onset Coupler for U23,V2,proV2. The CTD Sea Bird SBEa 37 Is a high technology instrument (Fig, 7.1) Measures salinity, temperature and pressure at 3 decimal values. However, as used by EDIMAR it is designed for oceanic waters down to 6000 meters, was useful and very reliable but was used only as a control, because it is a high technology instrument and it can be frequently internationally calibrated. Most of this equipment or its equivalent exists at WSNP. Figure 7.1 shows a CTD Sea Bird SBEa 37 being deployed.

Chapter 7. METHODOLOGY 1

Figure 7.1. CTD Sea Bird SBEa 37 being deployed.

The HOBOS system include temperature and light sensors, they are small and have proved to be very reliable. These HOBOS were placed in at least 3 points at each salina. The HOBO was attached to a steel bar (Fig 7.2). HOBOS for temperature and light intensity were placed at surface water, connected to a buoy and a marking tape. With a rope long enough to allow water movements. (Fig 7.3). Another type of HOBOS used was the Water level loggers HOBO U20-001-01. (Fig 7.4). This sensor allows measuring the water depth and so was used to measure the tidal influence at salinas. They were attached fixed to a steel bar in vertical position. A YSI 556 MPS, multiparamtric probe, was used to get in situ data of salinity, temperature, and oxygen. The data was recorded in situ after instrument readings stabilized (Fig. 7.7)

Chapter 7. METHODOLOGY 2

Figure. 7.2. HOBO Temp in place.

Figure. 7.3. HOBO Temp floating freely

Chapter 7. METHODOLOGY 3

Figure 7.4. Barometric HOBO in place. At the far-upper-right a Temp and Light intensity HOBO sensor is shown.-

Figure 7.5. Access to sampling sites was not always easy.

Chapter 7. METHODOLOGY 4

Figure 7.6. To locate sampling sites, especially during dark nights, GPS, torches and buoys were used.

Figure 7.7. Reading YSI 556 MPS, multiparamtric probe data.

Chapter 7. METHODOLOGY 5

Figure 7.8. Access to sampling sites when water levels were low, like in this picture at Matijs in June, was easily done by walking.

Chapter 7. METHODOLOGY 6

Figure 7.9. Access to sampling sites during the rainy season was a little more complicated, like in these pictures at Funchi in november (top) and January (bottom).

Chapter 7. METHODOLOGY 7

Figure 7.9. To access sampling sites in large or deep salinas like Goto, Slagbaai, Matijs and Tam during rainy season a kayak was used.

Chapter 7. METHODOLOGY 8

Figure 7.10. Nutrient water samples were taken by hand in special bottles previously washed with the same water to be sampled.

Data transcript from field tables to computer data sheets was done as soon as being back at “Kas Sientifico” at park facilities. Back up of data sheets were done daily using pen drives and burning CD’s (Fig. 7.11).

Figure 7.11. Data transcript.

Chapter 7. METHODOLOGY 9 7.2. Laboratory analysis

At lab, analyzes were done using the following equipment: - Ultra Basic UP-25. Denver instruments (Fig 7.12) - Nitrate sensor, Nitrate Epoxy Body. Denver Instruments. - Ammonia sensor. ammonia Epoxy Body” Denver Instruments.

Figure 7.12. Ultra Basic UP-25. Denver instruments.

Due to the limitations imposed by these methodological conditions, some key water quality indicators, as Phosphates could not be studied. It is possible that in a near future, technological advances, will allow to include some other variables to the monitoring program. Meanwhile, is the idea of the team, with the wise use of this data and some surrogates of non-available variables will help to monitor the salinas water quality. Table 7.1 shows the sampling effort during the 9 months study. As is possible to see, over 50 K data samples were taken. This is a very intensive effort that consumed an estimated of 3168 work/hours of field effort and 4224 work/hours of processing time. This makes a total of 7392 work/hours.

Chapter 7. METHODOLOGY 10

Figure 7.13. A locally made system to hold electrodes.

Table 7.1. Sampling effort during the Physico-Chemical Indicator´s Monitoring for “Salinas” Water Quality in Washington Slagbaai National Park program.

DISCRETE SAMPLES CONTINUOUS SAMPLES TOTAL Sensors BY Total data Loc/time Sensors Sensors BAROMETRI SALIN SALINA number Variables1 sites T˚ C LUMEX C A Matijs 265 11 41 1734 1734 867 4600 Bartol 342 11 60 2601 2601 1445 6989 Funchi 170 11 41 2312 2312 1156 5950 Wayaka 254 11 55 2601 2601 1445 6901 Slagbaai 513 11 96 2601 2601 1156 6871 Goto 837 11 133 3468 3468 1445 9218 Tam 183 11 45 2601 2601 867 6252 Frans 208 11 40 2312 2312 867 5699

TOTALS 2772 511 20230 20230 9248 52480

1 Variables: Salina, date, Location, time, T°C, O2 mg/l, Salinity, Water depth, Sample depth, Sechi disk depth, Ammonia PPM, Nitrates PPM. The selection of sampling stations had to be done according to the unique characteristics of each salina. Topics as salina area, environmental variety and geographical position, were considered to select the sampling stations. Table 7.2 shows the 39 sampling stations used during the three campaigns to Bonaire WSNP. Individual samplings stations in map format are show at each salina chapter

Chapter 7. METHODOLOGY 11 (Chapter 8). A total of 39 fixed sample sites were selected based in multiple criteria decision (coordinates show table 7.2).

Table 7.2. Sampling locations during Physico-Chemical Indicator´s Monitoring for “Salinas” Water Quality in Washington Slagbaai National Park Program. Coordinate system is the N.A. Cadastral coordinate system.

SAMPLING N.A. SALINA STATIONS N.A. EAST NORTH STAT_1 12362 32104 STAT_3 12008 31816 STAT_2 12093 32101 MATIJS STAT_4 11874 32296 STAT_1 7372 34844 STAT_2 7403 34703 STAT_3 7888 34317 STAT_4 8082 34500 STAT_5 7678 34516 BARTOL STAT_6 7441 34988 STAT_1 5646 32692 STAT_2 5646 32634 FUNCHI STAT_3 5990 32510 STAT_1 5793 31084 STAT_2 5593 31070 STAT_3 5721 31055 STAT_4 6115 31444 WAYAKA STAT_5 6046 31453 STAT1 5711 30553 STAT2 5884 30517 STAT3 6243 29942 STAT4 6036 30602 SLAGBAAI STAT5 6144 30455 STAT1 9697 26009 STAT2 9735 26073 STAT3 9815 26142 STAT4 9962 26434 STAT5 9882 26186 GOTO STAT6 9999 26809 STAT7 9949 27093 STAT8 9732 28568 STAT9 9802 27784 STAT10 10176 27292 STAT1 7097 26115 TAM STAT2 7227 26010 STAT3 7220 26175 STAT1 5690 28715 FRANS STAT2 5734 28663 STAT3 5950 28825

Chapter 7. METHODOLOGY 12

Figure 7.14. Training park manager in nutrient analysis.

7.3. Sampling scheme.

The seasonal variability in salinas conditions was studied combining the rainy season and hurricane season. In the Southeastern Caribbean, the wettest months are November to January (Fig 7.15). The hurricane period starts in June and ends in November. So it was possible to combine the two aspects. As some of the main objectives were, to define salinas tidal and circadian cycles, each salina was sampled every six hours during 24 hours.

Chapter 7. METHODOLOGY 13 120 SECOND SAMPLING (top of wet season) 100

LAST SAMPLING (end of 60 wet season) Rain (mm) Rain FIRST SAMPLING (end of 40 dry season)

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DIC Month

Figure 7.15. Seasonality of the sampling expeditions according to the Bonaire weather (After Simal 2005).

To process the barometric data from sensors, it is necessary additional information. Water level sensors measures pressure, and pressure in water is a combination of atmospheric pressure, water pressure due to water level (depth) and water density. The theoretical density of fresh water at salina temperature is necessary to estimate density effect in pressure, the density of the salina water is a must. Water pressure is directly related to water density, and this is directly related to water salinity and inversely related to water temperature. As sensors were located at shallow depths the atmospheric pressure is also very important. Both variables; saline water density and atmospheric pressure are very unpredictable at some seasons at Bonaire. As salina water density and pressure applied by water column (However small) on sensor, the water level scale is just relative; because more dense salinas will show higher levels than low density salinas, this can be compensated with more measures if the interest is to know the real Sea Level of Salina’s water, but the important aspect, at least in the contest of this WSNP project is the absolute amount of variability in water level due to tides and to runoff, because those aspects are critical to Salina’s management options. The atmospheric pressure was measured directly using a Water level loggers HOBO U20-001-01 placed at the scientific house in park facilities. Atmospheric pressure, and precipitation data were also obtained from. Chapter 7. METHODOLOGY 14 http://www.tutiempo.net/en/Climate/Flamingo_Airport_Bonaire/789900.htm It is clear that precipitation data from Flamingo Airport differs from WSNP, but was the nearest location. A useful web site used in the process is: http://www.csgnetwork.com/h2odenscalc.html Tides at Bonaire were obtained from http://www.mobilegeographics.com:81/locations/3169.html?y=2008&m=11&d=25

Chapter 7. METHODOLOGY 15 8. RESULTS

8.1. SALINAS BASINS

Northwest Bonaire drainage system comprises 18 different basins. Nine of them include a salina; all salinas except Benge were considered in this work. Studied 6 2 salinas, at maximum flood level, cover 4 * 10 m , or 7.15% of WSNP area. However, they drain up to 73.2 % of park area. Including Benge, a non-studied small salina, reaches 76.7 % of WSNP area. As expected in most protected areas, basins and park limits, do not match. The two large catchments in park eastern region; Goto with a catchment area of 14 416 374 m2 has 3 704 688 m2 outside the park, over a quarter of its basin area, and Salina Matjis with a total basin of 8 950 083 m2 has 176 318 m2 outside the park, or 1.97% of total catchment. Some park areas drain to coastal zones that, although they are inside STINAPA protected areas system below hide tide level, are not controlled by WSNP. Salina Matjis has 529 434 m2, including the most used park areas, draining towards Boka Pampar, just southeast of park limits. A large area of 1 194 916 m2 north of BOPEC facilities, is inside park borders, but drain through BOPEC locations towards the sea. Fig 8.1. Shows salinas location and the catchment areas, both basins with salinas related and those draining directly to the ocean. The WSNP drainage system includes some 120 roois and creeks, all of them seasonal, with a total length of 182 km, including a 1 kilometer creek draining to the most eastern park area towards Boka Pamper outside the park. Table 8.1. Shows the creeks and roois system for each salina. Figure 8.2. Show the basins, salinas and the drainage system.

TABLE 8.1. SALINAS BASINS MATIJS BARTOL FUNCHI WAYAKA SLAGBAAI FRANS TAM GOTO BASIN (m2) 8 950 083 5 260 991 2 086 264 1 353 043 7 654 232 2 205 629 3 278 467 14 416 374 SALINA (m2) 678 935 282 007 67 586 165 682 741 530 51 568 72 973 2 025 516 SALINA PERIMETER (m) 6 648 3 924 1 653 2 850 13 239 1 511 2 384 13 828 SALINA MAX DEPTH (m) 1 7 1 1 8 1 2 14 BASIN_OUT SIDE PARK (m2) 168 181 0 0 0 0 0 0 3 636 718 PARK OUT BASINS (m2) 529 586 0 0 0 0 0 0 0 ROOIS (m) 29 302 16 044 5 617 2 071 25 489 7 282 13 572 47 609

Chapter 8. RESULTS 1

Figure 8.1. Catchment areas in Northwest Bonaire and studied Salinas. ------denotes basin limits outside WSNP.

Chapter 8. RESULTS 2

Figure 8.2. Drainage system of the 18 basins in Northwest Bonaire.

8.2. A GENERAL VIEW OF NORTHWEST BONAIRE SALINAS.

The 8 salinas studied differ in many aspects, earlier we remarked that the maximum flooded area varied from 51 568 sq meters in Frans to 2 025 516 in Goto. Other aspects related to the hydrographic characteristics of the salinas also differ strongly. Some salinas like Goto, Tam, Slagbaai, Wayaka and Funchi, keep a seasonally stable area and volume, while Matijs area changes from 678 935 sq meters during a strong rainy season to a few hundreds of sq meters at the end of the dry season. Figure 8.3. Shows the salinas percentage change between maximum observed inundation levels and minimum.

Chapter 8. RESULTS 3

Figure 8.3 Percent seasonal area change for the 8 salinas studied at WSNP. < 10% 10 - 20% 20 - 30 % > 30%

The salinity and temperature variations in the salinas also show important differences between them. Salinas are extreme environments so the maximum and minimum data are more useful than their means to understand their seasonal behavior. Figures 8.4 and 8.5 show the maximum and minimum temperatures observed in the salinas during the sampling process. Wayaka and Slagbaai have a strong non-convecting solar pond behavior. It means, in short, that brine near the bottom keeps and accumulate heat down there, due to its high density. In a later chapter this aspect will be treated in more detail. Maximum temperatures recorded in Wayaka reached 46.2 °C while in Slagbaai was 57.3 °C. Minimum temperature (Fig 8.5.) also shows a large variation. In Slagbaai, temperatures below 20 °C (18.3 °C) were recorded, in the other hand, in salinas as Goto and Bartol temperatures never get below 25 °C. As far as the salinity range Figures 8.6 and 8.7 present the maximum and the minimum observed values for each salina.

Chapter 8. RESULTS 4

Figure 8.4. Salinas maximum observed temperature. 30-40 ° C 40-50 ° C > 50 ° C

Figure 8.5. Salinas minimum observed temperature. < 20 ° C 20 - 25 ° C > 25 ° C

Chapter 8. RESULTS 5

Figure 8.6. Salinas maximum recorded salinity. < 50SU 50-100 SU 100 - 150 SU 150-200 SU > 200 SU

Figure 8.7. Salinas minimum salinity recorded. < 20 SU 20 - 40 SU 40 - 60 SU > 60 SU

Chapter 8. RESULTS 6

Three salinas showed maximum salinities over 200 SU, Wayaka, Slagbaai and Frans. Matijs maximum observed salinity was lower than in oceanic waters, being the only salina that can not be called hypersaline. Minimum salinities observed varied from 4.4 SU in Bartol, 4.6 SU in Matjis and 6.4 SU in Funchi to 82.5 SU in Goto. Using the range, mean and Standard Deviation of Temperature, Salinity and Dissolved oxygen a similarity cluster analysis using the nearest neighbor method was done. It shows 3 groups of salinas. Slagbaai is clearly different to the others salinas. A second group is formed by Goto and Wayaka and a third cluster include the other 5 salinas.

Distance (SQ EUCLIDEAN) (SQ Distance 0

TAM GOTO

FRANZ MATIJS FUNCHI BARTOL WAYAKA LAGBAAI S

Figure 8.8. Cluster analysis of salinas using physicochemical data (mean observed temperature, standard deviation of temperature, maximum and minimum temperature, mean dissolved oxygen, oxygen standard deviation, maximum and minimum dissolved oxygen, mean salinity, salinity standard deviation, maximum and minimum salinity).

At present, all WSNP salinas are anchialine, this trait seems to be only over a century old, when in 1877 the “Bocas” were closed by a storm (see chapter 8.2.1.1). Before that, the most probable scenario was a system of lagoons connected with the sea via ephemeral tidal inlets open when strong rains filled the basin and closed when waves moved sand and coral rubbles up to the beach, very much as exists in many coastal areas, (Dussaillant, 2009). The opening of the sand bar turned this hypersaline lagoon into a tidal system. This coupled with an increase in freshwater inflow to the lagoon reduced the salinity (Gordon, 2000).

Chapter 8. RESULTS 7 The term “anchialine” was proposed by Holthuis (1973) for a type of habitat defined as “pools with no surface connection with the sea, containing salt or brackish water, which fluctuates with the tides”. The term has been widely accepted since, but some doubts on his application to small, mostly cave located habitats have arisen (Stock, 1986). Anchialine pools are surface expressions of complex, subterranean brackish water systems formed by the interaction of fresh and saline groundwater and lacking surface connections to the sea. They are rare worldwide, (Brock 1985; Maciolek, 1987). The surface pools function as “windows” into groundwater systems that may extend for long distances. They are tidally influenced: water levels rise as the tide comes in and fall as it goes out, though there is a time delay depending on the distance to the ocean and directness of the subsurface connection to the ocean. In many regions anchialine pools are mainly or only present in the form of caves (Bozanic, 1984). Characteristic pool biota includes algal mats, the aquatic wigeon-grass (Ruppia maritima), several different types of shrimp and snails, isopods, amphipods, and polychaetes (Maciolek 1983). Marine or amphidromous fish species, shorebirds, and other water birds are also found in the pools. Several rare, threatened, or candidate endangered species are found, including several shrimp, a snail, and several insects, particularly damselflies (Stephens and Daniels, 2006).

8.2.1. HISTORICAL BACKGROUND

In Bonaire the geological composition consists of an inner volcanic core (Upper Cretaceous) which is surrounded by mostly 5–8 m (3–12 m maximum range) high Pleistocene coral reef limestone cliffs and terraces (De Buisonjé, 1974). During geological and historical times, fossil reefs and reef terraces were a potential source area for material transported by high energy wave events. Wave cut notches at the base of cliffs generally promote an active retreat of the shoreline terraces. Undermining beneath those notches causes blocks to fall into the sea from where they can be transported onshore. Recent reef growth, especially on the leeward western parts of the islands where most of the Salinas in WSNP are located, provides material for landward transport during high energy wave events and partly shelters the coasts due to the potential of reefs to reflect and break waves. High-energy wave deposits are present as (1) coast-parallel ridges (up to some kilometers in length, 50 m in width and 3 m in height) consisting of coral rubble and reef limestone cobbles and (2) as isolated boulders of single coral species or lithotypes, or a combination of coral species within a calcarenite matrix (Spiske et al., 2007). Also, the objects that put up the geological composition of the island have a pretransport condition which it is important because they determine the way that seawater may pass trough objects given that they may have high effective porosity but variable degrees of permeability (Nott, 2003). For example, Serpulid reef rock only float when they are wrapped and their pores remain filled with air. As soon as it is unwrapped and put into water its well connected and spacious pores fill with water and the sample sinks. On the other hand, Diploria

Chapter 8. RESULTS 8 sp. pores are not well connected and therefore do not completely fill with water, so that the coral may float (Spiske et al., 2007). There is a growing recognition among scientific communities that anecdotic and folk knowledge may help to fulfill some of the multiples “data holes” and give key to reasons of present day situations. In some cases like fisheries and most marine and coastal science ecology (Pauly, 1996), where hardcore data is relatively recent, information transmitted from generation to generation, and paleontological evidence is every day increasingly being use as a tool to understand the shaping of present day reality. WSNP Chief Ranger “Kultura” and his family have lived for generation at Washington plantation. He recalled oral tradition stories and produced a series of maps showed in figures 8.9 to 8.15 depicting different stages of salinas since 1792 to present day. We show these maps here because we think is valuable oral tradition data, as far as we know, have not been published and therefore is prone to be lost. In many other cases similar oral information was used to describe natural and human coastal changes in the Caribbean (Vernette et al., 1977) and later hard evidence confirmed the inferences made from primitive sketches and texts. Hand painted maps were photographed, later orthogonally corrected to minimize angular error and georeferenced.

Figure 8.9. Northwest Bonaire salinas before 1792.

As shown, most salinas had direct connection with the sea and this situation continued until 1867, when a hurricane sends enough amount of coral ruble to the coast to close some salinas entrances (Fig 8.11). Salinas with collapsed entrance channels due to this hurricane were mainly Matijs and Bartol. Funchi,

Chapter 8. RESULTS 9 Frans, and Tam apparently did not have a full connection with the sea even before this storm.

Figure 8.10. Northwest Bonaire salinas between 1800 and 1867.

Figures 8.12 to 8.15 show a different story. Those were years of intense activities in plantations that now form WSNP. Fig 8.12 and 8.13 indicate, for instance that Goto entrance was some how closed during the first half of the XX century and that the coastal connection between Wayaka and Slagbaai changed also in both directions during this period.

Chapter 8. RESULTS 10

Figure 8.11. Northwest Bonaire salinas from 1868 to 1919

Figure 8.12. Northwest Bonaire salinas from 1936 to 1969.

Chapter 8. RESULTS 11

Figure 8.13. Northwest Bonaire salinas from 1960 to 1977.

Figure 8.14. Northwest Bonaire salinas from 1969 to 1979.

Chapter 8. RESULTS 12

Figure 8.15. Northwest Bonaire salinas from 1979 to 2006.

8.2.1.1 STORMS

Storms are key to landform changes in coastal areas and so they alter heavily the conditions in the salinas. During the 9 months field period of the study no storm affecting salinas occurred, but certainly they do happen, and this certainly should be carefully monitored. Bonaire and the other ABC Islands are privileged, compared to the islands of the Greater Antilles and the Windward Islands. The hurricane frequency on the ABC Islands is low, due to their position at the southern fringe of the hurricane belt. From 1605–2005 36 tropical storms or hurricanes of category 1–2 passed within 100 nautical miles (185 km) north of the ABC Islands according to the Meteorological Service of the Netherlands Antilles and Aruba, (2006). Thus, within the past 400 yrs, the frequency for tropical storms and category 1–2 hurricanes is one event every 11 yrs. No category 3–5 hurricanes came within 100 km of the ABC Islands since 1851 (NOAA, 2006). Nevertheless, hurricane Lenny in 1999 (375 km north; Bries et al., 2004) and Ivan in 2004 (130 km north), both category 4, caused severe impacts on the ABC Islands (Meteorological Service of the Netherlands Antilles and Aruba, 2006). From 1988–2007, when the tropical storms Joan (1988), Bret (1993) and Cesar (1996), as well as the category 4 hurricanes Lenny (1999) and Ivan (2004) and category 5 hurricane Felix (2007) affected the ABC Islands, the average

Chapter 8. RESULTS 13 frequency of tropical storms and hurricanes increased to one hurricane every 3 yrs (Meteorological Service of the Netherlands Antilles and Aruba, 2006). How this affects the coastal geomorphology, including the salinas is the main objective of this study; “bay barriers” between salinas and the sea are critical points. The formation of coastal structures at Bonaire as the deposition of huge boulders along rocky coasts is either a consequence of storm, hurricane or tsunami transport (Nott, 2003), Hurricane Ivan whose distal extensions hit Bonaire in 2004 did indeed break off and transport boulders up to 35 m3 and destroyed cliff sections more than 20 m long (Scheffers and Scheffers, 2006). Hurricane Ivan overtopped the cliffs and terraces broke off and transported boulders up to ca. 100 m inland and its swash line reached ca. 500 m inland. Due to the elapsed time of thousands of years, a long-term deposition of the coral rubble ridges, such as by multiple hurricanes is more coherent (Morton et al., 2006). For boulders on the ABC Islands, a probable hurricane emplacement is inferred from our calculated minimum wave heights necessary to transport boulders, and from the position of the deposits on the windward side, where impacts of hurricanes are most severe. Moreover, a high hurricane frequency and the lack of reported tsunami, support this scenario (Spiske et al, 2007). Storms also destroy reefs, both by overturning colonies and affecting water conditions, especially turbidity. Storm Joan passed the southern tip of Bonaire as it moved from east to west, generating wave heights of 2– 3 m along the leeward coast of Bonaire. Kobluk and Lysenko (1992) reported an extensive list of damage features at five sites along the southern leeward coast ranging from 1.5 to 37-m depth. Overturned and displaced massive coral colonies were observed everywhere, affecting colonies up to 2 m in diameter. In 1993, Tropical Storm Bret also passed to the south of Bonaire (Bries et al., 2004).

Historical account of Storms affecting Bonaire and its relation to historical reference data.

The official report of Bonaire as a “nearly” storm free area, do not agree with multiple, published or oral transmitted information, or it has to be used as “comparative to other Caribbean areas”. So, data from storms since 1850 were plotted against Bonaire and region maps and all storms crossing at 100 nautical miles or nearer were mapped. Data was plotted according to info taken from NOAA, (2006). Storms were not named before the second half of XX century, but historical references of hurricanes have been recovered and systematized}. Figure 8.16 shows probably the most important data to understand today’s salinas ecology. As is seen a hurricane in 1886 with winds at 85 Knots from August 17 to August 18 passed at a minimum of 20.9 km North of Bonaire. Just a year after that, a Hurricane in 1877 with winds estimated at 90 knots passed in September 23 at a minimum of 22.5 km South of Bonaire. It is certainly very probable that theses hurricanes generated the main changes in salinas “bocas” geomorphology that are referred in the oral tradition showed in the previous section. During the first quarter of the XX century only two important storms may have changed Bonaire coastal landscape. During 1901 a storm passed just a few

Chapter 8. RESULTS 14 kilometers north of Bonaire. And in 1918 a H1 hurricane passed just north of Bonaire (Fig 8.17).

Figure 8.16. Storms route and intensity near Bonaire prior to 1900.

Chapter 8. RESULTS 15

Figure 8.17. Storms route and intensity near Bonaire from 1900 to 1925. Legend on storm intensity is show in Fig 8.16

During 1932 an H1 category hurricane passed during Nov 3 at some 121 km north of Bonaire. During June 29 1933 a Tropical storm with winds up to 50 knots passed just 15 km south of Bonaire’s Southern tip.

Figure 8. 18. Legend on storm intensity is show in Fig 8.16. Storms route and intensity near Bonaire from 1925 to 1950.

Chapter 8. RESULTS 16 Storms from the southwest Caribbean usually formed in late November and early December are much more dangerous to ABC islands and in general to south Caribbean coast, that typical storms from June-July to November that are, stronger, but very fast running, staying few hours in a same place and going northwest quickly.

Figure 8. 18. Storms route and intensity near Bonaire from 1950 to 1975. Legend on storm intensity is show in Fig 8.16.

The strongest measured hurricanes in Bonaire’s area are Ivan with a category 4 and winds up to 130 knots during September 8 and 9, 2004. In 12 hours Ivan ran from -65.5 to -68.3.

Figure 8. 19. Storms route and intensity near Bonaire from 1975 to 2000. Legend on storm intensity is show in Fig 8.16.

Chapter 8. RESULTS 17

Figure 8. 20. Storms route and intensity near Bonaire from 2000 to present (2009). Legend on storm intensity is show in Fig 8.16.

Emily, during 2005, reached latitudes under 12 degrees, with H1 category winds, and stayed near N.A for 5 days. The strength of the effects of a storm in a piece of land it’s not only directly related to the strength of the storm itself and the distance of the hurricane eye to the affected area. But, also the storm time affecting an area, and the relative position, land - storm. In the same context that evaluating human and humane infrastructure damages, effects of a storm in a natural, not intruded coast line depends, of course of the type of coast. But given this aspect equal, the displacement speed of the storm, more than the storm winds speeds themselves, are crucial. The time winds and rain stay over a region is a critical factor. Another aspect important is the route of the storm. The displacement direction establishes the wind (cyclonic of course) and wave partners. Caribbean landforms have been shaped by thousands of years of storms and trade winds. A storm affecting in the “protected” areas of the islands can be much more damaging than much stronger storms in the usual way.

Chapter 8. RESULTS 18 8.2.1.2. SALINAS “BAY BARRIER” MORPHODYNAMIC MONITORING

Salinas’s communication with the sea is at present time, only underground by infiltration. However, as seen before, this was not the situation some years ago and may be different in a future; some salinas may get a surface contact with the sea, especially those salinas with less human intervention. Ephemeral sand bar openings may change dramatically the physical, chemical and biological characteristics of a lagoon (Suzuki et al., 1998). The monitoring of physical changes in beaches is then an important aspect of any coastal area. It has been considered as a key aspect in coastal management (UNESCO, 2003) (Fig, 8.21) Shorelines are areas of continuous change where the natural forces of wind and water interact with the land. Here, both natural forces such as storms and hurricanes, and human activities such as sand mining and construction too close to the beach, result in changes, which are often dramatic in nature. Such changes have taken on paramount importance in the Caribbean islands since tourism has become one of the major industries. In many Caribbean islands beaches are a scarce resource, so beach monitoring is a must. At Northwest Bonaire not all the barriers could be monitored, half of them especially in the southwest area are completely interfered and stabilized by human infrastructure. Hence, only Matijs, Bartol, Wayaka and Slagbaai were studied. Some of these beaches had been also heavily altered, as Slagbaai, but some sectors still deserve a natural mobility consideration.

Chapter 8. RESULTS 19

Figure 8.21. Costal changes are being monitoring all around the world. In the Caribbean as in most island nations, these changes are critical.

NORTHWEST BONAIRE BEACHES AND SALINAS BARRIERS

At Northwest Bonaire all barriers, meaning the sand or rocky contact between see and salina, have been strongly altered during the last centuries. Probably the least modified has been Playa Chikitu, the barrier of salina Matijs (Fig, 8.22). To measure the seasonal changes happening at beaches a very simple method call “beach profiles” was used. The original method (Emery, 1961) has been modified, over simplified and adapted to different beach characteristics, but always provides a fast view of beach form. However to be useful this technique has to be used for long term monitoring. Beaches, as weather and oceanographic conditions, vary from year to year in cycles of different length.

Salina Matijs Barrier

As told before Salina Matijs Barrier is Playa Chikitu a very small beach with strong waves (Fig 8.23, 8.24 and 8.27). Figures 8.25 and 8.26 shown the technique used to measure the beach profile. For this very dynamic beach 3 profiles were selected the first in the northern part, the second in the middle and the last one at the southern area. Resultant profiles are presented in figures 8.28 to 8.30.

Chapter 8. RESULTS 20

Figure 8.22. Playa Chikitu at strong wave season (November).

Figure 8.23. Playa Chikitu at low wave season (June).

Chapter 8. RESULTS 21

Figure 8.24. Playa Chikitu waves in June.

Figure 8.25. Measuring Playa Chikitu’s beach profile.

Chapter 8. RESULTS 22

Figure 8.26. Measuring Playa Chikitu’s beach profile.

Figure 8.27. Playa Chikitu Google earth image.

Chapter 8. RESULTS 23 4.5 4 3.5 3 2.5 2 1.5 1

Meters over level sea 0.5 0 0 5 10 15 20 25 30 Distance from high tide line (m)

Figure 8.28. Beach profile made at the northern extreme of playa Chikitu. Yellow line shows the June profile, Orange line November and Blue line January.

As it is possible to see, the November profile is the steepest one. A common characteristic to most Caribbean windward or northern facing beaches and caused by the long period waves, formed in the north Atlantic during fall and entering the Caribbean mainly by the Mona and Anegada passes. During June profile is very flat and January has the highest sand accumulation.

5 4.5 4 3.5 3 2.5 2 1.5 1

Meters over sea level 0.5 0 0 5 10 15 20 25 30 Distance from high tide line (m)

Figure 8.29. Beach profile made at the central part of Playa Chikitu. Yellow line shows the June profile, Orange line November and Blue line January.

This figure shows that in the central area of Playa Chikitu the profile is convex, and not concave as in the northern end. As in June, the largest sand volume is present at January and the lowest at November.

Chapter 8. RESULTS 24

4 3.5 3 2.5 2 1.5 1

Meters over level sea 0.5 0 0 1020304050 Distance from high tide line (m)

Figure 8.30. Beach profile made at the southern end of playa Chikitu. Yellow line shows the June profile, Orange line November and Blue line January.

Figure 8.30. Shows than in this part of the beach seasonal changes are small. It is logical because the most protected zone. However, this may change dramatically in the event of a storm with northerly winds.

Salina Bartol Barrier.

Salina Bartol has a rocky coast. The barrier is made of coral boulders left by past storms. This type of shores show much less seasonal variability, but their changes may be dramatic in an episodic time frame. Figures 8.31 and 8.32 show some characteristics of Bartol barrier. Any way as this is an almost not intervened coast line beach profiles were done. Fig 8.33, shows the measures taken at Bartol.

Chapter 8. RESULTS 25

Figure 8.31. Boka Bartol Barrier

Figure 8.32. Boka Bartol Barrier facing south.

Chapter 8. RESULTS 26

Figure 8.33. Measuring profile at Boka Bartol .

Although, coral boulders and rubble may seem static, they move with the waves and tides. Fig 8.34 shows the beach profiles at Bartol southern tip. As it is possible to see, during June, the coast forms a concave. It changes to a convex coast profile during November. As it should be expected the largest amount of sediments (of any size) accumulates during January.

2.5

1.5

1 Meters over sea level 0.5

0 0 2 4 6 8 10 12 14 Distance from tide line (m)

Figure 8.34. Beach profile measured at south end of Boka Bartol. Yellow line shows the June profile, Orange line November and Blue January.

Chapter 8. RESULTS 27 4

3.5

2.5

2 1.5

1 Meters over sea level 0.5

0 0 5 10 15 20 Distance from tide line (m)

Figure 8.35. Beach profile measured at north end of Boka Bartol . Yellow line shows the June profile, Orange line November and Blue January.

At the northern area of Boka Bartol the situation is not very different. The softest profile occurs during June and the steepest during November.

Salina Funchi Barrier.

At Playa Funchi it was not possible to find a sector not heavily altered by human activities. So no profile was taken and is considered a human stabilized coast line.

Figure 8.36. Altered shoreline at Playa Funchi.

Chapter 8. RESULTS 28 Salina Wayaka Barrier.

Salina Wayaka has another heavily intervened barrier that however has not been completely controlled by human activities. In Playa Wayaka activities as digging channels to connect the salina with the sea, have happened many tines in the past (Fig. 8.39). It is also one of the cases studies on the effects of storms, after Hurricane Lenny, November 1999. (Bries et al., 2004) Fig 8.40.

Figure 8.37. Shoreline at Playa Wayaka.

Figure 8.38. Shoreline at Playa Wayaka.

Chapter 8. RESULTS 29

Figure 8.39 a. Shoreline at Playa Wayaka. Some years ago, channels were dig to easy communication between the salina and the sea, experiment with shrimp farming. These changes strongly altered the morphodynamics of the barriers and probably the Physical-chemical and biological characteristics of the salinas.

Figure 8. 39 b. Shoreline at Playa Wayaka, after Hurricane Lenny, November 1999. Picture taken looking south. After Bries et al., (2004)

Chapter 8. RESULTS 30 3

2.5

1.5

0.5 Meters aboveMeters sea level

0 02468 Distance from Tide line (m)

Figure 8.40. Beach profile measured at North end of Wayaka beach. Yellow line shows the June profile, Orange line November and Blue January.

Salina Slagbaai Barrier.

Slagbaai is probably one of the more affected salinas of WSNP. During decades was used as a harbor and for probably some centuries was partially dammed for salt production. At its Barrier with the ocean, exits the most important coastal infrastructures at WSNP. However, due to the fact that some partially empty beach zones exists, an analysis of beach geomorphology was done. Figures 8.41 and 8.42 show some views north to south of Boka Slagbaai.

Figure 8.41. Boka Slagbaai barrier view from the northwest.

Chapter 8. RESULTS 31

Figure 8.42. Slagbaai building facilities exists since at least 150 year and have stabilized the coast.

1.5

0.5 Meters abobe sea level 0 0 5 10 15 20 25 30 35 40 Distance from tide line (m)

Figure 8.43. Beach profile measured at South end of Boka Slagbaai. Yellow line shows the June profile, Orange line November and Blue January.

As it is possible to see, June profile is somehow softer than in November, and January that are practically identical.

Chapter 8. RESULTS 32 2.5

1.5

0.5 Meters above sea level Meters 0 -5 5 15253545 Distance from tide line (m)

Figure 8.44. Beach profile measured at north end of Boka Slagbaai. Yellow line shows the June profile, Orange line November and Blue January.

The beach profile shows an atypical parabolic form during June, while during November and January the form is very similar.

Salina Goto barrier, Salina Tam barrier and Salina Frans barrier.

The barriers of these three salinas had been too much interfered at their mouth to be considered for monitoring. They connection with the sea can be only underground, and each day diminished by human activities.

Chapter 8. RESULTS 33 8.3. INDIVIDUAL SALINAS 8. 3.1 MATJIS Matjis is the only salina in WSNP facing the windward, eastern coast of Bonaire. This salina presents the largest changes in volume and area. At the end of the dry season – June - water surface was limited to a small very shallow pond at the seaward end of the salina. In November during the peak of the rainy season, the salina occupied over 678 935 ha with a perimeter of 6 648 m but a maximum depth of 1m. Salinas’s behavior and threats vary according to two main sources; basin modification and the anchihaline relationship trough the mouth or “boca”. Table 8.2. Show the Basic data for Matijs Basin, including Landscape Units, Vegetation types of High Conservation Value (HCV), Soils and Landtypes (De Freitas et al., 2005).

Table 8.2. Basic statistics for Matijs Basin

BASIC STATISTICS FOR MATIJS BASIN

BASIN (m2) 8 950 083 PERIMETER (m) 21 319 BASIN_WITH OUT_SALINA (m2) 8 272 349 SALINA (m2) 678 935 SALINA PERIMETER (m) 6 648 SALINA MAX DEPTH (m) 1 HIGHER POINT IN BASIN (MOSL) 203 YUANA

WASHINGT 7 122 381 81.0 % PLANTATIONS (m2) SLAGBAAI 1 669 975 19.0 % COAST (m) 1 489 WASHINGTON_R 361 43.4 % PARK BORDERS IN BASIN (m) SLAGBAAI_RIN 471 56.6 % BASIN_OUT SIDE PARK (m2) 168 181 PARK OUT BASINS (Washington) (m2) 529 586 ROOIS IN MATIJS (TOTAL=119;182183 m ) 16 29 302 13 % FENCES (m) 2 868 ROUTES (m) 5 9 422 WALKING TRAILS (m) 3 2 112

Chapter 8. RESULTS 39

LANDSCAPE UNITS IN MATIJS BASIN

m2 % A AGRI_BUILTUP 255 772 2.8 B1 Sesuvium-Li 44 832 0.5 D1 Eragrostis_ 524 575 5.8 D2 Haematoxylo 1 814 289 20.1 D3 Prosopis_Ca 4 660 238 51.7 D4 Prosopis_Su 220 211 2.4 D5 Prosopis_Op 94 770 1.1 S2 Sesuvium sa 681 449 7.6 TL1 Lithophila 179 211 2.0 TL8 Prosopis_C 299 443 3.3 TL9 Prosopis_S 231 914 2.6

% OF HCV % OF TOTAL IN IN HIGH CONSERVATION VALUE (HCV) m2 BASIN PARK

D5 Prosopis 94 770 1.1 0.3 D3 Prosopis 4 660 238 51.7 16.8 Tabaku di peskatore 0.0 TOTAL HCV 4 755 009 53 17.1

SOILS AND LANDTYPES IN MATJIS BASIN m2 % cAB9 rooi bottom 369 337 4.5 IWu SOILS PLAINS 262 418 3.2 ROCKLAND DEPOSIT 175 789 2.1 Tc CORAL BEACHS 16 612 0.2 TL LOWER TERRACE 648 731 7.9 Wr_Wi Hilly land 5 748 792 69.7 Ws_Wx Stonyland 1 026 529 12.4

8.3.1.1. MATJIS BASIN LAND COVER The soils and land types in Matjis basin are almost completely (81%) from the Washikemba formation. These comprises two landscapes called “hilly land” the “very high and high hills” (WR in (Freitas et al., 2005) denomination, and the medium-high and low hills). So, the High Conservation Value (HCV) vegetation type D3, covers most of the basin. However, the main characteristic of this area is that is the only basin facing windward, also is the only place inside WSNP that has an area covered by “Tabaku di piskado”, and host half the High Conservation Value (HCV) area of Landscape unit D5 Prosopis-Opuntia rooi, present only where roois and creeks are important.

Chapter 8. RESULTS 40 Figure 8.47 shows the distribution of Landscape units in Matijs basin. Although D3 type is dominant, it is outstanding the number of different units present (11). Figure 8.48 shows the distribution soils in Matijs basin. Fig 8.49 Shows the High Conservation Value (HCV) areas of the basin. It is an important aspect to remark that near Matijs basin is the only WSNP area that partially drains outside park limits, as well as part of Matijs basin is outside of park boundaries. Fig. 8.50. Show the drainage system in and in the vicinity, of Matijs basin. As is possible to see, a large part of the most used area of the park (entrance and facilities) drain outside the park. On the other hand, some areas of the basin are outside park limits.

Figure 8.47: Landscape units in Matijs’ basin.

Chapter 8. RESULTS 41

Figure 8.48. Soil types in Matijs’ catchment area

Figure 8.49. Matijs’ High Conservation Value (HCV) areas.

Chapter 8. RESULTS 42

Figure 8.50. Drainage system in and in the vicinity of Matijs’ basin.

8.3.1.2. SALINA MATJIS WATER BALANCE; TIDAL AND RAIN INFLUENCE. At present Salina Matijs salina is anchihaline, this trait seems to be only over a century old, when most, or all “Bocas” at Bonaire northern tip, were closed by a storm (see chapter 8.2.1). As explained before, the pressure sensors placed to measure water level change in the salinas, were intended to ratify the anchihaline character of these water bodies, but they served other proposes, as measuring flooding water levels. In the case of Matijs, in June 2008 (end of dry season), the water level (max 15 cm) was to low to place a sensor. In November 2008 (full rainy season), and January 2009 (end of rainy season) the sensors could be placed and showed very interesting results. November 2008 was a very rainy season for Bonaire. In November 19, the sampling day for Matijs, precipitation at Flamingo Airport (Bonaire Weather Report, 2009) was 21.08 mm. This figure means, in a basin 8 950 083 m2, an

Chapter 8. RESULTS 43 input of 188 667 750 liters. Salina Matijs area is 678 935 sq meters, meaning an input of 278 mm of rain-water per salina sq-meter. The effect of the large amount of fresh water entering Matijs salina, joined to the tidal influence as can be seen at Figure 8.51 As it is possible to see, water level increased 10 cm in just 3 hours.

1 MATIJS NOVEMBER 0.9

0.8

0.7

0.6

Water level (m) RAIN 21.08 (mm) 0.5 H T

0.4 TIDAL INFLUENCE

TIDAL INFLUENCE 0.3 06:50:00 09:05:00 11:20:00 01:35:00 03:50:00 06:10:00 08:25:00 12:15:00 02:30:00 04:45:00 07:00:00 09:15:00 11:30:00 01:45:00 04:00:00 06:15:00 08:30:00 10:45:00 01:00:00 03:15:00 05:30:00 Time Nov 19_ 20

Figure 8.51. Water level changes in Matijs salina during sampling time in November 2008.

The total amount of water entering the salina is higher than the rain captured. Salina levels increased 0.51 m between 15:20 Nov 19 and 06:06 Nov 20. That means 345 217 m3 So water exchange with the sea was strong during this day. During January 2009, although rain was still strong and over average, the tidal influence was not so remarked. Figure 8.52. shows the behavior of water level at the salina and its delayed time between tides at the ocean (Bonaire Weather Report, 2009) and high and low water levels at Matijs salina. The behavior of Matijs salina water level, although with less amplitude than in the ocean (3-4 cm in salina and 25 – 30 cm at the ocean) is clearly related. It is important to remark the difference between the two high tides observed in this time span. At Kralendijk the difference between two consecutive high tides is less than 10% of tidal amplitude at Matijs salina is over 30%. We think it’s due to the rain input, at this time not very important, but still representing 7.87 liters/ m2 in Matijs basin. This data shows that Salina Matijs collects over 70 thousand m3 of rain water during a few (c 3) hours. This heavy runoff matched with high tide. During the next 4 hours of descending tide, from before midnight, to before

Chapter 8. RESULTS 44 sunrise, Salina Matijs discharged to the ocean an estimate of 5072 m3 /per hour of mixed tide water and runoff water.

0.6 MATIJS JANUARY

0.595

0.59

0.585

0.58

Water levelWater (m) 0.575

0.57 HIGH TIDE LOW TIDE

0.565

0.56 07:25:00 08:45:00 10:25:00 12:15:00 01:25:00 02:10:00 03:05:00 04:45:00 06:35:00 07:20:00 08:15:00 09:30:00 11:10:00 12:00:00 12:50:00 01:35:00 02:25:00 03:10:00 04:15:00 05:15:00 Time Jan 26 _27

Figure 8.52. Water level fluctuation at Salina Matijs during sampling dates 26 to 27 January 2009. L and H arrows show Low and High tides for Kralendijk.

8.3. 1.3. SALINA MATIJS HYDROGRAPHIC CONDITIONS

Salinas water conditions were analyzed by two methods, as explained in Chapter 7. Sensors for temperature and light intensity placed in different sampling stations and discrete samples both of water for nutrients (Nitrogen compounds) analysis and in situ measurements of T, O2, Salinity and Sechii disc depth, among other variables. Figure 8.53. Show the sampling localities at Salina Matijs. Light penetration is essential for salinas environment. Light allows photosynthesis, and many of the chemical reactions that regulate the water conditions. Figure 8.54 shows, as an example, light intensity measured by two different systems during November 19, 2008. Above are the results of continuous temperature and light intensity sensor (HOBO™ UA-002-64) programmed to take a data every five minutes and below a traditional system for discrete light penetration depth, a Sechii disk. Both show similar results, growing light penetration during the morning followed by and abrupt drop in the afternoon due to heavy rains.

Chapter 8. RESULTS 45

. Figure 8.53. Water sampling stations at Salina Matijs.

Chapter 8. RESULTS 46

Figure 8.54. Light intensity considered by two different systems during November 19, 2008 at Matijs. A) Temperature and light intensity sensor (HOBO™ UA-002-64) programmed to take a data each five minutes. B) Traditional system for discrete light penetration depth, a Sechii disk.

Table 8.3. shows the mean observed temperature for each studied month, the standard error and the upper and lower limits (mean ± 1 SE).

Table 8.3. Normal values for surface water temperature at Matijs.

Month Mean Standard error Lower limit (-1SE) Upper Limit (+1 SE 1 27.22 0.273 26.94 27.49 6 29.21 2.38 26.83 31.6 11 26.86 0.30 26.56 27.17

Chapter 8. RESULTS 47 Figure 8.55 is designed to allow to determine whether the data come from a process which is in a state of statistical control. When measures are outside UCL or LCL further study of the circumstances is needed.

30 UCL = 28.9

29 CTR = 27.3

LCL = 25.7 28

T oC 27

25 1611 Month

Figure 8.55. The CTR= Center Line indicates the general Temperature mean observed for all seasons at Matijs salina. UCL = Upper Control Limit, is placed 3 sigmas (98.5%) above is a warning level and LCL= Lower Control Limit, placed 3 sigmas (98.5%) below is the other limit.

Table 8.4. shows the mean observed oxygen values for each month sampled, the standard error and the upper and lower limits (mean ± 1 SE).

Table 8.4. Values that can be considered normal for surface water oxygen at Matijs.

Month Mean Standard error Lower limit (-1SE) Upper Limit (+1 SE 1 9.1 0.69 8.48 9.86 6 4.99 0.82 4.17 5.81 11 8 0.46 7.54 8.47

Chapter 8. RESULTS 48

Figure 8.56. Allows determining whether the data come from a process which is in a state of statistical control. When measures are outside UCL or LCL need a further study of the circumstances.

9.9 UCL = 9.7 8.9 CTR = 8.1

LCL = 6.5 7.9 O2 6.9

5.9

4.9 024681012 Month

Figure 8.56. The CTR= Center Line indicates the general O2 mean levels observed for all seasons at Matijs salina. UCL = Upper Control Limit, is placed 3 sigmas (98.5%) above is a warning level and LCL= Lower Control Limit, placed 3 sigmas (98.5%) below, is the other limit.

Table 8.5 shows the mean observed salinity values for each month sampled, the standard error and the upper and lower limits (mean ± 1 SE).

Table 8.5. Values that can be considered normal for surface water salinity at Matijs. Month Mean Standard error Lower limit (-1SE) Upper Limit (+1 SE 1 10.69 0.036 10.66 10.73 6 31.78 2.38 29.4 34.16 11 11.25 1.09 10.17 12.34

Figure 8.57. Allows determining whether the data come from a process which is in a state of statistical control. When measures are outside UCL or LCL a further study of the circumstances is needed. In the case of Matijs, although levels never exceed usual Oceanic Southern Caribbean levels, the difference found between June samples and other seasons is large.

Chapter 8. RESULTS 49 34 UCL = 16.0

30 CTR = 13.6

26 LCL = 11.1

Salinity 18

10 024681012 Month

Figure 8.57. The CTR= Center Line indicates the general salinity mean levels observed for all seasons at Matijs salina. UCL = Upper Control Limit, is placed 3 sigmas (98.5%) above is a warning level and LCL= Lower Control Limit, placed 3 sigmas (98.5%) below, is the other limit.

Fig 8.58. It is a plot of the individual salinity measured values, organized by subgroup. As it is possible to notice the general salinity levels are higher in June, but variability is largest al November.

40 USL = Nominal = 30 LSL =

Salinity

0 036912 Month

Figure 8.58. Tolerance Chart of the individual measured values, organized by subgroup. Horizontal line mean of the observations. Vertical lines indicate the subgroup ranges.

Salinity and temperature are the main physical variables associated to salinas behavior. Matijs, as explained before, is the only WSNP salina that can not be considered hypersaline. Exploring the spatio-temporal relations with physical variables we did not found, using a Multiple Regression Analysis, a significant relationship between the season (sampling month), the locality (sampling station), and the time of the day for water temperature. However, for salinity we did found, using also a Multiple Regression Analysis, a significant relationship between season and locality (R2=18.23 n=38 p= 0.022 being the model equation S % = 19.9373 +

Chapter 8. RESULTS 50 0.135941*MONTH - 3.48082*LOC). In fact temperature and salinity at Matijs showed a highly significant relationship (R2=19.11 n=39 p= 0.004). As showed in Fig 8.59.

Figure 8.59. Relationship between Salinity and temperature at Matijs salina

Nutrients and Water Balance.

The main reason for this study is the implicit relationship between water quality at Bonaire’s reef and runoff from the inland. Data indicates that heavy inputs of nutrients and sediments from Islands activities will also heavily impact reef health, and in Bonaire’s case, economical aspects. This project was not depicted to show the impact of an Inland Park (WSNP) runoff to the reef. Objectives were just to find values to establish a base line. However, the results show some critical points to control. In June sampling period Matijs salina was almost dry. The small shallow water area was heavily eutrophic and Nitrates concentrations were very high, with a mean of 40.1 ppm. During the heavy rains of November 2008, during the first few hours, nutrients in Matijs salina waters show an increase over 100%. However, even as water level was increasing as rain continued, nitrates levels in salina waters decreased. Fig 8.60. Show this double relationship. The decrease in nutrients in salinas water could be due to three different processes; dilution in a larger water volume, absorbed by the salina system or exported to the ocean with the outflow. Table 8.6. Show basic data of Nitrates concentrations in Matijs salina. The data shows that although the water volume increase was 60% the total Nitrates amount diminish. So the possible alternatives are “nitrates are absorbed by the salina system or exported to the ocean with the outflow”. The

Chapter 8. RESULTS 51 amount of export to the ocean its unknown but as Nitrates accumulate in sediments and are available to restart cycles, this is probably the main fate.

Table 8.6. Nitrates concentration and total NO3 amounts in Matijs salina during November.

3 3 ppm (gNO3)/m NO3 in sal (kg) salina Vol m 18.8 5 193 276 221 8.26 3 834 464 167

Nitrates input diminish due to basin washout Nitrates increase due to first runoff.

Figure 8.60. Water level variability and Nitrates concentration at Matijs salina on Nov 19 -20.

In January the situation changed slightly. It was still raining; during the sampling period and the day before, precipitation reached a total of 10.66 mm. Nitrates concentrations were smaller than in November and after an initial increase during the day hours stabilized with variations of only around 0.1 ppm (Fig 8.61). Ammonia levels on the other hand diminished as water levels decreased (Fig 8.63).

Chapter 8. RESULTS 52 0.6 7.7

0.595 7.6

0.59 7.5

0.585 7.4

Water level (m) 0.58 7.3 NITRA Nitrates (ppm) Water level(m) 0.575 HIGH TIDE LOW TIDE 7.2

0.57 7.1

0.565 7

0.56 6.9

0 :00 :00 :00 :00 :00 4:0 5 0:00 5:00 5:00 0:00 5:00 5 0:00 0:00 0 0:00 5:00 0 5:00 5:00 0 5:00 0:00 -0 3 :4 :2 3 :1 :2 5 :3 :5 3:1 1:2 4:5 08:3 09: 11 12:5 01:40:0002 0 04:4 06: 07 07:5 08:50:0010 1 12:1 12: 01 02:2 03:00:0003 0 05:35:00 , GMT ime T Time Jan 26 _ 27

Figure 8.61. Relationship between water levels and Nitrate concentration.

0.6 7.7 y = 4E-07x3 - 0.0001x2 + 0.0162x + 6.9464 0.595 R2 = 0.9635 7.6

0.59 7.5

0.585 7.4

0.58 7.3 Nitrates (ppm)Nitrates

Water level (m) level Water 0.575 7.2

0.57 MATIJS 7.1

0.565 7

0.56 6.9 08:15:00 09:00:00 10:20:00 11:45:00 12:30:00 01:15:00 01:50:00 02:20:00 03:00:00 04:15:00 05:00:00 06:30:00 07:00:00 07:30:00 08:10:00 08:50:00 09:40:00 10:50:00 11:40:00 12:10:00 12:45:00 01:15:00 01:45:00 02:20:00 02:50:00 03:30:00 04:10:00 04:50:00 05:25:00 Time, GMT-04:00 Time, Time Jan 26 _ 27

Figure 8.62. Relationship between water levels and Nitrate concentration at Matijs during January.

Chapter 8. RESULTS 53 0.6 0.35

0.595

0.3 0.59

0.585 0.25 Water level (m) 0.58 AMO 0.2

Water level (m) level Water 0.575 Ammonia (ppm) Ammonia

0.57 0.15

0.565

0.56 0.1 07:25:00 08:45:00 10:25:00 12:15:00 01:25:00 02:10:00 03:05:00 04:45:00 06:35:00 07:20:00 08:15:00 09:30:00 11:10:00 12:00:00 12:50:00 01:35:00 02:25:00 03:10:00 04:15:00 05:15:00 Time Jan 26 _ 27

Figure 8.63. Relationship between Water levels (due to tides and rain input) and Ammonia concentrations.

Dissolved oxygen and nitrogen compounds (ammonia and nitrate) are probably the most critical chemical aspects in a salina environment, both for its need for saline life scheme, and in this case for salinas influences on off shore reefs. The relation between the dissolved oxygen and physical characteristics as T°C, sampling site depth and salinity showed a significant negative relationship, being the negative aspect mostly due to salinity (R2=35.78 n=39 p= 0.0009). Equation predicting oxygen levels for Matijs is O2 mg.l = -6.512 + 0.497*T°C + 4.446*SITE depth m - 0.098*S). Figure 8.64.shows this relationship.

6.5

4.5

2.5

0.5

component effect -1.5

-3.5 0 10203040

S (su)

Figure 8.64. Relationship between the combined (component effect over dissolved oxygen levels, of physical characteristics as T°C, sampling site depth and salinity), plotted against salinity levels

Chapter 8. RESULTS 54 The relationship between chemical variables, as nitrogen compounds and oxygen levels and physical variables is an important aspect for water management. The biochemical transformation of nitrogen compounds is affected for both, the physical characteristics of water and oxygen levels. At Matijs the relationship between O2 and physical-chemical variables like Ammonia, Nitrates, T°C, SITE depth and Salinity is showed at Fig 8.65. As can be seen is a strongly negative relationship between the combined effect on oxygen levels (driven primarily for nitrate concentration) following the equation O2 mg/l = 31.32 + 0.00005* Ammonia - 3.42* Nitrates 0.176*T°C + 11.27* SITE depth m + 0.014*S %.

0.45

0.25

0.05

-0.15 component effect

-0.35 24 27 30 33

T C

Figure 8.65. Effects of Physical-chemical variables on oxygen levels (plotted as an example against temperature).

The relationship between the ammonia concentration and the oxygen is shown in Fig 8.66.

(X 1000) 55

-5 component effect

-25 5678910 O2

Figure 8.66. Effects of Physical-chemical variables on ammonia levels (plotted, as an example against oxygen levels).

Chapter 8. RESULTS 55

In contrast, the relationship between nitrates and oxygen is inverse, using O2, as the control variable. Fig 8.67 shows the results.

0.41 0.21 0.01

-0.19

component effect -0.39

-0.59 5678910

Figure 8.67. Effects of Physical-chemical variables on nitrate levels (plotted, as an example against oxygen levels).

Multivariate methods A principal component analysis was used to detect relationships between the different variables measured. Results using only physical and chemical data (without nutrients) show some clear relations (Fig 8.68). Using Physical and Chemical data without nutrient information the relationship represented as Principal Components show a logical scheme. The first three principal components account for 71 % of the variance. The Principal Component I includes; COMP I = - 0.094*Month + 0.39*LOC - 0.092*Time - 0.206*T°C + 0.376*O2 mg_l - 0.56*S % + 0.57*SITE depth m. It shows a clear tendency (negative correlation for salinity and site depth); this is of course, because when salina levels were high, due to heavy rains, salinity was low. More interesting are the relationships between O2, Sal and T°C. Salinity and Temperature are, as expected in a highly rain water influenced salina, lowly correlated. In the same way O2 and S and T are in neutral (near 0 correlation areas). On the other hand, when nitrogen compounds data are includied, (Fig 8.69) the results vary. Using the same procedure of Principal Component analysis and results shows that Ammonia is inversely correlated to water depth. That nitrates concentration is inversely related to salinity, and that T°C, Oxygen temperature and nitrates are independent.

Chapter 8. RESULTS 56

Figure 8.68. Byplot of the two first principal components of Physical and Chemical data at Matijs salina.

Figure 8.69. Byplot of the two first principal components of Physical and Chemical data at Matijs salina including nitrogen compounds.

Chapter 8. RESULTS 57 8.3.1.5. BEACH DUNES AND TABAKU THE PISKADO

This plant also know as Sea Rosemary and Sea Lavender is considered endangered at the Southern Caribbean. At WSNP the main site are the dunes at Boka chikitu. The area covered in two sections divided by the road to the beach are at present 729 sq.meters to the north and 920 sq meters to the south. No difference in area cover was detected from June to January samples. The sediment accumulation zones preserve the few existent dunes, habitat for a Caribbean-wide endangered species the locally called “tabaku di piscado” (Tournefortia gnaphaloides), a species especially susceptible to human activities at the dunes. For this reason its coverage must be monitored.

8.3.1.6. MANAGEMENT RECOMENDATIONS

Probably the main issue in salinas’s conservation and management strategies, but not the only one, is its possible influence in reef barrier water quality. So an important item is the way salinas act as a buffer over runoff water conditions. The key aspect to discern in this topic is how, when and how much, water exchange occurs between salinas and the sea. Other additional questions include, do all the salinas behave the same? Is the water exchange similar year around? What conditions regulate this exchange?. Probably the answer to all these questions is negative. Not all the salinas are even similar as far as their basic characteristics, so there is no reason to expect they behave in a similar way. However a handicap in this study is that we do not measure water quality variables at Open Ocean. It is difficult and expensive but some near shore measures can be taken. It is a recommendation of this report that during extreme events like the heavy rains of November data and water samples should be taken in the outer part of the “boka”. Other important aspect in salinas conservation and management efforts is their importance as water bird habitat. Flamingos are an emblematic species for Bonaire, but salinas are important for many other species of birds as well. In the case of Matijs, the basin or catchment area, is probably one of the most altered in WSNP, because is the most visited, has the larger mileage of roads and tracks and the large amount of goats and other feral mammals present.

Chapter 8. RESULTS 58 8. 3. 2. BARTOL

Bartol is the most northern salina in Bonaire. Is a deep salina (max 7 m), one of the two salinas in WSNP with a semi-permanent freshwater well in its basin (Pos Mangel), (Goto has a very important freshwater spring but is outside park boundaries) most of it’s basin surface is covered by High Conservation Value vegetation types and over 70% of the basin consist of hilly land. Salinas’s behavior and threats vary according to two main sources; basin modification and the anchihaline relationship trough the mouth or “boka”.

Table 8.5 shows the Basic data for Bartol Basin, including Landscape Units, Vegetation types of High Conservation Value (HCV), Soils and Land types (Freitas et al., 2005).

BASIC STATISTICS FOR BARTOL BASIN

BASIN (m2) 5 260 991 PERIMETER (m) 14 796 BASIN_WITH OUT_SALINA (m2) 4 978 984 SALINA (m2) 282 007 SALINA PERIMETER (m) 3 924 SALINA MAX DEPTH (m) 7 HIGHER POINT IN BASIN (mosl) 186 HOBAS WASHINGTON 5 143 270 97.8 % PLANTATIONS (m2) SLAGBAAI 117 699 2.2 % COAST (m) 1 478 PARK BORDERS IN BASIN (m) 0 PARK OUT BASIN 0 ROOIS IN BARTOL (TOTAL=119;182 m ) 4 16 044 3.4 8.8 POS IN BASIN POS MANGEL 1 FENCES (m) 0 ROUTES (m) 3 4 826 WALKING TRAILS (m) 0

LANDSCAPE UNITS IN BARTOL BASIN m2 % B1 Sesuvium-Li 27 079 0.5 D1 Eragrostis_ 1 331 151 26.5 D3 Prosopis_Ca 3 289 435 65.4 D5 Prosopis_Op 93 851 1.9 S2 Sesuvium sa 62 877 1.3 TL 7 Croton Prosopis LT 221 880 4.4

5 026 274 100

% % OF OF HIGH CONSERVATION VALUE (HCV) TOTAL HCV D5 Prosopi 93851.37 1.9 2.8 D3 Prosopi 3289435.09 66.1 97.2

Chapter 8 RESULTS. 59 TOTAL 3383286.46 67.9513423 100

SOILS AND LANDTYPES IN BARTOL BASIN m2 % cAB9 rooi bottom 364 018 7.3 Tc CORAL BEACHS 19 184 0.4 TL LOWER TERRACE 242 515 4.9 Tr Terrace remanents 12 340 0.2 Wr_Wi Hilly land 3 513 791 70.4 Ws_Wx Stonyland 840 906 16.8

4 992 754 100

8.3.2.1. BARTOL BASIN LAND COVER.

The vegetation and landscape types of Bartol basin after (De Freitas et al., 2005) are shown in Fig 8.70. As is clear most of the basin is covered by D3 (after De Freitas et al., 2005) vegetation type. However this salina basin together with Matijs are the only ones that have vegetation type D5 (Prosopis - Opuntia Rooi). Over 68% of Bartol Basin is covered by High Conservation Value (HCV) landscapes. This makes it an important conservation basin in WSNP. Fig 8.71 Shows the soil types at Bartol basin. Here is a clear geological boundary. The southern part of the basin is almost completely Wr_Wi Hilly land and Ws_Wx Stonyland. However the northern part is built by more recent, calcareous soils.

Figure 8.70. Landscape units in Bartol Basin. Keys to colors and patterns are in Figure 5.5

Chapter 8 RESULTS. 60

Figure 8.71. High Conservation Value (HCV) landscapes in Bartol Basin. Keys to colors and patterns are in Figure 5.6

Figure 8.72. Soil types at Bartol basin. Keys to colors and patterns are in Figure 5.4.

Chapter 8 RESULTS. 61

Figure 8.73 Drainage system of Bartol Basin.

Salina Bartol drainage system is important. Although Salina Bartol is only fifth in maximum flooded area, only larger than Frans, Funchi, Tam and Wayaka, it has a catchment area double of Wayaka and Funchi and well over Frans and Tam. In its basin there is one of the only two semi permanent fresh water wells in the park (Pos Mangel). All this traits make Bartol Basin an important conservation area.

8.3.2.2. SALINA BARTOL WATER BALANCE; TIDAL AND RAIN INFLUENCE. At present Salina Bartol is anchialine, this trait seems to be only over a century old, when most, or all “Bokas” at Bonaire northern tip, were closed by a storm (see chapter 9.1). During June sampling time no rain felled at Bartol Basin. The Water level (Fig 8.74 and 8.75) shows a good correspondence with tides at “Playa”. The tidal difference was of less than 5 cm. This is about a 1/6 of oceanic tidal amplitude. During November in the middle of an especially strong rainy season situation was different. Figure 8.76, shows the important impact of rains. Bartol salina level increased over 20 cm in 24 hours. During January rains were of less magnitude but still have an important influence at Bartol water level (Fig. 8.77).

Chapter 8 RESULTS. 62 0.94 BARTOL JUNE

0.935

0.93

0.925

0.92

0.915 Water level (m)

0.91 HIGH TIDE

LOW TIDE 0.905 LOW TIDE

0.9 08:25:00 08:55:00 09:20:00 09:45:00 10:10:00 11:15:00 12:45:00 01:10:00 01:45:00 02:10:00 02:35:00 03:10:00 03:35:00 04:00:00 04:25:00 05:00:00 05:25:00 05:50:00 06:15:00 Time Jun 18_19

Figure 8.74. Water level change during June at Salina Bartol and its relation with tide levels at “Playa”.

BARTOL JUNE 1.42

1.41

1.4

1.39

1.38 LOW TIDE

Water level (m) HIGH TIDE

1.37

1.36 7:50:00 2:25:00 3:35:00 4:45:00 6:45:00 7:50:00 8:45:00 9:40:00 1:30:00 3:50:00 5:00:00 10:10:00 11:25:00 12:40:00 11:00:00 12:25:00 Time Jun 18 _ 19

Figure 8.75. Water level change during June at Salina Bartol at another sampling station, and its relation with tide levels at “Playa”.

Chapter 8 RESULTS. 63

BARTOL NOVEMBER 0.8

0.75

0.7

Water level (m) Water 0.65 RAIN 22.1 mm

0.6 08:15:00 09:35:00 10:55:00 12:15:00 01:35:00 02:55:00 04:15:00 05:35:00 06:55:00 08:15:00 09:35:00 10:55:00 12:15:00 01:35:00 02:55:00 04:15:00 05:35:00 06:55:00 Time Nov 20 -21

Figure 8.76. Water level fluctuations at Salina Bartol during November. Tidal influence, clear during June samples is blocked by rain input.

0.92 BARTOL JANUARY 0.91

0.9

0.89

0.88

Water level (m) 0.87

0.86 HIGH TIDE

LOW TIDE 0.85

0.84 07:10:00 08:25:00 09:30:00 10:50:00 12:50:00 01:50:00 03:00:00 04:30:00 05:45:00 06:45:00 07:45:00 09:05:00 10:25:00 11:45:00 01:10:00 02:10:00 03:10:00 04:10:00 Time Jan 27 _ 28

Figure 8.77. Salina Bartol water level variation during January.

Chapter 8 RESULTS. 64 8.3. 2. 3. SALINA BARTOL HYDROGRAPHIC CONDITIONS

Figure 8.78 show the location of the water sampling stations at Bartol.

Figure 8.78. Salina Bartol water sampling stations.

Figures 8.79 and 8.80 show the results of hobo sensors for temperature and light. In 8.79 is possible to see a clouded day during June with only a brief sunny moment just before 8:00 am. Light penetration was very low during all sampling time. Temperature behaves as expected varying from 28.5 early in the morning to 33.2 at 6:00 pm Figure 8.80, with data taken during January, shows a different aspect. At this time of the year climate over southern Caribbean typically show a high albedo with clear nights when most heat losses to upper atmosphere. This is clearly seen in the over 2 ˚C drop during dawn both sampling days at Bartol. Figure 9.81 shows the seasonal variation of water temperature, means and confidence intervals and Fig 9.82 and 9.83 the same data but for oxygen and salinity. Temperature varies from means over 29 ˚C in June to below 27 ˚C.

Chapter 8 RESULTS. 65

Figure 8.79. Temperature and light intensity data at Salina Bartol during June sampling period.

Figure 8.80. Temperature and light intensity data at Salina Bartol during January sampling period.

Chapter 8 RESULTS. 66

Table 8.6 Shows basic Physical-chemical water data for Bartol Salina.

TEMPERATURE Month Count Average Median SD 1 15 27.7293 27.35 0.983133 6 15 29.8947 29.42 1.59448 11 30 26.424 26.375 0.770936 OXIGEN Month Count Mean Standard error Lower limit Upper limit 1 15 5.52 0.336948 4 4.79732 6.24268 6 15 4.524 0.677712 3 3.07045 5.97755 11 30 10.2987 0.425498 9 9.42842 11.1689 Standard error Lower limit Upper limit SALINITY Month Count Mean 1 15 62.282 0.134936 6 61.9926 62.5714 6 15 117.332 0.890792 1 115.421 119.243 11 30 22.8117 2.69264 1 17.3046 28..3187

Mean and 95%Mean confidencelimits 26 1611

Figure 8.81. Mean temperature and 95Mo %nth confidence registered at Bartol during sampling days.

Figure 8.81. Temperature data at Salina Bartol during sampling period.

Chapter 8 RESULTS. 67

3 1611

Dissolved Oxigen Mean and 95% confidence limits mg/l Month

Figure 8.82. Mean dissolved oxygen levels and 95 % confidence registered at Bartol during sampling days.

120

105

90 75

60 45

15 Salinity(SI) Mean and 95% confidencelimits 1611 Month

Figure 8.83. Mean Salinity levels and 95 % confidence registered at Salina Bartol during sampling days.

Nutrients data.

Ammonia data for Bartol are only available for November (one sample) and January. Initial Study for Ammonia at Bartol Number of observations = 6 X Chart (Fig 8.84) is designed to allow you to determine whether the data come from a process which is in a state of statistical control. The control charts are constructed under the assumption that the data come from a normal distribution

Chapter 8 RESULTS. 68 with a mean equal to 25.5667 and a standard deviation equal to 32.1507. These parameters were estimated from the data. UCL: +3.0 sigma = 122.019 Centerline = 25.5667 LCL: -3.0 sigma = -70.8855 MR(2) Chart UCL: +3.0 sigma = 118.54 Centerline = 36.266 LCL: -3.0 sigma = 0.0 We cannot reject the hypothesis that the process (samples) is in a state of statistical control at the 90% or higher confidence level.

X Chart for Ammonia 130 120 UCL = 122.0 110 100 CTR = 25.6 90 80 LCL = -70.9 70 60 50 40 Ammonia (ppm) Ammonia 30 20 10 0

1.0 1.0 1.0 1.0 1.0 11.0

Figure 8.84. X Chart for ammonia at Bartol.

X Chart for Nitrates

80 UCL = 76.9 70 CTR = 45.9 60

50 LCL = 15.0

40 30 Nitrates (ppm) Nitrates 20 10 0 1.0 1.0 1.0 1.0 1.0 11.0 11.0

Figure 8.85. X Chart for Nitrates at Bartol.

Chapter 8 RESULTS. 69 NITRATES X and MR(2) - Initial Study for Nitrates Number of observations = 16 X Chart UCL: +3.0 sigma = 76.9133 Centerline = 45.9487 LCL: -3.0 sigma = 14.9842 8 beyond limits MR(2) Chart UCL: +3.0 sigma = 38.0554 Centerline = 11.6427 LCL: -3.0 sigma = 0.0 1 beyond limits Estimates Process mean = 45.9487 Process sigma = 10.3215 Mean MR(2) = 11.6427 X Chart (Fig 8.85) is designed to allow determining whether the data come from a process which is in a state of statistical control. The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to UCL: +3.0 sigma = 76.9133; Centerline = 45.9487 LCL: -3.0 sigma = 14.9842.

Relationship between Nutrients, Tides and Rain

The relationship between these variables is very interesting. During June with only tidal influence nitrates varied in a similar way as tidal influence (water level), with some 4 – 5 hours lag (Fig 8.86).

1.42 90

85 1.41 BARTOL

80 1.4

1.39

70 Nitrates (ppm) Water level (m) Water

1.38 65

1.37 60 RAIN 22.1 mm

1.36 55 0 0 00 00 00 00 :00 :00 00 00 :00 :00 :00 5 5 :05: :25: 55: :25: 15: 5 4 1: 0: 2: 07:50:0010:05: 11 12 0 02:45 04:00:0005:05 06:50:0007:50:008:40:009 1 11: 1 02:50 04:00:0005:05:00 Time Jun 6 _ 7

Figure 8.86. Nitrates and water level variability during June sampling days

Chapter 8 RESULTS. 70

During November, heavy rains that increased salina water level over 20 cm in less than 3 hours is related with a pronounced diminish in Nitrates concentration (Fig. 8.87). During January sampling period at Bartol salina water level was lowering after 3 days with accumulated precipitations of 10.66mm. Nitrates concentration lowered with the water level and later stabilized (Figs 8.88 and 8.89) Ammonia levels showed a more erratic behavior.

1 18 BARTOL 0.95 16 0.9

0.85 14

0.8

0.75 12 Nitrates (ppm) Nitrates Water level (m) 0.7 y = -3E-06x3 + 0.0011x2 - 0.1237x + 17.586 10 0.65 R2 = 0.9433

0.6 8

0 :00 :00 :00 :00 :00 :00 :00 0:00 5:00 :15 :30 :45 :00 :15 :30 :0 :1 :30:00 :15:00:30:00:45:00:00 :15:0 :30:00:45:00 7 08 09 10 12 01 02 03:45:0005 06 0 08:45:0010:00:0011 12 01 03 04 05 06 Time Nov 20 _ 21

Figure 8.87. Nitrates and water level variability during November sampling days

Chapter 8 RESULTS. 71 0.92 41

0.91 40.5

0.9 40

0.89 39.5 0.88 39

0.87 Nitrate (ppm) Water level (m) 38.5 0.86

0.85 38

0.84 37.5

0 0 0 0 :00 :00 0 :00 :00 :00 0 :00 :00 0: 5: 5: 5 00 30 4 :45 05 25 4 :10 10 3: 9: 3: 07:10:0008:25:0009:30:0010: 12:50:0001:50:000 04: 05: 06:45:0007 0 10: 11: 01:10:0002 0 04:10:00 Time Jan 27 _ 28

Figure 8.88. Nitrates and water level variability during January sampling days.

0.92 41.5 y = 4E-07x3 + 5E-05x2 - 0.0342x + 40.915 2 0.91 R = 0.9872 41

0.9 40.5

0.89 40

0.88 39.5

0.87 39 (ppm) Nitrate Water level (m) level Water

0.86 38.5

0.85 38

0.84 37.5

0 0 0 0 0 0 0 0 0 0 :0 0 0 :0 0 :0 :0 0 :0 0 0 0: 5: 5 0: 0 5 0: 0 5: :15:00:2 3 2 :20:00:3 5 :55:00:5 :0 2 :55:00:5 4 :05:00 8 9 0: 2 3 6 7 9 2 1 5 07:10:000 0 1 12: 01:25:000 0 04: 06:00:000 0 0 10: 11:35:001 0 02: 03:40:000 Time Jan 27 _ 28

Figure 8.89. Nitrates and water level variability during January sampling days

Chapter 8 RESULTS. 72 0.92 11 BARTOL JANUARY

0.91 10

0.9 9 0.89 8 0.88 7 0.87 Water level (m) Ammonia (ppm) 6 0.86

0.85 5

0.84 4

0 0 0 0 0: 0: 0:00 0:00 5:00 5:00 5:00 :1 :3 :5 :0 :4 4 2 7: 0: 1:10:00 3:10:00 07 08:25:0009 10:50:0012 01:50:0003 04:30:0005 06:45:000 09:05:001 11:45:000 02:10:000 04:10:00 Time Jan 27 _ 28

Figure 8.90. Ammonia and water level variability during January sampling days

8.3.2. 4. MANAGEMENT RECOMENDATIONS

The relationship between Nutrients, tides and rain is probably the most important item to monitor. As rain at Bonaire can be unpredictable in occurrence and intensity a sampling program can be difficult to plan. However, an emergency plan designed to, at the beginning of heavy rains, measure variables as water levels, salinity and nutrients both at salinas and at sea near shore bokas is a strong recommendation of this report.

Chapter 8 RESULTS. 73 8. 3. 3. FUNCHI

Funchi is a small salina, just south of Benge, a salina not included in this study. Funchi is the smallest salina in WSNP, and after Wayaka has the smallest basin area in WSNP. Funchi has the influence of two “great” mountains in WSNP including Brandaris. So rain flooding water should be expected to have a large influence. Vegetation types at Funchi are not very diverse. Most of the basin area is covered by Prosopis_Ca The only HCV (High conservation value) in the basin.

Table 8.7 Shows the Basic data for Funchi Basin, including Landscape Units, Vegetation types of High Conservation Value (HCV), Soils and Land types (De Freitas et al., 2005).

BASIC STATISTICS FOR FUNCHI BASIN m 2 BASIN (m2) 2 086 264 PERIMETER (m) 6 934 BASIN_WITH OUT_SALINA (m2) 2 018 678 SALINA (m2) 67 586 SALINA PERIMETER (m) 1 653 SALINA MAX DEPTH (m) 1 HIGHER POINT IN BASIN (mosl) 240 BRANDARIS WASHINGTON 1 113 632 53.4 % PLANTATIONS (m2) SLAGBAAI 971 246 46.6 % COAST (m) PARK BORDERS IN BASIN (m) PARK OUT BASIN ROOIS IN FUNCHI TOTAL=119;182183 m ) 4 5 617 3.4% POS IN BASIN FENCES (m) ROUTES (m) 3 2 590 WALKING TRAILS (m) LANDSCAPE UNITS IN FUNCHI BASIN m2 % B1 Sesuvium-Li 3 637 0.2 D1 Eragrostis_ 566 419 28.0 D2 Haematoxylo 80 464 4.0 D3 Prosopis_Ca 1 186 305 58.7 S2 Sesuvium sa 19 359 1.0 TL 7 Croton Prosopis LT 164 073 8.1

HIGH CONSERVATION VALUE (HCV) % OF TOTAL

D3 Prosopis_Ca 1 186 305 58.7 TOTAL 1 186 305 58.7 SOILS AND LANDTYPES IN FUNCHI BASIN cAB9 rooi bottom 443 507 21.9 Tc CORAL BEACHS 5 257 0.3 TL LOWER TERRACE 160 091 7.9 Wr_Wi Hilly land 944 497 46.6 Ws_Wx Stonyland 473 733 23.4

Chapter 8. RESULTS 74 8.3.3.1. FUNCHI BASIN LAND COVER

A large amount of Funchi basin is covered by D3 Prosopis_Ca vegetation type. (Fig 8.91).

Figure 8.91. Funchi basin landscape types. Keys to colors and patterns are in Fig 5.5

Chapter 8. RESULTS 75

Figure 8.92. Funchi High Conservation Value (HCV) areas. Keys to colors and patterns are in Fig. 5.6

Figure 8.93 Funchi basin soil types. Keys to colors and patterns are in Figure 5.4.

Chapter 8. RESULTS 76

Figure 8.94. Funchi drainage system

8.3.3.2. SALINA FUNCHI WATER BALANCE, TIDAL AND RAIN INFLUENCE.

As in most salinas during June sampling days the tidal influence was clear. Figures 8.95 and 8.96 show the tidal influence at two different sampling stations.

1.88

1.875

1.87

1.865

1.86

1.855

1.85

Water level (m) 1.845

1.84 LOW TIDE 1.835 HIGH TIDE

1.83 07:50:00 08:50:00 10:00:00 11:20:00 12:25:00 01:40:00 02:25:00 03:40:00 04:40:00 05:55:00 06:55:00 07:55:00 08:45:00 09:30:00 10:40:00 12:05:00 01:00:00 02:10:00 03:15:00 04:50:00 05:40:00 Time June 19 _ 20

Figure 8.95. Water level variation at Funchi during June.

Chapter 8. RESULTS 77 1.27

1.26

1.25

1.24 LOW TIDE Water level (m) level Water 1.23 HIGH TIDE

1.22 9:00:00 2:25:00 3:35:00 4:40:00 6:25:00 7:40:00 8:50:00 9:55:00 1:10:00 2:45:00 4:00:00 5:25:00 6:40:00 10:05:00 11:30:00 12:45:00 11:20:00 Time 19-20 June

Figure 8.96. Water level variation at Funchi during June at another sampling station.

0.99 FUNCHI NOVEMBER 0.98

0.97

0.96 NO RAIN (0.51 mm) ON 22 0.95 RAIN 0.94

0.93 Water level (m) level Water 0.92 LOW TIDE HIGH TIDE 0.91 LOW TIDE HIGH TIDE 0.9 11:30:00 12:40:00 01:50:00 03:00:00 04:10:00 05:20:00 06:30:00 07:40:00 08:50:00 10:00:00 11:10:00 12:20:00 01:30:00 02:40:00 03:50:00 05:00:00 06:10:00 07:20:00 08:30:00 09:40:00 10:50:00 12:00:00 Time Nov 21 - 22

Figure 8.97 Water level variation at Funchi during November

During November rain influence was clear. The first sampling day (21) heavy rains had occurred. So tidal influence was overlapped.

Chapter 8. RESULTS 78 0.8

0.79

0.78

0.77

Water level (m) 0.76

LOW TIDE 0.75 HIGH TIDE

0.74

:00 :00 :00 :00 :00 :00 :00 :00 5 :10 :10 07:3 08:40:0010:50:0012:00:0001:00:0002:10:0003:10:0004:10:0005:10:0006:10:0007:10:0008:10:0009:10 10:10 11:10 12 01 02:30:0004:45 05:45 Time JAN 28 _ 29

Figure 8.98. Tidal influence at Salina Funchi.

8.3. 3.3. SALINA FUNCHI HYDROGRAPHIC CONDITIONS

Figure 8.99. Water quality sampling stations at Funchi.

Chapter 8. RESULTS 79

The most remarkable aspect of Funchi’s water characteristics is its large seasonal variability and very small daily difference. Figure 8.100. Shows the salinity difference. In table 8.8 is possible to see that difference between mean salinity at November and June is over 185 salinity units but, differences among the 11 and 15 samples for each month do not exceed one unit.

Figure 8.100. Salinity variability at Funchi.

12 11 10 9 8 7 6 5 4 3 2 1 0 OxigenMean and 95% confidencelimits mg/l 1116

Month

Figure 8.101. Oxigen variability at Funchi.

Chapter 8. RESULTS 80

35 34 33 32 31 30 29 28 27 26 25 C limits 95% confidence and Mean Temperature 1116

Month

Figure 8.102. Temperature variability at Funchi.

Table 8.8. Means, Standard error and 95% confidence limits for variables

SALINITY Standard Lower Upper Month Count Mean Error Limit Limit 1 15 33.6227 0.456744 32.643 34.6023 11 15 10.182 0.814862 8.43429 11.9297 6 11 196.678 0.456138 195.662 197.695 Total 41 68.7934 12.3513 43.8304 93.7564 OXIGEN Standard Lower Upper Code Count Mean Error Limit Limit 1 14 9.67143 0.93933 7.64213 11.7007 11 15 1.65 0.0848865 1.46794 1.83206 6 11 1.23909 0.0715727 1.07962 1.39857 Total 40 4.3445 0.704797 2.91891 5.77009 TEMPERATURE Standard Lower Upper Code Count Mean Error Limit Limit 1 15 26.878 0.464196 25.8824 27.8736 11 15 27.86 0.192312 27.4475 28.2725 6 11 32.4764 1.02389 30.195 34.7577 Total 41 28.7393 0.484845 27.7594 29.7192

Nutrients data.

Ammonia data for Funchi are limited to one sample in November and 5 in January. The following data explain the control charts showed in Figures 8.103 (Ammonia) and 8.104 (Nitrates). X-bar and Range - Initial Study for Ammonia

Chapter 8. RESULTS 81 X-bar Chart UCL: +3.0 sigma = 2.61344 Centerline = 0.940883 LCL: -3.0 sigma = -0.731676 0 beyond limits Range Chart UCL: +3.0 sigma = 4.20735 Centerline = 1.63485 LCL: -3.0 sigma = 0.0 0 beyond limits Estimates Process mean = 0.940883 Process sigma = 0.965653 Mean range = 1.63485 This procedure creates X-Bar and R charts for Ammonia. It is designed to allow determining whether the data come from a process which is in a state of statistical control. The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 0.940883 and a standard deviation equal to 0.965653. These parameters were estimated from the data. Of the 2 nonexcluded points shown on the charts, 0 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart. Since the probability of seeing 0 or more points beyond the limits just by chance is 1.0 if the data comes from the assumed distribution, we cannot reject the hypothesis that the process is in a state of statistical control at the 90% or higher confidence level. For Nitrates results show a different situation, with a high variability and out of control data. X-bar and Range - Initial Study for Nitrates X-bar Chart UCL: +3.0 sigma = 20.5838 Centerline = 18.0845 LCL: -3.0 sigma = 15.5853 2 beyond limits Range Chart UCL: +3.0 sigma = 7.31422 Centerline = 3.09 LCL: -3.0 sigma = 0.0 1 beyond limits Estimates Process mean = 18.0845 Process sigma = 1.59525 Mean range = 3.09 The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 18.0845 and a standard deviation equal to 1.59525. These parameters were estimated from the data. Of the points shown on the charts, 2 are beyond the control limits on the first chart while 1 is beyond the limits on the second chart. Since the probability of seeing 2 or more points beyond the limits just by chance is 2.10054E-9 if the data comes from the assumed distribution, we can declare the process to be out of control at the 99% confidence level.

Chapter 8. RESULTS 82 X-bar Chart for Ammonia 3 UCL = 2.6 CTR = 0.9 2 LCL = -0.7

1 (ppm) Ammonia

0 1357911 Month

Figure 8.103. Control charts for Ammonia at Funchi.

X-bar Chart for Nitrates

30 UCL = 20.6 25 CTR = 18.1

LCL = 15.6 20

15 Nitrates (ppm) 10

5 024681012

Month

Figure 8.104. Control chart for Nitrates at Funchi.

Relationship between nutrients tides and rain

Figures 8.105 to 8.107 show the relation between tides and rain water flood and nutrients. The relation between lowering water level and Nitrates for November is highly significant. This situation was after a heavy rain. Nitrates at salina increased and may have been exported by infiltration to the nearby sea or could be reabsorbed by sediments. Nitrates during January show a special feature. It is seen an increase with the lowering waters but also show a cycle decreasing as water level increases with the tide.

Chapter 8. RESULTS 83

0.99 y = 1E-05x2 - 0.0021x + 5.8881 6.5 R2 = 0.9039 0.98 6.4 0.97 6.3 0.96 6.2

0.95 6.1 0.94

6 Nitrates (ppm) Nitrates (m) level Water 0.93

5.9 0.92 5.8 0.91 0.9 5.7

0 0 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :0 :00 0 5 0 5 3 5 2 4 10:0 5: 7: 8: 0: 11:30:0012:55:0002:20 03:45 05:10 06:35 08:00 09:25 10:50 12:15 01:40 03:05 04: 0 0 0 1 11:35 Time Nov 21 _ 22

Figure 8.105. Relationship between water level and nitrates at Funchi.

0.8 y = -1E-06x3 + 0.0005x2 - 0.0735x + 3.3794 4 R2 = 0.9749 3.5 0.79 3 0.78 2.5

0.77 2

1.5

0.76 (ppm) Ammonia Water level (m) level Water 1 0.75 0.5

0.74 0 07:35:00 08:40:00 10:50:00 12:00:00 01:00:00 02:10:00 03:10:00 04:10:00 05:10:00 06:10:00 07:10:00 08:10:00 09:10:00 10:10:00 11:10:00 12:10:00 01:10:00 02:30:00 04:45:00 05:45:00 Time Jan 28 _ 29

Figure 8.106. Relation between water level and Ammonia during January at Funchi.

Chapter 8. RESULTS 84

y = 5E-06x3 - 0.0018x2 + 0.1889x + 23.12 0.8 31 R2 = 0.8972 30 0.79 29

0.78 28

27 0.77 26 Nitrates (ppm) Nitrates

Water level (m) level Water 0.76 25

24 0.75 23

0.74 22 07:35:00 08:40:00 10:50:00 12:00:00 01:00:00 02:10:00 03:10:00 04:10:00 05:10:00 06:10:00 07:10:00 08:10:00 09:10:00 10:10:00 11:10:00 12:10:00 01:10:00 02:30:00 04:45:00 05:45:00 Time Jan 28 _ 29

Figure 8.107. Relation between water level and Nitrates during January at Funchi.

8.3.3. 4. MANAGEMENT RECOMENDATIONS

Salina Funchi, because of it small size could be taken as an example to deepen in the relation between tides, rain run off and nutrients. Its amazing stability in salinity during the different seasons should be confirmed.

Chapter 8. RESULTS 85 8. 3. 4. WAYAKA

Salina Wayaka is very close to Slagbaai a heavily intervened salina. It is one of the salinas that show strongly non convection stratification. This means high water temperatures and salinities at depth.

8.3.4.1. WAYAKA BASIN LAND COVER

Table 8.9. BASIC STATISTICS FOR WAYAKA BASIN

m 2 BASIN (m2) 1 353 043 PERIMETER (m) 6 071 BASIN_WITH OUT_SALINA (m2) 1 187 362 SALINA (m2) 165 682 SALINA PERIMETER (m) 2 850 SALINA MAX DEPTH (m) 1 HIGHER POINT IN BASIN (mosl) 240 BRANDARIS PLANTATIONS (m2) SLAGBAAI 1 353 043 100 % COAST (m) 852 PARK BORDERS IN BASIN (m) 0 PARK OUT BASIN 0 ROOIS IN WAYAKA TOTAL=119;182183 m ) 5 2 071 4.2 1.1 % POS IN BASIN (Pos Nobo) 1 FENCES (m) 0 ROUTES (m) 2 3 426 WALKING TRAILS (m) 0

LANDSCAPE UNITS IN WAYAKA BASIN m2 % B1 Sesuvium-Li 10 776 0.9 D1 Eragrostis_ 110 289 9.1 D3 Prosopis_Ca 794 424 65.6 S2 Sesuvium sa 29 618 2.4 TL 7 Croton Prosopis LT 237 660 19.6 TM9 Prosopis_Euphorbia MT 28 249 2.3

1 211 016 100

HIGH CONSERVATION VALUE (HCV) % OF TOTAL % OF HCV

D3 Prosopis_Ca 794 424 65.6 100.0 TOTAL 794 424 65.6 100

SOILS AND LANDTYPES IN WAYAKA BASIN m2 % Tc CORAL BEACHS 18 231 1.5 TL LOWER TERRACE 211 547 18.0 Tr Terrace remnants 74 416 6.3 Wr_Wi Hilly land 285 155 24.2 Ws_Wx Stonyland 588 590 50.0

1 177 939 100

Chapter 8. RESULTS. 86

Figure 8.108. Wayaka Landscape units. Key to colors and patterns is in Fig 5.5.

Figure 8.109. Wayaka High Conservation Value areas. Key to colors and patterns is in Fig 5.6.

Chapter 8. RESULTS. 87

Figure 8.110. Wayaka soils and land types. Key to colors and patterns is in Fig 5.4.

Figure 8.111. Wayaka Drainage system.

8.3.4.2. SALINA WAYAKA WATER BALANCE, TIDAL vs RAIN INFLUENCE.

Similar to other salinas, Wayaka, during June shown a normal tidal behavior with amplitudes near 3 cm and time lags around 5 hours. During November amplitude increased to 4 cm.

Chapter 8. RESULTS. 88

LOW TIDE

HIGH TIDE

Figure 8.112. Water level variation at Wayaka during June.

1.38 WAYAKA JUNE 1.375

1.37

1.365

1.36

1.355

1.35

Water level (m) 1.345

1.34 HIGH TIDE 1.335 LOW TIDE 1.33 07:30:00 08:35:00 09:50:00 10:55:00 12:00:00 01:40:00 02:45:00 03:50:00 04:55:00 06:00:00 07:10:00 08:25:00 09:40:00 11:20:00 12:35:00 01:40:00 02:45:00 03:55:00 05:15:00 06:30:00 Time June 20 - 21

Figure 8.113 Water level variations at another sampling station at Wayaka during June.

Chapter 8. RESULTS. 89

0.91

0.9

0.89

0.88 Water level (m) level Water

HIGH TIDE 0.87 HIGH TIDE

LOW TIDE LOW TIDE 0.86 07:10:00 07:55:00 08:40:00 09:25:00 10:10:00 11:00:00 11:45:00 12:30:00 01:15:00 03:05:00 03:50:00 04:35:00 05:20:00 06:25:00 07:30:00 08:45:00 09:45:00 11:15:00 01:10:00 02:05:00 03:05:00 04:50:00 05:50:00 Time Nov 22 - 23

Figure 8.114 Salina Wayaka water level variations during November.

0.97 WAYAKA JANUARY 0.965

0.96

0.955

0.95

0.945

0.94 Water level (m) level Water

0.935

0.93 HIGH TIDE LOW TIDE

0.925

0.92

00 00 00 00 00 00 :00 00 :00 00 :00 :00 :00 0 0 0 5 0 25: 25: 30: 20: 3 :30: 2 :00: 5 5 3 07: 08:15:0009: 10:40:0011: 12:30:0001: 03:00:0004:50: 06:40:0007:40: 08: 09 10: 12 12: 01:40:0002: 04:05:0005: Time Jan 29 _ 30

Figure 8.115. Salina Wayaka water level variations during January.

Chapter 8. RESULTS. 90

8.3. 4.3. SALINA WAYAKA HYDROGRAPHIC CONDITIONS

As in Funchi, the stability of variables as salinity in a same season is remarkable. Salinity is always higher than in Funchi but only during June deviations larger than 10 salinity units were recorded and this is caused by the strong, non-convective estratiphication ( See chapter 8.2.1.3) that keeps hot and hypersaline water near the bottom.

Figure 8.116. Wayaka water sampling stations

Table 9.10. Summary of hydrographic data for Salina Wayaka.

TEMPERATURE Month Count Average Median Deviation 1 15 26.442 26.25 1.5918 6 15 31.1287 28.8 5.53525 11 25 28.1372 27.55 1.26071 Total 55 28.4907 27.55 3.53037 OXIGEN Month Count Average Median Deviation 1 15 6.24867 6.1 2.35606 6 15 2.67933 2.65 0.836006 11 25 8.6824 8.96 1.42448 Total 55 6.38145 7.36 2.964 SALINITY Month Count Average Median Deviation 1 15 74.1853 74.11 0.367732 6 15 169.827 165.52 19.58 11 25 42.8548 42.37 2.79109 Total 55 86.0282 73.93 54.3635

Chapter 8. RESULTS. 91

34 33 32

C limits 95 % and confidence Temperature Mean 1611

Month

Figure 8.117. Temperature variability at Wayaka

2 Oxigenlevels and 95 % confidencelimits (mg/l ) 1611 Month

Figure 8.118. Seasonal dissolved oxygen variability at Wayaka.

Chapter 8. RESULTS. 92

180

160 140

120

100 80

limits. confidence % 95 and Salinity 40 1611 Month

Figure 8.119. Seasonal salinity variability at Wayaka.

Nutrients data

X-bar and S-squared - Initial Study for Ammonia UCL: +3.0 sigma = 2.71422 Centerline = 2.02667 LCL: -3.0 sigma = 1.33912 2 beyond limits S-squared Chart UCL: +3.0 sigma = 1.0412 Centerline = 0.157575 LCL: -3.0 sigma = 0.000212864 1 beyond limits Estimates Process mean = 2.02667 Process sigma = 0.396957 Pooled variance = 0.157575 The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 2.02667 and a standard deviation equal to 0.396957. These parameters were estimated from the data. 2 points are beyond the control limits.

Chapter 8. RESULTS. 93

X-bar Chart for Ammonia

9 UCL = 2.7

CTR = 2.0 6 LCL = 1.3

(ppm) Ammonia

0 1611 Month

Figure 8.120. Control chart for ammonia at Wayaka.

X-bar Chart for Nitrates

88 UCL = 54.1 78 CTR = 49.6

LCL = 45.1 68

(ppm) Nitrates 48

38 0 2 4 6 8 10 12 Month

Figure 8.121. Control chart for nitrates at Wayaka.

Chapter 8. RESULTS. 94

X-bar and S-squared - Initial Study for Nitrates UCL: +3.0 sigma = 54.0595 Centerline = 49.5727 LCL: -3.0 sigma = 45.086 3 beyond limits S-squared Chart UCL: +3.0 sigma = 45.7032 Centerline = 8.2015 LCL: -3.0 sigma = 0.0495473 1 beyond limits Process mean = 49.5727 Process sigma = 2.86383 Pooled variance = 8.2015 Since the probability of seeing 3 or more points beyond the limits just by chance is 0.0 if the data comes from the assumed distribution, we can declare the process to be out of control.

Nutrients, tides and rains

The most remarkable aspect of this relationship between nitrogen compounds, and water level due to tides and rain is that after 3 days of dry (no or low rain) weather nitrates increased while ammonia decreased (Figures 8.122 to 8.124).

0.97 WAYAKA JANUARY 62

60 0.96

58 0.95 56

Nitrates (ppm) Nitrates (m) level Water 0.94 54

0.93 52

07:25:00 08:20:00 09:35:00 10:55:00 11:50:00 12:55:00 02:25:00 04:20:00 06:30:00 07:35:00 08:30:00 09:35:00 10:30:00 12:15:00 01:10:00 02:05:00 03:40:00 04:45:00 Time Jan 29 _ 30

Figure 8.122. Relationship between Nitrates and water level at Wayaka during January.

Chapter 8. RESULTS. 95 0.97 y = 3E-06x3 - 0.0005x2 + 0.024x + 52.447 62 R2 = 0.9946

60 0.96

0.95

56 Nitrates (ppm) Water level (m)

0.94 RAIN 54

0.93 52 07:25:00 08:20:00 09:35:00 10:55:00 11:50:00 12:55:00 02:25:00 04:20:00 06:30:00 07:35:00 08:30:00 09:35:00 10:30:00 12:15:00 01:10:00 02:05:00 03:40:00 04:45:00 Time Jan 29 _ 30

Figure 8.123. Relationship between Nitrates and water level at Wayaka during January.

0.97 y = -5E-07x3 + 0.0002x2 - 0.0212x + 1.3078 1.4 R2 = 0.9911

1.2

0.96

0.95 0.8 Water level (m) 0.6 (ppm) Ammonia

0.94

0.4

0.93 0.2 07:25:00 08:20:00 09:35:00 10:55:00 11:50:00 12:55:00 02:25:00 04:20:00 06:30:00 07:35:00 08:30:00 09:35:00 10:30:00 12:15:00 01:10:00 02:05:00 03:40:00 04:45:00 Time Jan 29 _ 30

Figure 8.124. Relationship between Ammonia and water level at Wayaka during January.

Chapter 8. RESULTS. 96

1.38 WAYAKA JUNE 1.2

1.37 1

1.36 0.8

1.35 0.6 Water level (m) Ammonia (ppm)

1.34 0.4

1.33 0.2 07:30:00 08:35:00 09:50:00 10:55:00 12:00:00 01:40:00 02:45:00 03:50:00 04:55:00 06:00:00 07:10:00 08:25:00 09:40:00 11:20:00 12:35:00 01:40:00 02:45:00 03:55:00 05:15:00 06:30:00 Time June 20 _ 21

Figure 8.125. Relationship between Ammonia and water level at Wayaka during June.

0.91 39.5 WAYAKA NOVEMBER

39 0.9

38.5

0.89

38 Nitrates (ppm) Nitrates

Water levelWater (m) 0.88

37.5

0.87 37

0.86 36.5 0 00 00 00 :00 :00 00 :00 00 :00 00 :00 00 :00 00 0 00 0: 0: 5: 0: 0: 0: 5: 5: 5: :0 :4 :2 :4 :4 :1 :5 :0 :00: :1 8 9 1 4 6 9 2 3 5 6 07:10:000 08:50:000 10:30:001 12:15:0001:05 03:00:0003:50 0 05:40 0 08:00 0 10:30 1 01:55 0 0 0 Time Nov 22 _ 23

Figure 8.126. Relationship between Ammonia and water level at Wayaka during November.

Chapter 8. RESULTS. 97

Figure 8.127 shows the strong relationship between tide level and water nitrates concentration.

0.91 39.5

39 0.9

38.5

0.89

38 Nitrates (ppm) Nitrates

Water level(m) Water 0.88

37.5

0.87 37

y = -6E-08x4 + 2E-05x3 - 0.0033x2 + 0.1528x + 36.507 R2 = 1

0.86 36.5 0 0 0 0 0 0 0 0 :0 :0 :0 :0 0 :0 :0 :0 0 0 0 5 0: 0 0 5 :1 :00:00:5 :40:00:3 :25:00:1 :05:00 0 :50:00:40:00:40:00:40:00:0 :10:00:3 :55:00:5 :05:00 :15:00 7 8 8 9 1 1 3 4 5 6 8 9 0 2 1 3 6 0 0 0 0 10 1 12 0 03: 0 0 0 0 0 0 1 1 0 0 05:00:000 Time Nov 22 _ 23

Figure 8.127. Relationship between Ammonia and water level at Wayaka during November.

8.3. 4. 4. MANAGEMENT RECOMENDATIONS

It is possible that the rocky coast at Wayaka Boka, and the channels that have been dug in it, increase the sea water flow to Salina Wayaka. This may be the reason for the strong correlation between tides and water nitrates levels. Tide water inflow from below removes nitrogen accumulated in sediments and put them available in salinas water to be used by ecosystem. It is a strong recommendation of this report to study this possible connection. It could be a good opportunity to study the nitrogen cycle in salinas. The relationship between Nutrients, tides and rain is probably the most important item to monitor. As rain at Bonaire can be unpredictable in occurrence and intensity a sampling program can be difficult to plan. However, an emergency plan designed to, at the beginning of heavy rains, measure variables as water levels, salinity and nutrients both at salinas and at sea near shore bokas is a strong recommendation of this report.

Chapter 8. RESULTS. 98 8. 3.5. SLAGBAAI

8.3.5.1. SLAGBAAI BASIN LAND COVER

Table 8.11. BASIC STATISTICS FOR SLAGBAAI BASIN

m 2 BASIN (m2) 7 654 232 PERIMETER (m) 14 593 BASIN_WITH OUT_SALINA (m2) 6 912 685 SALINA (m2) 741 530 SALINA PERIMETER (m) 13 239 SALINA MAX DEPTH (m) 8 HIGHER POINT IN BASIN (mosl) 240 BRANDARIS PLANTATIONS (m2) SLAGBAAI 6 454 141 93.4 % LABRA 319 189 4.6 WASHINGTON 79 067 1.1 BRASIL 59 742 0.9 COAST (m) 549 PARK BORDERS IN BASIN (m) 0 PARK OUT BASIN 0 ROOIS IN SLAGBAAI TOTAL=119;182183 m ) 45 25 489 37.8 14.0 % POS IN BASIN 0 FENCES (m) 0 ROUTES (m) 4 541 WALKING TRAILS (m) 631 SUBIBRANDARIS

LANDSCAPE UNITS IN SLAGBAAI BASIN m2 % B1 Sesuvium-Li 14 891 0.2 D1 Eragrostis_ 544 886 7.9 D2 (Haematoxylon_ casearia) 1 419 434 20.5 D3 Prosopis_Ca 4 794 627 69.2 S2 Sesuvium sa 87 112 1.3 TL 7 Croton Prosopis LT 58 171 0.8 TM8 (Haematoxylon_ casearia) 12 662 0.2 TM9 Prosopis_Euphorbia MT 463 0.0 6 932 245 100

HIGH CONSERVATION VALUE (HCV) % OF TOTAL % OF HCV D3 Prosopis_Ca 4 794 627 69.2 100.0 TOTAL 4794627 69.1641351 100

SOILS AND LANDTYPES IN SLAGBAAI BASIN m2 % IWu soils of the plains WASHIKEMBA 139 781 2.0 Tc CORAL BEACHS 22 622 0.3 TL LOWER TERRACE 87 141 1.3 Tr Terrace remnants 6 744 0.1 Wr_Wi Hilly land 2 443 306 35.4 Ws_Wx Stonyland 4 194 472 60.8

6 894 067 100

Chapter 8. RESULTS. 99

Figure 8.128. Slagbaai basin landscape types. Keys to colors and patterns are in Figure 5.5.

Figure 8.129 High Conservation Value (HCV) areas in Slagbaai basin. Key to colors and patterns are in Figure 5.6.

Chapter 8. RESULTS. 100

Figure 8.130. Soils and Landtypes in Slagbaai basin. Key to colors and patterns are in Figure 5.4.

Figure 8.131. Slagbaai basin drainage system.

Chapter 8. RESULTS. 101 8.3.5.2. SALINA SLAGBAAI WATER BALANCE, TIDAL vs RAIN INFLUENCE.

Slagbaai, the second largest salina of northern Bonaire is also one of the most disturbed by humans. During decades was used as a harbor and its inner areas as salt ponds.

1.39

1.38

1.37

Water level (m) level Water LOW TIDE 1.36 HIGH

1.35

09:10:00 10:35:00 11:55:00 12:55:00 02:00:00 03:00:00 04:05:00 05:15:00 06:50:00 08:00:00 09:00:00 10:25:00 11:50:00 12:50:00 01:55:00 03:05:00 04:30:00 05:55:00 07:10:00 Time June 21_22

Figure 8.132. Tidal variability at Slagbaai during June.

Despite that Slagbaai boka is heavily constructed and altered salina water exchange with the sea is strong. During June an almost 3 cm tidal influence was observed. This amount for a salina 74 hectares in area means over 22 thousand cubic meters of water exchange with the sea in a daily base.

Chapter 8. RESULTS. 102 1.46 SLAGBAAI NOVEMBER

1.45

1.44

1.43 Water level (m) level Water

1.42 HIGH TIDE HIGH TIDE

LOW TIDE 1.41

:00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 5 :45 :35 :35 :55 06:35 07:30 08:40:0009:35 11 12:50 01:45 02:45:0004:05 05 06:35 07:45 08:35:0009:40 10 11:40 01:00 01:5 02:55 03 05:25 Time Nov 23 - 24

Figure 8.133. Tidal variability at Slagbaai during November.

1.49 SLAGBAAI JANUARY

1.48

1.47

1.46 Water level (m) level Water

1.45

LOW TIDE HIGH TIDE

1.44 08:25:00 09:25:00 10:10:00 11:05:00 12:30:00 01:05:00 01:45:00 02:25:00 03:10:00 04:15:00 05:30:00 06:25:00 07:05:00 07:50:00 08:25:00 10:20:00 11:40:00 01:05:00 01:50:00 02:40:00 04:10:00 04:55:00 05:55:00 06:30:00 Time Jan 30 _ 31

Figure 8.134. Tidal variability at Slagbaai during January.

Chapter 8. RESULTS. 103 8.3. 5.3. SALINA SLAGBAAI HYDROGRAPHIC CONDITIONS

Slagbaai is a very large salina, efforts to sample most areas were made, but only the south-western zone was studied.

Figure 8.135. Slagbaai water sampling sites

Table 8.12. Main water variables at Slagbaai salina.

TEMPERATURE Standard Lower Upper Month Count Mean Error Limit Limit 1 3 7 32.899 51.7 12513 1.1873 4.612 6 20 30.915 0.395965 30.519 31.311 11 39 36.359 1.72199 34.637 38.081 Total 96 33.8915 0.98294 32.9085 34.8744 OXYGEN Month Count Mean E rror L imit L imit 1 3 7 5.330 81 0.4851 4.346986.3 1464 6 20 2.5355 0.329751 1.84532 3.22568 11 39 7.5759 0.641277 6.2777 8.8741 Total 96 5.66052 0.378512 4.90908 6.41196 SALINITY Month Count Mean E rror Limit Limit 1 3 7 123 .0886 .98510 8.92113 7.254 6 20 171.582 11.971 146.526 196.638 11 39 100.548 9.23175 81.859 119.236 Total 96 124.034 5.85375 112.413 135.655

Chapter 8. RESULTS. 104

39 37

29 1611

Temperature C Mean 95 % and confidence limits Month

Figure 8.136. Slagbaai temperature variability.

Slagbaai is a very interesting salina. Despite of the large amount of water exchange with the ocean and the large basin, with important rain runoff the water is very stable. However to see the outliers, or data that is not expected a box and whiskers plot was made for each basic variable. This plot, which is particularly useful for comparing parallel batches of data, divides the data into four equal areas of frequency. A box encloses the middle 50 percent, where the median is represented as a small point by the middle of the vertical line. Values that fall beyond the whiskers, but within 3 interquartile ranges (suspect outliers), are plotted as individual points.

Box-and-Whisker Plot

62 58 54 50

46 42

T°C 38 34 30 26 22 18 1611

Month

Figure 8.137. Slagbaai temperature variability.

Chapter 8. RESULTS. 105

200

180 160

140

120

100

80 1611

SI and limits %95 confidence Salinity Mean Month

Figure 8.138. Slagbaai salinity variability.

Box-and-Whisker Plot

240

200

160

120

Salinity 80

0 1611

Month

Figure 8.139. Slagbaai salinity variability.

Chapter 8. RESULTS. 106

3 1611 mg/l %95 limits and confidence Oxigen Mean Month

Figure 8.140. Slagbaai Oxygen variability.

Box-and-Whisker Plot

15 12

mg_l_1 O2 6

0 1611

Month

Figure 8.141. Slagbaai Oxygen variability.

Chapter 8. RESULTS. 107 Nutrients data

X-bar and S-squared - Initial Study for Nitrates UCL: +3.0 sigma = 30.041 Centerline = 24.8233 LCL: -3.0 sigma = 19.6057 2 beyond limits S-squared Chart UCL: +3.0 sigma = 63.0407 Centerline = 12.0996 LCL: -3.0 sigma = 0.119828 1 beyond limits Estimates Process mean = 24.8233 Process sigma = 3.47844 Pooled variance = 12.0996 The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 24.8233 and a standard deviation equal to 3.47844. These parameters were estimated from the data. We can declare the process to be out of control at the 99% confidence level.

1.39 SLAGBAAI JUNE 34

32 1.38 30

28 1.37

26 Nitrates (ppm) Water level (m) 1.36 24

1.35 20 09:10:00 10:35:00 11:55:00 12:55:00 02:00:00 03:00:00 04:05:00 05:15:00 06:50:00 08:00:00 09:00:00 10:25:00 11:50:00 12:50:00 01:55:00 03:05:00 04:30:00 05:55:00 07:10:00 Time June 21 _ 22

Figure 8.142. Slagbaai nitrates variability during June.

Chapter 8. RESULTS. 108

X-bar and S-squared - Initial Study for Ammonia UCL: +3.0 sigma = 0.43814 Centerline = 0.28475 LCL: -3.0 sigma = 0.13136 1 beyond limits S-squared Chart UCL: +3.0 sigma = 0.0515332 Centerline = 0.00697144 LCL: -3.0 sigma = 0.00000279954 1 beyond limits Estimates Process mean = 0.28475 Process sigma = 0.0834951 Pooled variance = 0.00697144 The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 0.28475 and a standard deviation equal to 0.0834951. These parameters were estimated from the data.

1.39 0.6 SLAGBAAI JUNE

0.55

1.38

0.5

1.37 0.45 Water level (m) level Water Ammonia (ppm) Ammonia 0.4

1.36

0.35

1.35 0.3

0 0 0 0 0 :0 :00 :00 :0 :00 :00 :0 :00 :00 0: 5:00 0:00 5:00 1 :30 :45 0 :05 :10 4 3 40 :10 9: 3: 0 10 11 12:40 01:40:0002:35:0003:30:0004:30:0006: 07 08 09:05 10:25:0011:45:0012:40:0001: 02: 0 05 06:25 Time June 21 _ 22

Figure 8.143. Slagbaai Ammonia variability during June.

Chapter 8. RESULTS. 109 1.46 42 SLAGBAAI NOVEMBER

1.45 40

1.44 38

1.43 36 Nitrates (ppm) Nitrates Water level(m)

1.42 34

1.41 32

0 0 0 0 0 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :00 :0 :0 :0 :0 :0 5 0 5 0 5 5 5 5 5 5 0 40 :55 40 40 55 15 2: 1:4 3:1 4:5 6:1 7:3 8:3 9:5 0 2: 1: 2: 4: 06:3 07:4 09:0 11:2 1 0 0 0 0 0 0 0 1 1 0 0 0 Time Nov 23 _ 24

Figure 8.144. Slagbaai Ammonia variability during November.

1.46 3 2 42 y = 1E-05x - 0.0026x + 0.1478x + 36.525 R2 = 0.9586 1.45 40

1.44 SLAGBAAI 38

1.43 36 Nitrates (ppm) Water level (m)

1.42 34

1.41 32

0 0 0 0 0 0 0 0 0 0 0 00 :00 00 00 00 00 5:0 0:0 5:0 0:0 0: 5:0 5:0 5:0 5:0 5:0 5:0 0:0 0: 0: 5: 5: :3 :4 :0 :2 :3 :5 6 7 9 1 8 9 0 0 0 1 12:4 01:4 03:1 04:5 06:1 07:3 0 0 10:5512:4 01:4 02:5 04:1 Time Nov 23 _ 24

Figure 8.145. Slagbaai Nitrates variability during November.

Chapter 8. RESULTS. 110 1.7 0.3

1.69

1.68

0.2 1.67 Water level (m) Ammonia (ppm)

1.66

1.65 0.1 0 0 0 0 0 0 0 0 0 0 0 :00 :00 :00 :00 :00 :00 0:0 5:0 0:0 0:0 0:0 0:0 0:0 0:0 5:0 0:0 5:0 :0 :1 :0 :4 :30 4 :55 0 :00 5 :45 4 :30 3 :25 :1 :0 9 1 1 2 3 4: 5 7: 8 8: 9 0: 1 2: 1 3 5 0 10:10:001 12:05:000 01:50:000 0 0 0 0 0 0 0 1 1 1 0 02:15:000 04:15:000 Time Jan 30 _ 31

Figure 8.146. Slagbaai Nitrates variability during January.

1.7 y = -1E-06x3 + 0.0006x2 - 0.0551x + 11.761 13 R2 = 0.8849 1.695

1.69

1.685 12 1.68

1.675

1.67 Nitrates (ppm) Nitrates Water level (m) 11 1.665

1.66

1.655

1.65 10

0 0 0 0 0 00 00 00 00 00 00 00 5:00 5:00 5:00 5:00 5:00 :00:0 40: :45:0 :55:0 :00:0 05: 25: :00:0 20: 35: 45: 5 1 20: 2 3 5 0: 3: 4: 7: 8: 9: 0: 2: 1: 3: 4: 09 1 11 12 02 0 0 06 0 0 0 1 1 0 02: 0 0 Time Jan 30 _ 31

Figure 8.147. Slagbaai Nitrates variability during January.

Chapter 8. RESULTS. 111 1.7 0.3

y = 8E-08x3 - 3E-05x2 + 0.0027x + 0.2074 R2 = 0.7113 1.69

1.68

0.2

1.67 Water level (m) level Water Ammonia (ppm)

1.66

1.65 0.1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 :0 0 :0 5:0 0:0 0:0 0:0 0:0 5:0 :00:00:10: :15 :0 :0 :5 :4 :3 :40:0 :55:0 :45:0 :15:00:10: :15 :0 9 0 1 2 2 3 4 5 0 1 1 1 01 01 02 03 04 05 07:00:0008:00:0008:50:0009 10:40:0011:30:0012:35:0001:25:000 0 0 0 Time Jan 30 _ 31

Figure 8.148. Slagbaai Ammonia variability during January.

8.3.5. 4. MANAGEMENT RECOMENDATIONS

Slagbaai should be monitored with especial intensity. The trait of a heavy temperature difference between deep waters and shallow waters could be used as an experimental, non-conventional, energy source. However this trait could be disrupted by human interference causing water layers mixing (i.e. navigation, changes in runoff patterns, and changes in tidal influence). A permanent station measuring this peculiarity is a strong recommendation of this report. The relationship between Nutrients, tides and rain is probably the most important item to monitor. As rain at Bonaire can be unpredictable in occurrence and intensity a sampling program can be difficult to plan. However, an emergency plan designed to, at the beginning of heavy rains, measure variables as water levels, salinity and nutrients both at salinas and at sea near shore bokas is a strong recommendation of this report.

Chapter 8. RESULTS. 112 8.3.6. FRANS.

8.3.6.1. FRANS BASIN LAND COVER

Table 8.13. BASIC STATISTICS FOR FRANS BASIN

m 2 BASIN (m2) 2 205 629 PERIMETER (m) 7 240 BASIN_WITH OUT_SALINA (m2) 2 154 091 SALINA (m2) 51 568 SALINA PERIMETER (m) 1 511 SALINA MAX DEPTH (m) 1 HIGHER POINT IN BASIN (mosl) 64 SERU CHUBATU PLANTATIONS (m2) SLAGBAAI 347 361 15.8 % LABRA 1 016 678 46.1 % WASHINGTON BRASIL 840 693 38.1 % COAST (m) 519 PARK BORDERS IN BASIN (m) 0 PARK OUT BASIN 0 ROOIS IN FRANS TOTAL=119;182183 m ) 6 7 282 5 4 % POS IN BASIN 0 FENCES (m) 2 108 ROUTES (m) 0 WALKING TRAILS (m) 0

LANDSCAPE UNITS IN FRANS BASIN m2 % B1 Sesuvium-Li 15 654 0.7 D2 (Haematoxylon_ casearia) 1 544 944 71.7 D3 Prosopis_Ca 467 283 21.7 S2 Sesuvium sa 45 758 2.1 TL8 Prosopis Capparis LT 18 711 0.9 TM8 (Haematoxylon_ casearia) 62 450 2.9

2 154 800 100 HIGH CONSERVATION VALUE (HCV) % OF TOTAL % OF HCV D3 Prosopis_Ca 467 283 21.7 100.0 TOTAL 467282.66 21.6856653 100 SOILS AND LANDTYPES IN FRANS BASIN m2 % IWu soils of the plains WASHIKEMBA 930 110 43.6 Tc CORAL BEACHS 19 482 0.9 TL LOWER TERRACE 62 555 2.9 Tr Terrace remnants 62 567 2.9 Wr_Wi Hilly land 199 480 9.3 Ws_Wx Stonyland 860 935 40.3

2 135 129 100

Chapter 8. RESULTS. 113

Figure 8.149. Frans basin landscape types. Keys to colors and patterns are in Figure 5.5

Figure 8.150. High Conservation Value (HCV) areas in Frans basin. Keys to colors and patterns are in Figure 5.6

Chapter 8. RESULTS. 114

Figure 8.151. Soils and Landtypes in Frans basin. Key to colors and patterns are in Figure 5.6

Figure 8.152. Frans basin drainage system.

Chapter 8. RESULTS. 115

8.3.6.2. SALINA FRANS WATER BALANCE, TIDAL vs RAIN INFLUENCE.

Figure 8.153 Frans is probably the most disrupted salina.

Figure 8.154 Taking water samples at Frans

Chapter 8. RESULTS. 116 1.2 FRANS JUNE

1.19

RAIN 1.18 Water level (m) level Water

1.17 HIGH LOW

1.16 08:45:00 09:55:00 11:25:00 12:40:00 01:55:00 02:55:00 04:05:00 05:05:00 06:20:00 07:30:00 08:35:00 09:40:00 10:55:00 11:55:00 12:55:00 01:55:00 03:00:00 05:15:00 Time June 24-25

Figure 8.155. Water level variations at Frans during June 24-25 and its relation with rain and tides.

0.65 FRANS NOVEMBER

0.64

0.63

0.62

0.61

Water level (m) level Water LOW TIDE HIGH TIDE 0.6

0.59

0.58 07:45:00 08:55:00 09:55:00 10:55:00 12:05:00 01:20:00 02:30:00 04:00:00 05:35:00 07:00:00 08:00:00 08:55:00 10:00:00 11:00:00 12:05:00 01:25:00 02:20:00 03:15:00 04:30:00 05:25:00 Time Nov 26 _ 27

Figure 8.156. Water level variations at Frans during Nov 26- 27 and its relation with rain and tides.

Chapter 8. RESULTS. 117 0.61 FRANS FEBRUARY 0.605

0.6 26.42 mm OF RAIN 0.595

0.59

0.585

0.58

Water level(m) 0.575

0.57 LOW TIDE 0.565 LOW TIDE

0.56 07:50:00 08:50:00 09:50:00 11:25:00 12:25:00 01:30:00 02:35:00 03:35:00 04:35:00 05:35:00 06:45:00 07:45:00 08:45:00 10:55:00 12:45:00 02:25:00 03:40:00 04:40:00 05:40:00 Time Feb 2 _ 3

Figure 8.157. Water level variations at Frans during Feb 2-3 and its relation with rain and tides.

0.5 FRANS FEBRUARY

0.49

0.48

26.42 mm OF RAIN

0.47 Water level (m) level Water

0.46

0.45 LOW TIDE

LOW TIDE

0.44 08:05:00 09:00:00 10:30:00 12:15:00 01:15:00 02:40:00 04:05:00 05:25:00 07:00:00 07:55:00 08:50:00 09:45:00 10:35:00 11:25:00 12:40:00 01:55:00 02:50:00 03:40:00 04:30:00 05:20:00 Time Feb 2 _ 3

Figure 8.158 Water level variations at a different station at Frans during Feb 2- 3 and its relation with rain and tides.

Chapter 8. RESULTS. 118

8.3. 6.3. SALINA FRANS HYDROGRAPHIC CONDITIONS

Figure 8.159 Frans Water sampling stations

Table 8.14. Main water characteristics at Frans.

TEMPERATURE Month Count Mean Error Limit Limit 1 15 26.8987 0.575338 26.3233 27.474 6 10 29.241 0.984056 28.2569 30.2251 11 15 28.2287 0.498069 27.7306 28.7267 Total 40 27.983 0.394781 27.5882 28.3778 OXIGEN Month Count Mean Error Limit Limit 1 12 4.5775 0.61329 3.22766 5.92734 6 10 2.704 0.317827 1.98502 3.42298 11 15 11.5613 0.841227 9.75708 13.3656 Total 37 6.90243 0.762317 5.35638 8.44849 SALINITY Month Count Mean Error Limit Limit 1 15 36.87 0.460626 35.8821 37.8579 6 10 148.066 11.0168 123.144 172.988 11 15 22.0947 0.0920655 21.8972 22.2921 Total 40 59.1283 8.69986 41.5311 76.7254

Chapter 8. RESULTS. 119

28 27

Mean Temperature and 95 % Confident limits C 1611

Month

Figure 8.160. Temperature variability at Frans

2 1611 limits mg/l % 95 Confident and oxigen disolved Mean Month

Figure 8. 161. Oxygen variability at Frans

Chapter 8. RESULTS. 120

180 160

140 120

100

80 60

MeanSalinity and % 95 Confident limitsSI 20 1611

Month

Figure 8.162. Salinity variability at Frans.

Nutrients

1.2 FRANS 160

1.195 140

1.19 120 1.185 100 1.18 80 1.175 Nitrates (ppm) Water level(m) 60 1.17 y = -0.0001x3 + 0.039x2 - 2.2958x + 52.177 1.165 40 R2 = 1 1.16 20 08:45:00 09:55:00 11:25:00 12:40:00 01:55:00 02:55:00 04:05:00 05:05:00 06:20:00 07:30:00 08:35:00 09:40:00 10:55:00 11:55:00 12:55:00 01:55:00 03:00:00 05:15:00 Time June 24 _25

Figure 8. 163. Relationship between tides and Nitrates at Frans

Chapter 8. RESULTS. 121

3 2 1.2 y = 5E-07x - 0.0001x + 0.01x + 0.1606 0.5 R2 = 1

0.45

1.19 0.4

0.35 1.18 0.3 FRANS Water level (m) Ammonia (ppm) Ammonia 0.25 1.17

0.2

1.16 0.15 08:45:00 09:55:00 11:25:00 12:40:00 01:55:00 02:55:00 04:05:00 05:05:00 06:20:00 07:30:00 08:35:00 09:40:00 10:55:00 11:55:00 12:55:00 01:55:00 03:00:00 05:15:00 Time June 24 _ 25

Figure 8. 164. Relationship between tides and Ammonia at Frans

0.66 FRANS NOVEMBER 29.5 0.65 29 0.64 0.63 28.5 0.62 0.61 28

0.6 27.5 0.59 Nitrates (ppm) Water level (m) level Water 0.58 27 0.57 y = -2E-07x3 + 0.0002x2 - 0.0339x + 29.288 R2 = 0.9922 26.5 0.56 0.55 26 07:45:00 09:05:00 10:20:00 11:40:00 01:00:00 02:25:00 04:05:00 05:50:00 07:25:00 08:35:00 09:50:00 11:00:00 12:15:00 01:45:00 02:50:00 04:10:00 05:20:00 Time Nov 26 _ 27

Figure 8.165. Nitrates variability during November at Frans

Chapter 8. RESULTS. 122

y = -4E-05x3 + 0.0143x2 - 1.4469x + 48.338 0.61 R2 = 0.8438

0.6

0.59 22

0.58 Nitrates (ppm)

Water level (m) 17

0.57 12

0.56 7

0 0 0 00 00 00 0 00 45: 00: 1:40: 1:20: 07:50:0008:55:0010:25:1 12: 02: 03:05:0004:10:0005:15:0006:30:0007:35:0008:40:0010:55:0 02:35:0003:55:0005:00:0006:05:00 Time Feb 2 _ 3

Figure 8.166. Nitrates variability during February at Frans

0.61 0.35

2 y = -6E-06x + 0.0006x + 0.3071 0.33 0.6 R2 = 0.6996 0.31

0.29 0.59 0.27

0.58 0.25 Water level (m) 0.23 (ppm) Ammonia

0.57 0.21

0.19

0.56 0.17 07:50:00 08:55:00 10:25:00 11:40:00 12:45:00 02:00:00 03:05:00 04:10:00 05:15:00 06:30:00 07:35:00 08:40:00 10:55:00 01:20:00 02:35:00 03:55:00 05:00:00 06:05:00 Time Feb 2 _ 3

Figure 8.167. Ammonia variability during February at Frans

Chapter 8. RESULTS. 123

0.61 FRANS FEBRUARY 0.35

0.33 0.6 0.31

0.29 0.59 0.27

0.58 0.25 RAIN RUNOFF 26.42 mm Water level (m)

0.23 (ppm) Ammonia

0.57 0.21 0.19

0.56 0.17 07:50:00 08:55:00 10:25:00 11:40:00 12:45:00 02:00:00 03:05:00 04:10:00 05:15:00 06:30:00 07:35:00 08:40:00 10:55:00 01:20:00 02:35:00 03:55:00 05:00:00 06:05:00 Time Feb 2 _ 3

Figure 8.168. Ammonia variability during February at Frans.

X-bar and S - Initial Study for Ammonia UCL: +3.0 sigma = 0.414857 Centerline = 0.269556 LCL: -3.0 sigma = 0.124254 0 beyond limits S Chart UCL: +3.0 sigma = 0.207584 Centerline = 0.0957414 LCL: -3.0 sigma = 0.0 0 beyond limits Estimates Process mean = 0.269556 Process sigma = 0.102744 Mean sigma = 0.0957414

Chapter 8. RESULTS. 124

X-bar Chart for Ammonia

0.42 UCL = 0.4 0.37 CTR = 0.3 0.32 LCL = 0.1

0.27

0.22 (ppm) Ammonia 0.17

0.12 16 Month

Figure 8.169. Control chart for Ammonia at Frans.

X-bar and S - Initial Study for Nitrates UCL: +3.0 sigma = 51.2084 Centerline = 31.5029 LCL: -3.0 sigma = 11.7973 1 beyond limits S Chart UCL: +3.0 sigma = 28.3845 Centerline = 1 3.2643 LCL: -3.0 sigma = 0.0 1 beyond limits Estimates Process mean = 31.5029 Process sigma = 14.1896 Mean sigma = 13.2643

Chapter 8. RESULTS. 125

X-bar Chart for Nitrates

60 UCL = 51.2

50 CTR = 31.5

LCL = 11.8 40

Nitrates (ppm) 20

10 0 2 4 6 8 10 12

Month

Figure 8. 170. Control chart for Nitrates at Frans.

8.3. 6.4. MANAGEMENT RECOMMENDATIONS

Salina Frans has been changed by human activities very heavily. It also has a small and step basin. Nutrients and rain-tide water balance relation is not as clear as in other salinas. Frans barrier is heavily intervened, but Frans basin is not. So it may be an example of differences of nutrients basin-salina-sea interchange. The relationship between Nutrients, tides and rain is probably the most important item to monitor. As rain at Bonaire can be unpredictable in occurrence and intensity a sampling program can be difficult to plan. However, an emergency plan designed to, at the beginning of heavy rains, measure variables as water levels, salinity and nutrients both at salinas and at sea near shore bokas is a strong recommendation of this report. The fact that Frans Boka is strongly stabilized by the road, is difficult to manage. The only practical solution is to establish a permanent “natural” connection between salina and the sea. To restore the original connection is practically impossible but may be a large enough connection (> 20 m) in the form of a bridge over the boka could help.

Chapter 8. RESULTS. 126 8. 3.7. TAM

8.3.7.1. TAM BASIN LAND COVER

Table 8.15. BASIC STATISTICS FOR TAM BASIN

m 2 BASIN (m2) 3 278 467 PERIMETER (m) 9 188 BASIN_WITH OUT_SALINA (m2) 3 205 494 SALINA (m2) 72 973 SALINA PERIMETER (m) 2 384 SALINA MAX DEPTH (m) 2 HIGHER POINT IN BASIN (mosl) 83 WASAO PLANTATIONS (m2) BRASIL 3 278 467 100.0 % COAST (m) 1 748 PARK BORDERS IN BASIN (m) 0 PARK OUT BASIN 0 ROOIS IN TAM (TOTAL=119;182183 m ) 6 13 572 5 7 POS IN BASIN 0 FENCES (m) 0 ROUTES (m) 0 WALKING TRAILS (m) 0

LANDSCAPE UNITS IN TAM BASIN m2 % B1 Sesuvium-Li 52 349 1.6 D1 Eragrostis_ 351 680 10.9 D2 (Haematoxylon_ casearia) 1 821 776 56.6 D3 Prosopis_Ca 658 336 20.4 LT6 Caesalpina Metopium LT 166 256 5.2 S2 Sesuvium sa 92 982 2.9 TH1 Haematoxylon Croton HT 48 023 1.5 TM6 Haematoxylon Croton MT 28 899 0.9 3 220 300 100

HIGH CONSERVATION VALUE (HCV) % OF TOTAL % OF HCV D3 Prosopis_Ca 658 336 20.4 100.0 TOTAL 658 336 20.4 100 SOILS AND LANDTYPES IN TAM BASIN m2 % Tm Rock land of depositional_erosional terrace 106 548 3.4 Tc CORAL BEACHS 75 309 2.4 TL LOWER TERRACE 140 596 4.5 Tr Terrace remnants 47 119 1.5 Tx PLATEAU LAND IP 57 187 1.8 Wr_Wi Hilly land 2 467 148 79.3 Ws_Wx Stonyland 216 701 7.0

3 110 608 100

Chapter 8. RESULTS. 127

Figure 8.171. Tam basin landscape types. Keys to colors and patterns are in Figure 5.5.

Chapter 8. RESULTS. 128

Figure 8.172. High Conservation Value (HCV) areas in Tam basin. Keys to colors and patterns are in Figure 5.6.

Chapter 8. RESULTS. 129

Figure 8.173. Soils and Landtypes in Tam basin. Key to colors and patterns is in Figure 5.4.

Chapter 8. RESULTS. 130

Figure 8.174. Tam basin drainage system.

8.3.7.2. TAM SALINA WATER BALANCE, TIDAL vs RAIN INFLUENCE.

Water level at Tam behaves very similar to other salinas. During June sampling days, no heavy rains occurred, so the main influence was tidal (Fig. 8.175). In November, rain and tides shared effects in water level (Fig. 8.176). During the sampling time at January (February) a strong rain increased Tam salina level 5 cm in less than an hour (Fig. 8.177).

Chapter 8. RESULTS. 131 1.23 TAM JUNE

1.22

1.21

1.2 Water level (m) level Water

LOW TIDE 1.19 HIGH TIDE

1.18

0 0 0 0 0 :00 :00 :00 :0 00 :0 0 0:0 5:00 0 5 0 0:0 45 :2 4 :3 :50:0 00: :3 :0 1: 2:0 3 4:45:005: 7:2 8:20:009 2: 1:25:002 3:55:005 6:25:00 10:05:001 10 1 Time June 23 - 24

Figure 8.175. Tam tidal influence during June.

0.94 TAM NOVEMBER

0.93

0.92

0.91

0.9 Water level (m) level Water

HIGH TIDE 0.89

LOW TIDE 0.88

0.87

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 :0 0 0 :0 :0 :0 :0 0 0 :0 :0 0 0 :0 0 0 0 :3 :20: :25: :55 :55 :1 :15:0 :10:0 :00 :45: :00: :50:0 :40 :4 :30: :40: :55:0 :05 1 0 3 09 10 1 12:45:0001 02 04 05:15:0006 07 08 08 1 10:55:0011 12 01 02 0 04 06 Time Nov 25 _ 26

Figure 8.176. Tam water level variations during November sampling days.

Chapter 8. RESULTS. 132 0.57 TAM FEBRUARY

0.56

0.55

0.54

RAIN RUNOFF 0.53 (16.26 mm) Water level (m) 0.52 HIGH TIDE

LOW TIDE 0.51

0.5 08:30:00 09:30:00 10:25:00 11:35:00 12:30:00 01:25:00 02:25:00 03:20:00 04:55:00 05:50:00 06:45:00 08:10:00 09:25:00 10:50:00 11:45:00 12:40:00 01:40:00 02:35:00 03:50:00 04:45:00 05:40:00 Time Feb 1 _ 2

Figure 8.177. Tam water level variations at February sampling time.

8.3. 7.3. SALINA TAM HYDROGRAPHIC CONDITIONS

Figure 8.178. Tam water sampling stations

Chapter 8. RESULTS. 133 Table 8.16. Main water quality data from salna Tam.

TEMPERATURE Standard Lower Upper Month Count Mean Error Limit Limit 1 15 25.8313 0.204478 25.6269 26.0358 6 15 28.64 0.481836 28.1582 29.1218 11 15 27.062 0.41013 26.6519 27.4721 Total 45 27.1778 0.277366 26.9004 27.4551 OXYGEN Standard Lower Upper Month Count Mean Error Limit Limit 1 1 11.15 0 6 15 4.81733 0.401444 3.95632 5.67835 11 14 11.2293 0.704132 9.7081 12.7505 Total 30 8.02067 0.704603 6.57959 9.46175 SALINITY Standard Lower Upper Month Count Mean Error Limit Limit 1 15 30.2627 0.0407135 30.1753 30.35 6 15 69.8653 1.54427 66.5532 73.1775 11 15 22.482 0.614379 21.1643 23.7997 Total 45 40.87 3.1743 34.4726 47.2674

25 MeanTemperature and 95% Confidence limits C 1611

Month

Figure 8.179. Temperature variability at Tam salina.

Chapter 8. RESULTS. 134

13 12 11

9 8 7 6 5 4 3 MeanOxigen and 95%Confidence limits mg/l 1611 Month

Figure 8.180. Oxygen variability at Salina Tam.

MeanSalinity and 95% Confidence limits SI 20 1611 Month

Figure 8.181. Salinity variability at Tam.

Chapter 8. RESULTS. 135 Nutrients

X-bar and S - Initial Study for Ammonia UCL: +3.0 sigma = 0.25286 Centerline = 0.211714 LCL: -3.0 sigma = 0.170568 1 beyond limits S Chart UCL: +3.0 sigma = 0.0517708 Centerline = 0.0175793 LCL: -3.0 sigma = 0.0 1 beyond limits Estimates Process mean = 0.211714 Process sigma = 0.0209506 Mean sigma = 0.0175793

X-bar Chart for Ammonia

0.37 UCL = 0.3 0.33 CTR = 0.2 LCL = 0.2 0.29

0.25

Ammonia (ppm) Ammonia 0.21

0.17 1611 Month

Figure 8.182. Control chart of Ammonia at Tam.

X-bar and S - Initial Study for Nitrates

Chapter 8. RESULTS. 136 UCL: +3.0 sigma = 16.9571 Centerline = 16.4445 LCL: -3.0 sigma = 15.932 3 beyond limits S Chart UCL: +3.0 sigma = 0.700499 Centerline = 0.298466 LCL: -3.0 sigma = 0.0 0 beyond limits Estimates Process mean = 16.4445 Process sigma = 0.327171 Mean sigma = 0.298466

X-bar Chart for Nitrates

30 UCL = 17.0

25 CTR = 16.4 LCL = 15.9 pm) 20

es 15

itrat N 10

5 1611 Month

Figure 8.183. Nitrates control chart for Tam.

Chapter 8. RESULTS. 137

Figure 8.184. Relationship between water level and Nitrates at Tam.

Figure 8.185. Relationship between water level and Ammonia at Tam.

Chapter 8. RESULTS. 138

Figure 8.186. Relationship between water level and Nitrates at Tam.

Figure 8.187. Relationship between water level and Ammonia at Tam.

Chapter 8. RESULTS. 139

Figure 8.188. Relationship between water level and Nitrates at Tam.

Figure 8.189. Relationship between water level and Nitrates at Tam.

Chapter 8. RESULTS. 140 8.3.7.2. MANAGEMENT RECOMMENDATIONS FOR SALINA TAM .

Salina Tam is especially important as far as fauna and flora are concerned. It has the second largest mangrove area in salinas of Northwest Bonaire. Some bird species, rare in other Salinas are frequent here and the Wideon grass area is large and dense. Tam is very close to BOPEC facilities, so is especially vulnerable to oil activities risks. Is a Management recommendation of this report to keep a close monitoring of possible licks in the run off coming to the salina from the south and oil evidence on water surface. The fact that Tam Boka is strongly stabilized by the road, is difficult to manage. The only practical solution is to establish a permanent “natural” connection between salina and the sea. To restore the original connection is practically impossible but may be a large enough connection (> 20 m) in the form of a bridge over the boka could help.

Chapter 8. RESULTS. 141 8. 3.8. GOTO

8.3.8.1. GOTO BASIN LAND COVER

Nearly 30% of Goto basin is outside park limits. This area outside park boundaries is considered ecologically important. There are two freshwater springs (DOS POS and POS CHIKITU) and includes two landscape types not existing inside the park (E1 Prosopis_Casearia Esc. and TM7 Acacia Croton MT) (Fig. 8.190). E1 is considered a High Conservation Value landscape (Fig 8.192).

Table 8.17. BASIC STATISTICS FOR GOTO BASIN m 2 BASIN (m2) 14 416 374 PERIMETER (m) 18 830 BASIN_WITH OUT_SALINA (m2) 12 390 858 SALINA (m2) 2 025 516 SALINA PERIMETER (m) 13 828 SALINA MAX DEPTH (m) 14 HIGHER POINT IN BASIN (mosl) 203 YUANA PLANTATIONS (m2) SLAG BAAI 5 782 902 65.5 % LABRA 634 086 7.2 % BRASI L 2 415 157 27.3 % COAST (m) 421 PARK BORDERS IN BASIN (m) 4 621 BASIN IN PARK (m2) 8 754 140 70.6 % W/O SALINA BASIN_OUT SIDE PARK (m2) 3 636 718 29.4 % W/O SALINA PARK OUT BASIN 0 ROOIS IN GOTO BASIN 27 47 609 ROOIS GOTO BASIN IN PARK(TOTAL=119;182183 m ) 22 37 157 18 20 % POS IN BASIN 2 (OUTSIDE PARK.) FENCES (m) 4 675 ROUTES (m) 2 057 WALKING TRAILS (m) 0

LANDSCAPE UNITS IN GOTO BASIN BASIN PARK A Agrarian/Human Use 194 552 1.5 977 0.01 B1 Sesuvium-Li 42 765 0.3 21 884 0.2 D1 Eragrostis_ 382 161 3.0 221 605 2.4 D2 (Haematoxylon_ casearia) 4 156 784 32.7 3 690 738 40.7 D3 Prosopis_Ca 6 107 915 48.0 4 196 962 46.2 E1 Prosopis_Casearia Esc. 101 271 0.8 0 0 LT6 Caesalpina Metopium LT 210 270 1.7 153 349 1.7 S2 Sesuvium sa 481 299 3.8 479 497 5.3 TH1 Haematoxylon Croton HT 393 434 3.1 155 703 1.7 TM6 Haematoxylon Croton MT 443 093 3.5 157 235 1.7 TM7 Acacia Croton MT 205 426 1.6 0 0

12 718 971 100 9 077 950 100

Chapter 8. RESULTS 142

% % OF HIGH CONSERVATION VALUE % OF OF TOT % OF (HCV) TOTAL HCV AL HCV BASIN PARK D3 Prosopis_Ca 6 107 915 48.0 86.7 4 196 962 46.2 93.1 E1 Prosopis_Casearia Esc. 101 271 0.8 1.4 0 0 0 TH1 Haematoxylon Croton HT 393 434 3.1 5.6 155 703 1.7 3.5 TM6 Haematoxylon Croton MT 443 093 3.5 6.3 157 235 1.7 3.5

TOTAL 7 045 714 55.4 100 4 509 901 50 100.0

SOILS AND LANDTYPES IN GOTO BASIN BASIN PARK

IWu soils of the plains WASHIKEMBA 41 598 41 598 Tc CORAL BEACHS 45 300 23 636 TL LOWER TERRACE 72 840 20 485 Tm Rock land of depositional_erosional terrace 716 019 265 210 Tx PLATEAU LAND IP 542 714 153 300 Wr_Wi Hilly land 5 507 500 3 522 701 Ws_Wx Stonyland 5 472 680 4 730 031

12 398 652 8 756 961

Chapter 8. RESULTS 143

Figure 8.190. Goto basin vegetation types. Key to colors and patterns is at Figure 5.5.

Figure 8.191. Goto basin soil types. Key to colors and patterns is at Figure 5.4.

Chapter 8. RESULTS 144

Figure 8.192. Goto basin HCV vegetation types. Key to colors and patterns is at Figure 5.6.

Figure 8.193. Goto drainage system.

8.3.8.2. SALINA GOTO WATER BALANCE, TIDAL vs RAIN INFLUENCE.

As in all salinas, during June sampling season tides dominated salina’s water level, with variations of 3 to 5 cm and a lag 4 to 5 hours in reference to ocean tides.

Chapter 8. RESULTS 145

Figure 8.194. Salina Goto water level variations during June

In November is possible to see a level increase of 8 cm due to the combination of tides and a 14.73 mm rain input. During January the level difference was of 7 cm for a short but strong rain of 24.9 mm.

Figure 8.195 Water level variations at Goto during November.

Chapter 8. RESULTS 146

Figure 8.196. Water level variations at Goto during January.

8.3. 8.3. SALINA GOTO HYDROGRAPHIC CONDITIONS

Figure 8.197. Goto water sampling stations.

Chapter 8. RESULTS 147 Table 8.18. Shows the general hydrographic parameters obtained at Goto.

SALINITY Standard Lower Upper Month Count Mean Error Limit Limit 1 50 129.35 1.08217 127.175 131.524 6 28 155.667 2.3438 150.858 160.477 11 55 103.825 2.14404 99.5269 108.124 Total 133 124.335 2.03294 120.314 128.356 OXYGEN Standard Lower Upper Month Count Mean Error Limit Limit 1 40 4.82625 0.229545 4.36195 5.29055 6 28 2.61464 0.213134 2.17733 3.05196 11 55 7.38036 0.209342 6.96066 7.80007 Total 123 5.46488 0.214787 5.03969 5.89007 TEMPERATURE Standard Lower Upper Month Count Mean Error Limit Limit 1 50 30.0766 0.437413 29.1976 30.9556 6 28 30.0011 0.252435 29.4831 30.519 11 55 29.7416 0.392668 28.9544 30.5289 Total 133 29.9222 0.236028 29.4553 30.3891

160 150 140

130

120 110 100

90 1611 and SI 95 % limits Salinity confidence Mean Month

Figure 8.198. Goto Salinity variability.

Figure 8. 199 shows a Box-and-Whisker Plot. It is a graphical summary of the presence of outliers in data for one or two variables. This plot, which is particularly useful for comparing parallel batches of data, divides the data into four equal areas of frequency. A box encloses the middle 50 percent, where the median is represented small point at the middle of the line. Vertical lines, called whiskers, extend from each end of the box. The lower whisker is drawn from the lower quartile to the smallest point within 1.5 interquartile ranges from the lower quartile. The other whisker is drawn from the upper quartile to the largest point within 1.5 interquartile ranges from the upper quartile. Values that fall beyond the whiskers, but within 3 interquartile ranges (suspect outliers), are

Chapter 8. RESULTS 148 plotted as individual points. Far outside points (outliers) are distinguished by a special character (a point with a + through it). Outliers are points more than 3 interquartile ranges below the lower quartile or above the upper quartile.

Box-and-Whisker Plot

162

142

122

Salinity

102

82 1611

Month

Figure 8.199. Goto Salinity variability.

7 6 5

2 1 1611

mg/l and 95 % Oxigen confidence limits Mean Month

Figure 8. 200. Oxygen variability at Goto.

Chapter 8. RESULTS 149

Box-and-Whisker Plot

mg/lOxigen 4 2

0 1611 Month

Figure 8. 201. Oxygen variability at Goto.

28 C limits %confidence 95 and Temperature Mean 1611 Month

Figure 8. 202. Temperature variability at Goto.

Chapter 8. RESULTS 150

Box-and-Whisker Plot

35 33

29 Temperature mg/l

25 1611 Month

Figure 8. 203. Temperature variability at Goto.

Nutrients.

X-bar and S-squared - Initial Study for Ammonia UCL: +3.0 sigma = 1.37531 Centerline = 0.443214 LCL: -3.0 sigma = -0.488881 0 beyond limits S-squared Chart UCL: +3.0 sigma = 1.95469 Centerline = 0.337868 LCL: -3.0 sigma = 0.00151545 0 beyond limits Estimates Process mean = 0.443214 Process sigma = 0.581264 Pooled variance = 0.337868

Chapter 8. RESULTS 151

X-bar Chart for Ammonia

1.5 UCL = 1.4 CTR = 0.4

1 LCL = -0.5

0.5

(ppm) Ammonia

0 024681012 Month

Figure 8.204. Goto control chart for Ammonia

X-bar and S - Initial Study for Nitrates UCL: +3.0 sigma = 55.1198 Centerline = 47.66 LCL: -3.0 sigma = 40.2002 2 beyond limits S Chart UCL: +3.0 sigma = 10.9186 Centerline = 5.22645 LCL: -3.0 sigma = 0.0 0 beyond limits Estimates Process mean = 47.66 Process sigma = 5.5602 Mean sigma = 5.22645

Chapter 8. RESULTS 152

X-bar Chart for Nitrates

80 UCL = 55.1 70 CTR = 47.7 60 LCL = 40.2 50

Nitrates (ppm) 30

1611

Month

Figure 8.205. Goto control chart for Nitrates.

Figure 8.206. Relationship between Nitrates and water level during November.

Chapter 8. RESULTS 153

Figure 8.206. Relationship between Nitrates and water level during January.

Figure 8.207. Relationship between Ammonia and water level during January.

8.3.8.2. MANAGEMENT RECOMMENDATIONS FOR SALINA GOTO.

Salina Goto, as the most public and accessible salina in Northwest Bonaire, requires special management measures. The protection, with some sort of measure of the entire basin is the most important. As in Tam’s case, the neighborhood with BOPEC facilities comprise a larger number of oil related risks and they should be monitored. Also the presence of an open, paved road along saline’s Southeastern coast, imply larger patrolling effort. Goto is close to BOPEC facilities and vulnerable to oil activities risks. To keep a close monitoring of possible licks in the run off coming to the salina from BOPEC is a must.

Chapter 8. RESULTS 154 9. REFERENCES

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