Investigation into echinoderm species
richness and abundance within the rockpool
habitats of the Watamu Marine National
Park, Kenya
Chloe Naylor
BSc Biology 2014
Supervisor: Dr. Felix Eigenbrod
Word count: 8,636
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Summary for the Layman
Threats made to echinoderms are increasing due to commercial enterprises such as the
trade in echinoderms for aquariums and souvenirs. Besides that, the effects of commercial
fishing, particularly ‘dredging’ which involves clearing the sea bed, also have negative
impacts as some echinoderms are caught as by-catch and their habitats are destroyed.
These threats highlight the importance of Marine Parks whose conservation methods are
vital in sustaining echinoderm species richness and abundance worldwide. One such park is
the Watamu National Marine Park of Kenya. Within the park many echinoderms inhabit, not
only the coral reefs but also the rockpools that are located on the rock platforms along the
Watamu Marine Park beach. However little research has been conducted to investigate
exactly what species of echinoderms live in these habitats or their distribution amongst the
platforms. Research of this kind could contribute significantly to the park’s conservation
management.
This study investigated what echinoderm species could be found amongst these rock
platforms and whether their species richness and abundance varied within and between the
platforms. 151 transects were conducted on 11 rock platforms along the beach and 5
species of brittlestars, 1 of starfish, 1 of sea cucumber and 4 species of sea urchin were
found and it was determined that their abundance and species richness did vary significantly
within and between the platforms. The habitat variables that were included in the analysis
to determine whether they had an effect on this distribution were rockpool number within
the transect, rockpool depths, percentage of rockpools that had living seagrass and the type
of substratum found on the rockpool floor. The results were that none of these factors
affected echinoderm species richness while only floor substratum type affected total
echinoderm abundance. It is likely that there were biotic and abiotic variables that were not
measured in this study which were the main factors affecting echinoderm distribution; for
instance the tide level at which the platforms were submerged underwater. As well as
determining that these habitat factors did not affect echinoderm distribution, the study also
concluded that there were rock platform hot spots of relatively high echinoderm species
richness. This knowledge as well as the echinoderm data collection should contribute to the
database of the Marine Park and aid in conservation management decision making.
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Abstract
The Watamu National Park of Kenya has a series of rock platforms where there are
rockpools. These rockpools have had little research on the fauna that inhabit them and so
this study aimed to investigate the echinoderm species richness and total abundance in
these rockpools. Besides looking at whether these varied within and between rock
platforms the habitat variables of rockpool number, rockpool depth, percentage rockpools
with living seagrass and rockpool floor substratum type were also compared within the
analysis in order to determine whether they were effecting species richness and abundance
distribution. 151 transects were sampled across 11 rock platforms and 11 species across 4
echinoderm classes were found. The results of the analysis were that the echinoderm
species richness and abundance did vary significantly within and between rock platforms
however none of the habitat variables had an effect on this variation with the exception
that floor substratum type did have an effect on the distribution of the echinoderm total
abundance. When investigating whether the habitat variables had an effect on the
distribution of individual echinoderm species it was again found that, on the whole, these
habitat variables did affect their distribution. The conclusions of this study were that the
habitat variables measured did not affect the distribution of the echinoderm species
richness and abundance and that it must be either biotic or abiotic variables that were not
measured in the study which are causing the variation within and between the rock
platforms.
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Content
1. Literature Review...................................................................................................6
1.1 Watamu Marine National Park...............................................................................6
1.2 Phylum Echinodermata...........................................................................................6
1.3 Importance of Echinoderms....................................................................................8
1.4 Threats to Echinoderms........................................................................................10
1.5 Studies conducted on Echinoderm Distribution and Abundance.........................11
2. Aims of the project................................................................................................13
3. Materials and Methods.........................................................................................14
3.1 Study sites.............................................................................................................14
3.2 Transect Procedure...............................................................................................15
3.3 Measuring Rockpool Habitat data.........................................................................15
3.4 Finding, Handling and Documenting Echinoderms...............................................16
3.5 Analysis..................................................................................................................17
4. Results..................................................................................................................17
4.1 Echinoderm species found in the study................................................................17
4.2 Echinoderm species richness distribution.............................................................18
4.2.1 General linear model.................................................................................18
4.2.2 Distance class post-hoc analysis................................................................18
4.2.3 Rock platform visual comparison..............................................................19
4.3 Echinoderm abundance distribution.....................................................................21
4.3.1 General linear model.................................................................................21
4.3.2 Distance class post-hoc analysis................................................................22
4.3.3 Rock platform graph analysis....................................................................23
4.3.4 Floor substratum graph analysis...............................................................24
4.4 Effect of habitat variables on individual Echinoderm species distribution...........24
4.4.1 Logistic regression analysis of distribution of species Ophiocoma
scolopendrina............................................................................................25
4.4.2 Logistic regression analysis of distribution of species Amphiura
dejectoides................................................................................................25
4.4.3 Logistic regression analysis of distribution of species Echinometra
mathaei.....................................................................................................26
4.4.4 Logistic regression analysis of distribution of species Echinothrix
diadema.....................................................................................................27
4.4.5 Logistic regression analysis of distribution of species Tripneustes
gratilla.......................................................................................................28
4.5 Association between Echinothrix diadema and Echinometra mathaei abundance
and distribution.....................................................................................................29
5. Discussion.............................................................................................................29
5.1 Echinoderms found in the study...........................................................................29
5.2 Echinoderm species richness and the habitat variables.......................................30
5.3 Echinoderm abundance and the habitat variables...............................................31
5.4 Individual echinoderm species and the habitat variables....................................32
5.5 Echinometra mathaei and Echinothrix diadema...................................................33
5.6 Recommendations for future research and relevancy of study to conservation
management.........................................................................................................34
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6. References............................................................................................................36
7. Appendix..............................................................................................................40
7.1 Study site Locations..............................................................................................40
7.2 Assumptions of general linear model for Echinoderm species richness..............41
7.3 Assumptions of general linear model for Echinoderm abundance......................42
7.4 Assumptions of the Logistic regression analyses.................................................44
7.5 Results of Logistic regression analysis..................................................................47
7.6 Monotopic assumption of Spearman’s rank correlation between Echinothrix
diadema and Echinometra mathaei.....................................................................52
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1. Literature review
1.1 Watamu Marine National Park
The original primary objective of marine national parks has been to assist conservation or
non-fisheries use of use of an area or resource (Arara and Rose, 2004). Kenya’s 450 km of
coastline has several such marine national parks which prohibit fishing and collection of
marine organisms (Samoilys, 1988). Often these marine parks focus around coral reefs;
within Kenya, the coral reefs are typically shallow mainland fringing reefs, often enclosing a
lagoon or moat (Hamilton and Brake, 1984). Easily accessible coral reefs are important to
Kenya’s economy as a source of revenue from the tourism industry as approximately
124,000 tourists visit the coast per year (Samoilys, 1988).
The Malindi and Watamu Marine Parks are some of these important marine parks located
on the Kenyan coast. They were the first Marine National parks to be established in the
country in 1968 and they were third in the world (KWS, 2014). The purpose of these parks
was to protect biodiversity, manage resources in a sustainable way to protect the
livelihoods of coastal communities and manage tourism (Sluka et al, 2012). These two parks
were enclosed into the Malindi National Reserve under UNESCO in 1979 although the two
parks are maintained separately (KWS, 2014). The reserve encompasses 34 km2 while the
Watamu Marine Park is 10 km2 (KWS, 2014). The Marine Park encompasses a complex of
marine and tidal habitats including a mangrove forest in a creek characterized by tidal mud
flats and a shallow coral reef. The beach adjacent to this coral reef is sandy with large rock
outcrops close to the shore, which have multiple rockpools. These rockpools contain a wide
range of biota including fish, corals, echinoderms, sponges and other invertebrates.
Research is being undergone to provide baseline biodiversity and ecology monitoring in the
coral reef and among these rock platforms (Sluka et al, 2012).
1.2 Phylum Echinodermata
Echinodermata is a phylum that is classified under the superphylum of Deuterostomia which
it shares with Chordata and Hemichodata (Cameron et al, 2000). The phylum comprises of
five extant classes of echinoderms; Asteroidea, Ophiuroidea, Echinoidea, Holothuroidea and
Crinoidea (Pawson, 2007). There was, at one point, speculation over whether
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concentricycloids should make up a sixth class (Baker et al, 1986), however due to further
morphological, cladistic and molecular evidence, concentricycloids are grouped in the class
of Asteroidea (Mah, 2006).The phylum includes approximately 7000 living species and
13,000 fossil species; this group of animals can be briefly defined as containing a calcium
carbonate skeleton in the form of calcite, a unique water-vascular system which is used for
feeding and locomotion and a five-part radial symmetry (Pawson, 2007). However, there
are many exceptions to this general body plan; some taxa of Holothuroidea lack calcite in
their walls, some Asteroidea have more than five part radial symmetry (some sea stars have
as many as 50 arms) and many echinoderms have superimposed bilateral symmetry on the
radial pattern (Pawson, 2007). It has been suggested that the only unifying taxonomic
characteristic of the phylum is that all its extant members are marine (Pawson, 2007).
The Asteroidea class contains the sea stars of which there are 2100 species inhabiting the
oceans today (Pawson, 2007). Sea stars typically have a central disk out of which project five
arms which are narrow at the tip and then widens at the base (Barnes, 1980). The mouth is
located at the centre of the underside of the disk and from the mouth a wide furrow
extends into each arm within which there are two or four rows of small tubular projections
or ‘tube feet’ which are used for locomotion (Barnes, 1980). Asteroidea are carnivores that
tend to feed on invertebrates, such as bivalves, polychaetes and crustaceans though the
particular diet varies among species (Barnes, 1980).
The Ophiuroidea is a class that contains basket stars and serpent stars or brittle stars and
encompasses 2000 living species (Pawson, 2007). Similar to Asteroidea, Ophuiroids also
possess arms however these arms are more sharply set off from the central disk; they
appear jointed due to the presence of four longitudinal rows of shields (Barnes, 1980). They
also do not use their tube feet for locomotion, instead they use their arms to rapidly crawl
and some species can even swim (Barnes, 1980). Ophuiroids are scavengers, deposit feeders
or filter feeders; many species use their arms to trap detritus on the mucus strand which run
between the spines on the arms that they lift upward and wave about in the water (Barnes,
1980).
Echinoidea are made up of 800 species of echinoderms known as sea urchins, heart urchins
and sand dollars (Pawson, 2007). Echinoidea, unlike Asteroidea and Ophiuroidea, do not
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have arms; they have spherical or oval bodies typically covered by movable spines (Barnes,
1980). They are generally adapted to living on rocks or other types of hard bottoms and use
their spines to crawl along the ground (Barnes, 1980). Echinoids are generalist grazers which
have a mouth apparatus known as an Aristotle’s lantern that is composed of five calcareous
plates known as pyramids (Barnes, 1980). The echinoids scrape the substrates on which they
live with their Aristotle’s lantern, eating mostly algae but also other plant and animal
material they happen to come across; what each species will eat will depend on the area in
which they live and what is available (Barnes, 1980).
Holothurians number approximately 1400 living species more commonly known as sea
cucumbers (Pawson, 2007). Like echinoids, holothurians do not have arms; they are unique
in that their polar axis is lengthened giving the body an elongated shape (Barnes, 1980).
They are relatively sluggish animals that live on the bottom surface or burrow into mud,
those that do move do so with either their tube feet similar to asteroids or by contracting
their circular and longitudinal muscles in an action akin to the borrowing of earthworms
(Barnes, 1980). Holothurians are chiefly deposit or suspension feeders that use their
tentacles around their mouths to sweep over the bottom surface or hold them out in the
water to trap particulate matter (Barnes, 1980).
The Crinoidea class consist of 650 extant species of feather stars and sea lilies (Pawson,
2007). This investigation will not focus on crinoids as they often live at depths of 100 meters
(Barnes, 1980) and so are not relevant to this study which focuses on echinoderms which
can be found in rockpools.
1.3 Importance of Echinoderms
Echinoderms are an ecologically and economically significant group of invertebrates. They
are prime candidates for model toxicological test organisms for marine ecosystems (Zito et
al, 2005). This is due to reasons such as their ubiquitous distribution, their susceptibility to
micropollutants stored in marine sediments and their sensitivity to many types of
contaminants (Micael et al, 2009). The classes of echinoderms are uniquely important to the
environment, for instance deposit feeding holothurians are important bioturbators, altering
the stratification and stability of muddy and sandy bottoms (Anderson et al, 2011). On coral
reefs sea cucumbers can bioturbate the upper 5 mm of sediment once a year (4600 kg dry
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weight year-1 1000 m-2) significantly reducing microalgal biomass (Uthicke, 1999) and
recycling nutrients, thereby maintaining the high productivity of these oligotrophic
ecosystems (Uthicke, 2001). Holothurians also serve as prey, particularly for crustaceans,
asteroideans and fish (Francour, 1997). In addition to their ecological impact, holothurian
fisheries are of great economic and social importance to many coastal communities
(Anderson et al, 2011). They form the main source of income in the Solomon Islands (Nash
and Ramofafia, 2006) and for 5000 families in Sri Lanka (Dissanayake et al, 2010) as well as
representing a large proportion of non-fish marine products from the Maldives (Joseph,
2005).
Echinoids also play a major ecological role in coral reefs as their grazing can contribute to
the control of benthic community structures (Sammarco, 1982). This control is manifested in
several ways including direct alteration and abundance of sessile prey (Sammarco, 1982)
and controlling the distribution and abundance of coral spat (Sammarco, 1980). Echinoids
are also of economic value as the harvest of the roe or gonads of edible species such as
Tripneustes gratilla form a major source of livelihood for the local fishing communities in
such countries as Bolinao, Pangasinan and the Philippines (Juinio-Menez et al, 1998).
Areas of high abundance of suspension-feeding Ophiuroids are an important link between
benthic and pelagic ecosystems (Allen, 1998), in that their feeding behaviour is thought to
create a flux of organic matter between these two ecosystems and is, therefore, thought to
be a part of the nitrogen and carbon cycles of the coastal ecosystem (Davoult et al, 1992).
Asteriods have been documented as Keystone species for various ecosystems, in fact when
Paine first coined the term ‘Keystone species’ he was using the example of the Pisaster
ochraceus starfish which lives on the Pacific coast of North America and whose predation
contributes greatly to the biodiversity of the coastal system (Paine, 1966). Other examples
of starfish species performing critical roles include the Acanthaster planci which preys upon
stony corals and Charona sp. which preys upon the Acanthaster, thus preserving the balance
in coral reef ecosystems (Cristancho & Vining, 2004).
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1.4 Threats to Echinoderms
The collecting of echinoderms for various commercial enterprises has been a growing global
threat to the phylum (Micael et al, 2009). Sea urchins and sea cucumbers are under high
commercial fishing pressure and aspects of their biology compounds the effects of
overexploitation on their wild populations (Micael et al, 2009). These aspects include
temporal variability in the population density of sea urchins, sporadic and unpredictable
recruitment of juveniles and the uncertainty of reproductive success of both species (Micael
et al, 2009). Their sedentary lifestyle and habitat requirements also makes them an easier
target and more susceptible to overfishing (Micael et al, 2009). Besides targeted commercial
fishing, echinoderms are frequent by-catches from use of general fishing hardware (Micael
et al, 2009). Echinoderms are indirectly affected by the impacts of trawls and dredges on the
benthic habitat though these effects vary according to the fragility of the habitat and the
severity of natural disturbance (Micael et al, 2009).
Apart from the direct and indirect effects of fishing, echinoderms are also exploited through
the global trade of echinoderms for souvenirs, home aquaria and biomedical products
(Micael et al, 2009). Echinoderms occupy 17% of the global trade for aquariums (Wabnitz et
al, 2003) and little is known of the full extent of the global use of echinoderms as souvenirs
(Micael et al, 2009). To aid conservation, it is recommended that there be an improvement
of the collection of echinoderm catch data as well as a development of a global database on
the biology, ecology, threats, monitoring and conservation of echinoderm species (Micael et
al, 2009).
A current topical threat to marine ecosystems and organisms is ocean acidification, brought
about through increased ocean CO2 levels due to climate change. This reduction of the
ocean’s pH is particularly concerning for organisms with calcium carbonate shells or
skeletons which are potentially susceptible to dissolution in acidic waters (Orr et al, 2005).
As mentioned earlier in section 1.2, echinoderms have calcium carbonate skeletons and so
they fall under this category. It has been concluded that echinoderms are robust to near
future ocean acidification as a whole however they are likely to be impacted at the
ecosystem level (Dupont et al, 2010). Having said that, it is also concluded that the effect of
ocean acidification is species specific and will have worse effects on certain species than it
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will on others; an example of this is the brittle star Ophiothrix fragilis which is predicted
under certain scenarios to go extinct by 2050 due to near future ocean conditions (Dupont
et al, 2010). The effects of ocean acidification will also affect specific processes differently
between the echinoderm classes, for instance ocean acidification causes high mortality of
brittlestar larvae while sea urchin larvae are comparatively robust to the ocean’s drop in pH
(Dupont et al, 2008). Evidently the acidification of the ocean may have an effect on
echinoderms depending on the species but it will certainly have negative effects on
echinoderms in general at the ecosystem level.
Other stressors upon echinoderm populations associated with climate change include
physical disturbance associated with storm events (which are predicted to increase)
(Solomon et al, 2007), rising ocean temperatures, rising sea levels (which are predicted to
particularly effect invertebrate nurseries of sea grass beds and mangroves) (Lovelock and
Ellison, 2007) and changes in salinity and coastal runoff due to changes in rainfall
(Przeslawski et al, 2008). These both directly and indirectly impact echinoderm populations
as changing conditions leads to ecosystem community shifts which adversely affect
echinoderm biodiversity as well as the biodiversity of other marine invertebrates
(Przeslawski et al, 2008).
1.5 Studies conducted on Echinoderm distribution and abundance
There have been many studies conducted on echinoderm species spatial distributions and
densities as such data provides useful baselines to manage local pollutions in light of the
increasing harvest of echinoderms for aquarium trade and souvenirs (see section 1.4)
(Guzman and Guevara, 2002). One such studied species is the tropical starfish Oreaster
reticulates which inhabits mangroves, lagoons and shallow reef environments (Hendler et al,
1995). High densities of Oreaster r. tended to be found in sea grass areas or on mixed
substratum of semi-coarse to fine calcareous sand where there is less run-off from rivers
(Guzman and Guevara, 2002). It was also discovered that high densities and high
recruitment rates of these starfish were found around the Bocas del Toro archipelago of
Panama (Guzman and Guevara, 2002). Considering the fact that only 2.8% of the shallow
habitats around this area are protected, it has been recommended that the limits of these
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protected areas be expanded to include more developed and diverse coral reefs and
seagrass habitats (Guzman and Guevara, 2002).
Other studies have investigated the distribution and abundance of echinoderms within the
larger investigation of benthic communities along tropical subtidal habitats such as the
study conducted in Southeastern Brazil (Oigman-Pszczol et al, 2004). It was found that
echinoderm abundances were comparatively low compared to the phylums of other
invertebrates (Oigman-Pszczol et al, 2004). Sea urchins were the most abundant
echinoderm species and the analysis revealed that abundance of the sea urchins varied
significantly between sites and depths; their abundance tended to decrease with depth
(Oigman-Pszczol et al, 2004). The area under study was suffering from urban growth which
could be potentially impacting the marine populations, communities and ecosystems and so
it was recommended that the biota of the subtidal zones be monitored with taxonomic
precision (Oigman-Pszczol et al, 2004).
Studies have also been conducted on echinoderm distribution on a much larger scale such
as the one conducted on near-shore rocky habitats in 2010 (Iken et al). The study focused on
latitudinal trends in echinoderm species richness and large regional hotspots (Iken et al,
2010). The study was conducted over 76 global-distributed sites with 12 ecoregions and it
was found that sample-based species richness was overall low; <1-5 species per site (Iken et
al, 2010). Intertidal species richness and abundance was highest in Caribbean ecoregions
whose echinoderm assemblages were generally dominated by echinoids (Iken et al, 2010).
In contrast, the intertidal ecoregions with high abundance and species richness in the
Northeast Pacific were dominated by asteroids and holothurians (Iken et al, 2010). Distinct
latitudinal trends were found for intertidal assemblages with greater species richness and
abundance at high northern latitudes while no such trends were found for subtidal
echinoderm assemblages (Iken et al, 2010). It was also found that latitudinal gradients
tended to be superseded by regional hotspots whose assemblages were driven by processes
such as evolutionary history and overall productivity (Iken et al, 2010). In this study natural
and anthropogenic variables were also found to strongly correlate with the echinoderm
assemblages; these included salinity, sea-surface temperature, chlorophyll a, primary
productivity, inorganic pollution and nutrient contamination (Iken et al, 2010).
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Evidently multiple studies have been conducted on echinoderm species distribution and
abundance, of which the studies discussed are but a few examples. They have been
conducted at scales as specific as a single echinoderm species or as general as the benthic
fauna of an entire region. They have been investigations at the small scale of a single beach
to large scales that span the entire globe. These assessments in marine systems are
important not only from the ecological standpoint but also the public and management
standpoints; they create greater understanding for managing marine resource use and
identifying conservation priorities (Gray, 1997).
2. Aims of the Study
The primary aims of this study were, first to establish what species of Echinoderms were
present on these particular platforms and second, to determine what possible habitat
variables were affecting the distribution of the Echinoderm species richness and abundance
across the rock platforms. Knowing what species are present and where there is higher
biodiversity and abundance of these species in the Marine Reserve will be important for
conservation management purposes. Determining which factors are contributing towards
the diversity and abundance of the Echinoderm organisms will also be important for
conservation management.
Other secondary aims in the study were to investigate if and how habitat variables were
affecting the abundance and distribution of individual Echinoderm species. Also, if there
were any species that were observed to occur frequently together during data collection,
then one of the aims of the study was to establish whether there is an association of the
distribution and abundance between the two species. This will contribute towards
determining what factors may contribute to the species distribution and will also have
relevancy to the conservation of those particular species.
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3. Materials and Methods
3.1 Study sites
Eleven rock platforms were selected as study sites from among the rock platforms that can
be found along the Watamu Marine Reserve Beach between Turtle Bay and Geroda, Kenya
(see Appendix 7.1). The study sites were selected based upon whether they were close
enough to the shore that, during the low tides, the sides closest the land were in contact
with the beach and there was very little or no body of water between the rock platform and
the beach. This was to ensure that the study site could be categorized into the three
distance classes of ‘flanking the sea’ or ‘sea’, ‘flanking the beach’ or ‘land’ and ‘the centre of
the platform between the distance classes ‘sea’ and ‘land’’ or ‘top’.
Data collection at these study sites were conducted between the 15 th of August and the 15th
of September 2013. Data collection took place at the low tides of which the time of day and
the length of time of low tide varied from day to day. Data collection was not conducted at
night as the darkness would have made it harder to locate and identify Echinoderms and it
may have caused a change in the echinoderms behaviour and distribution compared to the
daytime. Belt transects were used to sample the study sites and though the number of belt
transects varied between study sites due to the variation in size of the platforms however
the number of transects tended to vary between 4 and 8 transects per distance class of the
rock platform.
Figure 1. A sample picture of some of the rockpools found on one of the rock platforms
that were researched in this investigation. The picture was taken August 2013 by Chloe
Naylor.
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3.2 Transect procedure
The transects were set up using a 100 m long measuring tape which was laid along the rock
platform. The starting point of the platform was always at the western end and the data
collection always began on the distance class categorized as ‘sea’. 10 m of tape would be
laid out at a 90° angle to the sea and then a meter long pole would be used to determine
the width of the transect as the person conducting the data collection would walk down the
measuring tape holding out the pole so that one end would be over the tape and the other
would be facing the sea. Using this pole to judge what rockpools came within the transect,
the echinoderm species and the number of individuals within that species within the
rockpools would then be counted. In total, 151 transects were conducted on the 11 study
site rock platforms.
What was considered a rock pool was a depression within the rock platform which had a
volume of water within that depression. Sometimes rockpools would be connected together
but there would be two distinct depressions within the rock platform although they may
have shared the same volume of water. If that were the case then those two depressions
would be counted as two separated rockpools as they had two different deepest volumes. A
rockpool that counted as being within the transect was any that had come within the metal
pole; even those that were not completely within the transect were counted, although only
those echinoderms within the transect boundary were counted; those that were found in
rockpools which were partly found within the transect but the echinoderms themselves
which were not physically within the transect were not counted. That being said, the
echinoderm species which were seen on the rock platforms but were not found within the
transects were noted down.
3.3 Measuring rockpool habitat data
As well as counting the echinoderm species and the number of individuals, habitat data was
also taken into account. The depths of the rockpools that were found within the transects
were measured using a reed pole that had lines carved into the pole every 10 cm so that the
pools estimated depths could be taken as 0-10cm, 10-20cm, etc. Each pool was consistently
measured at its deepest point, even those that were only partly found within the transects.
As well as pool depth, sea grass coverage was also measured using a number system to
15
judge and estimate the amount of sea grass coverage per pool. If the pool was ¼ covered by
sea grass then it received the number 1; if it covered up to half of the pool it was given the
number 2 and if it was over half it was given the number 3. This system was decided as it
was difficult to judge how much of the pool was covered by sea grass above that of ½ the
pool. The percentages of rockpools that had living seagrass in the transects were then
calculated from this data. As well as this, the number of rockpools per transect were
counted including those that were only partly found within the transects. Typically
combinations of sand, rock rubble and/or dead seagrass consisted as the substratum of the
rockpools and so four categories of floor substratum were devised: sand, sand and dead
seagrass, sand and rock rubble, and sand, rock rubble and dead seagrass. It was recorded
which of the categories the floor substratums belonged to in each of the rockpools within
the transects.
3.4 Finding, handling and documenting echinoderms
If a new species of echinoderm was found then the individual would be placed in a white
tub and a picture would be taken of the specimen for later identification. There were
individuals that were too difficult to take a picture of, for example there were brittle star
species that tended to be found in crevices and therefore were difficult to pull out in order
to be placed in the white tub. Each rock pool within the transect was carefully inspected in
order to observe any brittlestars which tended to protrude their tentacles out of the
crevices when the tide started to come back in. If the rock pool was completely covered in a
thick layer of dead seagrass then it was difficult to observe any brittlestar species. This
hindered data collection but not to a significant extent as it would be unusually for brittle
stars to be in those rockpools as they are filter feeders (Barnes, 1980) and preferred
rockpools with an unobstructed flow of water at high tide. In order to determine whether
any sea urchins were under the layer of dead sea grass within the rock pools, a wooden pole
would be used to move the dead sea grass from side to side in order to get a better view of
the rock pool bottom. As well as this, the wooden pole would be pulled along the rockpool
bottom in order to feel whether there were any urchins on the bottom of the pool. The
dead seagrass would also be searched through by hand as sometimes juvenile urchins could
be found sticking to the strands of sea grass.
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3.5 Analysis
The data was handled using Microsoft Excel 2007 and were analysed using IBM SPSS
Statistics 21. The data was determined to be normally distributed using visual inspection of
their bar charts and Q-Q normal plots and then they were determined to be homogenously
distributed with visual inspection of the scatterplots. General linear models were then run
on the echinoderm data and the rockpool habitat data. Logistic regression analyses for
individual echinoderm species’ data and the habitat data sets. Finally a spearman’s rank
correlation was used to investigate an association between the abundance and distribution
data of two species of echinoids.
4. Results
4.1 Echinoderm species found in the study
Within this study 5 species of Ophuiroidea, 1 species of Asteroidea, 1 species of
Holothuroidea and 4 species of Echinoidea were found. Ophuiroidea, therefore, was the
most diverse echinoderm class found during the investigation followed by Echinoidea. The
most highly abundance species was Echinomertra mathaei and then Ohiphiocoma
scolopendrina and Echinodthrix diadema. The most highly distributed echinoderm species
was Ophiocoma scolopendrina followed by Echinometra mathaei and Echinothrix diadema
(see Table 1). Outside of the transects other species of echinoderms, the starfish
Protoreaster lincki and Nardoa variolata, were also discovered on the study sites, however
they were not included in the analysis of this study. This indicates that the methods of this
investigation did not sample all the echinoderm species that inhabit the rockpools of the
study sites.
Table 1. The list of the echinoderm species found in the study with the total abundances
and how many rock platforms they were found on.
Echinoderm
Family
Ophuiroidea
Asteroidea
Holothuroidea
Echinoderm species
Ophiocoma
scolopendrina
Amphiura dejectoides
Ophiomastix venosa
Unidentified species
1
Unidentified species
2
Monachaster sanderi
Actinopyga
Number of rock platforms on which it
was found
10
17
Total
abundance
268
3
2
2
41
7
4
1
2
1
1
1
1
Echinoidea
mauritiana
Echinometra mathaei
Echinothrix diadema
Tripneustes gratilla
Diadema setosum
6
5
4
1
275
161
11
1
4.2 Echinoderm species richness distribution
4.2.1. General linear model
To understand which, if any, of the independent abiotic factors measured were affecting the
Echinoderm phylum distribution amongst the rockpools of the study sites, a general linear
model was conducted. This model investigated whether there was a statistically significant
difference between the Echinoderm species richness adjusted means of the independent
variable groupings; distance class, rock platform, rockpool number, percentage of live
seagrass coverage, rockpool depth and rockpool floor substratum type.
The species richness data were determined to be normally distributed through visual
inspection of its histogram and normal Q-Q plot (see Appendix 7.2 figures 15 and 16) and
they were determined to have homogenous variance through visual inspection of the
scatterplot of predicted values and standardized residuals (see Appendix 7.2 figure 17). The
results of the General Linear model were that there was statistical significance difference in
echinoderm species richness between the rock platform distance classes, F(2,129) = 11.374
(p<0.001) partial Ƞ2 = 0.150 and between the different rock platforms, F(10, 129) = 4.112
(p<0.001) partial Ƞ2 = 0.242. There was no statistical significance difference in echinoderm
species richness between the floor substratum types, rockpool numbers, percentages of
rockpools with living seagrass, or rockpool depths.
4.2.2 Distance class post hoc analysis
To investigate how species richness differs between the distance class groupings, post-hoc
pair-wise comparison analysis that included a Bonderroni adjustment was conducted. The
results of the analysis indicates that the mean echinoderm species richness of distance class
‘land’ was significantly lower than that of distance class ‘sea’ (p<0.001) and significantly
lower than that of distance class ‘top’ (p=0.002). However there was no significant difference
between the echinoderm species richness of classes ‘sea’ and ‘top’ (p= 0.203) despite the
visual observation of the ‘top’ echinoderm species richness mean being lower than that of
‘sea’ (see figure 2).
18
2
Mean echinoderm species number
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
sea
top
land
Distance class
Figure 2. The mean echinoderm species number of the distance classes across all the rock
platforms.
4.2.3 Rock platform graph analysis
As stated in section 4.1.1, the Echinoderm species richness means were, in general,
statistically different between the different rock platforms. Figure 3 indicates that the rock
platforms with the largest mean echinoderm species richness were Geroda Rock 1 and 2, Wishing
Rock 1 and 2 and Hemmingways Rock 1. Those with the lowest average number of echinoderm
species were Turtle Bay Rock 1 and 3, Wishing Rock 3 and Geroda Rock 3. This is confirmed by
figures 4 and 5 which give a visual indication as to where each species was found on the study sites
along the beach.
19
Mean echinoderm species number
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Rock platforms
Figure 3. Mean echinoderm species number of each rock platform.
Figure 4. Map indicating the locations of the echinoderm species found at the study sites
(between Wishing Rock 1 and Hemmingways Rock 1). The locations of the echinoderm
20
markers on the rock platforms are not representative of where on the rock platforms these
echinoderm species were found.
Figure 5. Map indicating the locations of the echinoderm species found in this study at the
platform level (between Geroda Rock 1 and Geroda Rock 3). Similar to Figure C, the
locations of the echinoderm markers on the rock platforms are not representative of where
on the rock platforms these echinoderm species were found.
4.3 Echinoderm abundance distribution
4.3.1 General linear model
Similar to the general linear model conducted in section 4.1.1, this model was using the
same independent variable groupings to discover whether there is a statistically significant
difference between the mean abundances of the total Echinoderm organism counts.
Standardized residuals of the total number of echinoderm organism were seen to not be
normally distributed through the visual inspection of the bar chart (see Appendix 7.3 figure
18). As a result of this, the data was transformed to the square-root and the subsequent
residuals of the transformed data were shown to be normally distributed through the visual
inspection of the bar chart (see Appendix 7.3 figure 19) and the normal Q-Q plot (Appendix
7.3 figure 20). The data was also shown to be relatively but not completely, homogenously
21
varied through visual inspection of the scatter-plot of the predicted values and standardized
residuals (see Appendix 7.3 figure 21) however due to the robustness of the model analysis
and the high number of data points, it was decided that the test could be attempted despite
this, although its results should be interpreted with caution.
The results of the general linear model were that the difference between the numbers of
echinoderm organisms were statistically significant across the distance classes F(2,129) =
15.44 (p<0.001) partial Ƞ2 = 0.193, across the different rock platforms F(10,129) = 5.239
(p<0.001) partial Ƞ2 = 0.289 and across the different floor substratum types F(2,129) = 2.986
(p=0.034) partial Ƞ2 = 0.065. There was not a statistically significant difference between the
total echinoderm numbers in relation to rockpool number, percentage of rockpools with
living seagrass and rockpool depths.
4.3.2 Distance class post hoc analysis
Post hoc pair-wise comparison analysis with a Bonferroni adjustment of the distance class variable
revealed that the difference in the echinoderm organism count was significant between each of the
three categories (p= 0.001, p<0.001, 0=0.029). As can be seen from figure 6, the ‘sea’ class had
significantly more echinoderm organisms than classes ‘top’ and ‘land’ while ‘top’ had a significantly
higher number of echinoderm organisms than ‘land’.
Mean total echinoderm organism count
(Squareroot transformation)
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
sea
top
land
Distance classes
Figure 6. A bar-chart showing the average echinoderm organism counts across the three
distance classes.
22
4.3.3 Rock platform graph analysis
Figure 7 shows that the rock platforms with the highest total number of echinoderm
organisms were Wishing Rock 1 and 2 and Geroda Rock 1 and 2. This means that not only do
Wishing Rock 1 and 2 and Geroda Rock 1 and 2 have the high echinoderm species richness
but it also has high abundances of these echinoderms. Geroda Rock 2 though it was found
to have relatively low species richness; when it is compared to Geroda Rock 1 and Wishing
Rocks 1 and 2, it was found to have the highest echinoderm abundance of all the rock
platforms. This indicates that though Geroda Rock 2 may not have conditions which allow a
high variety of these species to exist, of what species that do manage to survive, the
conditions enable them to live in high numbers. Hemmingways Rock 1 on the other hand,
though shown to have high species richness, did not have relatively high abundances of the
echinoderms when compared to the other rock platforms. Turtle Bay Rock 3, Wishing Rock 3
and Geroda Rock 3 have been consistently found to be low in both echinoderm species
richness and abundance. No species and therefore no abundances were found on Turtle Bay
Mean total number of echinoderm organisms
(squareroot transformation)
Rock 1.
6
5
4
3
2
1
0
Rock platform
Figure 7. A bar-chart of the mean, total echinoderm organism count per rock platform.
23
4.3.4 Floor substratum type graph analysis
As was previously stated in section 4.2.1, the variable of the rockpool floor substratum type
was shown to have a significant effect on the number of echinoderm organisms in the
rockpools. Figure 8 illustrates that the floor substratum type that had, on average, the
highest number of echinoderm organisms was that of the sand, rock rubble and dead
Mean echinoderm organism count (squareroot
transformation)
seagrass while that which had the lowest was sand and dead sea grass.
6
5
4
3
2
1
0
sand
sand and dead
seagrass
sand and rock
rubble
sand, rock rubble
and dead seagrass
Floor substratum category
Figure 8. A chart of the mean of the total echinoderm organism count for the different
floor substratum types of the rockpools.
4.4 Effect of habitat variables on individual Echinoderm species distribution
Having established what effects these habitat variables have on the species richness and
abundance of Echinoderms as a phylum, it then would be worthwhile to deduce what
effects these habitat variables will have on the distribution of individual species. Logistic
regression analysis was preformed to ascertain the effects of rockpool umber, percentage of
rockpools with living seagrass, rockpool depth and rockpool floor substratum on the
likelihood that a certain species of echinoderm will be present in a transect.
24
All species data that was put through the linear regression analysis met the assumption of
the continuous independent variables being linearly related to the logit of the dependent
variables (see Appendix 7.4 ,Tables 2-6).
4.4.1 Logistic regression analysis of distribution of species Ophiocoma scolopendrina
Logistic regression analysis was preformed investigating whether these factors had an effect
on the presence or absence of the brittlestar species Ophiocoma scolopendrina (see figure
9) which was one of the most widely distributed species that was found on the study sites
(see figures 4 and 5). The logistic regression model for this species was a good fit as was
indicated by the statistically insignificant results of the Hosmer and Lemeshow test; X2 =
7.082, df = 8, p=0.528. The model explained 12.5% (Nagelkerke R2) of the variance in the
distribution of Ophiocoma scolopendrina and correctly classified 68.2% of transects.
Sensitivity of the model was 19.1%, specificity was 90.4%, positive predictive value was
47.4% and negative predictive value was 71.2%. The model showed that none of the
predictor variables were statistically significant in determining the presence or absence of
Ophiocoma scolopendrina (see Appendix 7.5 table 7).
Figure 9. Photo of Ophiocoma scolopendrina taken by Chloe Naylor August 2013
4.4.2 Logistic regression analysis of distribution of species Amphiura dejectoides
Logistic regression analysis was also used to investigate whether these factors have an
effect on the distribution of Amphiura dejectoides (see figure 10), another brittlestar species
25
commonly found on the rock platforms that were studied in this investigation (see figure C
and D).This logistic regression model was a good fit in predicting the distribution of the
species as is shown by the Hosmer and Lemeshow test; X2 = 3.921, df = 8, p=0.864. The
model explained 24.6% of the variance of the Amphiura dejectoides (Nagelkerke R2)
distribution and correctly classified 93.4% of transects. Sensitivity of the model was 0.1%,
specificity was 100%, the positive predictive value was 100% and the negative predictive
value was 93.3%. Of the predictor variables, rockpool number was the only variable to have
a statistically significant effect on the distribution of Amphiura dejectoides, Wald = 4.269, df
= 1, sig = 0.039. Increasing the rockpool number by 1 would increase in the likelihood of
finding Amphiura dejectoides in that transect 1.084 times (see Appendix 7.5, table 8).
Figure 10. Photo of Amphiura dejectoides taken by Chloe Naylor August 2013
4.4.3 Logistic regression analysis of distribution of species Echinometra mathaei
The next species whose distribution was compared to the independent variables was
Echinometra mathaei (see figure 11), a species of sea urchin which was common and widely
distributed among the study sites (see figures 4 and 5). The model is a good fit to the
distribution of Echinometra mathaei as shown by the results of the Hosmer and Lemeshow
test; X2 = 10.443, df = 8, p = 0.235. The model explained 14.8% (Nagelkerke R2) of the
variance in the distribution of the species and correctly classified 86.1% of transects. The
sensitivity of the model was 8.7%, its specificity was 100%, the positive predictive value was
100% and the negative predictive value was 85.9%. The model indicates that none of the
26
predictive values are statistically significant in predicting the presence or absence of
Echinometra mathaei (see Appendix 7.5 table 9).
Figure 11. Photo of Echinometra mathaei taken by Chloe Naylor August 2013
4.4.4 Logistic regression analysis of distribution of species Echinothrix diadema
The next species to be analysed with logistic regression was Echinothrix diadema (see figure
12), another species of sea urchin that was commonly found at the study sites. The results
of the Hosmer and Lemeshow test indicate that the model of the regression was a good fit,
X2 = 2.047, df = 8, p = 0.980. The model explained 16.6% (Nagelkerke R2) of the variance in
the Echinothrix diadema distribution and correctly classified 90.7% of transects. Sensitivity
of the model was 0%, specificity was 100%, the positive predictive value was 0% and the
negative predictive value was 90.7%. The model shows that none of the predictive values
are statistically significant in predicting the presence or absence of Echinothrix diadema (see
Appendix 7.5 table 10).
27
Figure 12. Photo of Echinothrix diadema taken by Chloe Naylor August 2013
4.4.5 Logistic regression analysis of distribution of species Tripneustes gratilla
The final logistic analysis was conducted on another species of sea urchin, Tripneustes
gratilla (see figure 13), which was not commonly found on the study sites but when it was
observed, it tended to be observed in rockpools with high percentage coverage of living
seagrass. The model was a good fit as was shown by the results of the Hosmer and
Lemeshow test; X2 = 4.830, df = 8, p = 0.776. The model explained 10.3% of the variance in
the Tripneustes gratilla distribution and correctly classified 94% of transects. The sensitivity
of the model was 0%, specificity was 100%, the positive predictive value was 0% and the
negative predictive value was 94%. The model shows that none of the predictive values are
statistically significant in predicting the presence or absence of Tripneustes gratilla (see
Appendix 7.5 table 11). The other species recorded in this study were found too rarely to
make do a logistic regression analysis.
28
Figure 13. Photo of Tripneustes gratilla taken by Chloe Naylor August 2013
4.5 Association between Echinothrix diadema and Echinometra mathaei abundance and
distribution
It was observed that Echinothrix diadema and Echinometra mathaei were often found together and
when they were found together they tended to occur in high abundances. A spearman’s rank
correlation test was implemented to see whether there was an association between these two
species’ abundances. The abundance data was transformed using natural log to meet the
assumption of the data being monotopic (see Appendix 7.6). Once the test was run it was found that
there was a strong positive correlation between the abundance distribution of Echinothrix diadema
and that of Echinometra mathaei, rs(11) = 0.731, p = 0.005.
5. Discussion
5.1 Echinoderms found in the study
The results of this study indicated that the most abundant echinoderm to be found on the
rock platforms of the Watamu Marine Park beach was Echinometra mathaei. While other
studies of echinoderms have not found Echinometra m. to be the most abundant of all
echinoderm species, they have found that Echinometra m. is the dominant echinoid on the
Diani Beach in Kenya (Khamala, 1971). This perhaps is due to the prime environment that is
29
ideal for the Echinometra m. to thrive or it may be a robust species that can adapt to a
range of environmental factors.
The most diverse echinoderm class was Ophuiroidea which may be representative to how
well adapted the class is to exploiting the rockpool type of habitat. Other studies have
revealed that the intertidal zone makes a favourable habitat for Ophuiroids as it affords the
ability to exploit a nutrient-rich water surface and provides them protection from predators
such as fish which are excluded from these platforms at low tide (Oak and Scheibling,
2006).The Asteroidea and Holothuroidea may not have been so diverse and abundant due
to the fact that they are mainly shallow water species and so only a relatively few individuals
of the larger population living on the coral reefs would occupy the rocky habitats at the
fringes of where the majority of these organisms live (Barnes, 1980).
5.2 Echinoderm species richness and the habitat variables
According to the general linear model that was conducted in this study, species richness is
only significantly different across the distance classes and the rock platforms themselves
(see section 4.2.1). The species richness of the echinoderms did not vary significantly
between different rockpool numbers, rockpool depths, percentage of rockpools with living
sea grass or floor substratum types. That being the case, it is likely that other factors that
were not measured during the study, either abiotic or biotic which causes there to be a
variation between the species richness of the echinoderms of the different rock platforms. A
highly possible variable that was not measured was the amount of time that the rock
platforms are submerged during high tide. Though this variable was not measured directly,
it was noted during data collection that the rock platforms were exposed at different levels
of low tide. Other studies have found that fluctuating levels of salinity have an effect on
echinoderm diversity, abundance and population structure and that this sensitivity can be
explained by the high permeability of their outer surfaces (Drouin et al, 1985). The amount
of time that the rockpools are submerged and exposed would affect salinity levels of the
rockpools and so it likely that those rockpools that are closer to land and are exposed for
longer periods of time would have higher salinity fluctuations and therefore a lower
diversity and abundance of echinoderms. This concurs with what was found in this study as,
according to the result of the post-hoc analysis, there was no significant difference between
30
the echinoderm species richness between that the of the ‘sea’ and ‘top’ distance classes
however there was a significant difference between ‘land’ and the these other two distance
classes (see section 4.2.2). Other variables may also be related to this variation in
echinoderm diversity such as the availability of food resources. The land section is exposed
more frequently than the ‘top’ and ‘sea’ distance classes and it is less likely that the algal
growth will live on the section of the rock platform closest to land as that will be submerged
the least amount of time; this will mean it is will be less likely to find echinoid species on this
distance class as they are fairly sedentary grazers which scrape algae off the rocks (Barnes,
180). Similarly, ophuiroids are filter feeders and are more likely to inhabit rockpools closer
to the seaward side of the rock platforms in order to be nearest to the source of influx of
organic matter which they filter out of the seawater with their tentacles (Barnes, 1980).
Holothuroids are could be predictable found where they can filter organic materials out of
the sediment (Barnes, 180) and that sediment will be more likely to be found on the
seaward side of the rock platforms. The asteroids are predators (Barnes, 180) and could
predictably be found where their prey is located and so are more likely to be found where
the other echinoderms are located which has been established to be on the ‘top’ and ‘sea’
distance classes. The fact that species richness is not significantly different between the
distance classes of ‘sea’ and ‘top’ mean abiotic and biotic variables of these two distance
classes are similar enough to allow for the same variety of echinoderms to inhabit their
rockpools.
5.3 Echinoderm abundance and the habitat variables
Similar to the results of species richness, it was found that echinoderm abundance varied
significantly between the distance classes and the rock platforms (see section 4.3.1). It is
likely that it is for the same reasons of the unmeasured abiotic and biotic variables that
were not measured in this study, such length of time the habitats are submerged or
availability of food, which are affecting the distribution of the echinoderm numbers along
the study sites. The post-hoc analysis conducted on the distance classes revealed that there
was a significant difference between the abundance of echinoderms across all three
distance classes (see section 4.3.2). This indicates that while, the abiotic and biotic variables
were similar enough between the ‘sea’ and ‘top’ distance classes to allow for the same
variety of echinoderm species to inhabit their rockpools, the differences between them are
31
enough to mean that there is a higher abundance of echinoderm organisms found on the
distance class closest to the sea. This could mean that a biotic variable such as availability of
food occurs for a wide variety of echinoderm species across both the ‘top’ and ‘sea’ distance
classes however; the availability of food is greater within the ‘sea’ distance classes allowing
for a greater abundance of echinoderm organisms.
Unlike the results investigating echinoderm species richness however, the results of the
analysis showed that the variable of floor substratum types does have an effect on the
distribution of total number of echinoderm organisms, though the level of its significance
was not as high as that of distance class and rock platforms (see section 4.3.1). The evidence
from figure 8 indicates that the floor substratum type which allowed for the highest number
of echinoderms was sand, dead seagrass and rock rubble. This is interesting as many studies
have found that a positive correlation between habitat heterogeneity and diversity (Tews et
al, 2004) which predicts that a more complex rockpool floor substratum would mean
greater species richness but not necessarily a greater number of echinoderm organisms.
Also figure 8 indicated that the floor substratum type that had the least abundance was
sand and dead seagrass rather than just sand. This could indicate that dead seagrass has a
detrimental effect on the number of echinoderms that can live within the rockpools. This
would be logical for the brittlestar species as the dead seagrass would inhibit their ability to
filter feed however, one would not have thought that it would have a detrimental effect on
the other classes of echinoderms. Besides this, it was found that sand and rock rubble was
floor substratum type with the second highest number of echinoderms; this could indicate
that the presence of rock rubble is what causes there to be a higher number of echinoderm
organisms. This would be worth investigating in further study.
5.4 Individual species and the habitat variables
According to the results of this study, none of the habitat variables measured had an effect
on the distribution of the individual echinoderm species Ophiocoma scolopendrina,
Amphiura dejectoides, Echinometra mathaei, Echinothrix diadema, and Tripneustes gratilla
(see section 4.4). There was one exception in that the results predicted that the likelihood of
finding Amphuira d. in a transect was predicted to increase 1.084 times with an increase of
one rockpool. This could mean that there are species-specific abiotic or biotic variables that
32
were not measured in the study that predict the distribution of individual echinoderm
species and that it is impossible to use generalized habitat variables in order to predict their
individual distribution. For instance, a study conducted on Ophiocoma scolopendrina
determined that its feeding behaviour (surface-film feeding) is regulated by the tidal cycle
and is triggered by suspended particles (Oak and Scheibling, 2006). In order to optimize
their ability to obtain organic matter from filtering the seawater, it is likely that they will be
located in rockpools that are closest to the water of the rising tide that covers the rock
platforms. The abiotic variable that could potentially best predict their distribution would be
the amount of time it takes for the water of the rising tide to reach the pool. This would be
completely different to what best predicts the distribution of sea urchins which do not filter
feed but tend to scrape the surface of substrates. Within one species there can also be
variations of habitat preferences which could complicate what factors influence the species
distribution. Echinometra mathaei, for instance shows great colour variation and it has be
demonstrated in previous studies that these morphological variants have different habitat
preferences; for instance one type preferred protected tidepools with rock rubble floor
substratums while another type preferred intertidal areas exposed to wave action and
which had deep burrows (Nishihara et al, 1991). Clearly not only the variation between the
lifestyles of different echinoderm species but also possibly within echinoderm species may
mean that shared abiotic variables will not be able to predict individual echinoderm species
distributions.
5.5 Echinometra mathaei and Echinothrix diadema
The distribution of the abundance of Echinometra m. was found to be strongly associated
with that of Echinothrix d (see section 4.5). This could be put down to the similar lifestyles
that these two echinoids have; they are both omnivorous grazers that scrape algae and
other organic matter of the rock substratum (Barnes, 1980). The fact that they occur in high
abundances together is an indication that in those particular areas the conditions are
favourable for high number of organisms with that particular lifestyle. However they cannot
share the exact same niche as it would be predicted that the better adapted of the two sea
urchin species would have outcompeted the other sea urchin species and prevented it from
occupying the same habitat. That being the case, the two species have similar enough life
33
styles that they occur in high abundances in the same favourable conditions however their
niches are dissimilar enough to allow them to cohabitate the same rock platforms.
5.6 Recommendations for future research and relevancy of study to conservation
management
This study established that the habitat variables measured had little effect on the
distribution of the echinoderm abundance and species richness though it did determine that
they varied significantly between the distance classes of the rock platforms and the rock
platforms themselves. With this in mind, it might be worthwhile to investigate whether
biotic factors such as food availability or presence of non-echinoderm predators have a
greater effect on the distribution of the species richness and abundance of the
echinoderms. Other abiotic factors that could not be measured in this study due to lack of
materials and time but would be worth investigating in future studies were salinity and
temperature variation among the rockpools and the amount of time that the rock platforms
are exposed at the different tide levels. As well as this there may be anthropogenic effects
on the distribution of the echinoderms which could not be measured in this study such as
the possibility of echinoderms being illegally collected by tourists and souvenir sellers along
the beach.
This study established that rockpool floor substratum types did have an effect on the
distribution of the echinoderm abundances. A worthwhile further study to this may be to
conduct a comparative investigation between the floor substratum types in order to
determine whether it is the presence of rock rubble which causes there to be a greater
abundance of echinoderms and whether the presence of dead seagrass causes there to be a
reduction in the number of echinoderms that can live in the rockpools.
This study also established that generalized habitat variables do not predict the distribution
of individual echinoderm species and so, other studies would need to conduct more speciesspecific surveys in order to determine what are the most significant factors affecting the
distribution and abundance of the individual echinoderm species among the rock platforms.
This study determined that the rock platforms investigated that had the greatest
echinoderm species richness were Geroda Rock 1 and 2, Wishing Rock 1 and 2 and
34
Hemmingways Rock 1 (see section 4.2.3). These then could be considered rock platform
hotspots for echinoderm diversity and this should be considered when planning future
conservation management. It may be prudent to enforce greater protection for those
particular areas so there are fewer disturbances to the echinoderm populations in order to
maintain their high biodiversity. It also determined that some of the rarer echinoderms to
be found on the rock platforms belonged to the Asteriodea and Holothuroidea classes which
could mean that they should be carefully monitored as their relatively lower populations
would be less resistant to disturbances to the rockpool ecosystems.
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38
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39
7. Appendix
7.1. Study site Locations
Figure 14. A habitat map of the Watamu Marine Reserve. The rocky platforms used as
study sites in the investigation are circled in red and the names of the rock platforms that
were given them during the investigation are written beside each study site. The Map was
provided by A Rocha Kenya.
The Hemmingways Rock 1 and Turtle Bay Rock 2 study sites were connected however, due
to the fact that most of the time the platforms appear separated as the connected rock part
of the platform is underwater, the rock platform was considered as two study sites.
40
7.2. Assumptions of general linear model for Echinoderm species richness
Figure 15. The bar chart of the standardized residuals of the number of echinoderm
species.
Figure 16. The normal Q-Q plot of the standardized residuals for the number of
echinoderm species
41
Figure 17. A scatterplot of the predicted values and standardized residuals of the
number of echinoderm species.
There were no outliers in the echinoderm species richness data as determined by there being
no cases with standardized residuals greater than +/-3 standard deviations.
7.3 Assumptions of general linear model for Echinoderm abundance
Figure 18. A bar chart of the standardized residuals of the Echinoderm total organism
count.
42
Figure 19. A bar chart of the standardized residuals of the total echinoderm organism
count that had been transformed using square-root.
Figure 20. The Normal Q-Q plot of the standardized residuals of the total echinoderm
organism count transformed through square-root.
43
Figure 21. The scatter-plot of the predicted values and standardized residuals of the
total echinoderm organism count that had been transformed through square-root.
7.4. Assumptions of the Logistic regression analyses
Table 2. The results of the logistic analysis for the species Ophiocoma scolopendrina. The
interaction terms between the seagrass cover percentages and their natural logs and the
rockpool number and their natural logs are not statistically significant indicating that the
original independent variables are linearly related to the logit of the dependent variable
thereby meeting this assumption of logistic regression analysis.
Variables in the Equation
B
Mode_rockpool_depth
Floor_substratum
Arcsine_seagrass_cover_percentages
Step
1
a
Rockpool_number
LnRockpool_number by
S.E.
Wald
df
Sig.
Exp(B)
.155
.324
.230
1
.632
1.168
-.234
.270
.751
1
.386
.791
-1.119
1.027
1.186
1
.276
.327
-.279
.292
.913
1
.339
.757
.065
.071
.839
1
.360
1.067
.339
2.101
.026
1
.872
1.403
1.335
1.985
.452
1
.501
3.800
Rockpool_number
Arcsine_seagrass_cover_percentages by
LnArcs_living_seagrass_proportions
Constant
a. Variable(s) entered on step 1: Mode_rockpool_depth, Floor_substratum,
Arcsine_seagrass_cover_percentages, Rockpool_number, LnRockpool_number * Rockpool_number ,
Arcsine_seagrass_cover_percentages * LnArcs_living_seagrass_proportions .
44
Table 3. The results of the logistic analysis species Amphiura dejectoides. The interaction
terms between the seagrass cover percentages and their natural logs and the rockpool
number and their natural logs are not statistically significant indicating that the original
independent variables are linearly related to the logit of the dependent variable thereby
meeting this assumption of logistic regression analysis.
Variables in the Equation
B
1
a
Wald
df
Sig.
Exp(B)
Rockpool_number
-.669
.622
1.156
1
.282
.512
Floor_substratum
-.266
.604
.195
1
.659
.766
.743
.586
1.611
1
.204
2.103
-1.053
4.164
.064
1
.800
.349
-15.640
12.047
1.685
1
.194
.000
.193
.152
1.602
1
.206
1.212
-6.385
4.565
1.957
1
.162
.002
Mode_rockpool_depth
Step
S.E.
Arcsine_seagrass_cover_percentages
Arcsine_seagrass_cover_percentages by
LnArcs_living_seagrass_proportions
LnRockpool_number by Rockpool_number
Constant
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages, Arcsine_seagrass_cover_percentages *
LnArcs_living_seagrass_proportions , LnRockpool_number * Rockpool_number .
Table 4. The results of the logistic for the species Echinometra mathaei. The interaction
terms between the seagrass cover percentages and their natural logs and the rockpool
number and their natural logs are not statistically significant indicating that the original
independent variables are linearly related to the logit of the dependent variable thereby
meeting this assumption of logistic regression analysis.
Variables in the Equation
B
S.E.
Wald
df
Sig.
Rockpool_number
1.548
1.403
1.218
1
.270
4.702
Floor_substratum
-.640
.377
2.875
1
.090
.527
.279
.449
.386
1
.534
1.322
-1.898
1.409
1.815
1
.178
.150
1.589
2.895
.301
1
.583
4.899
-.495
.407
1.475
1
.225
.610
-3.091
4.811
.413
1
.521
.045
Mode_rockpool_depth
Step
1
a
Arcsine_seagrass_cover_percentages
Arcsine_seagrass_cover_percentages by
Exp(B)
LnArcs_living_seagrass_proportions
LnRockpool_number by Rockpool_number
Constant
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages, Arcsine_seagrass_cover_percentages *
LnArcs_living_seagrass_proportions , LnRockpool_number * Rockpool_number .
45
Table 5. The results of the logistic analysis testing whether the continuous independent
variables were linearly related to the logit of the dependent variable for the species
Echinothrix diadema. The interaction terms between the seagrass cover percentages and
their natural logs and the rockpool number and their natural logs are not statistically
significant indicating that the original independent variables are linearly related to the logit
of the dependent variable thereby meeting this assumption of logistic regression analysis.
Variables in the Equation
B
S.E.
Rockpool_number
.659
1.506
Floor_substratum
-.045
df
Sig.
.191
1
.662
1.932
.435
.011
1
.917
.956
.651
.525
1.537
1
.215
1.917
-2.979
1.892
2.479
1
.115
.051
3.357
3.434
.956
1
.328
28.697
-.231
.439
.278
1
.598
.793
-2.348
5.280
.198
1
.657
.096
Mode_rockpool_depth
Step
1
a
Arcsine_seagrass_cover_percentages
Arcsine_seagrass_cover_percentages by
Wald
Exp(B)
Ln_seagrass_cover_percentages
Ln_rockpool_number by Rockpool_number
Constant
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages, Arcsine_seagrass_cover_percentages *
Ln_seagrass_cover_percentages , Ln_rockpool_number * Rockpool_number .
Table 6. The results of the logistic analysis testing whether the continuous independent
variables were linearly related to the logit of the dependent variable for the species
Tripneustes gratilla. The interaction terms between the seagrass cover percentages and
their natural logs and the rockpool number and their natural logs are not statistically
significant indicating that the original independent variables are linearly related to the logit
of the dependent variable thereby meeting this assumption of logistic regression analysis .
Variables in the Equation
B
S.E.
Wald
df
Sig.
14.670
12.728
1.328
1
.249
2349598.565
-.202
.624
.105
1
.746
.817
.635
.788
.650
1
.420
1.888
Arcsine_seagrass_cover_percentages
-2.166
2.944
.541
1
.462
.115
Arcsine_seagrass_cover_percentages by
-1.620
6.820
.056
1
.812
.198
-4.076
3.492
1.363
1
.243
.017
-57.034
49.235
1.342
1
.247
.000
Rockpool_number
Floor_substratum
Mode_rockpool_depth
Step
1
a
Exp(B)
Ln_seagrass_cover_percentages
Ln_rockpool_number by Rockpool_number
Constant
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages, Arcsine_seagrass_cover_percentages *
Ln_seagrass_cover_percentages , Ln_rockpool_number * Rockpool_number .
46
7.5 Results of Logistic regression analysis
Table 7. Logistic regression analysis of effect of predictor variables on the presence or
absence of Ophiocoma scolopendrina.
Variables in the Equation
B
S.E.
Wal
d
d
f
Sig.
Exp(B)
95% C.I.for
EXP(B)
Lowe Upper
r
Rockpool_number
.020
.024
Floor_substratum
Floor_substratum(1)
Floor_substratum(2)
Floor_substratum(3)
Floor_substratum(4)
p1
.957 44708.88
Mode_rockpool_depth(3)
Mode_rockpool_depth(4)
Arcsine_seagrass_cover_percenta
3.61 4
.461
.000 1
0
1.069
1.130
1.020
.973
1.069
1.00
2.604
.000
.
2.912
.318
26.68
0
.895 1
.344
7
.502
1.178
.182 1
.670
1.653
1.502
1.163
1.66 1
.197
4.489
.459
7
16.63
5.73 3
43.85
2
.125
9
19.84 19580.83
8
.000 1
.999
416923473.06
4
20.78 19580.83
4
.000 1
.999
4
20.02 19580.83
1062302748.1
.000 1
.999
498330083.30
4
-.666
.735
.821 1
.365
.514
- 19580.83
.000 1
.999
.000
0
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
47
.000
.
.000
.
.122
2.170
1
4
Arcsine_seagrass_cover_percentages.
.
87
7
22.16
.000
2
ges
Constant
.164
5
Mode_rockpool_depth
Mode_rockpool_depth(2)
.407
2
Ste
a
.687 1
Table 8. Logistic regression analysis of effect of predictor variables on the presense or
absence of Amphiura dejectoides.
Variables in the Equation
B
S.E.
Wald d
Sig.
Exp(B)
95% C.I.for
f
EXP(B)
Lowe Uppe
r
Rockpool_number
.081
.039
Floor_substratum(2)
Floor_substratum(3)
Floor_substratum(4)
17.66
45879.27
9
7
18.06
12778.04
9
2
-1.027
14425.98
p1
18.46
12778.04
9
2
18059.77
4
2
18.01
18059.77
5
2
17.53
18059.77
3
2
Arcsine_seagrass_cover_percentag -1.494
Mode_rockpool_depth(4)
.000 1
1.00 47164135.795
.
0
.000 1
.999 70338015.399
.000
.
.000 1
1.00
.358
.000
.
.999 104955586.79
.000
.
0
.000 1
6
.532
.000 1
.999 21728931.856
.000
.
.000 1
.999 66635253.897
.000
.
.000 1
.999 41170038.359
.000
.
1.779
.706 1
.401
.224
.007 7.331
-
22123.13
.000 1
.999
.000
38.87
7
es
Constant
.000
0
16.89
Mode_rockpool_depth(3)
.997
2.20 3
Mode_rockpool_depth
Mode_rockpool_depth(2)
1.084 1.004 1.171
.170 4
9
Ste
a
.039
9
Floor_substratum
Floor_substratum(1)
4.26 1
r
2
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages.
48
Table 9. Logistic regression analysis of effect of predictor variables on the presense or
absence of Echinometra mathaei in the transects.
Variables in the Equation
B
S.E.
Wald df
Sig.
Exp(B)
95% C.I.for
EXP(B)
Lower
Rockpool_number
-.067
.042 2.583
1
.108
4.701
4
.319
Floor_substratum
Floor_substratum(1)
Step
1
a
- 40192.970
Upper
.935
.862
1.015
.000
1 1.000
.000
.000
.
21.714
Floor_substratum(2)
-.610
.960
.404
1
.525
.543
.083
3.566
Floor_substratum(3)
.652
.964
.457
1
.499
1.919
.290
12.695
Floor_substratum(4)
-.129
.975
.017
1
.895
.879
.130
5.940
2.467
3
.481
Mode_rockpool_depth
Mode_rockpool_depth(2)
-1.250
1.095 1.304
1
.253
.286
.033
2.449
Mode_rockpool_depth(3)
-1.385
1.127 1.511
1
.219
.250
.027
2.279
Mode_rockpool_depth(4)
-.645
1.191
.293
1
.588
.525
.051
5.420
Arcsine_seagrass_cover_percentages
-.823
.923
.796
1
.372
.439
.072
2.679
.511
1.218
.176
1
.675
1.667
Constant
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages.
49
Table 10. Logistic regression analysis of effect of predictor variables on the presense or
absence of Echinothrix diadema in the transects.
Variables in the Equation
B
S.E.
Wald df
Sig.
Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Rockpool_number
-.042
.051
.959
.867
1.060
1 1.000
.000
.000
.
1.077 3.189
1
.074
.146
.018
1.206
Floor_substratum
Floor_substratum(1)
Step
1
a
- 40192.970
.672
1
.412
4.735
4
.316
.000
21.180
Floor_substratum(2)
-1.923
Floor_substratum(3)
-.523
1.028
.259
1
.611
.593
.079
4.445
Floor_substratum(4)
-.441
.999
.195
1
.659
.643
.091
4.554
3.322
3
.345
Mode_rockpool_depth
Mode_rockpool_depth(2)
-.753
1.291
.340
1
.560
.471
.038
5.909
Mode_rockpool_depth(3)
-.711
1.323
.289
1
.591
.491
.037
6.565
Mode_rockpool_depth(4)
.534
1.364
.153
1
.696
1.705
1.307 1.914
1
.167
.164
1.371
1
.987
.977
Arcsine_seagrass_cover_percentages
Constant
-1.808
-.023
.000
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages.
50
.118 24.715
.013
2.124
Table 11. Logistic regression analysis of effect of predictor variables on the presense or
absence of Tripneustes gratilla.
Variables in the Equation
B
S.E.
Wald d
Sig.
Exp(B)
95% C.I.for
f
EXP(B)
Lowe Uppe
r
Rockpool_number
-.072
.058
Floor_substratum(2)
Floor_substratum(3)
Floor_substratum(4)
.218
.931
.830 1.043
6
Floor_substratum
Floor_substratum(1)
1.51 1
r
17.75
46297.65
1
5
18.67
13002.17
8
9
18.99
13002.17
7
9
17.86
13002.17
7
9
.803 4
.938
.000 1
1.00 51167641.599
.000
.
.000
.
.000
.
.000
.
.000
.
.000
.
.000
.
0
.000 1
.999 129284052.52
0
.000 1
.999 177881599.02
7
.000 1
.999 57465098.780
.478 3
.924
.000 1
.999 138090004.97
Ste
p1
a
Mode_rockpool_depth
18.74
18945.74
3
0
18.60
18945.74
4
0
19.27
18945.74
2
0
Arcsine_seagrass_cover_percentag -1.291
1.636
.622 1
.430
.275
-
22978.20
.000 1
.999
.000
38.95
7
Mode_rockpool_depth(2)
Mode_rockpool_depth(3)
Mode_rockpool_depth(4)
5
.000 1
.999 120151846.43
8
.000 1
.999 234351918.85
4
es
Constant
4
a. Variable(s) entered on step 1: Rockpool_number, Floor_substratum, Mode_rockpool_depth,
Arcsine_seagrass_cover_percentages.
51
.011 6.796
7.6 Monotopic assumption of Spearman’s rank correlation between Echinothrix diadema and
Echinometra mathaei
Visual inspection of the scatter-plot (figure 23) of the paired abundance data of these two species
did not appear to be monotopic and so the data was transformed with the natural log in order to
produce a monotopic scatter-plot (figure 24), thus meeting this assumption.
Figure 23. The scatterplot comparing the paired abundance data of Echinometra mathaei
and Echinothrix diadema. The relationship does not appear to be strictly monotopic; as one
variable increases, the other one does not always increase.
52
Figure 24. The scatterplot comparing the natural log transformed, paired abundance data
of Echinometra mathaei and Echinothrix diadema. Here the relationship loosely appears to
be monotopic but not linearly monotopic.
53