A SURVEY OF THE BRANCHIOPODA AND COMMUNITY ASSOCIATES IN
ROCK POOLS WITH RESPECT TO ABIOTIC HABITAT PARAMETERS ACROSS
OUTCROPS IN WESTERN AUSTRALIA AND NORTHERN AZ, U.S.A.
A Thesis
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
Alissa Calabrese
August, 2009
A SURVEY OF THE BRANCHIOPODA AND COMMUNITY ASSOCIATES IN
ROCK POOLS WITH RESPECT TO ABIOTIC HABITAT PARAMETERS ACROSS
OUTCROPS IN WESTERN AUSTRALIA AND NORTHERN AZ, U.S.A.
Alissa Calabrese
Thesis
Approved: Accepted:
______Advisor Dean of the College Dr. Stephen C. Weeks Dr. Chand Midha
______Committee Member Dean of the Graduate School Dr. Peter Lavrentyev Dr. George R. Newkome
______Committee Member Date Dr. Randall Mitchell
______Department Chair Dr. Monte Turner
ii TABLE OF CONTENTS Page
LIST OF TABLES ...... v
LIST OF FIGURES ...... vi
CHAPTER
I. HABITAT CHARACTERISTICS OF EPHEMERAL ROCK POOLS MAY PREDICT THE DISTRIBUTION OF THE CLAM SHRIMP, LIMNADIA BADIA...... 1
Introduction ...... 1
Methods...... 4
Results ...... 7
Discussion ...... 15
II. ALGAL AND BACTERIAL DISTRIBUTION AND ABUNDANCE IN EPHEMERAL POOLS IN RELATION TO CLAM SHRIMP PRESENCE, INCLUDING A COMPARISON OF COMMUNITIES IN WESTERN AUSTRALIA AND ARIZONA, U.S.A...... 22
Introduction ...... 22
Methods...... 24
Results ...... 26
Discussion ...... 32
Conclusion ...... 38
III. HATCHING CUES IN CLAM SHRIMP: A LABORATORY TEST OF TEMPERATURE AS A MEANS OF SPECIES SEPARATION AND A SUGGESTED ALTERNATIVE MECHANISM FOR THE BET-HEDGING HYPOTHESIS ...... 40
iii Introduction ...... 40
Methods...... 42
Results ...... 43
Discussion ...... 46
Conclusion ...... 56
REFERENCES ...... 59
APPENDIX …………………………………………………………………………….. 64
iv LIST OF TABLES
Table Page
1.1. Abiotic habitat characteristics of 36 Australian pools with Limnadia present and absent. …………………………………………………………… 10
1.2. Percents of total variation in each parameter explained by outcrop and pool, with remaining variation treated as “residual variation.” …………....…. 12
1.3 Results of individual ANOVAs comparing habitat characteristics in pools with and without Limnadia (Holland and Dingo). ……………………….….. 12
2.1. Abiotic habitat characteristics of seven Australian pools and eight pools from Arizona all containing clam shrimp. …………………………………... 32
3.1 List of successful laboratory hydrations with quantities of Limnadia and Eulimnadia. ………………………………………………………………….. 45
iv LIST OF FIGURES
Figure Page
1.1. Illustrations of the diurnal variation in 5 physiochemical parameters of ephemeral pools: (a) Temperature, (b) Conductivity, (c) Dissolved oxygen, (d) pH, and (e) TDS (Total dissolved solids). ….………………….. 8
1.2. Ranges of water temperature, conductivity, dissolved oxygen, pH, and TDS for 36 temporary pools. …………………………………………… 11
1.3. Results of a canonical discriminate analysis on all eight dependent variables using data from Holland and Dingo only. …………………………………… 14
2.1. Densities of major categories of microbial inhabitants of 12 ephemeral pools on 4 granite outcrops in Western Australia. ..……………………….... 27
2.2. Changes in water quality parameters of Australian ephemeral pools over time on four different outcrops. …..…………………………………….. 29
2.3. Bacterial and total microbial density trends over time. …………………...… 30
2.4. Relationship between the density of heterotrophic nanoflagellates (cells/L) and the quantity of clam shrimp ( p=0.0025) and between surface area and the quantity of clam shrimp (p=0.0111). ………………….. 30
3.1. Number of hatching occurrences of Limnadia badia and Eulimnadia dahli incubated at 15°C and 27°C. ………………………………………….. 44
3.2. Theoretical distributions of hatching proportions versus multidimensional environmental profile in (a) populations from a more predictable environment and (b) from a less predictable environment. …...... … 53
v CHAPTER I
HABITAT CHARACTERISTICS OF EPHEMERAL ROCK POOLS MAY PREDICT
THE DISTRIBUTION OF THE CLAM SHRIMP, LIMNADIA BADIA
Introduction
The biological and abiotic factors that structure freshwater invertebrate
communities and determine the distribution and abundance of their members are variable
in time and space and depend on the type of community and the habitat in question.
Structuring in communities is generally thought to vary along a continuum from
permanent freshwater bodies to temporary freshwater environments (Wellborn et al.
1996, Euliss et al. 2004, Jocqué et al. 2007b). Some work has suggested that predation is
the dominant structuring force in the most permanent communities while competition is
more powerful in structuring pools that are less permanent, such as temporary ponds that
dry at least once per year (Wilbur 1987). However, bodies of water on the most
ephemeral edge of the continuum (lasting just days to weeks) seem to host communities
that are structured more by environmental conditions than by any biotic interactions
(Jocqué et al. 2007a).
Increasingly, temporary pools have been used as models to investigate community ecology (Srivastava et al. 2004). Often, several pools can be found in close proximity,
with each pool representing a microcosm that operates on a temporal and spatial scale
1 that is small enough to be well suited for field observations. However, if these pool communities are structured mainly on the basis of abiotic characteristics, such as aspects of water chemistry, they may not be suitable for the study of some major concepts in community ecology. Biotic interactions, such as coexistence, competition, and predation, may be observed but they may not be important for the organization of rock pool communities.
Multiple varieties of interspecific interactions have been observed in temporary pool branchiopods. Coexistence among large branchiopods is a reasonably common phenomenon (Maeda-Martínez et al. 1997). Anostraca (fairy shrimp), Notostraca
(tadpole shrimp), and Spinicaudata (clam shrimp) can regularly be found in the same pools (Dodson 1987, Maeda-Martínez et al. 1997). However, there is also evidence for successional, inter-species presence in Anostraca, in which one species at a time dominates a particular pool (Dumont & Negrea 2002). Whether this temporal separation is a result of competition or some other factor, like pre-existing differences in life cycle, is unknown. Petrov and Cvetković (1997) observed up to four anostracan species coexisting in one pond. Alternatively, pools on and around a granite outcrop in Texas presented a segregation of fairy shrimp species (Belk 1991). These non-overlapping distributions suggest competitive exclusion; however, upon closer inspection the segregating mechanism may have been a life history constraint (Belk 1991).
Branchinecta packardi reproduces rapidly enough to inhabit the very shallow pools at the peak of the rock, whereas Streptocephalus texanus reproduces too slowly to be able to maintain viable populations in those pools (Belk 1991). Interspecific interactions among branchiopods manifest in a number of different ways; however, when species are
2 segregated, the reasons seem to be related to life-history factors and habitat constraints,
such as those in the above example, rather than direct resource competition.
Temporary pool communities are more likely to be structured by abiotic characteristics and dispersal limitation (Jocqué et al. 2007a), especially in pools that favor the Spinicaudata. Differing distributions of seemingly similar species can be explained by a variety of factors aside from competition. Species may have specific tolerances to physiochemical parameters (e.g., conductivity; Hamer & Martens 1998) or life history characteristics may restrict survival in particular habitats. The exact cause for the dearth of reported clam shrimp coexistence is unclear, but environmental characteristics and life-history constraints may be the underlying factors.
In the current study, preliminary field examinations exploring the habitat characteristics of rock pools and the distributions of branchiopods, particularly clam shrimp, were conducted using temporary pools on granite outcrops in the semi-arid, wheatbelt region of Western Australia. Multiple pools were present on each outcrop and they differed in species composition and abundance. Clam shrimp species previously reported from these pools were Eulimnadia dahli Sars, 1896 and Limnadia badia Wolf,
1911. Physiochemical parameters (temperature, conductivity, dissolved oxygen, pH, total dissolved solids) of pools and densities of clam shrimp were assessed during the cool, rainy season when only L. badia was encountered. Whether these genera never coexist within the same pool or whether one succeeds the other in the same pools is still unknown.
Although temporary pools will form year-round, seasonal changes in the environment predict variation in the nature of the pools formed. Preliminary evidence
3 suggests that Limnadia may predominate in pools in the winter (rainy) season, while
Eulimnadia may be more common in the summer (Weeks, pers. comm.). My aims were
twofold: (1) to survey these pools to gather information on specific aspects of water
quality to describe the basic rock pool habitat during the autumn, and (2) to attempt to
use this information to describe the habitat distribution for the clam shrimp, Limnadia
badia.
Methods
Although the terrain of the region is generally flat, large granite monoliths can be
found throughout the area. Prior to the first rain of the season (late February through mid-
April), 21 of these rock outcrops were scouted as possible sampling locations. Sediment
from multiple depressions on each outcrop was collected for later laboratory hydrations
(see chapter 3) and the geographical coordinates (latitude and longitude) of each pool
were recorded.
Of the numerous outcrops scouted, only four received adequate rainfall for
sustained hydration: Holland Rock (Shire of Kent, 33°21.259’S; 118°44.639’E; 15
pools), Dingo Rock (Shire of Lake Grace, 33°0.558’S; 118°36.321’E; 8 pools), Wave
Rock (Shire of Kondinin, 32°26.712’S; 118°53.836’E; 10 pools), and Puntapin Rock
(Shire of Wagin, 33°19.495’S; 117°23.941’E; 7 pools). Most of the rocks in this region
are relatively undisturbed, although these four outcrops have reservoirs built around their
bases to collect drinking water. Pools were sampled from late April through late May,
2007. The rainy season in this part of Australia lasts approximately from May to October and is the milder time of the year, with mean maximum air temperatures ranging from
4 18.3°C to 22.3°C (Australian Bureau of Meteorology, http://www.bom.gov.au/). Of the
40 total pools studied, 30 had clam shrimp. Pools for observation were selected for variation in size and depth. In general, most of the pools on every outcrop were sampled.
Pools that were not sampled were generally too small to sample adequately or had already dried.
Sampling protocol
Each of the four sites was monitored for five consecutive days. At the start of
each sampling day, each pool was measured for maximum length, maximum width and mean depth (averaged from three measurements). Each pool was sampled at two hour
intervals throughout the day for a total of five samplings per day across five days for
temperature, pH, dissolved oxygen, conductivity, and total dissolved solids. Additionally,
three pools on each of three outcrops (due to time constraints Holland was excluded from
this analysis) were monitored at four hour intervals throughout the night in order to
observe the complete diurnal variation of environmental attributes. These readings were
taken using a YSI 556 multi-probe system (YSI, Incorporated). Pool volumes were
estimated from maximum length, maximum width, and average depth assuming the shape
to be half of an ellipsoid. Community composition was sampled with dip nets of two
mesh sizes (2.0mm and 0.5mm) on one occasion. Pools were netted across their entire
volume for three, 3-minute trials with each of these nets. This method of sampling differs
from a more common method of sampling exhaustively until no new species are collected
(Graham 2002). In the present study three rounds of sampling reduced the probability of
missing species while the timed aspect of the procedure allowed uniformity of sampling
5 from pool to pool. Over the three successive rounds of sampling, the number of clam
shrimp collected declined by approximately one half each round. After three rounds in
each pool it can be estimated that roughly 88% of the clam shrimp in the pool had been
recovered. Macroinvertebrates were sorted in trays, identified, counted, and then
returned to the pool. Densities were assessed by dividing the total number of collected individuals of each species by the estimated volume of each pool. Specimens not easily identified in the field were preserved in 90% ethanol for later species determination in the laboratory. Individuals identified as Limnadia were categorized as male, female, or juvenile. Females were determined by the presence of eggs in the brood chamber. Those lacking eggs can be differentiated from males by the absence of claspers when viewed under low magnification. Juveniles were determined by the absence of claspers and of eggs. Because of the occasional difficulty in seeing through the carapace, some females with eggs developing in the ovary, but none in the brood chamber may have been inadvertently categorized as juveniles.
Analysis of habitat variables and species distribution
Ranges and means of each physiochemical parameter for each pool with clam
shrimp were compared to the pools lacking clam shrimp over the course of the sampling
period. Analyses of variance (ANOVA) were used on pool means to determine if there were significant differences between the two groups (presence/absence) of Limnadia for these abiotic parameters. Four pools (H8, H14, P6, and W9) were excluded from all analyses on the basis of their high volume using the quartile test for outliers. The anomalously high volume of these four excluded pools resulted in them being quite
6 different from the remainder of the pools in a number of respects. Their greater depths
meant that the bottom sediment was obscured when looking down from the top, possibly
resulting in increased stratification of microbial populations, which are eaten by clam
shrimp. As a result of their size and lack of clarity, these pools could not be adequately
sampled using the same methods as the other pools, and thus they were excluded in all of
the below reported comparisons.
A second set of analyses were performed for pools to note whether rock outcrop
was a major component of variation for these abiotic pool measurements. A nested
ANOVA was run on all variables to partition total variation into within-pool, among-pool
and among-outcrop variation. Because outcrop was found to be an important determinant
of variation for most variables measured, the full data set was reduced to the two outcrops
that had both presence/absence pools and univariate ANOVAs were re-examined with this new data set. A multivariate analysis of variance was performed followed by a canonical discriminate analysis to determine the variation that found to be important in distinguishing between the pools that contain Limnadia from those that do not. Statistical analysis were performed in JMP 7.0.2 (Copyright © 2007, SAS Institute, Inc.).
Results
Filled pools ranged from small puddles that would not hold water for more than a few hours to basins that were several meters in diameter (volumes ranged from 0.3 litres,
when nearly dry, to 9140.1 litres for fully filled pools). Sediment type ranged from sand-
gravel to fine silt. Because these water bodies are reasonably small (average pool volume
7 was 216.2 litres, median volume was 173 litres) compared to permanent water bodies,
some physiochemical attributes were widely variable (Table 1).
22 0.8
20 a. b. 0.7 18 0.6 16
14 0.5
12 0.4
10 0.3 8 Temperature (°C)
Conductivity (mS/cm) 0.2 6 2D Graph 1 4 0.1 02:00:00 06:00:00 10:00:00 14:00:00 18:00:00 22:00:00 02:00:00 06:00:00 10:00:00 14:00:00 18:00:00 22:00:00 Time of Day Time of Day
150 7.0
6.8 140 c. d. 6.6 130 6.4
120 6.2
6.0 110
pH 5.8
100 5.6
90 5.4 5.2
Dissolved (%) oxygen 80 5.0
70 4.8 02:00:00 06:00:00 10:00:00 14:00:00 18:00:00 22:00:00 02:00:00 06:00:00 10:00:00 14:00:00 18:00:00 22:00:00 Time of Day Time of Day
0.6 e. 0.5
0.4
0.3 TDS (g/L)
0.2
0.1 02:00:00 06:00:00 10:00:00 14:00:00 18:00:00 22:00:00 Time of Day
Figure 1.1. Illustrations of the diurnal variation in 5 physiochemical parameters of ephemeral pools: (a) Temperature, (b) Conductivity, (c) Dissolved oxygen, (d) pH, and (e) TDS (Total dissolved solids). The 11 pools displayed are a random subset of the total pools sampled for which data were available for an entire 24 hour cycle. Each line represents one pool. 8 Figure 1.1 illustrates the diurnal variability of relevant physiochemical parameters
for the subset of 11 ephemeral rock pools for which data was available around the clock.
Mean water temperature across all pools was 18.4°C and ranged from 6.0 to 35.4°C
throughout the course of the sampling, sometimes varying up to 13.6° in a single pool
over a 24-hour period (Fig. 1.1a). Dissolved oxygen content (mean: 111.4%, range: 67.4-
141.8%; Fig 1.1c) and pH (mean: 6.4, range: 4.8-9.0; Fig. 1.1d) were also highly
variable, even throughout the course of a day. Some parameters, such as total dissolved
solids and conductivity, did not vary noticeably over the course of a day.
Species-habitat relationships
A total of 15 macroinvertebrate families was observed across all 40 pools, but no
single pool contained more than 10 families, with an average of 6 per pool (see
Appendix). Most of the pools examined were heavily populated with branchiopods with
very few other macroinvertebrates. Where Limnadia was present, it was the dominant macroinvertebrate species in the habitat, accounting for up to 99.9% of total macroinvertebrate individuals in the pool. However, in some pools there were no
Limnadia at all.
Pools with and without Limnadia were observed to have different abiotic conditions as illustrated in Table 1.1. Examined individually, there were two abiotic
parameters that differed significantly between the two groups: temperature and pH. Pools
containing Limnadia had, on average, lower temperatures and lower pH values. Figure
1.2 illustrates the ranges and means of temperature, conductivity, dissolved oxygen, pH,
TDS, volume, depth, and surface area for each of the 36 rock pools examined over the
9 course of 5 days each. Pools containing Limnadia had overlapping ranges with those
pools lacking Limnadia. However, all the pools lacking Limnadia had mean temperatures
above the grand mean of 18.3°C.
Table 1.1. Abiotic habitat characteristics of 36 Australian pools with Limnadia present and absent. Means, standard deviations (SD), minimums and maximums (min. and max.) are presented for all daytime readings at each outcrop. P-values represent results of ANOVAs comparing the means of pools with and without Limnadia. Cond. = conductivity, TDS = total dissolved solids, SA = surface area. Bold type indicates significance after sequential Bonferroni correction.
Pools without Limnadia Pools with Limnadia Mean SD Max Min Mean SD Max Min df P-value
Temp (°C) 20.93 1.00 22.42 19.52 18.02 2.58 21.24 13.16 35 0.0039
Cond (mS/cm) 0.24 0.09 0.37 0.13 0.38 0.23 0.91 0.09 35 0.1153
DO (%) 111.87 7.04 120.67 103.35 112.31 6.24 128.04 98.11 35 0.8655
pH 7.09 0.49 7.69 6.40 6.13 0.63 7.31 5.07 35 0.0003
TDS 0.17 0.06 0.26 0.09 0.29 0.18 0.72 0.06 35 0.0816
Depth (mm) 30.64 15.18 62.39 13.34 30.36 16.87 68.61 4.00 35 0.9674
Est. Vol. (L) 135.90 116.97 327.45 23.54 197.15 198.72 766.69 3.87 35 0.4143
Est. SA (m2) 7.30 3.98 12.45 1.49 9.47 7.07 26.00 1.14 35 0.4142
To note the potential differences among rock pools and overall rock outcrops
(which were sampled sequentially, and thus differed both in space and in time), the
amount of variation explained by differences among outcrops versus differences among
pools was calculated (Table 1.2). Some of the parameters were more highly influenced
by among-outcrop variation than among-pool variation, namely temperature and pH.
Nonetheless, both spatial components explained a good deal of variation in most of the measured variables.
10 1.0 0.9 0.8 0.7 0.6 0.5 0.4 TDS (g/L) 0.3 0.2 0.1 0.0
25 C) °
20
15
10 Temperature (
5 1.4
1.2 /cm)
1.0 mS
0.8
0.6
0.4
Conductivity ( Conductivity 0.2
0.0
150
140 130
120
110
100 DO (mg/L) 90
80
70 60
9
8
pH 7
6
5 D1 D1 D2 D2 D4 D4 D5 D5 D6 D6 D7 D7 D8 D8 D9 D9 H1 H1 H10 H10 H11 H11 H12 H12 H15 H15 H16 H16 H3 H3 H4 H4 H7 H7 H9 H9 P10 P10 P11 P11 P12 P12 P13 P13 P8 P8 P9 P9 W10 W10 W11 W11 W12 W12 W13 W13 W14 W14 W15 W15 W5 W5 W6 W6 W8 W8 H13 H13 H5 H5 H6 H6
Figure 1.2. Ranges of water temperature, conductivity, dissolved oxygen, pH, and TDS for 36 temporary pools. Filled circles = pools containing Limnadia. Open circles = pools without Limnadia. Dashed lines indicate the grand mean. Letters in pool names correspond to outcrops (D=Dingo, H=Holland, P=Puntaping, and W=Wave). Differences between means of the two groups are available in Table 1.1.
11 Table 1.2. Percents of total variation in each parameter explained by outcrop and pool, with remaining variation treated as “residual variation.” Parameter Outcrop (%) Pool (%) Residual (%) Temperature 48.91 -1.55 52.64 Volume -7.27 94.35 12.92 Conductivity 22.58 69.45 7.97 Dissolved oxygen 33.45 7.20 59.36 pH 65.20 5.07 29.73 TDS 29.53 64.00 6.27 Mean depth 1.06 82.17 16.77 Surface area -7.88 102.97 4.92
Clearly, outcrop-to-outcrop differences were important in these water-quality variables. Because only two outcrops contained pools that were lacking Limnadia
(Holland and Dingo), the other two outcrops (Wave and Puntaping) needed to be eliminated from further presence/absence analyses to avoid confounding presence and absence with outcrop-to-outcrop differences.
Table 1.3. Results of individual ANOVAs comparing habitat characteristics in pools with and without Limnadia (Holland and Dingo). No variables have significant differences between the two groups after sequential Bonferroni correction. Pools without Limnadia Pools with Limnadia Mean SD Max Min Mean SD Max Min p =
Temp (°C) 22.32 1.00 26.10 17.26 21.49 2.58 25.76 17.21 0.1717
Cond. (mS/cm) 0.25 0.09 0.47 0.06 0.27 0.23 0.93 0.04 0.6356
DO (%) 112.06 7.04 137.60 97.3 111.53 6.24 141.4 88.9 0.4483 pH 7.50 0.49 9.03 5.91 6.92 0.63 8.57 5.53 0.0460
TDS 0.17 0.06 0.32 0.04 0.19 0.18 0.62 0.03 0.5765
Depth (mm) 35.46 15.18 70.67 4.33 39.67 16.87 68.33 17.67 0.1306
Est. Vol. (L) 170.02 116.97 420.38 1.71 281.72 198.72 929.51 24.91 0.1060
Est. SA (m2) 8.24 3.98 14.17 0.75 12.33 7.07 28.45 1.98 0.1117
12 After reducing the original data set to the pools on two outcrops, individual
ANOVAs comparing the habitat parameters in pools with and without Limnadia were
repeated. In this case, no variables appeared significantly different between the two
groups after a sequential Bonferroni correction. Of these variables, pH has the strongest
signal at p=0.046.
A MANOVA was preformed with the reduced data set. Differences in the eight
variables across the two groups yielded no significant results (p=0.2737).
A canonical discriminate analysis (CDA) was used to determine which variables
best described the variation between groups (presence and absence of Limnadia). Results
of the CDA are in Figure 1.3. The first canonical correlation (CC1) explained 100% of the important variation that differed between pools with vs. those without L. badia. Four original dependent variables were correlated strongly with CC1. Volume, depth, and surface area were significantly negatively correlated with CC1 and temperature was significantly positively correlated with CC1. In its assignment of pools into groups that had either Limnadia or not, the CDA misclassified 5 pools, or 23.81% of the total pools in this comparison. The plot of CC1 versus CC2 in Figure 1.3 shows the means of the centroids of Limnadia-present pools and Limnadia-absent pools. Although the
MANOVA did not find significant overall differences among the present/absent pools, the two groups appear to cluster independently (Fig. 1.3).
13 8.5
8.0 Mean(Est. SA (m3))
7.5
7.0 Mean(Mean Depth (mm)) 6.5
6.0
5.5
5.0 Mean(DO % )Mean(pH)
4.5 1 0
4.0 Mean(TDS g/L) Mean(Cond mS/cm) 3.5 Mean(Temp C)
Canonical Correlation 2 3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5 Mean(Est. Vol. (dm3)) -1.0 25 26 27 28 29 30 Canonical Correlation 1
Eigenvectors: Canonical correlation 1 Canonical correlation 2 Temperature -0.357* -0.495 Estimated volume 0.005* -0.018 Conductivity -0.606 -2.939 Dissolved oxygen 0.154 0.064 pH 2.739** 0.678 TDS 7.349 -3.381 Mean depth -0.009* 0.132 Estimated surface area -0.232 0.363 Figure 1.3. Results of a canonical discriminate analysis on all eight dependent variables using data from Holland and Dingo only. Pools without Limnadia are plotted as empty circles, pools with Limnadia are represented with filled circles. Asterisks indicate significant correlations of the principle component scores with each of the original variables: ** = p<0.01; * = p<0.05. Canonical correlation 1 explains 100% of the differences between present/absent pools. 14 Discussion
Temporary pools are highly variable environments with conditions changing drastically over the course of short periods of time (Bayly 1982, Williams 2001). Pool inhabitants must cope with extended periods of drought between wet phases. Marked change also occurs over smaller periods of time. These habitats are subjected to diurnal variation in a number of physical parameters. Continuous daily monitoring of rock pools showed obvious cycling of temperature, dissolved oxygen, and pH. The trend in TDS and salinity were not as obvious (Figure 1.1). The most extreme daily variation was in water temperature, which changed drastically over the course of the day in response to air temperature and sun exposure. Dissolved oxygen changed as well, probably as a response to the temperature changes combined with effects of photosynthetic organisms in the water (Chan et al. 2005). The measurements of solutes remained relatively stable in comparison to the other variables. That some of these variables change so rapidly over the course of a single day presents an added challenge to clam shrimp in temporary pool habitats.
There is a wealth a literature available testing the habitat requirements for large branchiopods to break dormancy (Moore 1963, Bishop 1967, Belk 1977, Scott &
Grigarick 1979, Khalaf & Al-Jaafery 1985, Mitchell 1990, Brendonck 1996, Philippi et al. 2001, Schönbrunner & Eder 2006, Beladjal et al. 2007). However, there is comparably little information available on the habitat conditions that are associated with natural, healthy adult populations. Much of the available information is focused on the range of a single parameter (i.e., only temperature or pH) that promotes hatching or sustained populations. Here I have assessed whether a single parameter can sufficiently
15 explain the habitat preference of Limnadia badia in the field, or whether a suite of
characteristics may be necessary to describe the distribution of this species.
When single parameters were compared independently across all 36 pools, water
temperature and pH were found to be significantly higher in non-Limnadia pools.
Nevertheless, pool volume, surface area, conductivity, and TDS were higher in the pools
that had Limnadia populations, but not significantly so.
Because of the structure of the sampling regime, it was necessary to examine the variation between locations. Between-outcrop and between-pool variation was assessed and the results indicated that there was indeed high variation either between outcrops, between pools or between both for all of the dependent variables on all outcrops. This variation is reasonable, especially when noting that outcrops were sampled sequentially over the course of the month, which had both a seasonal component of change (i.e., air temperatures were getting cooler) as well as a pool hydration change (i.e., pools sampled later in the month were older than those sampled earlier in the month). Temporary pools are known to have rapid successional effects (Boix et al. 2004, Joćque et al. 2007b), and thus this “time since hydration” component of outcrop-to-outcrop variation in the
variables measured is to be expected. Because two outcrops did not have pools without L.
badia, a second analysis needed to be performed to assess for differences in abiotic
variables in the present/absent pools without the possible confounding effect of variation
among outcrops. Since the original analysis of individual variables (Table 1.1) examined
variation on all four outcrops, this result may have confounded outcrop-to-outcrop
variation with variation between presence/absence of L. badia within pools.
16 After removal of pools on the above noted outcrops, the eight parameters were
again independently compared. In this analysis, no variables were significantly different
between the two groups (Table 1.3). However, pH values stood out as being more
informative than the remainder of the variables for distinguishing presence from absence
of L. badia.
By combining all parameters into a multivariate analysis, I tested for the
possibility of an overall signal among the abiotic measurements that distinguished L. badia presence from absence. Such a signal was weak (p = 0.2737), and thus there was little extra information gleaned from considering all variables simultaneously.
Nonetheless, a canonical discriminate analysis was performed to note which suites of correlated characters may differentiate presence from absence of L. badia in the measured pools. Interestingly, the first canonical correlation explained 100% of the variation in the data and was highly correlated with volume, depth, surface area, and temperature. Because all of the important variation describing differences between pools with and without L. badia was described in the first canonical correlation, and because
TDS, conductivity and DO% were not correlated with Canon1, it appears that variation for these three variables was unimportant for distinguishing among pools with and without L. badia. Temperature is correlated in the opposite direction of volume, depth, and surface area, which is what would be expected: as pool sizes increase, their water temperatures decrease because larger pools will be more resistant to temperature change and will not warm as quickly throughout the day as the smaller pools. The sampling times used in this analysis were from the warmest parts of the diurnal cycle. The variables that described pool morphology were correlated with one another and with
17 CC1. CC1 can correctly predict which pools will contain Limnadia 75% of the time.
Pool morphology is an obvious habitat characteristic that can impact this species. Pools that are too small may evaporate too quickly for the maintenance of breeding populations.
There are, of course, other factors beyond morphology and temperature that could affect the distribution of this species. It is possible that populations had not yet dispersed to the non-Limnadia pools, although this scenario is unlikely considering the proximity of the pools to each other. Another possibility is that the presence of a predator excluded
Limnadia from some pools and not others, although this is not at all supported by the data. Clam shrimp are generally thought to be preyed upon by tadpole shrimp, which were not present in any pools. I observed some clam shrimp being consumed by predacious water beetle larvae (Dytiscidae), but not often. In every pool in which these larvae were present, Limnadia was also present. Additionally, 81.3% of pools that harbored anuran larvae also had Limnadia. Thus, L. badia do not appear to be excluded from any pools in this series by the presence of a suspected predator.
The only univariate abiotic measurement that appeared consistently different between pools with and without L. badia was pH (Tables 1.1 & 1.3) This component of water chemistry has been attributed to the metabolic activities of cyanobacteria, algae, and heterotrophs (Ramsing et al. 1993, Scholnick 1994, Chan et al. 2005), as noted above. These organisms may be the key in relating the pH levels of pools to the presence of Limnadia. Clam shrimp are primarily filter feeders and thus their food consists of planktonic animals and algae. It is likely that the favorable conditions for clam shrimp are also the favorable conditions for the organisms they consume. If clam shrimp are present, it follows that their food source must also be present in sufficient densities.
18 Because specific information about what organisms provide food for L. badia is
unknown, it is difficult to determine which environmental conditions are favorable to
them. However, because variation in pH is related so strongly to microscopic organisms
and is also related to L. badia presence, this may be the link between the two types of
organisms. A more detailed description of the planktonic life in these rock pools is
provided in Chapter 2.
Because these pools are small, short-lived, isolated from other water bodies, and supplied exclusively by rain water, aspects of their water chemistry and community composition can change dramatically from inundation to dry-down. Algal photosynthetic and animal respiratory activities both reduce the pH of the water (Chan et al. 2005). As
populations of these organisms grow after hatching or colonization, it follows that water
chemistry will change. Although there is undoubtedly cycling diurnally (see Figure 1),
there is also continuous directional change over the life of the pool. Throughout the
course of the sampling period at each outcrop (5 days), significant changes in
physiochemistry were observed. For example, at Holland Rock, temperature and pH
were reduced significantly (p=0.0252 and p<0.0001, respectively) while TDS, and
conductivity both increased significantly (p<0.0001 and p<0.0001, respectively) over just
five days (see Chapter 2 for details). In fact, dissolved oxygen content was the only
variable to have observable diurnal amplitude but no directional trend. These
successional trends are the combined result of biological activity and seasonal
progression. It would be interesting to learn how this seasonal progression affects
branchiopod populations.
19 Abiotic habitat parameters may be the most important factor structuring the distribution of the clam shrimp Limnadia badia across pools in Western Australia, especially since biological interactions, such as competition and predation, appear to have little influence (Jocqué et al. 2007a). After analyzing the variation in nine different environmental parameters over the course of several days on 36 different ephemeral pools, no significant pattern emerged to aid in description of the preferred habitat of this species. Nonetheless, the general pattern that emerged is that factors relating to habitat morphology (i.e., general surface area and overall volume) may be the most important determinants of whether L. badia will be present or absent from various rock pools.
Other measures (TDS, conductivity and dissolved oxygen) were unimportant for determining presence/absence, which suggests that the variation among pools in these factors is within the normal range of which L. badia can be found.
Thus, although a pattern for habitat characteristics predicting the presence of L. badia was not established here, a larger data set may be necessary to begin to filter through the complexity inherent in the system. Additionally, the sampling design should be altered to examine a set of pools continuously from inundation to dry-down.
Analyzing the changes in a single set of pools through the entire wet-phase would be more informative than sampling outcrops sequentially over time. The more favorable design was not feasible for this project because of the lack of time available for data collection in Australia. A more complete suite of characteristics may also be necessary.
Estimates of chlorophyll content, turbidity, and specific ions (e.g., Ca, Fe, Mg, etc.) may also play a role in the habitat of L. badia. Until the complexity in the system can be
20 addressed, the actual mechanisms preventing the occurrence of Limnadia in certain pools and not in others will be unexplained.
21 CHAPTER II
ALGAL AND BACTERIAL DISTRIBUTION AND ABUNDANCE IN EPHEMERAL
POOLS IN RELATION TO CLAM SHRIMP PRESENCE, INCLUDING A
COMPARISON OF COMMUNITIES IN WESTERN AUSTRALIA AND ARIZONA,
U.S.A.
Introduction
Ephemeral rock pools are temporary aquatic habitats that are formed as rain water collects in weathered depressions on outcrops of rock. They are of particular biological interest because of their diverse fauna, much of which cannot be found in more permanent habitats (Bayly 1982, King et al. 1996). The rock pools are typically very short-lived (lasting hours to several weeks) and host a variety of organisms that are specifically adapted to this kind of transient habitat. Most of the animal life in pools like these can usually be categorized into two different types: those that survive dry periods by using this habitat opportunistically for only a portion of their life cycle (e.g., flying insects with aquatic larval stages) and those that survive dry-down as dormant eggs or adults in the sediment that remain at the bottom of the depressions (Wiggins et al. 1980,
King et al. 1996, Jocqué et al. 2007a).
There is a growing amount of information available on seasonal changes in water quality and faunal composition in temporary pools worldwide (Moore 1970, Bayly 1982,
22 King et al. 1996, Maier et al. 1998, Serrano & Fahd 2005, and Boven et al. 2008). The
pools addressed in these studies are typically on the long-lived side of ‘temporary’, with durations from several weeks to several months. The present study will examine water quality, microbial and macroinvertebrate composition in pools that have shorter aqueous phases, in the range of days to weeks. Water chemistry of pools can be highly variable depending on the substrate, depth, elevation, regional climate, and biotic community
(Scholnick 1994, Chan et al. 2005, Boven et al. 2008). In fact, because of the small size
of these water bodies, many physiochemical parameters can fluctuate widely over the
course of a single 24 hour period (Williams 1987, Scholnick 1994; also see chapter 1).
Residents of these pools must therefore be physiologically capable of tolerating rapid and
dramatic changes in their environment in addition to extended periods of drought.
Ephemeral pools are an interesting and unique habitat containing organisms not
found anywhere else (Colburn 2004). Much attention has been given to the most iconic member of these communities (phyllopod crustaceans) but little work has been done to explore the dynamics of bacterial and algal populations on which these communities are built. Here I examine the microbial dwellers of ephemeral pools, with the specific intent of assessing their relationship to water quality and a common rock pool invertebrate: the
clam shrimp (Crustacea: Branchiopoda: Spinicaudata).
Clam shrimp are obligate dwellers of temporary pools. Resting eggs are dropped
onto the sediment or buried in small burrows (Zucker et al. 2002) and typically remain
dormant until the pool has dried and then been refilled by rain. Upon hydration and
appropriate conditions, eggs will hatch and develop quickly (5-6 days) to mature adults
who filter their food from the water (Weeks et al. 1997).
23 Exactly what they eat is not yet known, but as filter feeders their diet likely consists of a variety of coexisting planktonic and benthic organisms. Microorganisms found in temporary pools in Utah include cyanobacteria, aerobic heterotrophs, lichens, and algae (Chan et al., 2005). The current study will begin to assess the microbial life of the rock pools of Western Australia and northern Arizona.
Methods
Methodology in this section is comprised of both field sampling and laboratory techniques. Most of the field methods for this chapter are identical to those for Chapter 1 with the exception of those that follow.
Site selection and description / Sampling protocol
Clam shrimp can be found in ephemeral pools worldwide, in a variety of climates.
The focal pools of this study were located in the wheatbelt region of southwestern
Western Australia and in the Thousand Pockets area on the Paria Plateau in northern
Arizona, U.S.A., both semi-arid to arid regions.
Pools in Australia were sampled at the beginning of autumn, from late-April to early-May, at the beginning of the rainy season. There were 12 total pools across four outcrops covering a swath of area approximately 220km wide. Rock outcrops were all granite in composition but varied in their topography. Three of the four were dome- shaped while one was level with the surrounding terrain.
Pools at Thousand Pockets were sampled in early August at the height of summer and just days after the arrival of the monsoon rains. There were only eight accessible
24 pools of sufficient depth for study, all on the top of the same rocky ridge. The substrate
at Thousand Pockets was sandstone, a more porous rock than granite.
Processing of collected materials
Microbial plankton (bacteria, algae, heterotrophic protists and micrometazoa)
abundance was determined in water samples collected in three pools per outcrop. The samples were obtained on the final day of sampling at each outcrop (Holland Tank = 30
April, Dingo Rock = 7 May, Wave Rock = 16 May, Puntaping Rock = 21 May) and preserved in formalin and acid Lugol’s iodine (both at 2% final concentration).
To determine bacterial abundance, 2-ml aliquots of formalin-preserved samples were concentrated onto a 0.2 μm black polycarbonate filter, stained with DAPI and examined under an Olympus BX40 fluorescent microscope (total magnification 1,000x).
Ten random fields per slide were taken with a Spot 1.4.0 digital camera (Diagnostic
Instruments, Inc.). The volume of bacterial cells was determined using Image-Pro Plus software (version 4.5.1.22 for Windows XP Professional) (© 1993-2002 Media
Cybernetics, Inc.). The abundance of autotrophic and heterotrophic nanoplankton was also determined via fluorescence microscopy on 0.8 μm black polycarbonate filters.
Microzooplankton (ciliates, dinoflagellates, sarcodines, rotifers and other micrometazoa) preserved in Lugol’s were settled overnight in Utermohl chambers and counted under an
Olympus IX-70 inverted microscope equipped with differential interference contrast.
25 Results
The 12 Australian pools in this survey varied in size and depth. However, they
were generally shallow (42 ±13 mm) and small (16 ± 10 m2). Pool volume varied greatly
(395 ± 299 L) across outcrops. A fine layer of sediment covered the pool basins, which
was measured prior to the wet phase of the pools. These sediment layers were measured
to be between 0.5 and 3.5 cm deep and were of varying consistency, ranging from sand to
silt. The water was generally clear, except toward the end of the sampling period when algal growth became dense in some pools. All pools were in direct sunlight, as there was no significant vegetation on the surface of the rock. The water was slightly acidic at a pH
6.19 ± 0.57 and temperatures averaged 17.42 ± 3°C.
Microbial inhabitants and microinvertebrates generally included bacteria,
heterotrophic nanoflagellates, autotrophic algae, ciliates, dinoflagellates, amoeba,
rotifers, ostracods, cladocerans, and cyclopoid copepods. These organisms were found in
varying densities in different pools (see Figure 2.1 for detail). Pool W12 had extremely
high densities of ciliates and autotrophic algae compared to the other pools in the series.
This could possibly be explained by the fact that this was a relatively large pool that had
dried down quite a bit during the course of sampling, thus condensing the original
populations into a smaller area (surface area on 12 May: 13.36 m2; surface area on 16
May: 7.13 m2).
26 2.5e+7 1e+10
2.0e+7 8e+9
1.5e+7 6e+9
1.0e+7 4e+9 HNF(cells/L) Bacteria (cells/L) Bacteria
5.0e+6 2e+9
0.0 0 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 8e+8 80000
6e+8 60000
4e+8 40000 PNAN (cells/L) PNAN 2e+8 20000 Ciliates (individuals/L)
0 0 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 1200 1e+5
1000 8e+4
800
6e+4
600
4e+4 400
2e+4 Amoeba(individuals/L) 200 Dinoflagellates (individuals/L)
0 0 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 1200 350
300 1000
250 800
200 600 150
400 100 Rotifers (individuals/L) Rotifers 200 50 Microcrustaceans (individuals/L) 0 0 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 H4 H9 H10 D6 D7 D8 W8 W9 W12 P9 P11 P13 Figure 2.1. Densities of major categories of microbial inhabitants of 12 ephemeral pools on 4 granite outcrops in Western Australia. X-axis delineates specific pools. H = Holland Rock, D = Dingo Rock, W = Wave Rock, P = Puntaping Rock. HNF = Heterotrophic nanoflagellates, PNAN = photosynthetic nanoflagellates. Note: the x-axis is aligned by sampling date, with Holland Rock sampled first and Puntaping Rock sampled last.
27 Successional trends – Australian pools
Directional trends were evident during the course of sampling for several of the abiotic parameters measured. Although these trends over time were confounded by geographic location, they are nonetheless presented here. Since there was no difference in average pool size, depth, or substrate between the four outcrops, and because these same trends were observed on a smaller time-scale within the sampling period at each outcrop, the patterns presented here are considered to be greatly influenced by successional changes over time (Figure 2.2). At Holland Rock, temperature and pH were reduced significantly (p=0.0252 and p<0.0001, respectively) while TDS and conductivity both increased significantly (p<0.0001 and p<0.0001) over the five day sampling period
(results not shown).
Bacterial densities climbed continuously over time (Fig. 2.3), while algal densities peaked in one pool after 3 weeks but otherwise remained relatively constant (see Figure
2.1). Overall density of microbial and macroinvertebrate life (total density) increased significantly over time (Fig. 2.3), however this is probably just a reflection of the bacterial numbers as there were no other observable trends in microbial densities over time (Fig. 2.1).
Relationship to clam shrimp abundance
Clam shrimp were present in all pools in this series, but in varying densities.
There were no significant trends in clam shrimp abundance between pools, outcrops, or over time.
28 22 122
120
20 118 116
114 18 112
110 16 108
Temperature (°C) 106
14 Dissolved (%) Oxygen 104
102
12 100 30 apr 07 7 may 07 16 may 07 21 may 07 30 apr 07 7 may 07 16 may 07 21 may 07
7.2 0.7
7.0 0.6 6.8
6.6 0.5
6.4 0.4 6.2 pH 0.3 6.0
5.8 0.2
5.6 Conductivity (mS/cm) 0.1 5.4
5.2 0.0 30 apr 07 7 may 07 16 may 07 21 may 07 30 apr 07 7 may 07 16 may 07 21 may 07
X Data 0.6 1200
0.5 1000
0.4 800
0.3 600 TDS (g/L) 0.2 Volume (L) 400
0.1 200
0.0 0 30 apr 07 7 may 07 16 may 07 21 may 07 30 apr 07 7 may 07 16 may 07 21 may 07
Parameter p -value R-square Temperature <0.0001 0.979 Dissolved Oxygen 0.0013 0.663 pH <0.0001 0.843 Conductivity 0.128 0.216 TDS 0.051 0.331 Volume 0.708 0.015
Figure 2.2. Changes in water quality parameters of Australian ephemeral pools over time on four different outcrops. TDS = Total dissolved solids. 29 There was, however, a significant negative relationship between the density of
heterotrophic nanoflagellates and the quantity of Limnadia badia across all pools as
illustrated in Figure 2.4. There were no significant relationships between the abundance
of clam shrimp and the densities of other possible food sources, such as bacteria
(p=0.4807, N=12), algae (p=0.1101, N=12), rotifers (p=0.9702, N=12), amoeba
(p=0.5601, N=12), or ciliates (p=0.0878, N=12).
9e+9 9e+9
8e+9 8e+9
7e+9 7e+9
6e+9 6e+9
5e+9 5e+9
4e+9 4e+9 Totaldensity Bacteria (cell/L) 3e+9 3e+9 2e+9 2e+9
1e+9 1e+9 30 Apr 07 7 May 07 16 May 07 21 May 07 30 Apr 07 7 May 07 16 May 07 21 May 07
Figure 2.3. Bacterial and total microbial density trends over time. Time periods differ significantly from one another. ANOVA p=0.0110 for the bacteria and p=0.0107 for the total density of all microbial life.
Figure 2.4. Relationship between the density of heterotrophic nanoflagellates (cells/L) and the quantity of clam shrimp (p=0.0025) and between surface area and the quantity of clam shrimp (p=0.0111).
30 Additional information regarding pools from the U.S.A
A comparison of sandstone pools in the U.S. and granite pools in Australia yielded some interesting differences and similarities (Table 2.1). Australian pools used in this analysis were from the first outcrop sampled (Holland) to try to minimize the difference in pool age between groups. Pools on Holland Rock were approximately eight days old at the start of sampling. Pools at Thousand Pockets in Arizona were younger, probably only four to five days old.
Morphologically, the pools in both localities were not significantly different.
However, although the differences were not significant, pools in Arizona may have had higher volumes and greater depths than Australian pools (Australian pool median volume: 121.84 versus 187.03 for Arizona pools; Australian pool median depth 34.65 versus 51.67 for Arizona pools).
There were also some striking differences in water quality parameters. The water temperatures were a full 10°C higher, on average, in Thousand Pockets; a reflection of the time of year the pools were filled (high summer in AZ versus autumn in WA).
Dissolved oxygen and pH showed no difference between the two groups of pools.
Measurements of solutes, such as conductivity and TDS, differed markedly between locations. These were significantly lower in the pools in Thousand Pockets than those in
Western Australia.
31 Table 2.1. Abiotic habitat characteristics of seven Australian pools and eight pools from Arizona all containing clam shrimp. Means, standard deviations (SD), minimums and maximums (min. and max.) are presented for all daytime readings at each outcrop. P- values represent results of ANOVAs comparing the means of pools in Australia to those in Arizona. Cond. = conductivity, TDS = total dissolved solids, SA = surface area, bold type indicates significance after Bonferroni correction.
Pools from Holland Tank, WA Pools from Thousand Pockets, AZ Mean SD Max Min Mean SD Max Min P-value
Temp (°C) 21.09 0.12 21.24 20.89 31.14 0.62 31.99 30.33 <0.0001
Cond (mS/cm) 0.19 0.08 0.33 0.09 0.06 0.04 0.15 0.03 0.0011
DO (%) 104.89 3.72 108.71 98.12 111.82 8.87 126.47 99.77 0.0773 pH 7.05 0.17 7.31 6.81 7.60 1.25 9.29 5.62 0.2675
TDS 0.13 0.05 0.23 0.06 0.04 0.02 0.08 0.02 0.0004
Depth (mm) 37.67 10.37 55.94 25.78 65.29 55.69 180.00 10.00 0.2205
Est. Vol. (L) 256.06 291.46 766.67 39.24 550.98 1019.25 3022.83 11.55 0.4745
Est. SA (m2) 10.85 10.10 26.00 2.30 9.42 10.07 32.07 1.74 0.7891
Discussion
Temporary pools are frequently used as model ecosystems because of their size and ease of study (Srivastava et al. 2004). Many authors have begun to analyze interspecific relationships and community organization in these systems (Wellborn et al.
1996, Euliss et al. 2004, Joćque et al. 2007a, 2007b). Yet almost no information is available on the microbial communities in these pools and how they change over time.
The microbial organisms form the base of the macroscopic community by providing food and oxygen to the other inhabitants of the pool (Felton et al. 1967, Chan et al. 2005). An understanding of the dynamics of the community on this level and their interactions with higher levels of organization is essential to fully understanding ephemeral rock pools.
32 Successional trends
In a recent study, Chan et al. (2005) examined the prokaryotic life in the biofilms
that form on the surface of the rock. This study found a number of microbial inhabitants
in the dry sediment, but did not measure quantify these inhabitants during the wet phase.
However, they did note that numbers aerobic heterotrophs in the dried sediment increased
dramatically after that sediment was hydrated (Chan et al. 2005).
Bacterial densities had previously been described from a temporary forest pool in
Louisiana (Felton et al. 1967). In this study, Felton et al. (1967) examined the bacterial content from the initial filling of the pool until it had dried and found that the content of
bacteria decreased with time. They attributed this to ingestion of bacteria by higher
organisms. The present study discovered the exact opposite: bacterial volumes increased
with the length of inundation. Although bacteria in this system are almost certainly being
consumed to some degree, it is obviously not enough to check population growth.
A partial explanation for this difference might involve the types of pools being
compared. The Louisiana pool was a deep (100cm) forest pool with little sunlight, high
leaf litter content and low pH (4.4-5.6) (Felton et al.1967). The rock pools in this study
were shallow (30cm), in direct light, with comparably little detritus and a slightly higher
pH (5.0-7.7). It is possible that whatever was eating bacteria in the Louisiana ponds (or
its trophic analog in rock pools) was simply not present (or not present in the same
quantities) in these Australia pools. Also, there is a great discrepancy in the longevity of
the pools. In Louisiana, the forest pool was measured over the course of nine months,
while the pools in this study were only monitored for approximately one month and
would be unlikely to persist through a nine month period. Since successional changes
33 occur rapidly in temporary pools, ecological communities and water chemistry can differ
with increasing hydroperiod.
Felton et al. (1967) also report very small amounts of autotrophic bacteria. The
current study does not differentiate between bacterial functions, but there is evidence for
high quantities of algae in the Australian rock pools, almost certainly a reflection of the
high light saturation.
An interesting trend was the changing quantity of photosynthetic nanoflagellates
over the course of the study period (Figure 2.3). It appears as though these organisms are
increasing in density through approximately three weeks and then crashing rapidly, most
likely as a result of nutrient depletion. There are few sources of nutrient input into these pools. Most of the available nutrients come from the decay of mosses and lichens, the excrement of mammals, and windblown pollen (Bayly 1982). Since nutrient input is
minimal, it is possible that after three weeks of continuous population growth, the algal
community has consumed much of the original resource base causing the population to
decline.
There is a general tendency for temporary pool communities to become more
complex over time since inundation. Many authors have noted an increase in the number
of species present as the length of inundation increases (Bayly 1982, King et al. 1996,
Serrano & Fahd 2005). These trends are generally attributed to allowing more time for
different species to colonize, especially species whose life-histories do not allow for them
to establish in the most ephemeral waters on the continuum. The design of this study
does not allow me to corroborate this trend, although total number of individual
organisms increased over the course of the study (see Figure 2.3).
34 Abiotic characteristics of the water showed distinct trends over time (Figure 2.2).
Temperature predictably decreased over the study period on all four outcrops as the season progressed toward winter. The long-term increase in the dissolved oxygen content can probably be explained as a function of the declining temperature: cooler liquids have a higher capacity to hold dissolved gases. Moore (1970) studied larger woodland pools and found a decline in dissolved oxygen over time that he suggested was the result of increasing bacterial biomass with little replacement of oxygen by algae. His pools were different from those in the current study because of their high level of shade.
The overall drop in pH across the month is likely due to photosynthetic activity (Chan et al. 2005). Conductivity and TDS both increased significantly and these concentrations are probably related to the gradual evaporation of the water leaving higher solute concentrations in the pools. Scholnick (1994) studied water chemistry in desert ephemeral pools and found that conductivity did not change over the course of their wet phase. He claimed that conductivity fluctuation was not an important factor in these systems. Conductivity has been reported elsewhere as being an important predictor of branchiopod presence (Hamer & Martens 1998, also see Chapter 1). However, conductivity does not appear to play an important role in the presence of Limnadia badia in Australian rock pools (see also Chapter 1). Because of these discrepancies, the role of conductivity in ephemeral pools needs to be explored in greater depth.
Admittedly, any temporal trends in these pools need to be evaluated with caution because, although all pools were filled with the same rain event, the pools were sampled sequentially by outcrop. Thus, the pools examined in the first week are on a different outcrop than those on the second week and so forth, confounding geographic locale with
35 sampling date. However, this potential conflict may be ameliorated by the observation of
similar trends within outcrops as were observed between them. The abiotic factors in
Figure 2.2, which changed predictably through time across all four outcrops, also changed in a similar fashion within each outcrop (results not shown), making it more plausible that these differences are true reflections of change over time in temporary rock pools rather than simply changes between pools in different locations.
Relationship to clam shrimp abundance
The abundance of clam shrimp in Australian rock pools was strongly related to a
number of abiotic characteristics of the pools themselves. Clam shrimp were found in
higher numbers in pools that had less surface area and possibly smaller volumes as well
(p=0.0111, p=0.0548, respectively). This is surprising because smaller pools are more
likely to have shorter wet-phases, which would seem to be detrimental to the maintenance
of breeding populations. Larger pools may also contain more predators, and in this case
there were significantly more anuran larvae in pools with larger surface areas (p=0.0075,
figure not shown). These higher tadpole quantities may have been detrimental to L.
badia success, although there is no evidence that shrimp were ever excluded from pools
because of the existence of a predator. That there are more clam shrimp in smaller pools
suggests that there is some factor preventing their success in larger pools, such as
predation. Results from Chapter 1 show that volume is one of a number of factors that may have an effect on the presence of Limnadia.
It is also possible that the conditions that promote hatching of L. badia were not ideal in the larger pools, resulting in a smaller initial hatch. This is impossible to
36 determine because the abiotic conditions that promote hatching in this species have not
yet been thoroughly explored (but see Chapter 3). In general, however, clam shrimp are a
warmer climate group than other branchiopods (Colburn 2004). Khalaf & Al-Jaafery
found a very narrow window of hatching success for Eocyzicus spinifer (1985).
Although they found E. spinifer to hatch at temperature between 28-30°C, they saw no
hatching at all at 26°C and below. It is possible that L. badia has a similarly small window of optimal hatching temperatures, although the results of Chapter 3 suggest otherwise.
Differences Between Australian and North American Temporary Pools
The abiotic parameters in both sets of pools differed in interesting ways. None of
the parameters that relate to the size of the pools (volume, depth, and surface area) were
significantly different between locations, although in general the Arizona pools were
larger. Temperatures were higher in Thousand Pockets because of the time of year these pools were sampled. The parameters that varied were measurements of solutes. TDS and conductivity were much lower in Arizona than in Western Australia. Chan et al. (2005), who studied pools with similar structure and substrate in southern Utah, suggest that this may be a result of resistant bedrock and low quantities of sediment in these basins. This explanation does not provide any insight into why these pools have lower solute values than those in WA. Sediment depths in WA ranged from 0.5 to 3.5cm, however no comparable data exist for Thousand Pockets, so a determination of the role that sediment plays in the solute concentrations in these pools is not possible. The pools in Thousand
Pockets were younger, just filling a few days prior to sampling, as opposed to the pools in
37 WA which were eight days of age. The additional time since inundation likely allowed
for evaporation to concentrate the solutes in the water. Additionally, the areas
surrounding the rock outcrops in WA were parched agricultural fields with salty
sediments which could become windblown thereby increasing the salinity of the pools
(Halse et al. 2003). This windblown addition to the salt component would probably be
greater in pools that had the lowest elevations. This was not so in the present data set, as
the lowest elevation pools (those on Holland Rock) also had the lowest salt content (mean
conductivity (mS/cm) on Holland=0.147, Wave=0.266, Dingo=0.442, Puntaping=0.446).
Conductivity has been noted as an important component of temporary pools to harbor large branchiopods (Hamer & Martens 1998). Clam shrimp were present in all pools at both localities. However the pools at Thousand Pockets contained fairy shrimp and tadpole shrimp as well. This is interesting because these were the pools with lower solute levels. This evidence, then, is in contradiction with the findings of Hamer &
Martens (1998) who found that conductivity was the sole variable that could be tied to the presence of branchiopods in South African pools.
Conclusion
Temporary pools are highly variable environments that not only change diurnally,
but also change over the course of longer periods of time. The study here examined some
abiotic and biological trends evident in temporary granite rock pools in Western Australia
and in northern Arizona. Although these two areas are geographically distant, the water
chemistry of the pools is quite similar. Because of their small size and their temporary
nature, they are subject to variability from the external environment in ways that more
38 permanent water bodies are buffered against. This creates an interesting dynamic in the ecological community, as all successful inhabitants must be able to withstand these changes. Clam shrimp in these pools are undoubtedly affected by their environment and by their food source, which is in turn subject to environmental constraints. Although these constraints appear to be quite rigid, the communities in temporary pools are robust.
The algae and bacteria, which form the base of these communities, have not been examined thoroughly in ephemeral rock pools. This preliminary information on this topic must be followed by more detailed studies in order to fully appreciate the complexity of these rock pool communities.
39 CHAPTER III
HATCHING CUES IN CLAM SHRIMP: A LABORATORY TEST OF
TEMPERATURE AS A MEANS OF SPECIES SEPARATION AND A SUGGESTED
ALTERNATIVE MECHANISM FOR THE BET-HEDGING HYPOTHESIS
Introduction
Clam shrimp are branchiopod crustaceans that are obligate dwellers of temporary waters. Permanent waters harbor too many predators for these slow moving organisms to maintain viable populations (Fryer 1986). Populations survive dry periods in the form of resting eggs dropped by mature females or hermaphrodites. The eggs lie in a dormant state until the appropriate conditions are met for hatching (Brendonck 1996). Upon inundation, many (but not all) resting eggs hatch, leaving behind a bank of eggs in the sediment (Brendonck 1996). On occasion, rain will fill the pool and eggs will hatch but not reach sexual maturity before the basin dries. In these instances, the egg bank is vital to the success of the population. Because not all eggs hatch with every new rain, there is much discussion in the literature about which environmental variables trigger eclosion and how the triggers vary across species within the Branchiopoda (Moore 1963, Bishop
1967, Belk 1977, Scott & Gragarick 1979, Khalaf & Al-Jaafery 1985, Mossin 1986,
Mitchell 1990, Brendonck 1996, Brendonck et al. 1996, Kuller & Gasith 1996,
Schonbrunner & Eder 2006, Beladjal et al. 2007).
40 Many physical habitat parameters, including light, dissolved oxygen, pH, osmotic
pressure, and temperature, have been implicated in hatching success or failure in large
branchiopods. Light has been noted as an important variable on many occasions (Bishop
1967, Mitchell 1990). Schönbrunner et al. (2006) noted that hatching success in Triops
cancriformis follows a normal distribution over a pH range of 4 to 9.9, peaking between
6 and 6.9. Scott & Grigarick (1979) observed the highest hatching rates at a pH of 5.6 for
Triops longicaudatus. Temperature, too, seems to be important in hatching. Mitchell
(1990) found that the proportion of hatching Streptocephalus macrourus eggs varied at
different temperatures. Moore (1963) monitored temperature in natural populations of
Streptocephalus seali and Eubranchipus holmani and found that temperature in
combination with dissolved oxygen levels were most likely the driving forces keeping
these two species from coexisting. Belk (1977) measured temperature against hatching
for nine Arizona anostracans and found that each species exhibited a preference for a
specific temperature. He observed that there was higher hatching at some temperatures
than others and that all nine species were significantly different in their preferences.
Bishop (1967) experimented with controlled gas environments to determine if
dissolved oxygen concentrations or sudden changes in gas concentrations had an effect
on hatching success in Limnadia stanleyana. He observed that low concentrations of
dissolved oxygen inhibited hatching. Interestingly he also noted that when resting eggs
were kept in uniform and favorable conditions, most of them hatched.
The common thread of all these reports is that there is undoubtedly an environmental role in the timing of hatching of large branchiopods. In addition to this environmental aspect, there is a role for phenotypic variability in eclosion to produce
41 temporal risk-spreading (Simovich & Hathaway 1997, Brendonck & Riddoch 2000). Bet-
hedging in this way complicates the process of determining the mechanisms behind
hatching in large branchiopods.
So it seems there is a suite of physiochemical characteristics that promote
hatching and that there is variation in this profile between species. The aim of the present
study is to determine whether water temperature plays a role in determining which of two
species present in an ephemeral rock pool will hatch. Resting eggs of clam shrimp
species used in the analysis (Eulimnadia dahli and Limnadia badia) were collected from
granite rock pools in the wheatbelt region of Western Australia. These species have been
observed to inhabit neighboring pools, but have never been collected from the same pool
at the same time (Weeks et al. 2006). Limnadia badia is thought to be more common in
the winter months while Eulimnadia dahli is more likely to hatch in the summer (S. C.
Weeks, personal communication). This seasonal separation suggests the possible role of
temperature as a mechanism driving these differences. A seasonal separation might also
indicate different temporal risk-spreading strategies between the two species.
Methods
Rock pools were chosen from granite outcrops in the wheatbelt region of Western
Australia. Prior to the first rain of the season (late February through mid-April), several
outcrops were scouted as possible sampling locations. These rocks were Holland Rock
(Shire of Kent), Dingo Rock (Shire of Lake Grace), Puntapin Rock (Shire of Wagin),
Cullimbin Rock (Shire of Dowerin), Tammin Rock (Shire of Tammin), Yorkrakine Rock
(Shire of Tammin), Elachbutting Rock (Shire of Westonia), Cairns Rock (Shire of
42 Narembeen), Wave Rock (Shire of Kondinin), The Humps (Shire of Kondinin), Bunjil
Rock (Shire of Perenjori), and Wanarra Rock (Shire of Morawa). Eight depressions were
selected from each outcrop and the geographical coordinates of each pool were recorded.
Sediment was collected from the depressions and bagged for hydration in the laboratory.
Sediment from each pool was thoroughly mixed and 250 ml were hydrated in 20 l of filtered (via reverse osmosis) water. Some hydrations were executed in smaller containers, but the sediment to volume ratio was always kept constant. All containers were housed under constant light and constant temperature, at either 27°C (+/-1°) or
15°C. The lower temperature for the trials was based on the mean temperature of natural pools where the sediment was collected (see Chapter 1, figure 1.2). The higher temperature was chosen because it is highly successful for other species in the genus
Eulimnadia, including those collected in Western Australia (Weeks et al. 2006). Upon maturation, individuals in each tank were identified to species by gross morphology and the lack of spines on the ventroposterior corner of the telson for Limnadia badia (Richter
& Timms 2005).
Statistical analysis of the results consisted of chi-square analysis of hatching success of both L. badia and E. dahli in low and high temperature situations.
Results
Species hatching was divided across and between outcrops. Most outcrops hatched out either one species or the other, even when both species were known to occur in those sediments. Two outcrops (Holland and Puntaping) had only one of the two species present: Eulimnadia was never observed from sediments collected on these two
43 rocks. All other rocks yielded both species, but usually only one per hydration (Table
3.1). Very little successful hatching occurred in containers incubated at 15°C in
comparison to the high amount of hatching at the 27°C level. Only six containers
produced adult clam shrimp at a constant temperature of 15°C and all of those tanks
contained only Limnadia badia. At 27°C, 56 containers yielded clam shrimp of the
genera Limnadia and Eulimnadia. Out of 76 total hydration attempts, only seven tanks
hatched both Eulimnadia and Limnadia on the same occasion (see Table 3.1 for full
results).
Clam shrimp occurrence at two temperatures
45
40 E. dahli
35 L. badia 30
25
20
15
# tanks hatched 10
5
0 15 27 Temperature (°C)
Figure 3.1. Number of hatching occurrences of Limnadia badia and Eulimnadia dahli incubated at 15°C and 27°C. Chi-square likelihood p=0.0012*.
The results in Figure 3.1 strongly suggest that Eulimnadia dahli does not break dormancy
at 15°C, although it hatches readily at 27°C. Although L. badia possesses the ability to
hatch at 15°C, it hatches more frequently at 27°C. All pools with known Eulimnadia
44 eggs that were hydrated at 15°C hatched only Limnadia or nothing at all. However at
27°C, Eulimnadia hatched more readily than Limnadia. Thus, there was a significant
2 difference in the proportions of each species hatching at the two temperatures. (χ (1) =
10.49; p=0.0012).
Table 3.1. List of successful laboratory hydrations with quantities of Limnadia and Eulimnadia. A (+) indicates the presence of a species when exact quantity was unavailable. Date Quantity Quantity Outcrop Pool Temperature hydrated Limnadia Eulimnadia Holland 2 28 30-Apr-08 + 0 Holland 4 28 30-Jul-07 1261 0 Holland 2 28 4-Apr-08 27 0 Holland 3 28 4-Apr-08 20 0 Holland 4 28 4-Apr-08 10 0 Humps 3 28 8-Jun-07 0 971 Humps 5 28 22-Jun-07 0 255 Humps 6 28 22-Jun-07 0 23 Wanarra 3 28 17-Mar-08 0 6 Wanarra 5 28 18-Mar-08 0 7 Wanarra 6 28 18-Mar-08 0 5 Wave 8 15 1-Feb-08 7 0 Wave 5 15 5-Sep-08 9 0 Wave 3 28 10-Jul-09 + 121 Wave 6 28 20-Aug 1 19 Wave 8 28 3-Oct-07 86 8 Yorkrakine 1 28 21-Nov-07 0 6 Yorkrakine 2 28 21-Nov-07 0 25 Tammin 1 28 13-Jul-07 20 0 Tammin 2 28 17-Oct-07 0 108 Tammin 4 28 17-Oct 0 46 Tammin 5 28 24-Oct-07 0 923 Tammin 6 28 24-Oct-07 0 910 Tammin 7 28 5-Nov-07 0 45 Puntaping 1 28 3-Aug-07 26 0 Puntaping 2 28 30-Apr-07 + 0 Puntaping 4 28 30-Apr-07 8 0 Puntaping 6 28 14-Apr-07 37 0 Puntaping 8 28 1-Apr-08 4 0 Elachbutting 1 28 17-Jul-07 648 0 Elachbutting 5 28 8-Feb-08 0 37 Elachbutting 6 28 8-Feb-08 1 0 Elachbutting 7 28 8-Feb-08 0 1 Dingo 2 15 1-Feb-08 11 0 45 Table 3.1. List of successful laboratory hydrations with quantities of Limnadia and Eulimnadia. A (+) indicates the presence of a species when exact quantity was unavailable (continued). Date Quantity Quantity Outcrop Pool Temperature hydrated Limnadia Eulimnadia Dingo 1 15 1-Feb-08 3 0 Dingo 1 28 17-Jul-07 123 0 Dingo 2 28 30-Jul-07 52 0 Dingo 4 28 31-Mar-08 0 1 Cairns 1 15 6-Sep-08 19 0 Cairns 2 15 6-Sep-08 7 0 Cairns 1 28 29-Jan-08 47 0 Cairns 1 28 13-Jul-07 118 15 Cairns 1 28 15-Nov-07 14 64 Cairns 2 28 28-Sep-07 0 5 Cairns 4 28 28-Sep-07 17 0 Cairns 5 28 3-Oct-07 0 55 Cairns 7 28 12-Oct-07 30 0 Bunjil 1 28 10-Aug-07 0 268 Bunjil 1 28 29-Apr-08 0 25 Bunjil 2 28 10-Aug-07 0 456 Bunjil 3 28 20-Aug-07 0 131 Bunjil 4 28 30-Aug-07 0 6 Bunjil 5 28 30-Aug-07 0 32 Bunjil 6 28 18-Sep-07 0 15 Bunjil 7 28 18-Sep-07 0 396 Bunjil 8 28 18-Sep-07 0 15 Bunjil 1 28 27-Sep-08 0 + Bunjil 6 28 16-Sep-08 + +
Discussion
It is widely accepted that branchiopod resting eggs receive environmental cues
and that these cues trigger eclosion when conditions are amenable (Moore 1963, Bishop
1967, Belk 1977, Scott & Gragarick 1979, Khalaf & Al-Jaafery 1985, Mossin 1986,
Mitchell 1990, Brendonck 1996, Brendonck et al. 1996, Kuller & Gasith 1996,
Schonbrunner & Eder 2006, Beladjal et al. 2007). The present experiment demonstrated that these environmental cues may function, at least partly, as a mechanism to prevent co- occurrence of similar species in the same pools.
46 The results presented here suggest that temperature may be a highly sensitive cue
for hatching in these two species of clam shrimp. This could be explained by the idea that
temperature is the most likely mechanism for preventing both species from hatching after
the same rain event. However, it does not fully explain the previously-suggested
separation of L. badia in the cooler months from E. dahli in the warmer months (Weeks
pers. comm.). The cool temperatures clearly prevent E. dahli from hatching, but the warmer temperatures did not preclude L. badia from hatching. Moore (1963) noted a similar situation in two species of fairy shrimp and suggested the possibility of a temperature-dissolved oxygen interaction preventing the two species appearing together.
Continuous aeration of all containers in the present experiment is likely to have reduced any possible differential oxygen effect and thus could be the reason why both species hatched out simultaneously at the higher temperature. Bishop noted that low oxygen content of pools will inhibit hatching in Limnadia stanleyana (1967). Perhaps the lower dissolved oxygen that would have been present in natural situations, due to increased temperature, prevents L. badia from hatching.
Another possibility is that the temperature cue observed here is more sensitive in
Eulimnadia dahli than in Limnadia badia, as evidenced by the fact that although L. badia was seen frequently in tanks incubated at the higher temperature, E. dahli was never observed at the lower temperature. Eulimnadia dahli was not observed in the field during the autumn-winter season when water temperatures were lower than they would have been during a summer hydration (personal observation). The reduced range of E. dahli
was also noted by Timms (2006) who found its frequency of occurrence to be moderate
47 in comparison with the high frequency of L. badia in a survey of WA rock pools. This evidence supports the possibility of a narrower temperature range for E. dahli.
Yet another possible explanation for the results is that because only two temperatures were tested, the actual high temperature limit of L. badia was not exceeded.
There may be an upper bound to hatching in this species that was above the 27°C level tested here. The lower temperature limit for hatching in E. dahli was not determined but it seems likely that 15°C is below the limit. If Eulimnadia does hatch out more often in the summer, then it must mature quickly to attain reproductive age before the pools dry.
In this case, temperature would be an important cue for the determination of growth rate.
Application to the bet-hedging hypothesis
Since these clam shrimp live in highly unpredictable environments, it is necessary
to have a mechanism for preventing all eggs from hatching during the same hydration
period. Without such a mechanism, if the pool should fill but dry before the animals
reach maturity, all the reproductive effort of the previous generation would be lost and
the local population would go extinct. This is the essence of the bet-hedging hypothesis: spreading reproductive risk over time (Seger & Brockman 1987, Simovich & Hathaway
1997). Three major theoretical predictions of this hypothesis, as pertaining to branchiopods, are laid out by Simovich & Hathaway (1997) as follows: (i) eggs maintaining dormancy should retain the ability to hatch later when conditions are again favorable, (ii) hatching fractions should be correlated with the probability that there will be successful reproduction, and (iii) differences in timing of hatching of eggs within a clutch should be due to phenotypic variability of each egg and not to genetic
48 polymorphism. These predictions were adapted originally from Seger & Brockman
(1987) who described bet-hedging as essentially being a process of potentially lowering
expected fitness with the benefit of also lowering the overall variance of fitness. I will
explore these three hypotheses one at a time for their application to this system and to
clam shrimp in general.
The findings presented here appear to partially support the first prediction of the
bet-hedging hypothesis (Simovich & Hathaway 1997), although this was not the aim of
this experiment. Presumably, these eggs are hatching when environmental signals are
favorable, although I have no direct evidence that individual eggs maintained dormancy
at one temperature only to hatch at the other, more favorable temperature. Although there
is no other data available for the most favorable hatching conditions for these two species
of clam shrimp, the maintenance of dormancy during unfavorable periods is a well-
accepted occurrence within the large branchiopods (Brendonck 1996).
According to the second prediction of the bet-hedging hypothesis of Seger &
Brockman (1987) as adapted by Simovich and Hathaway (1997), animals that are commonly found in pools during the summer months, when rains are more unpredictable and the length of inundation is typically shorter, should have lower hatching proportions than shrimp that are found in pools during the more predictable wet season. Many authors have reported such a pattern; namely, that there is a reduction in the hatching fraction for species found in more ephemeral environments as opposed to those in pools that are filled more predictably (Belk 1977, Mossin 1986, Simovich & Hathaway 1997).
The implication here is that species that have evolved in pools on the ephemeral end of the spectrum are more likely to spread their reproductive risk over greater amounts of
49 time. Pools that fill predictably every spring, such as those that form from snowmelt
water, should have larger hatching fractions with every inundation than pools that form
after sporadic desert rains because the probability that the water will remain long enough
for successful reproduction in the former situation is higher.
The last prediction of the Simovich & Hathaway (1997) hypothesis is that the
variation in hatching is the result of environmentally-induced phenotypic variability
between sibling eggs. There is clear variability in hatching of eggs in the same species,
same pool, and same clutch. Hatching fractions vary widely within and between species.
Beladjal et al. (2007) noted differences in hatching fractions of several species of
Anostraca relative to the presence of conspecific adults in the water; eggs could
apparently chemically detect the presence of adults and retain dormancy so as not to be in
competition with conspecifics. Schönbrunner & Eder (2006) experimented with hatching
Triops cancriformis over a range of pH values to determine the level of highest hatching and noted a normal distribution, peaking at near neutral pH. Mitchell (1990) found variation in streptocephalid hatching under differing light regimes.
Hatching variability appears common for all large branchiopods. What is unclear is the origin of the variation. Simovich & Hathaway (1997) proposed that there is phenotypic variation between eggs and tested their hypothesis by repeatedly hydrating
eggs from a single mating pair. They reported that a decreasing proportion of eggs
hatched out with every repeated hydration, suggesting that there was innate variability in
emergence timing between eggs. However, to qualify as diversified bet-hedging, the
variability cannot be due to genetic polymorphism; the alleles for timing of eclosion must
be identical among siblings (Seger & Brockman 1987, Evans & Dennehy 2005), and
50 there was no evidence provided by Simovich and Hathaway (1997) to suggest that these sibling eggs were genetically identical. If there is a genetic polymorphism controlling the timing of eclosion, then this would be better described as an evolutionary stable strategy than as an instance of bet-hedging (Seger & Brockman 1987). Alternative alleles may result in an increase in the variance of fitness (the possibility of total reproductive failure), while to qualify as bet-hedging, the phenotypic variability must reduce average fitness of the individual while reducing the variance of fitness (Seger & Brockman 1987).
In other words, there must be some initial reduction in expected immediate fitness that lowers the possibility of total failure. A genetic polymorphism in certain instances may actually increase the variance of fitness, not reduce it, and therefore cannot be considered a mechanism of diversified bet-hedging.
In fact, diversified bet-hedging is a difficult concept to grasp and many occurrences commonly referred to as bet-hedging probably do not meet the strict criteria
(Seger & Brockman 1987, Evans & Dennehy 2005). This term may be a catch-all, more clearly defined by what it is not than what it actually is. Bet-hedging is not the result of genetic polymorphism or predictive plasticity to environmental cues (Evans & Dennehy
2005). The phenotypic variation in hatching must be a response to a completely unpredictable future environment. Predictive plasticity does not qualify as bet-hedging because any predictability can be wagered upon and then bets would not be hedged.
Diversified bet-hedging is the result of a phenotype that affects the timing of hatching and is variable between individuals who carry identical alleles for hatch timing (Evans &
Dennehy 2005).
51 Natural selection operates by filtering through existing genetic variation in the
population, eliminating the detrimental and maintaining the beneficial. It never creates
new variation, it can only maintain or reduce that which already exists. It seems unlikely
that a bet-hedging process should promote a selective advantage to programming
phenotypic variability into branchiopod eggs for two reasons. First, the efficacy of natural
selection is directly related to the number of alleles available to manipulate for the trait in
question. The exception is variation that is retained in genes that are not directly related
to the timing of eclosion, but nonetheless impact hatching (e.g., egg shell thickness). The
mechanism that controls the development of the shell has an important effect of how the
egg will receive signals about external conditions. Retaining imperfection in the
mechanisms that control shell thickness would effectively maintain variability in
hatching. Mechanisms of this kind might be at risk of being ‘perfected’ by selection,
unless “sloppiness” itself was beneficial. If it is under genetic control and exerts a strong
influence on hatching, then shell thickness will eventually be selected toward a very
narrow margin after a few ideal inundations, unless there are alleles that promote a certain amount of imperfection in shell construction and therefore in shell variation.
Secondly, bet-hedging supposedly occurs only in situations in which the environmental fluctuations that affect fitness are highly unpredictable (Evans & Dennehy
2005). If organisms that occur in highly unpredictable environments would benefit by staggered hatching but there is no genetic variability, it is possible to extract that necessary variability from the inherent variation in their environment rather than from within their own genome.
52 It might be more parsimonious to admit the possibility that the selective pressure
being applied is toward a narrower window of favorable hatching conditions rather than
variable hatchability alone. In such a highly unpredictable environment, it makes sense to
allow the environment to supply some of the variability rather than the organisms alone,
especially if some hatching cues are reliable predictors of pool longevity. The risk is still
being spread; however, the organisms need not hedge their bets to the same extent as in
the previous application of this hypothesis to branchiopods. In this scenario the
environment is aiding in the risk-spreading exercise. This contribution may prevent
individuals from ‘going all in’ on their variable hatchability bet as in the above example
involving shell thickness.
Possessing the ability to perceive cues from the environment to predict when to
hatch would be a very beneficial trait. It appears there is a level of predictability in the
habitats of L. badia and E. dahli or there would be no differential hatching on the basis of temperature. A cue that is linked to the future environment indicates some predictability,
meaning that the bets of these organisms are not spread equally across the board as a
means to avoid total fitness loss. The environment must not be entirely unpredictable.
a. b. Hatching probability Hatching probability Hatching Environmental profile Environmental profile
Figure 3.2. Theoretical distributions of hatching proportions versus multidimensional environmental profile in (a) populations from a more predictable environment and (b) from a less predictable environment. 53 Temperatures at the perimeter may be largely different than those at the surface or the bottom. Dissolved oxygen as well as light intensity will change with depth. Resting eggs at the periphery will be exposed to different conditions than those in the center.
Eggs under even a few grains of sand will not be exposed to the same parameters as their closest neighbors.
Figure 3.2 is a visual representation of the possible mechanism behind the bet- hedging continuum from highly ephemeral pools to more predictable bodies of water. If there is a range of environmental conditions that promote hatching, it is reasonable to assume that the likelihood of eggs hatching along that range will follow a normal distribution, with the most hatching occurring at the most favorable conditions and the likelihood decreasing as conditions worsen in either direction. In reasonably predictable temporary pools, it may be obvious that any rain event will likely result in conditions that are favorable to successful mating. In pools that are filled more erratically, it would be much more difficult to predict that any particular rain event will lead to suitable mating conditions. Thus, in species that inhabit unpredictable ephemeral pools, natural selection may favor individuals that have a narrower range of favorable hatching conditions, which will be detailed below. In species from more predictable pools, there is no need to reduce the hatching fraction or to narrow the window of favorable conditions because almost every inundation will be successful. There is far less risk involved in hatching and there is no benefit to withholding reproductive output. Conversely, in less predictable temporary pools, there is certainly a selective advantage to waiting, especially if the majority of rainfall events do not maintain the water level long enough to grow mature breeding populations. In order for this trigger mechanism to work in an advantageous
54 way, there must be a predictable link between the conditions that induce eggs to hatch
and the water conditions later in the wet phase of the pool.
Although many authors have contributed to our collective knowledge of
environmental cues for egg hatching (Moore 1963, Bishop 1967, Belk 1977, Scott &
Gragarick 1979, Khalaf & Al-Jaafery 1985, Mossin 1986, Mitchell 1990, Brendonck
1996, Brendonck et al. 1996, Kuller & Gasith 1996, Schonbrunner & Eder 2006, Beladjal
et al. 2007), very little effort has been applied to understanding the water quality of
sustained wet-phase pools that support viable branchiopod populations, much less the
link between the cues for hatching and the habitat requirements for long-term physiological sustainability of branchiopod species.
Environmental conditions within pools vary widely. Rather than one environmental cue alone triggering hatching, a complex suite of cues is probably involved and the parameters within this suite are likely all correlated with pool size and permanence (see Chapter 1 for more information on environmental profiles). Likely contributors to this multidimensional profile include appropriate levels of dissolved oxygen, pH, TDS, conductivity, salinity, temperature, depth, surface area, etc.
Information available in physiochemical parameters like these could indicate conditions that are beneficial or detrimental to shrimp. By measuring the temperature and pH of a pool we may get an idea of the microbial composition of the pools and thus the food source of the animal. Measurements of depth and surface area may indicate the length of time the pool is likely to remain in its wet-phase – a vital piece of information for clam shrimp. By selecting for an ever narrower range of favorable conditions, risk of hatching at an inappropriate time should be reduced. The eggs that do emerge should be more
55 likely to be successful because they have been selected to hatch under only the most
promising conditions. An individual’s bets can be hedged by a selective refinement of
hatching cues in addition to an increase in the variability of hatching timing among eggs within a clutch. Species that inhabit pools primarily in the winter or rainy season will be under relatively weaker selection pressure to key in on a specific environmental profile as those species that inhabit pools in the more erratic summer or dry seasons.
None of this is to say that there is no innate variance in hatchability of eggs and that all eggs are solely triggered to hatch at the whims of the environment. Certainly there must be some variation imposed by maternal effects or other stochastic events.
There must be some range of risk-spreading, otherwise there would have been no available variability in hatching to buffer against long term climatic change that these species have most certainly endured. Clearly the window of suitable environmental characteristics is not so small as to prevent hatching altogether if, say, the temperature is one or two degrees off the ideal peak. This is merely a suggestion that much more work needs to be done in order to test the hypothesis that bet-hedging, as defined in the literature, is truly applicable to large branchiopods (Seger & Brockman 1987, Simovich
& Hathaway 1997, Evans & Dennehy 2005).
Conclusion
Branchiopod crustaceans of temporary pools have species-specific tolerances for a multitude of physiochemical parameters including temperature, dissolved oxygen, pH, and light. Eclosion in a number of species probably depends on combinations of these factors being within a favorable range. Ephemeral pool crustaceans, in conjunction with
56 responding to these environmental suites, are also are hedging their bets so as not to hatch
all resting eggs during the same hydration event. The construction of an egg bank in the
sediment is vital to continued population success. Rather than non-genetic, phenotypic
variability between eggs being the only mechanism for delayed hatching of certain
individuals, I propose that all eggs are being selected for a narrow range of environmental
cues and that even in carefully controlled conditions it is unlikely that each resting egg
will be exposed to the exact same suite of characteristics that would promote hatching.
The idea presented here rests on an assumption that the conditions eggs experience upon
inundation have the power to predict, with reasonable certainty, the length of time the
water is likely to remain. If this predictability is non-existent, then eggs must truly be forced to hedge their bets in the traditional sense. However, there is definitely a mechanism that restricts hatching to some degree based on environmental inputs, otherwise populations would be decimated when a few showers drop only enough water to dampen the bottom of the sediment. Realistically, there is probably a selection balance between maintaining enough variability in hatching success to remain viable and selecting strongly toward moderately predictive cues.
This hypothesis remains untested and is only presented here as a possible alternative to the prevailing bet-hedging ideas. It could be tested by experimentally controlling the hatching conditions for each egg to attempt to induce full hatching. If hatching is purely due to triggers imposed by the environmental profile rather than due to some innate variability, as proposed by Simovich & Hathaway (1997), then it should be possible to hatch all eggs in a clutch at the same time. According to those authors, they were able to successfully hatch individuals from the same clutch over the course of three
57 separate hydrations. This seems to be strong evidence in their favor; however their eggs were not necessarily genetically identical and the opposite has also been reported. With carefully controlled conditions, Bishop (1967) was able to hatch nearly all eggs of a clutch of Limnadia stanleyana in a single hydration. These kinds of tests should be repeated on many species within the Branchiopoda to discern the patterns of hatching from resting eggs in more detail.
.
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63 APPENDIX
Appendix. List of families of macroinvertebrates collected on four outcrops in Western Australia. Identifications made using Davis & Christidis 1999 and Williams 1968.
Outcrop A=Adult, L=Larva Puntaping Puntaping Puntaping Puntaping Puntaping Puntaping Puntaping Wave Wave Wave Wave Wave Wave Wave Wave Wave Wave Dingo Dingo Dingo Dingo Dingo Dingo Dingo Dingo Holland Holland Holland Holland Holland Holland Holland Holland Holland Holland Holland Holland Date 20-May 19-May 19-May 18-May 21-May 19-May 20-May 15-May 13-May 13-May 15-May 13-May 14-May 14-May 16-May 12-May 12-May 10-May 10-May 25-Apr 24-Apr 25-Apr 24-Apr 22-Apr 22-Apr 22-Apr 24-Apr 25-Apr 24-Apr 22-Apr 22-Apr 6-May 2-May 3-May 3-May 3-May 4-May Pool H16 H15 H14 H13 H12 H11 H10 H9 H7 P13 P12 P11 P10 P9 P8 P6 W15 W14 W13 W12 W11 W10 W9 W8 W6 W5 D9 D8 D7 D6 D5 D4 D2 D1 H4 H3 H1 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Limnadia X X X X X cyzicus X X X X X X X X X X X X X X X X X X X X X X Dytiscidae (A) X X X X X X X X X X X X X X X X X X Corixidae X X X X X X X X X X X X X X X X X X X X X X X X Calanoida
X Cyclopoida X X X X X X X X X X X tadpole X X X X X X X X X X X X X X X X X X X X X X X X X X X X Chironomidae X X X X X X X Planorbidae X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Turbellaria X X X X X Hydrophillidae X X X X X X X Notonectidae X X X X X X X X X X X X X X Dytiscidae (L)
X X X X Culicidae
X Stratiomydae
X X X X Anostraca
64