Ecology of phytoplankton 2006
551 pág.

Ecology of phytoplankton 2006


DisciplinaFitoplâncton12 materiais70 seguidores
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over the enclosure
surface, during windy conditions which intensi-
\ufb01ed in the subsequent few days. A near-uniform
distribution with depth was quickly established
(see Fig. 2.20). The spores (d = 32.80 ± 3.18;
\u3c1c = 1049 kg m\u22123; \u3d5r \u223c 2.2) had a measured sink-
ing rate (ws) of 15.75 µm s\u22121 at 17 \u25e6C, which,
adjusted for the density and viscosity of the water
at the 4\u20135 \u25e6C obtaining in the \ufb01eld, predicted an
in-situ intrinsic sinking rate of 0.96 m d\u22121. The
theoretical time for spores to eliminate the enclo-
sure (at the time, H \u223c 11.8 m) was thus calcu-
lated to be (t\u2032 = ) 12.3 days. In fact, the elimi-
nation proceeded smoothly, always from a near-
uniformly distributed residual population at an
average exponential rate of \u22120.10 m d\u22121, which
value corresponds to a 95% removal in (te =) 30
days. The ratio te/t\u2032 is lower than predicted in Sec-
tion 2.6.2 (2.44 against 3.0). This may be explained
by probable violation of the initial assumption of
full mixing of the water column throughout the
experiment. Although no signi\ufb01cant density gra-
dient developed, continuous and complete verti-
cal mixing of the enclosure cannot be veri\ufb01ed.
Nevertheless, the outcome is suf\ufb01ciently close to
the model (Fig. 2.20) solution for us not to reject
the hypothesis that entrained particles are lost
from suspension at an exponential rate close to
\u2212(ws/hm).
In the second experiment, commenced in
June, spores were dispersed at the top of the
THE SPATIAL DISTRIBUTION OF PHYTOPLANKTON 77
Figure 2.20 Modelled (M) and
actual (A) depth\u2013time distributions
of preserved Lycopodium spores (of
predetermined sinking
characteristics) introduced at the
water surface of one of the Blelham
enclosures on each of three
occasions (1, 9 January; 2, 3 June; 3,
9 September) during 1976, under
sharply differing conditions of
thermal stability. Lycopodium
concentrations plotted as cylindrical
curves; density gradients plotted as
dashed lines. \u2217 \u2013 indicates no field
data are available. Redrawn from
Reynolds (1984a).
strati\ufb01ed enclosure during relatively calm con-
ditions. Sampling within 30 minutes showed a
good dispersion but still restricted to the top 1
m only. However, 4 days later, spores were found
at all depths but the bulk of the original addition
was accounted for in a \u2018cloud\u2019 of spores located
at a depth of 5\u20137 m. After a further 7 days, mea-
surable concentrations were detected only in the
bottom 2 m of the column, meaning that, effec-
tively, the addition had cleared 10 m in 11 days,
at a rate not less than 0.91 m d\u22121. Adjusted for
the density and viscosity of the water at the top
of the water column, the predicted sinking rate
was 1.42 m d\u22121. Thus, overall, the value of t\u2032 for
the \ufb01rst 10 m (= 7 days) was exceeded by the
observed te (= 11 days) by a factor of only 1.57. Part
of the explanation is that sinking spores would
have sunk more slowly than 1.42 m d\u22121 in the
colder hypolimnion. However, the model expla-
nation envisages a daily export of the population
from the upper mixed layer (varying between 0.5
and 4 m during the course of the experiment),
calculated as N exp \u2212(ws/hm), whence it contin-
ues to settle unentrained at the rate ws m d\u22121. To
judge from Fig. 2.20, this is an oversimpli\ufb01cation
but the prediction of the elimination is reason-
able.
The same model was applied to predict the
distribution and settlement of Lycopodium spores
in the third experiment, conducted during the
autumnal period of weakening strati\ufb01cation and
mixed-layer deepening. Variability in wind forc-
ing was quite high and a certain degree of re-
entrainment is known to have occurred but the
time taken to achieve 95% elimination from
the upper 9 m of the water column (te = 18.0
days) at the calculated in-situ sinking rate (ws)
of 1.32 m d\u22121 exceeded the equivalent t\u2032 value
(9/1.32 = 6.82) by a factor of 2.6.
The three results are held to con\ufb01rm that the
depth of entrainment by mixing is the major con-
straint on elimination of non-motile plankters
heavier than water, that the eventual elimina-
tion is however delayed rather than avoided, and
that prolongation of the period of suspension is
in proportion to the depth of the mixed layer,
wherein u\u2217 \u2265 15 (ws).
2.7 The spatial distribution of
phytoplankton
The focus of this chapter, the conditions of
entrainment and embedding of phytoplankton in
the constant movement of natural water masses,
is now extended to the conditions where water
movements are either insuf\ufb01ciently strong or
insuf\ufb01ciently extensive to randomise the spa-
tial distribution of phytoplankton. This section
is concerned with the circumstances of plankters
becoming disentrained and the consequences of
78 ENTRAINMENT AND DISTRIBUTION IN THE PELAGIC
weakening entrainment for individuals, popula-
tions and communities, as augured by spatial dif-
ferentiation in the vertical and horizontal distri-
butions of natural assemblages.
Distributional variation is subject to issues of
scaling which need to be clari\ufb01ed. It has already
been made plain that aquatic environments are
manifestly heterogenous, owing to spatial differ-
ences in temperature, solute content, wind stress,
etc., and that each of these drivers is itself sub-
ject to almost continuous variation. However,
while precise values are impossible to predict,
the range of variability may be forecast with
some con\ufb01dence, either on the basis of averag-
ing or experience, or both. We may not be able
to predict the intensity of wind mixing in a
lake some three weeks or more into the future
but we may estimate from the knowledge base
the probability with which a given wind inten-
sity will prevail. The changes in temperature,
insolation, hydraulic exchanges and the delivery
of essential nutrients affecting a given stretch
of water also occur on simultaneously differ-
ing scales of temporal oscillation \u2013 over minutes
to hours, night\u2013day alternations, with changing
season, interannually and over much broader
scales of climatic change. The nesting of the
smaller temporal scales within the larger scales
holds consequences for phytoplankters in the
other direction, too, towards the probabilities of
being ingested by \ufb01lter-feeders, of the adequacy
of light at the depths to which entrained cells
may be circulated, even to the probability that
the energy of the next photon hitting the photo-
synthetic apparatus will be captured. The point
is that the reactions of individual organelles,
cells, populations and assemblages are now gen-
erally predictable, but the impacts can only be
judged at the relevant temporal scales. These
responses and their outcomes are considered in
later chapters in the context of the relevant pro-
cesses (photosynthesis, assimilation, growth and
population dynamics). However, the interrelation
of scales makes for fascinating study (see, for
instance, Reynolds, 1999a, 2002a): in the end,
the distinction is determined by the reactivity of
the response. This means that critical variations
alter more rapidly than the process of interest
can respond (for instance, light \ufb02uctuations are
more frequent than cell division) or so much less
rapidly that it is perceived as a constant (such as
annual temperature \ufb02uctuations having a much
lower frequency than cell division) which will
be no more relevant to today\u2019s populations than
is the onset of the next ice age to the mainte-
nance of present-day forests (Reynolds, 1993b). In
between, where driver and response scales are
more closely matched, the interactions are rather
more profound, as in the frequency with which
new generations are recruited to a water col-
umn mixed to a different extent on successive
days.
The variability in the instantaneous distribu-
tion of phytoplankton may be considered in rela-
tion to an analogous spatial scale. Consider \ufb01rst a
randomised suspension of unicellular \ufb02agellates,
such as Chlamydomonas or Dunaliella.