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EFFECTS OF PRIMARY PRODUCTIVITY ON THE DYNAMICS OF SPATIALLY
ASSEMBLED COMMUNITIES WITH OMNIVORY
Heather David, Co-P.I. Ashkaan Fahimipour, P.I.: Dr. Kurt E. Anderson
Department of Biology, UC Riverside
ABSTRACT
The structure of ecological systems is heavily influenced by
spatial flows of colonists (i.e., colonization) from outside
systems. Whether variation in a community’s primary
productivity alters the effects of colonization remain unclear.
Studies of specialized consumers in three level food webs show
marked biomass increase with increased nutrient enrichment.
However, in systems with omnivory, predators may interrupt the
food chain by directly consuming basal resources. Thus, the
interaction between productivity and colonization rate remains
unknown for systems characterized by omnivory. Here, we use
an array of laboratory microcosms to test the hypothesis that the
effects of colonization on the structure and dynamics of
communities characterized by omnivory are influenced by
productivity manipulation. Replicate microcosms containing
protists; Blepharisma sp.(omnivore), Chilomonas
sp.(intermediate consumer), and mixed bacteria; Serratia
marscecens, Bacillus cereus and Bacillus subtilis; were
established in 150 mL bottles (“mainlands”), which supply small
30 mL “islands” of varying productivity levels with colonists.
Productivity (bacteria) is manipulated by altering nutrient
concentrations in the “islands” which affects enrichment and
maximum density. Fresh individuals are dispersed to the
“islands” three times a week in amounts which simulate
proximate and isolated spatial locations. We expect a clear
indication of how primary productivity influences the community
either by facilitating or inhibiting protist population growth. This
study will inform ecological theory by demonstrating interactions
between broad scale spatial processes, and local resource
conditions.
INTRODUCTION
Ecosystems in nature are spatially
subdivided and are connected by
dispersal of individuals (Levin 1992).
How dispersal affects these
ecosystems is our area of focused
interest.
"Seneca River in Jordan NY" by MTBradley - Own work.
Licensed under CC BY-SA 3.0 via Wikimedia Commons http://commons.wikimedia.org/wiki/File:Seneca_River_in_Jo
rdan_NY.jpg#mediaviewer/File:Seneca_River_in_Jordan_N
Y.jpg
Within ecosystems, system
dynamics with predator-prey
and competition interactions
have been well documented
(DeAngelis 1992).
The protist Tetrahymena hunts E. coli in this photo illustration, which features a
microscope image of Tetrahymena (left). Credit: University at Buffalo
Read more at: http://phys.org/news/2013-06-dangerous-strains-coli-lingerlonger.html#jCp
However, systems with omnivory have features differing from
systems previously studied (Diehl and Feissel 2000, Pimm and
Lawton 1978; Matsuda et al. 1986; Law and Blackford 1992;
Thingstad et al. 1996; Holt and Polis 1997). Statistical analyses of
empirical data and studies of analytical models have hinted that
other factors in a food web with omnivory and variation in
colonization rate (i.e., the rate at which new individuals enter a
community) among habitats cause the disaccord between trophic
cascade theory and data, but studies that directly test these
hypotheses are scant (Polis et al. 1997; Polis et al. 2000; Bruno &
O’Connor 2005; Borer et al. 2005; Fox 2007; Holt et al. 2010).
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METHODS
DISCUSSION
Spatially subdivided ecosystems were established in
laboratory microcosms. Five mainlands were each
connected to three proximate islands and three isolated
islands (pictured right).
The data for Blepharisma are highly non-linear, and will require
alternative analyses that account for these non-linearities. It is clear
that the relationship between productivity and Blepharisma densities in
high colonization rate bottles is hump-shaped. To formally analyze
such data, we need to employ likelihood based non-linear model fitting.
This will be a goal for future analyses.
It's clear from these preliminary results that productivity and
colonization rate have complex and non-intuitive consequences for
population dynamics in this system. Determining what these effects are
will require sophisticated time-series analyses of population dynamics.
These include likelihood-based model fitting and information-theoretic
techniques. However, the general result – that productivity and
colonization rate interact to influence population dynamics in food
webs – is one that is of great interest to the broader ecological
community, given the ubiquity of both of these processes in nature.
CITATIONS
Above is the omnivore Blepharisma sp. in its carnivorous morphological state.
Taken with a Leica microscope camera. 100x.
Mixed Bacteria: Taken with a Leica microscope camera at 400x.
The microcosm replicates were heat-sterilized 150mL Nalgene bottles, containing liquid medium and nutrients. The food webs consisted of mixed
bacteria resources, the bactivorous protist prey species, Chilomonas sp., and the generalist omnivore, Blepharisma americanum. There were two
dispersal treatments, high and low, crossed with three productivity treatments. Dispersal rates were manipulated by introducing new individuals into
replicates three times a week following published intervals (Holyoak 2000). Productivity differences were established by providing three levels of
bacteria resources (0.1 pellet/L, 1.0 pellet /L and 3.0 pellet/L). We ran the experiment for 40 days – roughly 50 protist generations and the normative
time for simple bottle communities to reach dynamical equilibrium. We collected fine-scale time series data of protist densities and cell volumes
(sampling every other day) from each bottle using published methods (Fox 2007). In brief, protists are counted under a microscope in aliquots from a
small (0.1mL) subsample taken from each homogenized bottle.
RESULTS
Fig. 1. Mean long-term species density as a function of colonization rate and
productivity. Results of linear mixed effects models (LME): Chilomonas densities
are higher in low colonization rate ponds than in high colonization rate ponds
(negative effect of colonization rate on Chilomonas density; F1,20 = 5.389, P =
0.0309). Productivity has no effect on Chilomonas densities when colonization rate
is low, but Chilomonas densities decrease with productivity when colonization rate
is high(colonization rate x productivity interaction; F2,20 = 2.852, P = 0.0413).
Fig. 2. Coefficient of variation (CV) of population dynamics as a function of
colonization rate and productivity. The coefficient of variation is a measure of
stability, with the idea being higher CVs are less stable because populations
are fluctuating more, and therefore are more variable. For Chilomonas: CVs
are higher when colonization rates are high (F1,20 = 7.42994, P = 0.0130). CV
increases with productivity when colonization rate is high, but we observe no
effect when colonization rate is low (colonization rate x productivity
interaction; F2,20 = 3.456, P = 0.0498). For Blepharisma: we observe a
marginally-significant effect of productivity on CV (F2,20 = 3.163, P = 0.064),
indicating CVs decrease with productivity across all colonization rate
treatments. We did not observe an effect of colonization rate on Blepharisma
CVs.
Borer, E.T. et al. (2005) What determines the strength of a trophic cascade? Ecology 86:
528-537.
Bruno, J., O'Connor, M. (2005) Cascading effects of predator diversity and omnivory in a
marine food web. Ecology Letters 8(10), 1048-1056.
DeAngelis, D. L. (1992). Dynamics of nutrient cycling and food webs. Chapman & Hall,
London.
Diehl, S., Feissel, M. (2000). Effects of Enrichment on Three-Level Food Chains with
Omnivory. The American Naturalist 155 (2): 200-218.
Fox, W. (2007). The dynamics of top‐ down and bottom‐ up effects in food webs of varying
prey diversity, composition, and productivity. Oikos 116(2), 189–200.
Gellner, G., McCann, K.S. (2012) Reconciling the omnivory-stability debate. The American
Naturalist 179 (1): 22-37.
Holt, R.D. et al. (2010). Theoretical perspectives on trophic cascades. In J. Terborgh and
J.A. Estes, eds. Trophic Cascades: Predators, Prey and the Changing Dynamics of
Nature. Island Press.
Holt, R. D., and Polis, G. A. (1997). A theoretical framework for intraguild predation.
American Naturalist 149: 745–764.
Holyoak, M. (2000). Habitat Subdivision Causes Changes in Food Web Structure.
Ecology Letters 3: 509 - 515.
Law, R., and Blackford, J. C. (1992). Self-assembling food webs: a global viewpoint of
coexistence of species in Lotka-Volterra communities. Ecology 73:567–578.
Levin, S.A. (1992). The problem of pattern and scale in ecology. Ecology 73 (6): 19431976.
Matsuda, H., Kawasaki, K., Shigesada, N., Teramoto, E., and Ricciardi, L. M. (1986).
Switching effect on the stability of the predator-prey system with three trophic levels.
Journal of Theoretical Biology 122:251–262.
Pimm, S. L., and Lawton, J. H. (1978). On feeding on more than one trophic level. Nature
(London) 275:542–544.
Polis, G.A., Sears, A., Huxel, G.R. (2000) When is a trophic cascade a trophic
cascade? Trends in Ecology & Evolution 15(11).
Polis, G. A., Holt, R.D., and Menge, B. A. (1997). Time, space, and life history: influences
on food webs. Food webs: integration of pattens and processes.
Thingstad, T. F., Havskum, H., Garde, K. and Riemann, B. (1996). On the strategy of
“eating your competitor”: a mathematical analysis of algal mixotrophy. Ecology 77: 2108–
2118.
ACKNOWLEDGEMENTS
The author thanks NSF and the UC Riverside CAMP program for
funding, as well as the University of California, Riverside HSI-STEM
program, as well as the entire Anderson Lab for their support.