Download Can we use food web theory to evaluate how robust communities

Can we use food web theory to evaluate how
robust communities are to species loss?
Dunne et al. (2002) Ecology Letters 5:558
Motivated by network theory that explores how complex networks
(eg. Power grid, WWW, neural networks) are influenced by node
loss. Used a set of 16 well characterized food webs differing in
richness and connectedness to explore how robust they are to
secondary extinctions following species elimination.
Are there ‘rivet-like thresholds’ where enough primary extinctions
result in community collapse? S= trophic species (share the same predators/prey)
Res = % of taxa identified; O = fraction of species that are omnivores
For each food web:
Simulate extinction by sequentially removing (trophic) species
according to various criteria (e.g., random, most connected spp,
least connected spp). “Stability”: number of potential secondary extinctions, which
would occur if a species lost all its prey items.
“Robustness”: How many species do you need to remove to
eliminate half of all species in the web (secondary extinctions)
Correlated robustness against food web metrics (S, connectedness,
omnivory) Food web robustness strongly influenced by connectance Robustness here
is proportion of
primary spp
removals
necessary to
generate 50%
extinction from
food web. Staniczenko et al. (2010) Structural dynamics and
robustness of foodwebs. Ecology Letters13:891-899 Follow up to Dunne et al. (2002)
Many examples where species change diet when competition for
prey items changes (examples?)
Asked: What are the consequences of incorporating predator-prey
“rewiring” for food web robustness?
Example of foodweb rewiring:
Remove species 4
Competition for
prey of species
4 reduced
Food web is rewired
as sp 6 consumes sp
1
Results
Fraction of sp removals
until no spp remains PIR = proportional
increase in robustness
Staniczenko et al. argue that cannot predict outcome of species
loss using a static food web
Compensatory effects have potentially strong impacts on food
web robustness.
Robustness then depends on overlap spp that can shift diet
when prey species is removed (rather than S or c) .
Dunne/Staniczenko work highlights limitation of two approaches
to exploring foodweb stability
Lotka-Volterra approach (May, Pimm, Lawton, Morin)
- per capita effects difficult to parameterize with emprical data
- limited in the trophic species richness that can handle
- cannot portray complex topology of food webs
Structural approach (Dunne and later papers)
- tractable to empirical data for large numbers of taxa
- lack information on population size, dynamics
- cannot quantify species links as energy flow or interaction
strength Future of food web research (Thompson et al. in press)
Utility of food web research is linking community structure to
ecosystem function. Need to:
Incorporate energy flux into food webs to evaluate ecosystem
function
Link individual traits to food web structure – for example size or
metabolic varition Incorporate temporal and spatial variation in food web structure
Predict impacts of biodiversity loss or invasion on ecosystem
function
Summary
Food web studies explore how trophic relationships influence the
stability of communities. When linked to explorations of energy
flow they can (could) provide a framework for examining top
down and bottom up effects in communities Early modeling efforts made unrealistic assumptions about the
distribution of interaction strengths in communities. The
distribution of strengths is now better understood but
application to food webs is limited.
Food web studies provide opportunities to predict how species
losses propagate through communities and influence community
stability and ecosystem services.
Priority effects and assembly rules
What are the consequences of phenological patterns for
interspecific interactions?
Harper (1961) planted two species of grass: Bromus rigidus and B.
madritensis either simultaneously or with B. rigidus sown 3 weeks
after B. madritensis
Grown together: B. rigidus accounted for 75 % of biomass
Sown later: B. rigidus accounted for 10 % of biomass
What might determine a priority effect like this??
Why is it important for understanding species coexistence?
Priority effects aren’t always tied to phenology
Schulman (1983) looked at recruitment of marine reef fish from the
larval stage on newly created artificial reefs
- Recruitment of fish was inhibited by prior occupation by two
species of beaugregory (territorial damselfish) and juvenile snapper
- New territories on the reef open at random. Species that have
more settling larvae available when the territory opens up will have
a higher probability of filling it.
Other examples of priority effects like this one?? Priority effects are an example of an ‘Assembly Rule’
What is an assembly rule?
Diamond: rules that govern how
communities are assembled!
Diamond’s work based on an
accumulation of observational
data on the distribution of bird
species on Bismark islands
around New Guinea
Jared Diamond (1975)
Interested to know if only
certain sets of spp drawn from a
regional species pool can
coexist at some local level
Bismarck Archipelago
Islands N and E of
Papua New Guinea
Diamond’s approach to examining coexistence
Influenced by MacArthur and his warblers…
Hypothesis: species fit together in a complementary way in
communities dictated by the strength of interspecific competition
Incidence functions: the probability that a particular species of
interest will occur in a particular community given some attribute
of the community
Attribute of the community that predicts occurrence = species
richness. Why species richness?
Probability of occurrence
Species called High-S require more specialized features of
communities that support a variety of other species. ‘Tramp’
species occur islands including those with low spp richness
“Tramp sp”
“High-S sp”
(dove)
(cuckoo)
“Tramp sp”
“Supertramp”
(flower pecker)
Species richness
(cuckoo-dove)
Diamond’s hypothesis for these patterns:
Species use/consume resources (e.g., food, nesting sites) out of the
total resource pool available on the island. Resource pool
determined mostly by island size?
Under what conditions would you predict that species can coexist?
4 spp guild with different resource requirements. Solid line
represents resource production. Dashed line resource use by each
species
Small island - only sp # 3 exists
Larger island spp 2 and 4 but
not 2,3,4 can coexist Larger island, sp # 1 could
invade island occupied by spp
2 and 4
Diamond codified the patterns he observed into a set of “Assembly rules”
1. Considering all combinations that could be found for a group of
related spp. only certain ones exist in nature
2. Those permissible combinations resist invaders that would
transform them to forbidden combinations
3. “Checkerboard rule” - some pairs of species never coexist either
by themselves or as part of a larger combination
How would you test if these assembly rules actually operate??
Example of Diamond’s rule that some spp pairs never coexist
Various tests of Diamond’s rules using null models
• Connor and Simberloff (1979) and other papers - looked
at whether fewer species combinations occurred in nature than
expected at random. Could NOT reject the null model
• Gotelli and McCabe (2002) - more complete analysis of
particular assembly rules - first test of the checkerboard assembly
rule
-Assembled data from 96 studies of species occurrences from
scales of 1-1010 m2 used Monte Carlo randomizations to examine
whether there are species co-occurrences that are less likely than
expected at random
-Found general SUPPORT for assembly rules
Assembly of Hawaiian spider communities (Gillespie 2004)
Hawaiian island archipelago – isolated,
topographically diverse, and range of
island ages (Hawaii <1 Mya – Kauai 5
Mya.
How have communities assembled on
these islands? Tetragnatha radiation of spider species on
Hawaii
- One clade of Tetragnatha = ‘spiny leg’
clade (16 spp) – hunting spiders that
abandoned web building Assembly of Hawaiian spider communities (Gillespie 2004)
A. Green ecomorph. Leaf dwelling, feed
on small insects
B. Maroon ecomorph. Mossdwelling, weakly flying insects
C. Small brown ecomorph. Twig
dwelling, feeds on small insects
D. Large brown. Slow moving, lives
on bark, feeds on caterpillars
Assembly of Hawaiian spider communities (Gillespie 2004)
Each community: 2-4 ecomorphs,
regardless of island/volcano age
Never find 2 spp of the same
ecomorph in the same community
Assembly highly non-random
Distribution of spider ecomorphs across Hawaiian islands
What processes could generate this pattern?
Molecular phylogeny of Tetragnatha
What pattern do you infer?
Assembly of Hawaiian spider communities (Gillespie 2004)
Ecomorphs originate within habitats by:
(i)  In situ evolution of one ecomorph into
another (e.g. green-maroon – Oahu)
(ii)  Dispersal without speciation (e.g.
quasimodo).
Assembly hypothesis: Young islands –
initial colonization followed by species
radiation, then additional colonization
increases species richness. Then
competition after species accumulate to
fine tune community composition??
Community phylogenetics – the rebirth of assembly rules
Last 6-8 years – push to combine phylogenetic analysis of species
relationships with community assembly and structure
3 perspectives on how communities assemble:
(1) Niche-assembly rules dictated by local environmental filters
and the principle of competitive exclusion (Tilman, Diamond)
(2) Neutral assembly (the null model approach) where species are
assumed to be ecologically equivalent (Hubbell, Simberloff)
(3) History-based assembly. Starting conditions and historical
patterns of speciation matter more than local processes
(Ricklefs)
Phylogenetic approach: identification of processes that
underlie community assembly
- Emphasis on competitive exclusion/limiting similarity led to convenient
assumption that evolutionary processes are not relevant on the time scale of
ecological processes.
Cavender-Bares et al. (2009) Ecol. Lett. 12:693-715
The paradox of phenotypic similarity
…species of the same genus have usually, though by no
means invariably, some similarity in habits and
constitution… Darwin (1859)
So…closely related species should also experience strong
competitive interactions due to their ecological similarity.
One the one hand environmental filtering will select for species
with similar traits in the same environment. On the other hand
ecological similarity may prevent closely related species from
sharing environments. Community phylogenetics explores the relative importance of
competitive exclusion and ecological character displacement in
community assembly.
What might community phylogenetic structure look like?
Scenario #1
Strong phylogenetic signal
in community assembly
Clustering is a consequence of
trait conservatism – closely
related species have similar
ecologies
Phenotypic clustering in turn
results from environmental
filtering
What might community phylogenetic structure look like?
Scenario #1
Strong phylogenetic signal
in community assembly
Clustering is a consequence of
trait conservatism – closely
related species have similar
ecologies
Phenotypic clustering in turn
results from environmental
filtering
What might community phylogenetic structure look like?
Communities
composed of species
from different
branches of
phylogeny. Why?
Species on
different branches
converge on
similar traits
Environmental
filtering controls
what traits can
occur in a niche/
community
What might community phylogenetic structure look like?
Communities
composed of species
from different
branches of
phylogeny. Why?
Species on
different branches
converge on
similar traits
Environmental
filtering controls
what traits can
occur in a niche/
community
Explaining phylogenetic structure (Webb 2002)
If environmental filtering dominates, co-occurring species sharing the
same abiotic environment should have more similar traits
(phenotypically more similar) than expected (trait clustering)
Explaining phylogenetic structure (Webb 2002)
If competitive interactions dominate, co-occurring species sharing the
same abiotic environment should be phenotypically less similar than
expected (trait overdispersion)
Explaining phylogenetic structure (Webb 2002)
Which ecological process (filtering, competition) is important in
determining phylogenetic structure also depends on pattern of trait
evolution.
For phylogenetic overdispersion: competitive interactions must cause
overdispersion of conserved traits, or environmental filtering must
cause clustering of convergent traits.
Cavender-Bares et al. (2004) Phylogenetic
overdispersion of oak communities
17 oak species occur in North Central Florida in
sites that range in moisture availability.
Environmental filtering – oaks that live in similar
environments should show similar phenotypic
traits.
But… species that are too similar are unlikely to
co-occur because of competitive exclusion
Explored correlations between phylogenetic relatedness of oaks,
degree of co-occurrence, and similarity in physiological traits.
Floridian oak phylogeny and a mapped on trait (soil
moisture preference)
Any evidence for phylogenetic clustering?? Found:
Significant negative correlation between species differences in soil
moisture preference and phylogenetic distance… so, distantly
related species…
converge on the same habitat conditions.
Despite phylogenetic overdispersion there is evidence for
environmental filtering in this study:
Bark thickness, radial growth, rhizome resprouting potential,
seedling growth rate – all show phenotypic clustering, indicating
that co-occurring species across a soil moisture gradient were
phenotypically similar. Conclusions:
Assembly rules idea attractive to ecologists but languished until
recent development of community phylogenetics
- Very difficult to test for assembly rules based on spp presenceabsence patterns (too many explanatory variables that need to be
ruled out)
- Phylogenetic approach provides additional insight into
mechanisms leading to co-occurrence (dispersal, radiation) and
can detect potential effects of competition – a cornerstone of
traditional assembly theory, and allows us to incorporate
evolutionary processes occurring at larger spatial and temporal
scales.