Transcript Ground Rules, exams, etc. (no “make up” exams) Text: read

Third Exam Thursday 5 May 2016
Chapters 11-15, 17-18 plus 8 readings
Energy
Money
Land
Food
Water
Sewage
Solutions
Space Travel
26th Lecture
21 April 2016
Final Exam -- 13 May 2016, 9-12 am
Janzen’s Seedling Ring Hypothesis
Tamiasciurus squirrel seed predation
Community and Ecosystem Ecology
Macrodescriptors = Aggregate Variables
Compartment models, trophic structure, food webs,
connectance, rates of energy fixation and flow,
biogeochemical cycles, ecological energetics,
ecological efficiency, trophic continuum, guild structure,
ecological pyramids, successional stages, transition
atrix,
species diversity, stability, relative importance curves.
All 10 Sites: Total Number of lizards: 20,990
Total numbers of lizards of 67 species
collected on 10 desert study sites from
1966-2008 plotted against their ranks in
relative abundance. The 12 most common
species (blue) are named, along with 6 of
the 55 less common (green, 17 species) to
rare species (red 38 species).
Samples exceed 30 for 48 of the 67 species.
Discriminant function analysis showing clear separation of rare species
based on 9 ecological variables including body size, number of sites,
fecundity, niche breadths and overlaps for diet, microhabitat, and habitat.
H 1. Body size-trophic level hypothesis.
Larger species are uncommon either because they are top predators
(monitor lizards) or for other reasons.
H 2. Fecundity hypothesis.
Some species could be uncommon due to their low fecundity.
H 3. Geographic range hypothesis.
Rare species could have narrow geographic ranges, occurring at
only a few sites (Rabinowitz et al. 1986).
H 4. The niche breadth hypothesis.
Rare species are uncommon because they are specialized with
narrow niche requirements. Resources such as habitats, microhabitats,
or foods might be scarce or limited. These alternatives can be tested
with data on niche breadths. However, some generalists are rare
and some abundant species are specialists.
H 5. Diffuse competition hypothesis.
Rare species could be uncommon due to diffuse competition from
many other, more abundant, species (MacArthur 1972b).
H 6. Physical tolerance hypothesis.
Rare species might have narrow tolerances to physical environments.
H 7. Sink versus source hypothesis.
Rare species might be uncommon only locally in 'sink' populations,
but might be more abundant in nearby 'source' areas.
H 8. Dispersal hypothesis.
Rare species could be rare because they do not have dispersal powers
necessary to find and invade suitable habitats. Are rare species merely
accidentals, dispersing from one habitat to another?
H 9. Predator hypothesis.
Predators could hold population densities of uncommon species at
low levels.
Some related questions that can be asked about rare species include:
How can rare species find mates and continue to exist?
Is rarity an illusion due to cryptic behavior making putative rare
species difficult to find?
Are rare species vital to community function?" Do rare species
persist in more stable communities in spite of their rareness, or does
the presence of rare species enhance the stability of ecosystems?
Latitudinal Gradients in Species Richness
From: Schall and Pianka
1978 Science 201: 679-686.
Robert H. MacArthur
Geographical Ecology
1. Degree of Saturation
2. Range of Available Resources
3. Average Niche Breadth
4. Average Niche Overlap
Species Diversity = “Biodiversity”
Regional <—> Local <—> Point diversity
Saturation with species
Four ways in which diversity can differ
1. Range of available resources
2. Degree of saturation
3. Niche breadth
4. Degree of niche overlap
100
50
Cited by 1357
96
97 98
99 00 01
02 03
04 05
06
07 08 09
10
1
I was a mere graduate student, wet behind the ears, only 25
years old, when I wrote it. I don’t usually re-read my own
papers – but now, 5 decades later, I am pleased to find it
cerebral and fairly well written.
Productivity Hypothesis
Intermediate Disturbance Hypothesis
Latitudinal gradients in species diversity
Tropical tree species diversity
Seeding rings
Nutrient mosaic
Circular networks
Disturbance (epiphyte loads)
Sea otters as keystone species, alternative stable states
Types of stability
Constancy = variability
Inertia = resistance
Elasticity = resilience (Lyapunov stability)
Amplitude (domain of attraction)
Cyclic stability (neutral stability, limit cycles, strange attractors)
Trajectory stability (succession)
Traditional ecological wisdom: diversity begats stability
Tree Species Diversity in Tropical Rain Forests
Seed Predation Hypothesis
Nutrient Mosaic Hypothesis
Circular Networks Hypothesis
Disturbance Hypothesis
(Epiphyte Load Hypothesis)
Sea Otter (Enhydra lutris)
Amchitka
Sea Otters
Kelp
Sea Urchins
Chitons
Barnacles
Mussels
Greenling
Harbor Seals
Bald Eagles
20-30 km2
dense mats
8/m2, 2-34mm
1/m2
5/m2
4/m2
abundant
8/km
abundant
Shemya
only vagrants
heavily grazed
78/m2, 2-86mm
38/m2
1215/m2
722/m2
scarce or absent
l.5-2/km
scarce or absent
Community Stability
Traditional Ecological Wisdom
Diversity begats stability (Charles Elton)
More complex ecosystems with more
species have more checks and balances
Alternative stable states
http://www.nature.com/scitable/knowledge/library/alternative-stable-states-78274277
Types of Stability
Point Attractors <——> Repellers
Domains of Attraction, Multiple Stable States
Local Stability <——> Global Stability
Types of Stability
1. Persistence
2. Constancy = variability
3. Resistance = inertia
4. Resilience = elasticity (rate of return, Lyapunov stability)
5. Amplitude stability (Domain of attraction)
6. Cyclic stability, neutral stability, limit cycles, strange attractors
7. Trajectory stability
= Variability
= Resilience
= Resistance
(Domain of attraction)
Limit Cycle
Trajectory Stability
dx/dt = a(y - x)
dy/dt = bx - y - xz
dz/dt = yz – cz
Edward Lorenz
Strange
Attractor
“Butterfly Effect”
Traditional Ecological Wisdom:
Diversity begats Stability
MacArthur’s idea
Stability of an ecosystem should increase
with both the number of different trophic
links between species and with the
equitability of energy flow up various food
chains
Robert MacArthur
Robert May challenged
conventional ecological
thinking and asserted that
complex ecological systems
were likely to be less stable
than simpler systems
May analyzed sets of randomly assembled Model
Ecosystems. Jacobian matrices were
Assembled as follows: diagonal elements were defined
as – 1. All other interaction terms were equally likely to
be + or – (chosen from a uniform random distribution
ranging from +1 to –1). Thus 25% of interactions were
mutualisms, 25% were direct interspecific competitors
and 50% were prey-predator or parasite-host
interactions. Not known for any real ecological system!
May varied three aspects of community complexity:
1.Number of species
(dimensionality of the Jacobian matrix)
2. Average absolute magnitude of elements
(interaction strength)
3.Proportion of elements that were non-zero
(connectedness)
May’s challenge using random model systems
Real systems not constructed randomly
Real communities are far from random in construction,
but must obey various constraints.
Can be no more than 5-7 trophic levels, food chain loops
are disallowed, must be at least one producer in every
ecosystem, etc.
Astronomically large numbers of random systems : for
only 40 species, there are 10764 possible networks
of which only about 10500 are biologically reasonable —
realistic systems are so sparse that random sampling is
unlikely to find them. For just a 20 species network, if
one million hypothetical networks were generated on a
computer every second for ten years, among the
resulting 31.513 random systems produced, there is a
95% expectation of never encountering even one
realistic ecological system!