Energy Money Land Food Water Sewage Solutions Space Travel Third Exam Thursday 3 December 2015 Chapters 11-15, plus 8 readings Final Exam 9 December 2-5 PM 26 th Lecture 24 November 2015 Janzens 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 matrix, species diversity, stability, relative importance curves. Bottom Line: Balance of nature myth. Antagonisms All 10 Sites: Total Number of lizards: 20,990 Total numbers of lizards of 67 species collected on 10 desert study sites from 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: 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 Cited by 1357 I was a mere graduate student, wet behind the ears, only 25 years old, when I wrote it. I dont usually re-read my own papers but now, 5 decades later, I am pleased to find it cerebral and fairly well written. also range of available resources also range of available resources 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 Seed Predation Hypothesis Nutrient Mosaic Hypothesis Circular Networks Hypothesis Disturbance Hypothesis (Epiphyte Load Hypothesis) Tree Species Diversity in Tropical Rain Forests Sea Otter (Enhydra lutris) AmchitkaShemya Sea Otters km 2 only vagrants Kelp dense matsheavily grazed Sea Urchins 8/m 2, 2-34mm78/m 2, 2-86mm Chitons 1/m 2 38/m 2 Barnacles 5/m /m 2 Mussels 4/m 2 722/m 2 Greenling abundantscarce or absent Harbor Seals 8/kml.5-2/km Bald Eagles abundantscarce 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 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= Resistance = Resilience(Domain of attraction) Limit Cycle Trajectory Stability Edward Lorenz Strange Attractor Butterfly Effect dx/dt = a(y - x) dy/dt = bx - y - xz dz/dt = yz cz Traditional Ecological Wisdom: Diversity begats Stability MacArthurs 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) Mays 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 possible networks of which only about 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 random systems produced, there is a 95% expectation of never encountering even one realistic ecological system!