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Complex interactions and population control in nature: Lecture Content We’ll talk about three issues relevant to population control in nature, issues that expand our understanding of the complexity of ecological systems beyond what we’ve discussed so far… What factor(s) have the greatest impact controlling populations? What are “metapopulations”, and how does a population’s spatial distribution influence its stability and control? What is the relative impact of “top down” versus “bottomup” (trophic-dynamic) control of populations? What factor(s) control or influence population size in nature? Suppose you were studying the population of Nephila spiders in LaFitte National Historical Park Method: circular quadrats, as in lab (control for habitat, season) Density per quadrat dropped from 5 in 2001 to 0.5 in 2002 What factors would you need to investigate as possible explanations for the population stability or dynamics in this species? Population interactions Spatial impacts Weather How would you distinguish the most important factor(s)? Key-factor analysis Define key-factor analysis = method to identify which mortality factor has greatest impact shifting population away from equilibrium (i.e., limiting population) Identifies relative strengths of all mortality factors (ki) k-values = “killing power”, defined as log(Nt) -log(Nt+1), where Nt = population size at time t, before mortality factor i acts; Nt+1 = population at time t+1, after it acts Ki’s are mortality factor (like qx); their value is that they are additive, allow partition of mortality into components Method of analysis k-values measured for a number of years; graphed Key factor is the k-value that most closely mirrors overall mortality, K Regression of k ’s against total annual mortality, K Example of key-factor analysis Data from classic study by Varley, Gradwell, and colleagues on oak winter moth Methods A number of cohorts followed in field over 13 years Number of insects lost to variety of mortality factors quantified using several methods (see text) k-values calculated as described Results Loss of larvae over winter (k1) was largest k-factor, mirroring K; and also in regressions showed greatest (statistically significant) slope when plotted against K See example calculations of k-factors for one year (next slide) Oak winter moth data for one year (from Varley, Gradwell, & colleagues; see Stiling Table 13.1) Life-history s tage Adult females, 1955 Eggs (=females*150) Larvae (after winter loss) Killed by Cyzenis Killed by other parasites Killed by microsporidians Pupae Killed by predators Killed by Cratichneumon Adulte fem ales , 1956 Number alive/m^2 4.39 658 96.4 90.2 87.6 883 83 28.4 15 7.5 Log(no. alive) 2.82 1.98 1.95 1.94 1.92 1.92 1.45 1.18 k-value 0.84 = k1 0.03 = k2 0.01 = k3 0.02 = k4 0.47 = k5 0.27 = k6 K =1.64 Density-dependence of mortality factors (k-values) is next step in analysis, after identification of key factor(s) Density dependence identifies those mortality factors that could regulate population, i.e., return population size to some constant value (carrying capacity) In oak winter moth example, only two factors were regulatory (see overhead, Fig. 13.6, Stiling text) Pupal predation (k5) was positively density-dependent (mortality increases with density, log(N), i.e. regulatory Other larval parasites (k3) was inversely densitydependent, and thus potentially destabilizing to population growth(less mortality with increasing population is a positive feedback on population size!) Surveys of different organisms in nature reveals few generalizations Key factors in diverse populations? Some examples Sand dune annual plant--seed mortality in soil Colorado potato beetle--adult emigration Tawny owl--reduction in egg clutch size from max. size Gradd-mirid insect--no obvious key factor identified Few generalizations have emerged overall Density-dependence? (see Fig. 13.7,Stiling) Insects disproportionately regulated by parasites, diseases, predators Small birds and mammals by limited space, crowding Large mammals by mortality associated with limited food Spatial distribution of populations is another, new aspect of complexity that can stabilize or de-stabilize abundance Metapopulation Defined: group of isolated, interconnected populations Each sub-population may or may not act as an independent population Potential to stabilize total population (colonization rate > extinction rate) Classification of metapopulations (diagram, next slide) Classic metapopulation (rare; Richard Levins, 1969) Core-satellite metapopulation (common) Patchy population (common) Nonequilibrium metapopulation (rare) Source-sink populations (common; Ron Pulliam et al.) Classification of metapopulations Core-satellite metapopulation Classic metapopulation l>1 l<1 l<1 l<1 l>1 Source-sink populations Patchy population Nonequilibrium metapopulation Classic metapopulation can stabilize population Mechanism of stability? Dp/dt = mp(1-p) - xp, where p = number of occupied patches, m = rate of movement between patches, x = extinction rate of occupied patches At equilibrium, Dp/dt = 0 ==> p = 1- (x/m) Equilibrium reached if x < m Examples? Few examples of classic metapopulation have been described Best known is population of Bay ckeckerspot butterfly (Euphydryas editha) studied on Jasper Ridge,California, and other areas Its food plant found on serpentine soils (hi in Mg, Fe) Both extinction & re-colonization documented Spotted owl population--example of classic metapopulation? This is an endangered species,occupying patches of oldgrowth coniferous forest in W. U.S.; little is known about patch-patch dispersal Example of subdivided population--Everglades kite Trophic structure adds a third set of complex interactions among species Trophic structure of ecosystems is concept formalized by Lindeman: trophic-dynamics Flow of nutrients, energy in food chain from ultimate source (sun, or center of Earth)-->plants (1º producers) -->herbivores (1º consumers)-->2º consumers-->3º consumers-->detritivores Such trophic structure adds complexity:multiple controls “Bottom-up” control of populations is hypothesis (put forth by Lindeman) that all populations are controlled by entities lower in food chain E.g., plants controlled by energy in sunlight (& nutrients, water, of course) Top predators ultimately controlled by total energy in food chain Trophodynamics, continued “Top-down” control involves higher entities in food chain (or web) as control agents--e.g., predators, herbivores Hairston, Smith, & Slobodkin (HSS) ideas, already discussed, involve top-down control Marquis & Whelan study of birds eating caterpillars on white oak saplings (again, already discussed) Another example: Kelp-->sea urchins-->sea otters Pelagic marine food webs, often four trophic levels, also illustrate top-down forces: top predators (piscivores) reduce abundance of zooplankton feeders, which releases pressure on zooplankton, which become abundant enough to crop phytoplankton (oceans not “green”, unlike terrestrial ecosystems--see next slide) Diagramatic illustration of bottomup & top-down trophic control Top-down control allows for possibility of indirect trophic effects Carnivore - + Herbivore + - Plant Indirect effect of + carnivore on plant via control of herbivore Bottom-up control of zooplankton abundance: more algae-->more zooplankton feeding on the algae (from Ricklefs 2001) 0.1 1 10 100 Chlorophyll (mg per L) 1000 Top-down control of zooplankton: addition of fish (predators) leads to decrease zooplankton, increase algae Synthesis of bottom-up & top-down forces? One scheme is Ecosystem Exploitation Hypothesis (EEH)--Oksanen, Fretwell, and others Biomass at a given trophic level depends on how many trophic levels present (both bottom-up & top-down forces--see Fig. 13.12 Stiling text) The number of trophic levels present depends on total ecosystem (environmental) productivity Foregoing slides illustrate importance of both bottom-up & top-down forces in freshwater zooplankton-fish communities, supporting these ideas Algae in aquatic & marine food chains tend to be relatively edible (no woody support tissue, as in trees!), allowing more biomass, and longer food chains Conclusions: Multiple factors control populations of most, if not all organisms, necessitating methods (like key-factor analysis) to assess relative strengths of control Key factors identify factors that perturb populations, density-dependence identifies those that regulate Metapopulations add spatial-temporal complexity to population dynamics, and come in a variety of flavors, some of which can help stabilize population (e.g., satellite-core, source-sink, classic metapopulation) Trophic-dynamics adds complexity in terms of multiple possible controls (bottom-up, top-down), and indirect interactions (e.g., predators help plants by controlling herbivores) Acknowledgements: Some illustrations for this lecture from R.E. Ricklefs. 2001. The Economy of Nature, 5th Edition. W.H. Freeman and Company, New York.