To: All Msg #261, 930910 19:57:24 Subject: Dynamics of Animal Populations 930831 The text

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From: Wesley R. Elsberry To: All Msg #261, 93-09-10 19:57:24 Subject: Dynamics of Animal Populations 930831 The text that follows is my set of notes for the WFSC 624 course here at TAMU, "Dynamics of Animal Populations". The next few messages are further lecture notes. Anybody who doesn't like this stuff can holler via netmail to me here at 1:117/385. ========================================================================= WFSC 624 Dynamics of animal populations, Dr. Kirk O. Winemiller 930829 Labs count for 20% of the grade Last year, about 1/2 the class thought there was too much quantitative material in course, other 1/2 thought too little. This used to be taught as a modeling course (Folse, maybe?). Now, it is a broad survey of the field, concepts, natural history. Office hours: for grad course, not really necessary, just drop by. Text: last year, Pianka's Evolutionary Ecology, good for concepts. But includes geological ecology, etc., that we don't need. This year, Bingham and Mortimer's Population Ecology. Note the dates in the syllabus. Have to circumvent the enrollment cap... Rest of this first period... review of terms and definitions, background material. Dynamics -- change over time. A pattern or history of growth, change, or development of an entity. Field of inquiry which deals with motion and equilibrium of systems under the action of forces usually from outside the system. Animal -- heterotrophs. Population -- deme: conspecifics which interbreed in a locality. A group of conspecifics in a prescribed area. deme is the more specific term. evolution -- changes in allele frequencies in a population over time. Natural selection -- differential reproductive success. Phenotypic traits must have a genetic basis. directional selection -- selection favors one extreme phenotype. stabilizing selection -- selection favors the mean phenotype and against the extreme phenotypes. disruptive selection -- favors extremes of phenotypes simultaneously. Proposed as a mechanism for polymorphisms. Other hypotheses for polymorphisms: shifting directional selection heterosis -- heterozygous genotype has the advantage, e.g. sickle cell anemia. frequency depedent selection -- rare phenotype or allele is at a selective advantage. selective neutrality -- ??? End of polymorphism hypotheses Genetic drift -- another mechanism for evolution. Random changes in allele frequencies. Example: imagine hurricane wipes out half the class, instant change in allele frequency. gene flow -- interaction between populations, breeding members. meiotic drive -- segregation distortion during gametogenesis. Violates the independent assortment principle of Mendel. This can skew the gene frequencies. mutation pressure -- force of mutation that is always out there. 1/10^5 or thereabouts. Adaptation -- trait that enhances fitness, usually arrives via natural selection. fitness -- relative representation of an individual's offspring in the population. Group selection -- naive: "for the good of the species". deals with a meta-population view of the world. Modeling cooperative behavior. Problem with most group selection models is the existence of "cheaters". kin selection -- idea of inclusive fitness : instead of looking at fitness of individual, look at the fitness of a gene. Unit of selection then becomes a group of closely related individuals. Parent-child relation is 1/2. Sibling-sibling relation is 1/4. Cousins 1/8, aunt-nephew 1/8. End 930829 --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #262, 93-09-10 20:00:58 Subject: Dynamics of Animal Populations 930901 WFSC 624 Dynamics of Animal Populations 930901 (got in late... having a car in for repair does that to ya...) Law of parsimony Occam's Razor Assumptions Verhulst-Pearl logistic growth model -- assumes stable age distribution Model testing: Inductive method: induction -- if a prediction is true 9/10s of the time, it isprobably pretty good. Deductive method: deduction -- design an experiment or critical test to falsify the prediction of the model. We can't toss induction, since not all systems are amenable to the deductive method. Resource management field -- Carl Walters at U. Wash. "adaptive management". When you have to make a decision about an action, modify current action to have a component of the new action, so that you do two things, and see how it works out. Combination of both basic and applied research. Scale in modeling: critical observations, evaluation Hierarchy in modeling: Ecosystem Population Organisms Tissues, cells Genes Molecules Model selects which level one is looking at. Other biological hierarchies: phylogenetic evolutionary hierarchy Don't want more levels than required, or fewer than necessary. One of the problems in modeling in ecology is that the systems are complex, with lots of relationships and interactions, and most components are highly variable, and individuals that comprise a population are all different. The downside is that it is difficult to know that you got all the right factors. Phenotypic variation 1) ecophenotypic (environmental bias) 2) adaptive variation (genetic basis) [3) Random variation -- not really expected] End 930901 --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #263, 93-09-10 20:02:22 Subject: Dynamics of Animal Populations 930903 930903 Dynamics of Animal Populations 1840's Leibig's law of the minimum: resource in shortest supply has the most effect on characteristics of population ecology. Pianka states that the resources most frequently indicated as limiting are various nutrients, water, and temperature. Bernie Patten - complex networks: emphasis that indirect interactions between species have considerable effect. A predates B predates C, indirect interaction A -> C. Alan Berryman - perhaps Leibig's law has some validity. Empirical research does show that one thing usually does control a population. Shelford's law of tolerance - too much or too little of any environmental factor is detrimental to an organism. Implies a range of optimal levels for an organism to survice and perform. Example: jumping frogs and temperature. There will be some sort of distribution (usually normal) of performance that shows limits at both extremes. Principle of allocation - To increase performance results in a narrowing of tolerance limits, in terms of ecological strategies. Apparently impossible to build a superorganism, one that does everything well. MacArthur and "species packing". When lots of species are around, specialization is expected. Distribution of individuals and populations in space. Hope to finish this today... Environmental heterogeneity (patchiness): Virtually all habitats have this feature, and we can distinguish different scales (fine-grained vs. coarse-grained) fine-grained (FG): patchiness is for small patches that are fairly regular. Coarse-grained (CG): big patches relative to organism, so organism spends more time in any one patch. Whether we call a habitat FG or CG depends on patch size, organism size, patch distribution, patch density, mobility of patch or organism. Mobility: a continuum that encompasses sessile, sedentary territorial: defended space home-ranging: not defended, fuzzy free-ranging: organisms that roam broad landscapes Life history plays a big role in territoriality Population consequences of level of mobility Viscous populations v. fluid populations viscous pop. are comprised of relatively sedentary organisms fluid pop. are comprised mainly of free-ranging organisms gene flow tends to be greater in more fluid populations. tuna on the high seas, wildebeests, etc. gene flow implies less genetic variability associated with space, since organisms are so mobile, so you don't find much localized genetic variability. Weaker meta-population structure (sub-groups on a two or 3 dimensional landscape) in more fluid populations, stronger in more viscous populations. In populations that are relatively viscous, distribution often tells about how the individual organisms view resources. Even, uniform, hyper-dispersed distributions show regularity in distribution of individuals. Examples: Ted Case UCSD on ants in Australia, competition for limited resources. Aggregated distributions of individuals is often evidence that Allee effect is in operation: the fitness of individuals is enhanced when individuals are tightly packed. Reproductive swarms. Clonal reproduction: ??? Dan Janzen: recruitment rings in tropical forests. Parental tree has seedling recruits in a ring around the parent. The probability of a seed settling on the ground will have a negative binomial function of distance from the parent. Janzen also noted that the major source of mortality of seeds is predation. The prob. of a seed escaping predation is inversely related to distance. Combine these probabilities, and you find a ring of higher probability of survival. End 930903 --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #264, 93-09-10 20:03:30 Subject: Dynamics of Animal Populations 930906 Dynamics of Animal Populations 930906 Concepts from last lecture: Leibig's law of the minimum Shelford's law of tolerance Principle of allocation Distribution in space Fine-grained v. coarse-grained Sedentary <-> free-ranging viscous v. fluid pop.s uniform (even) v. clumped (aggregated) Tree recruitment rings Ants in Australia as hyperdispersed: even dist. of resources can be the determining factor, but other causes can possibly be in effect. Start on demography today Indices of dispersion: how to describe the distribution of organisms. Lots of these approaches rely on sample quadrat: a square area for sampling. The scale of the qradrat is critical. Count organisms in these qradrats, then test the distribution in the quadrats against a random expectation. (Read _ as an indication of subscript, ^ as exponentiation, strings as single symbols, string+bar is "string" with a superscripted bar) P_x = e^(-mu) (mu^x / x!) P = prob. x individuals in a qradrat x = integer count of individuals mu = mean of distribution Random expectation is the Poisson distribution. For Poisson, tails off to one side (skewed). The variance is equal to the mean. sigma^2 = xbar. I = sigma^2 / xbar. If I = 1.0, then the Poisson distribution is supported. If I > 1, then organisms are clumped. If I < 1, then organisms are evenly distributed in space. Chi square test of goodness of fit is applied. chi^2 = I(n-1), d.f. = n-1 (where n is number of qradrats). Standard chi square test: chi^2 = sum [(observed - expected)^2 / expected] Another index: nearest neighbor distance. Hopkin's index of dispersion. H = sum (x_i^2) / sum (r_i^2). x_i is distance from random point to nearest organism. r_i is distance from random organism to its nearest neighbor. I_h = h / (1 + h). If I_h = 1.0, then clumped. If I_h = 0, uniform (even). If I_h ~= 0.5, then random. Demography: meat & potatoes of this course. Study of demes: a population which is a genetic unit, shares a gene pool. Starting point: a life table: a record of birthdays and dates of death of individuals in a population. Two approaches: longitudinal sample (dynamic sample or cohort analysis): follow a cohort (a group of individuals all born within a time interval) until all are dead, recording dates of death. Horizontal sample (static sampling or segment): taking a snapshot in time of a population, figure out how old each individual in the population is. Assumes that the various rates are not changing within age classes. Life track graph, age v. time Cohort sample follows diagonal of life tracks, segment sampling is a vertical slice through the graph. Segment sampling requires a good method of aging individuals. Cohort sampling takes longer, but is unequivocal. Plotting deaths(y) against age(x). Frequency histogram. Infant mortality, old age mortality. The frequency histogram is not good for comparison to other cohorts or populations. Age specific death rate (q_x) provides a relative measure. Plot q_x v. age. q_x = d_x/ a_(x-1). Also termed the "force of mortality". a_x = number of individuals in an age class. d_x = number of deaths of individuals in an age class. Age specific survivorship (l_x). The proportion of the original cohort which survives through age class x. l_x = a_x / a_0 Monotonically decreasing function. Fraction of newborns that survive through age class x. All these can be computed with calculus, which leads to "survivorship curves". Type 1 curve: mammals, birds, humans: high survivorship early on. Type 2 has relative level force of mortality across age classes. Type 3 has extremely high infant mortality, but good survivorship in older age classes. Trees, inverts, and a lot of others have type 3 curves. Let's construct a life table. Five age classes. Number of individuals a_x. x a_x l_x q_x E_x ---------------------------- 0 100 1.0 0 3.3 1 90 0.9 0.1 2.55 2 70 0.7 0.22 2.0 3 50 0.5 0.29 1.4 4 20 0.2 0.6 1.0 5 0 0 1.0 0 E_x : expectation of future life. E_x = sum (l_y) / l_x for all values of y from x to omega (omega is last age interval). Know for exam***. This is the kind of measure life insurance companies use to determine premiums. x is a time interval. It is also an extremely handy concept for evolutionary biology modeling. Other measures discussed in the text are less often used. "Killing power" : K_x = log a_x - log a_(x+1) = log (a_x/a_(x+1)). P_x : P_x = (1 - q_x) is the probability of survivorship in an age class. End 930906 Next time: fecundity and life tables. --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #265, 93-09-10 20:06:10 Subject: Dynamics of Animal Populations 930908 Dynamics of Animal Populations 930908 Concepts from last lecture: Indices of Dispersion Qradrats, Nearest-neighbor Demography Life table Cohort (longitudinal sample) Static (horizontal sample) a_x, d_x, q_x, l_x, E_x, K_x, P_x =========================================== N is number of individuals in a population. N_(t+1) = N_t - (N_t (1-P_x)) = N_t - N_t(q_x) a_x = a_x - (a_x (1-P_x)) Look at table 1.2 in text when it comes in. Red deer in England. B_x : age-specific fecundity (also known as m_x), average number of offspring produced by a female of age x. Raw data v. smooth data: in horizontal study, some of the assumptions may not be met, or sampling error may be introduced. Smoothing is integration over a range of age classes to correct for some of this. Fecundity curves: m_x (y) v. x (x) Age of first reproduction is alpha, age of last reproduction is omega. For semelparous (one-time reproducer) organism alpha = TAU where TAU is mean generation time For iteroparous organisms, TAU ~= (alpha + omega) / 2 TAU = sum (x l_x m_x), for x from alpha to omega For fecundity curves, most of the time you only consider females, due to difficulty in determining paternity. Females limit the reproductive capacity of the population. Net Reproductive rate: R_0, average number of successful female offspring produced by average newborn female over its entire lifetime. R_0 tends to be near one, regardless of organism. R_0 = sum (l_x m_x), for x from 0 to infinity R_0 is also called the replacement rate of a population. It is an index of how individuals are replacing themselves in the population. R_0 = 1 indicates a stable population. R_0 > 1 indicates a growing population. R_0 < 1 indicates a declining population. If R_0 is near one all the time, then an inverse relationship must hold between l_x and m_x. This has implications for life history strategies. Reproductive value (due to R.A. Fisher) V_x, the age specific expectation of future offspring. The measure of the extent to which members of a specific age class contribute to the next generation. V_x = sum ((l_t/l_x) m_t), for t from x to omega for a newborn individual in a stable population, V_0 = R_0 = 1.0. V_x for a post-reproductive female equals 0. V_x starts at one, peaks at alpha, then tails off. Curve due to mortality before alpha. In a changing population, what does that do to the curve? If a pop. is growing the value V_x will be relatively lower for younger individuals. Opposite for a declining pop. V_x can be termed present value of future offspring. V_x can be partitioned into two components: V_x = m_x + sum ((l_t/l_(x+1)) m_(x+1)), for t = x+1 to omega Basically splits the terms into "now" and "future". If one can record deaths, births for a poulation, you can calculate alpha, omega, mean generation time, net reproductive rate, reproductive value, E_x. If l_x and m_x schedules don't change, then the population will achieve a "stable age distribution". This means that you have constant percentages of individuals in age classes. This is a "stationary" age distribution. Demography birth rate (b) = N births per X individuals per unit time death rate (d) = N deaths per X individuals per unit time If the total number of births exceeds the total number of deaths over some time interval, the population will increase. If total births < total deaths, then population will decline. In a closed population, the difference is the intrinsic rate of population change or intrinsic rate of population increase, r. (Malthusian parameter) = b - d = r For open population r = b + i - (d + e) where i is immigration rate and e is emigration rate. delta N / delta t = bN - dN (discrete equ.) dn/dt = rN (differential equ.) This is the basic model of exponential population growth, growth without limits, "J" growth curve. One model is E. coli. N ~= 2^t, t = 20 minutes. Start with 1 cell, after 36 hours, N = 2^108. End 930908 --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #266, 93-09-10 20:07:20 Subject: Dynamics of Animal Populations 930910 Dynamics of Animal Population 930910 Concepts from last lecture: Age-specific fecundity Mean generation time TAU = sum (x l_x m_x) / R_0 ====================================== N_t = N_0 e^(rt) Euler equation (Lotka's) sum ( e^(-rx) l_x m_x) = 1 Estimate of R_0 with basic life table data: r ~= (log R_0) / TAU Note inverse relation between mean generation time and r. This holds pretty well everywhere. r_max = per capita rate of increase for a population under conditions optimal for growth taxon r_max TAU(days) --------------------------------- E. coli 60 0.014 Tribolium 0.12 80 Rattus 0.015 150 Homo 0.0003 7000 Any population has inherent capacity for exponential pop. growth. delta N / delta t = constant constant really is r_max Say we measure two points (N v. t), the slope is an estimate of r If we track the trajectory of a population, we get the "J" curve In exponential growth, delta N / delta t = rN dN/dt = rN (now r is changing instantaneous rate of change per capita) This changing value of r is r_actual (r_act, r_a are synonyms) lambda = finite rate of increase lambda = e^r N_(t+1) = lambda N_t 1 = sum (lambda^-x l_x m_x) Intraspecific competition 1. Ultimate effect is to decrease the contribution of individuals to the next generation (decreasing the fitness of individuals within population) 2. Resource shortages (sometimes not apparent what the resources are) 3. Reciprocity - all individuals suffer a negative effect of increasing density. 4. density dependence (see hand notes for graphs) Other indices of density dependence for populations Exponential growth most often follows catastrophic population decline Populations that have "sawtooth" N v. t plots are called "opportunistic" (colonizing) species: high capacity for exponential growth Need a model that lets R_0 change with changing density: dealing with density dependent dynamics. Verhulst-Pearl logistic model: K is carrying capacity of the environment. K is defined by environment, expressed in units of population density, it is the density of a population that the environment can support. When population is at K, R_0 is 1.0, and r_act is 0. dN/dt = rN ((K-N)/K) = rN (1 - (N/K)) "sigmoidal" (s-shaped, logistic) growth curve results from this Assumptions: 1. all individuals are equal in pop. (same V_x) 2. r_max and K are constants 3. No time lag between change in r_act and change in pop. density N Very seldom will one find that these assumptions are supportable. Model is still widely used, though, because it gets decent results. End 930910 --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #267, 93-09-10 20:10:16 Subject: Fill in the life tables Here's a couple of life tables for exercise, should anyone out there be interested in applying the notes... Answer key later. x a_x m_x l_x q_x l_x*m_x x*l_x*m_x E_x V_x ------------------------------------------------------------- 0 400 0 1 150 0 2 100 1.0 3 25 4.0 4 10 10.0 5 5 20.0 6 0 0 R_0 = TAU = x a_x m_x l_x q_x l_x*m_x x*l_x*m_x E_x V_x ------------------------------------------------------------- 0 50 0 1 40 0.35 2 30 0.50 3 20 1.00 4 0 0 R_0 = TAU = --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #268, 93-09-10 20:48:30 Subject: Seminar on ecology notes 930910 Wildlife and Fisheries Sciences Departmental Seminar in Ecology series Seminar - Ed Rykiel of Biosystems Modeling section, Industrial Engineering Dept., TAMU Rykiel has been a site reviewer for DOE, NSF. Lots of other indicators of professional excellence, etc. Pres., Theoretical Ecology section of Ecological Society ========== Talk about something that we are familiar with: habitats and species. There is an interaction between plants and animals, habitats change with animal use. Cluster Phase Dynamics in Vegetation Current paradigm is "Gap phase dynamics" : fallen tree creates a gap that gets filled in. "Cluster Phase" : succession by clustering around a plant. - nurse plants - nucleation - change to a different vegetation phase Gap Phase - shifting mosaic - statistical ensemble - successional return to similar vegetation phase Gap phase - dynamics based on competition - close seed source - affected areas usually shrink - animals relatively unimportant - gaps do not interact Cluster phase - facilitation eventually gives way to competition - distant seed source - affected area expands: clusters grow outward - animals relatively important - clusters can interact Facilitation (out of fashion in ecology, due to assumption of benignity) - active - biotic processes are involved in facilitating differential establishment of vegetation - Passive - abiotic and biotic structure attracts seed dispersers; differential establishment is simply a matter of seed density The Role of animals - animals speed up the slow plant to plant and cluster to cluster interactions by facilitating the seed dispersal process - vegetation change occurs by a combination of rapid saltation and slow diffusion - woody vegetation can expand into open space opportunistic tree species - juniper with cedar waxwings (slide) Where do the CW's disperse the juniper seeds? (On my car, audience comment). Not particular about where they put the seeds. Fence line - we call it a fence line attractor: CW's drop seeds near fence lines. Power pole attractor - another abiotic structural feature for the dispersers to cluster around. Windmill attractor (That's a strange attractor, audience comment) Facilitation - population processes - enhanced disperasal - enhanced gemination - reduced mortality risk - nurse plant reduces mortality for seedlings - suppression of herbaceous competitors - physiological processes -- reduced energy load -- reduced moisture stress -- enhanced nutrient status -- increased soil moisture holding capacity Examples of cluster phase dymamics in Texas - post oak savannah - rio grande valley Post Oak Savannah - seed deposition under post oak Note difference of litter within and without the cluster (slide). [Litter within is relatively heavy and deep, opposite outside.] if the nurse plant is relatively small, it doesn't take long for the juniper to shade out the nurse plant. Hypothesized successional sequence Post oak with juniper seedlings Post oak being shaded out by junipers Juniper cluster grows Now for Rio Grande... Mesquite seedling - how they get estab. is another question Once mesquite is established, it becomes a biotic attractor. Shrub vegetation starts under mesquite. Cluster then expands around the mesquite nurse plant Mesquite (Prosopis glanulosa) is very deeply rooted, serves as nitrogen fixer, nitrogen pump, was well as water pump. Shrub patches inhibit the growth of mesquite seedlings. Nutrient cycling is being changed by cluster as well as by the animals. Eventually, mesquite dies, leaving cluster, groves can form with coalescence of clusters. Is nucleation/clustering a common phenom.? - USA: CA oak woodlands, sonoran desert, chihuahuan desert etc. ??? lots more examples flashed by too quickly - clustering has been observed in a wide variety of ecosystems - clustering is most likely to be important in ecosystems where at least seasonal drought occurs - savannah and ecotones appear to be good candidates for clustering landscape effects - interactions at the individual plant level lead to cluster formation and expansion - cluster expansion leads to interactions between clusters at the landscape level - the result is a meta-stable two phase vegetation system w/ clusters expanding and contracting in response to climate, fire, grazing, and other influences Conclusion - contrast w/ usual structuring paradigm (Gap Phase) - cluster phase succession commonly occurs - can be important in changing the system - explicit role of animals in veg. structure and composition - saltation v. diffusion dispersal process if you leave out anmals in system, you won't understand how system developed. Facilitation is a real ecological process: species are not fixed in role, roles can change over time. Cluster Phase succession is more likely to be seen in ecotones, savannas because you don't have canopy cover. Neill: How about advection as well as saltation? Neill: saltation implies random, advection implies direction. Neill: the diff. between gap and cluster seems to be reflected in moisture gradient: high rainfall -> gap; prairie, savanna -> cluster Winemiller: Leibig's law of the minimum may play a role rather than simply moisture gradient. What you'll find generally is that gap is dominant paradigm, grasslands folk force gap on observations. A number of questions remain about junipers: dieocious species, recruitment, seeding, etc. Deserts: structures in deserts can be seen at all scales. Facilitation is seen in any relationship that uncouples system from climatic conditions of environment. Neill: fractal analysis may point to dominant process. With landscape interest, someone should be looking at this. The question has not yet been framed well yet... This process is not uncommon: why has it been ignored? End --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385) From: Wesley R. Elsberry To: All Msg #269, 93-09-10 20:51:00 Subject: Event recorder Diane had been working on me for a couple of years to program at least a minimal event recorder. Well, taking the WFSC 422 Ethology course finally provided the activation energy, and last night I did a quick and dirty program for my trusty TRS-80 Model 100 portable computer in the Mod100 BASIC. What's an event recorder, some may ask. Well, I don't have a concrete example to work from, since they don't come cheap and my only contact has been reading about them in the literature. But the basic idea is that an event recorder provides a way for an observer to note emitted behaviors in subjects in real time. The type of behavior and a timestamp are the minimal pair to be recorded. Today, I tried it out during a student presentation. The program records single characters with a time stamp in the form of a string. Resolution is only down to the second. For my practice run, I came up with a repertoire of four behaviors, coded as "e": eye contact with audience "f": face touch with hands "h": any hand gesture excluding "f" "u": "uh" or "um" vocalizations The results are: Date: 09/10/93 u 12:05:23 u 12:05:28 e 12:05:31 h 12:05:39 e 12:05:51 e 12:05:56 e 12:05:58 u 12:06:01 e 12:06:04 h 12:06:05 e 12:06:08 e 12:06:11 e 12:06:15 h 12:06:16 h 12:06:18 h 12:06:21 e 12:06:23 h 12:06:24 f 12:06:26 e 12:06:30 e 12:06:34 u 12:06:40 u 12:06:42 e 12:06:44 u 12:06:50 e 12:06:53 h 12:06:55 e 12:07:05 h 12:07:07 u 12:07:08 e 12:07:10 h 12:07:14 e 12:07:15 u 12:07:17 h 12:07:20 e 12:07:30 e 12:07:33 e 12:07:53 e 12:08:06 e 12:08:11 h 12:08:21 e 12:08:26 e 12:08:40 h 12:08:48 h 12:09:00 f 12:09:16 h 12:09:27 f 12:09:32 e 12:09:35 f 12:09:45 e 12:09:46 h 12:09:51 u 12:09:52 f 12:09:53 f 12:09:56 e 12:09:57 h 12:10:04 u 12:10:05 u 12:10:09 f 12:10:11 e 12:10:12 u 12:10:12 e 12:10:14 u 12:10:19 f 12:10:21 e 12:10:24 f 12:10:29 u 12:10:31 e 12:10:35 h 12:10:42 f 12:10:43 e 12:10:44 u 12:10:44 e 12:10:54 u 12:10:56 e 12:11:03 u 12:11:14 h 12:11:17 u 12:11:21 u 12:11:24 e 12:11:25 u 12:11:26 u 12:11:31 h 12:11:33 u 12:11:38 e 12:11:50 h 12:11:57 f 12:11:59 u 12:12:00 h 12:12:03 f 12:12:05 e 12:12:10 h 12:12:13 h 12:12:14 u 12:12:16 h 12:12:18 f 12:12:21 f 12:12:23 e 12:12:23 u 12:12:29 h 12:12:30 e 12:12:36 h 12:12:38 end of talk, end of observations I'll be working on refining this tool over the course of the semester, and applying it to interesting behaviors later on. The program as it stands now, though, is this: 10 REM event recorder 20 ON KEY GOSUB 1000,2000 30 KEY (1) ON:KEY (2) ON 40 D=0:G=0 900 REM 910 IF (G = 1) AND (D = 0) THEN GOSUB 3000 920 IF D = 1 THEN GOTO 930 ELSE GOTO 900 930 KEY (1) OFF : KEY (2) OFF 940 CALL 23164,0,23366:CALL 27795:REM restore function keys 990 STOP 1000 REM F1 int. service 1010 INPUT "enter file name";F$ 1020 A$ = "ram:" + F$ + ".do" 1030 OPEN A$ FOR OUTPUT AS 1 1040 PRINT #1,"Date: ";DATE$ 1050 PRINT "Date: ",DATE$ 1060 G = 1 1070 RETURN 2000 REM F2 int. service 2010 CLOSE 1:D=1 2020 RETURN 3000 REM poll for key, then log it and time 3010 B$ = INKEY$ 3020 IF B$ = "" THEN RETURN 3030 PRINT #1,B$;" ";TIME$ 3040 PRINT B$,TIME$ 3050 RETURN That's all, folks. --- msgedsq 2.0.5 * Origin: Central Neural System 409-589-3338 (1:117/385)


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