Presentation is loading. Please wait.

Presentation is loading. Please wait.

CIMAT Taller de Modelos de Capture y Recaptura 2010 Introduction.

Similar presentations

Presentation on theme: "CIMAT Taller de Modelos de Capture y Recaptura 2010 Introduction."— Presentation transcript:

1 CIMAT Taller de Modelos de Capture y Recaptura 2010 Introduction

2 Gotelli defines a biological population as A group of plants, animals, or other organisms, all of the same species, that live together and reproduce Turchin defines a population as A group of individuals of the same species That live together in an area of sufficient size to permit normal dispersal and migration behavior In which population changes are largely determined by birth and death processes WHAT IS A POPULATION?

3 OTHER DEFINITIONS Geo-political Populations e.g., people in Guanajuato, Mexico, etc. Study Population (or Statistical Population) e.g., the fish in a study stream during 2009-2010 Management Population e.g., Desert Bighorn Sheep on Tiburon Island

4 Population Biology Population Genetics WHAT IS POPULATION BIOLOGY?

5 POPULATION GENETICS Genetic Diversity Within Across Populations Space and Time Change in allele frequencies within a population over time Natural Selection Genetic Drift: change in allele frequencies due to random events in small populations Founder Events Bottlenecks

6 OTHER POPULATION DEFINITIONS Theoretical Evolutionary Studies A group of individuals with a given allele A group of individuals with a given phenotype

7 Population Biology Population Ecology WHAT IS POPULATION BIOLOGY?

8 POPULATION ECOLOGY How individuals within a population interact with their surrounding environment How this affects: Distribution Abundance Growth and dynamics (i.e., rates of change) Of populations over space and time

9 Population Biology Population EcologyPopulation Genetics

10 Populations Communities Ecosystems Global Systems Individuals Organs Cells Organelles DNA EvolutionEcology Socio-Economics

11 Each definition of a population refers to a collection of individuals We denote the total # of individuals in a population as N (population abundance)

12 POPULATIONS IN NATURAL RESOURCE MANAGMENT Conservation Ensure healthy population growth to avoid small population size, and ultimately to avoid extinction Population abundance is a pivotal aspect of endangered species act Population Control Decrease population size, avoid population growth and spread over the landscape Pest control Control of invasive species Management Manage for sustainable population numbers over space and time to maintain plentiful game and viewing opportunities along with commercial activities

13 POPULATIONS IN HUMAN SOCIETIES Innovation & Infrastructure Within nations: more minds can lead to more innovators Societal infrastructure Demand for limited resources More people, more demand for finite resources Cost of living Quality of living Social Programs Age structure (e.g., ratio of dependents to working age to retired) Stimulate or strain social gov. programs (e.g., social security)

14 irthirth mmigrationmmigration eatheath migrationmigration There are only 4 processes that can affect N N future = N now + B + I – D – E

15 WHAT ARE MODELS FOR? “Essentially, all models are wrong, but some are useful” Box 1987 How so? By being a simplification of nature, a model can never be exactly correct Some can do a great job of explaining the general patterns in nature Simple enough to understand

16 WHAR ARE MODELS USEFUL FOR Force one to clearly state their assumptions Force one to simplify nature so that it can be understood Useful learning tool, e.g., can be used to project the consequences of different scenarios For making predictions

17 BIDE BALANCE MODEL N t+1 = N t + B + I – D - E (B occurs during discrete breeding season, I, D, and E occur over t to t+1) To relate BIDE numbers to average individual performance per time step (t+1) Convert BIDE to per capita rates by dividing by abundance

18 BIDE BALANCE MODEL Substitute per capita rates into the model Divide by N t Lambda (λ) ‘geometric’ rate of growth (or decline) over a ‘finite’ time interval (e.g., a year)

19 There are 2 ways to estimate lambda: If one has an estimate of lambda, population abundance can be projected over time: BIDE BALANCE MODEL

20 POPULATION HEALTH Abundance is important More to population biology than just knowing how many there are Are there more than there used to be? Are there fewer than there used to be? How is the population doing? Are they doing so well that they’re moving into new areas, or is the range contracting? Questions related to population growth over time and space

21 BIDE ESTIMATION Must follow the history of individuals Human Studies Field Studies in the wild 2 Categories of Event Knowledge for an individual life history Events are known with certainty Census, clinical trials, etc. Events are not known, but can be estimated conditional on other probabilities (detection probability) Captures of marked individuals

22 Survival curves for male subjects with Cystic Fibrosis (CF) but without diabetes (green, median survival 49.5 years), male subjects with CF and diabetes (blue, median 47.4 years), female subjects with CF but without diabetes (black, median 47.0 years), and female subjects with CF and diabetes (red, median survival 30.7 years).

23 Women's sample hazard functions of having the first child plotted against age by attractiveness groups.


25 25 How many children do you have? None of your business! Human demographers use surveys, census, etc What do population ecologists use? IN THE WILD

26 BIDE ESTIMATION Must follow the history of individuals Human Studies Field Studies in the wild 2 Categories of Event Knowledge for an individual life history Events are known with certainty Census, clinical trials, etc. Events are not known, but can be estimated conditional on other probabilities (detection probability)

27 27 CAPTURE-MARK-RECAPTURE Band RecoverySatellite tracking And also radio-telemetry, number-tags, paint-tags, pit tags, etc. You can then estimate vital rates (survival, movement, growth…), population size, capture probabilities, and more

28 Capture Mark Recapture

29 One can address questions in: Wildlife Management Evolutionary Biology Metapopulation Dynamics Conservation & Epidemiology CAPTURE MARK RECAPTURE MODELS

30 APPLICATION IN MANAGEMENT Snow Goose Demography (Chen caerulescens caerulescens) Snow Geese at La Pérouse Bay Near Churchill, MB

31 APPLICATION IN MANAGEMENT Snow Goose Demography Reproduction estimated by monitoring: Egg production in nests Probability of nests hatching young Gosling survival up to fledging

32 Survival estimated by: Capturing thousands of geese each year (most years) Banding them (USFWS leg band) APPLICATION IN MANAGEMENT Snow Goose Demography

33 Survival estimated using: Recaptures of live banded geese and hunter recoveries of known-age banded birds allows us to estimate age-specific survival APPLICATION IN MANAGEMENT Snow Goose Demography

34 SNOW GOOSE DEMOGRAPHY Age-specific Fertility and Survival for lesser snow geese (Chen caerulescens caerulescens) Studied by Fred Cooke, Rocky Rockwell, Evan Cooch, Dave Koons, Lise Aubry, and others LaPérouse Bay, Manitoba, Canada 1969-present Age ClassFertilitySurvival 100.816 20.1860.816 30.3630.816 40.4350.816 5 and older0.5040.816

35 SNOW GOOSE LIFE CYCLE MODEL 0.816 0.5040.435 0.363 0.186 Age ClassFertilitySurvival 100.816 20.1860.816 30.3630.816 40.4350.816 5 and older0.5040.816 12345+ 0.816

36 0.5040.435 0.363 0.186 12345+ 0.816 SNOW GOOSE LIFE CYCLE MODEL

37 APPLICATION IN MANAGEMENT Snow Goose Population Model Cross-seasonal drivers are causing overpopulation

38 SNOW GOOSE EXAMPLE Overpopulation of snow geese is degrading arctic habitat Experienced adults now nesting and raising their goslings away from traditional nesting colony such that goslings can graze on higher quality saltmarsh grasses


40 APPLICATION IN MANAGEMENT Elasticity of population growth to survival probabilities = 0.83 Elasticity of population growth to fertilities = 0.17 Population is almost 5 times more sensitive to proportional changes in survival than it is to fertility

41 APPLICATION IN MANAGEMENT Snow Goose Population Model Rather than addling eggs Reductions in adult survival (increased mortality) would be more effective at controlling rapid and destructive population growth In an attempt to do just this, managers have liberalized harvest regulations Try and increase harvest rates Hopefully reduce survival by a large enough amount To stabilize or reverse the population trend Has had some effect Now trying to compute the optimal kill rate required to decrease numbers and stabilize ecosystem dynamics

42 1. EVOLUTIONARY ECOLOGY Weddell seal study G. Hadley, R. Garrott, J. Rotella, K. Proffitt (Montana State) MORE EXAMPLES 2. METAPOPULATION DYNAMICS Florida Scrub Jay D.R. Breininger, J.D. Nichols, G.M. Carter, D.M. Oddy (Central Florida University) 3. EPIDEMIOLOGY & CONSERVATION Tasmanian Devil S. Lachish, M. Jones, H. McCallum (Queensland/ Tasmania / Canberra University)

43 1. EVOLUTIONARY ECOLOGY Weddell Seal (Leptonychotes weddellii) COST OF REPRODUCTION Trade-off between current reproduction and future reproduction and/or survival SURVIVAL: increases? decreases? or remains the same? REPRODUCTION : increases? decreases? or remains the same? Year t Year t+1 REPRODUCTION

44 DATA Known-age reproductive females 1979 to Present 2 states: Breeding (B) Non-breeding (N) COVARIATES OF INTEREST AFR: age at first reproduction Environmental Variability Breeding Experience THE STUDY

45 1. COST TO SURVIVAL = S B < S N Having babies is more costly in terms of survival than not having any 2. COST TO FUTURE REPRODUCTION = Ψ BB < Ψ NB If you were a breeder in year t, you are less likely to again be a breeder in year t+1 than an individual that was previously a non- breeder REPRODUCTIVE COSTS

46 CONCLUSIONS 1. Reproductive costs to survival: Mean annual survival was 0·91 for breeders versus 0·94 for non-breeders  Cost of reproduction to survival: S B < S N 2. Reproductive costs to subsequent reproduction: Mean probability of breeding in t+1 was 31.3% lower for first- time breeders at t compared to experienced breeders  Cost of reproduction to future reproduction for first time breeders only: Ψ BB > Ψ NB CONCLUSION: breeding experience helps!

47 Habitat-specific demography in a territorial, philopatric and cooperative breeder Only bird endemic to Florida Exclusively lives in Florida scrub habitat Nutrient poor habitat Occasional droughts Frequent fires 2. METAPOPULATION DYNAMICS Florida Scrub Jay (Aphelocoma coerulescens)

48 Birds marked from 1988 to present Trapped with baited Potter traps, drop traps, and mist nests Monthly surveys: Try to re-sight marked birds Family composition Breeding status (pair-bond behavior) Habitat in territory locale THE STUDY

49 4 Habitat types: ‘Short’: burned within 3 years, sandy with sparse oak shrubs ‘Optimal’: large sandy areas and patches of oak shrubs ‘Tall mix’: short and medium high shrubs and patches of tall oak ‘Tall’: connected shrub canopies THE STUDY

50 OBJECTIVES 1. Estimating: Habitat-specific survival probabilities Habitat-specific recapture probabilities Transition probabilities across habitat of different quality In a frequently disturbed ecosystem (fire dynamics)

51 2. Disentangling bird movement across habitats of different quality from habitat quality changing as a result of disturbance Habitat dynamics: disturbances (e.g., fires, severe droughts) Bird movement: processes of habitat selection (e.g., avoiding a high predation environment, moving to a location where resources or more abundant) OBJECTIVES

52 p  P rediction does not exactly match results RESULTS Expected Observed

53 RESULTS S  Prediction matches results S optimal > S tallmix > S tall > S short Short Optimal Short-mix Tall

54 RESULTS B ird did not move much, but their habitat changed over time as a response to disturbances (mainly fires), which in turn influenced Scrub Jay demography Ψ  Predictions match results

55 RESULTS Survival was found to be highest in optimal habitats, however, in this study, only 27% of habitats were ‘optimal’ Asymptotic distribution of habitats: 13% (short), 27% (optimal), 43% (tallmix), 17% (tall) For optimal habitats to become more available, occasional extensive fires might be needed

56 3. EPIDEMIOLOGY & CONSERVATION Tasmanian Devil (Sarcophilus harrisii)

57 3. EPIDEMIOLOGY & CONSERVATION Tasmanian Devil (Sarcophilus harrisii) Cedric is the only devil that was infected with DFTD but showed positive immune response Devil Facial Tumor Disease (DFTD) Infectious cancer observed starting in 1996 Listed as vulnerable in may 2008 Fatal disease (die of starvation within 3-7 months) Transmissible through social interactions Spread over 60% of Tasmanian's habitats

58 Estimate vital rates for infected and healthy devils Force of infection (function of disease prevalence) Population monitored since 1999, infected with DFTD in 2001 THE STUDY

59 1. Impact of DFTD on sex- and age-specific survival 2.Variation in infection rates to a disease state (transition rate from healthy to diseased) in response to disease prevalence 3.Effect of DFTD on lambda OBJECTIVES

60 Ear marked / transponders Juveniles are excluded, 1 and 2+ years old only 3 states: healthy sub-adult (hsa) healthy adult (ha) diseased adult (da) THE DATA

61 Survival for both sub-adults and adults declined as disease prevalence increased Sub-adults Adults DFTD prevalence RESULTS

62 Healthy sub-adults to diseased adults Healthy adults to diseased adults DFTD prevalence As disease prevalence increased, the probability for healthy sub-adults to become diseased adults increased, but the probability of healthy adults to become infected was stable RESULTS

63 CONCLUSIONS Initially all infections occurred in adults The majority of new infections appear to be occurring in recently matured adults In May 2009, the Australian government uplisted the Tasmanian devil from ‘vulnerable’ to ‘Endangered’ under national environmental law

64 2 Programs are ‘specialized’ in CMR models MARK (US team) M-surge/E-surge (French team) MARK is user friendly, not as flexible (constrained parameterization), and slow (big datasets) M-SURGE: slow start (GEMACO language), very flexible, fast (optimized algorithms) IN PRACTICE

65 Downloadable websites Online books MARK (Gary White & Evan Cooch) M-SURGE (Remi Choquet) Online Help for both softwares IN PRACTICE

66 2 R Packages RMARK For Capture-Mark-Recapture (Jeff Laake & Erik Rexstad) UNMARKED For habitat occupancy, distance estimation of abundance (Ian Fiske and Richard Chandler) IN PRACTICE

Download ppt "CIMAT Taller de Modelos de Capture y Recaptura 2010 Introduction."

Similar presentations

Ads by Google