Term
| What are 2 points to consider when estimating abundance? |
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Definition
1. Detection probability (β) is almost always <1.0
2. Detection probability also varies among samples
§Heterogeneity due to random variation in sampling
§β also varies over space, time, among observers, among sample units (i.e., individual animals within a population) |
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Term
How do you estimate abundance when β = 1?
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Definition
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Term
| How do you estimate abundance when β = 1? |
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Definition
[image]
Nhat = est abundance
M = ind sampling area
ybar = # animals counter per area/ number of areas
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Term
| What happens to your variance if B=1 and the entire area is sampled? |
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Definition
| There is no sample variance |
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Term
| How do you account for different sample sizes when B=1 and you are estimating abundance? |
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Definition
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Term
| What is a disadvantage to distance sampling? |
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Definition
| You need 60-80 minimum detections |
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Term
| What are advantages of distance sampling? |
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Definition
| est of density even when not every indiviudal is counted, can account for observer bias, even if youonly detect 10% you're good!, can have an unknwn sampes area size, efficient for large areas |
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Term
| What concept is central to the concept of distance sampling? |
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Definition
| The detection function! The p(detecting an object|distance from origin) |
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Term
| What are potential sources of bias in distance sampling? |
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Definition
| observer, time of day, weather, veg density, topography, bird response to obs, and so on... |
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Term
| Where is distance sampling most useful? |
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Definition
| Where reliable est of density are needed for comparisons between sites, though time, or for the investigation of a single pop (provided you can get 60 samples) |
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Term
| Name the main 4 models in distance |
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Definition
- Single species, single season
- Single species, multiple season
- Multiple species, single season
- Multiple species, multiple seasons |
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Term
| What is distance sampling? |
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Definition
| A widely used group of closely related methods for estimating the density/abundance of biological populations |
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Term
| What is the key to distance sampling analyses? |
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Definition
| To fit a detection function to the observed distances and use this fitted function to estimate the proportion of objects missed during the survey |
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Term
| What methods does distance sampling extend? |
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Definition
| Quadrat-based methods - by relaxing the assumption that all the objects within the strip or study area are counted |
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Term
| How do you estimate animal density? |
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Definition
Dhat = n/2wLPhat
Dhat = density
2wL = surveyed area
Phat = halfwidth/w |
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Term
| What type of animal are point counts used most often for? |
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Definition
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Term
| What are the perks of a point count? |
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Definition
| Less walking (good for difficult terrain), and the obs can sit and focus in the organisms observed |
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Term
| What are the disadvantages of a point count? |
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Definition
| Time spent traveling between points is a waste (you can't record species detected); this method is unsuited for species that need to be flushed to be detected |
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Term
| How do you calculate abundance? |
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Definition
Nhat = n/Phat
Abundance = number obs individuals/detection probability |
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Term
| What is the probability detection function (PDF)? |
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Definition
P = 1/f(0)w
P = P(detection)
f(0) = represents the PDF of the detected distances, evaluated at distance 0 |
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Term
| What is the standard formula to estimate density using distance methods? |
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Definition
Dhat = nf(0)/2L
f(0) = represents the PDF of the detected distances, evaluated at distance 0 |
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Term
| What are the 3 main assumptions of distance sampling? And the 2 little ones? Why are they little assumptions? |
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Definition
1. Object 0 distance from the line are all detected
2. Detections are recorded as a snapshot in time (objects don't move)
3. Measurements are precise
4. Object locations are independent of the line (is your line parallel to a road?)
5. Detections are independent events
They are little bc Distance is robust to violations to the last 2 |
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Term
| What is the difference between design-based and model-based estimation? |
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Definition
design-based estimation extrapolates from the sampled areas to the region
model-based estimation assumes animals are uniformly and independently distributed through the landscape - your estimates of precision suffer
We use a hybrid approach with program Distance |
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Term
| What are the 3 main assumptions of distance sampling? And the 2 little ones? Why are they little assumptions? |
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Definition
1. Object 0 distance from the line are all detected
2. Detections are recorded as a snapshot in time (objects don't move)
3. Measurements are precise
4. Object locations are independent of the line (is your line parallel to a road?)
5. Detections are independent events
They are little bc Distance is robust to violations to the last 2 |
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Term
| What is the difference between design-based and model-based estimation? |
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Definition
design-based estimation extrapolates from the sampled areas to the region
model-based estimation assumes animals are uniformly and independently distributed through the landscape - your estimates of precision suffer
We use a hybrid approach with program Distance |
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Term
| What is spiked data and what are some causes of it? |
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Definition
| Spiked data occur when there is a disproportionately high shoulder at zero (left skewed). This happens when observers are not accurate with measurements or animals are attracted to the observer. |
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Term
| What are the 3 stages of data analysis in Distance? |
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Definition
| Exploratory (make a histogram), model selection, and final analysis/inference |
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Term
| What are the assumptions of occupancy analysis? |
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Definition
1. Closed pop
2. Constant P(occupation) across all sites
3. Detection among sites is independent, unless modeled differently
4. (Added by Ben) No unexplained heterogeneity in P(presence) or P(detection) across sites |
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Term
| What are some perks of Presence? |
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Definition
More stable than Mark
More user-friendly than R |
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Term
| Name 2 secondary Occupancy models |
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Definition
- Falso positive detections
- Multi-state
- Season heterogenetiy
- Season staggered entry
- Spatial dependence
- Integrate habitat occupancy
- Heterogeneous detection probabilities |
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Term
| How often will your sampling covariance change in Presence? |
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Definition
| With each site visit (weather, observer, date, etc.) |
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Term
T or F
Occupancy< Naive occupancy |
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Definition
FALSE
Occupancy > Naive occupancy |
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Term
| What are 4 factors that influence density? |
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Definition
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Term
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Definition
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Term
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Definition
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Term
When do you use m/M?
Distance |
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Definition
| When sample area size is uneven |
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Term
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Definition
mean count
aka # animals counted/area/# areas |
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Term
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Definition
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Term
| What do you use the coefficient of variation for? |
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Definition
| To tell you what sample size you need to obtain a desired level of precision |
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Term
| Why is Distance more robust and sound than Presence or Mark? |
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Definition
| Euclidean geometry (very sound in mathematical theory) |
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Term
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Definition
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Term
| What is the AUC in distance sampling? |
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Definition
The effective half-width (w) of the samping plot (mew)
When detection is perfect, mew=w |
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Term
| List line transect assumptions |
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Definition
1. Objects 0 distance from the line have perfect detectability
2. Objects do not move prior to count in response to observer presence
3. Measurements are precise
4. Transects are randomly placed
5. Independent detections
6. 60 - 80 detections minimum |
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Term
What does this mean?
g(x) = P(object detected|x) |
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Definition
| THe probability an object is detected given distance x from the center of the line/point |
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Term
What does this mean?
f(x) = g(x)/mew |
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Definition
| probability an object is detected given the effective half width |
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Term
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Definition
| probability of an object given distance |
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Term
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Definition
| cumulative distribution of animals detected grouped by distance from the line |
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Term
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Definition
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Term
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Definition
| all detection probabilities |
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Term
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Definition
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Term
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Definition
| The shape of your histogram (how probablity drops off as you leave the line) |
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Term
| When do you use a uniform key function? |
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Definition
| It's outdated, don't do it |
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Term
| When do you use a half-normal key function? |
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Definition
| Good when your data declines markedly from the center line |
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Term
| When do you use a hazard rate key function? |
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Definition
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Term
| When do you use a exponential key function? |
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Definition
| salvage analysis of poorly collected data when there is strong reason to believe the data are spiked |
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