Factors Affecting Detection Probability
Lab
17 January 2012
Point counts are widely used to monitor bird abundance. They consist of one or
more observers recording all birds seen or heard during a fixed amount of time (usually
3-10 minutes) at a single location. Point counts are used extensively in research, and by
federal and state agencies to address ecological, conservation and management questions
about bird populations.
The likelihood that an observer records an individual bird during a count
(detection probability) varies due to many different factors. These factors may include:
observer skill or ability, time of day, time of season, weather conditions, and habitat
structure.
Standardizing sampling methods is one approach to controlling these factors. For
example, using the same observer or group of observers, not conducting point counts in
rainy or excessively windy conditions, only conducting counts between 5am-10am during
the breeding season, and stratifying your allocation of sampling units by different habitats
will all reduce the variability of factors affecting detection probability. Although
standardization reduces variability, point counts are still widely criticized because they
provide an index of abundance that assumes a linear relationship between the number of
birds detected and the true population. This means that if the true population increases
between location A and location B, then the count will increase in proportion to the true
population increase.
Unfortunately, this relationship does not always hold because birds that are
present, but not detected, are often missed by observers. For example, assume that you
are conducting two counts, one in a mixed deciduous forest with a very dense understory
and another in an open forest with hardly any understory at all. If you count more birds
in the open forest than in the dense forest you infer that there are more birds in the open
forest than in the dense forest. However, if you could not detect all of the birds in the
dense forest because of the dense understory, but you could detect all the birds in the
open forest, your inference about abundance could be wrong.
To account for birds present, but not detected, we collect additional information to
estimate the probability of detection, and adjust our counts by this probability. The
relationship is as follows:
E (C )  p * N
Therefore,
C
Nˆ 
pˆ
Where C = count, p = detection probability and N = population size. A hat over a
variable (e.g., N̂ ) represents an estimate, something that is estimated from the data.
Today we are going to examine different factors that affect detection probability
such as those discussed above (observer, habitat, environmental conditions, and time of
season/day).
Our objectives are:
1. To introduce you to a common method of estimating bird abundance.
2. To examine in more detail, several factors affecting the probability of detecting a
bird at a given location, and the effect these factors have on bird population
estimates.
Overview:
Today’s lab will be a hands-on experience conducting a standardized point count
using visual detections. We will use an artificial static “bird” population, which allows
easy comparison of our experimental results with the true results.
Part 1: Visual detections – double independent observer method
You will work with a partner for this section of the lab. During a three-minute
visual count, record on a data sheet the location and size of each target observed. Targets
will consist of pieces of craft foam in two colors and two sizes. After three minutes you
will compare your sheet to that of your partner. On your data sheet, circle the targets you
both identified, but do NOT change your sheet in any other way after the count is over.
Move to the second point and repeat the procedure, recording your observations on a
second data sheet. Summarize your data for each point. For each type of target, count the
total number observed by each partner (n1 and n2), then the total number you had in
common (m1). Afterwards we will provide you with an answer key. Think about the
factors that affected your results at each point.
Assignment:
Send a copy of your summarized data from the visual points to your TA by the end of the
day on Friday. Be sure to include the names of both partners with your data. The
compiled results from all pairs of observers will be posted on the website. Use the Excel
worksheet provided to calculate an average population and detectability estimate for each
“species” at each point. Review the material we went over in lab last week and consider
the factors affecting detection probability and how they can alter the interpretation of
sampling data. Write up a 1-2 page synopsis of your findings to turn in the following
week.
Each write up should include:
1. A summary of your results and a summary of the class results from the visual
points.
2. A comparison of your results to the true results, as well as to other members of the
class.
3. A discussion of the factors that affected your ability to detect "birds" on visual
counts.
4. You thoughts about other factors that may affect detection probabilities on real
point counts.