Evidence of Absence

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Detailed Description

This is a recorded presentation describing a statistical software package called "Evidence of Absence" that can be used to provide evidence of compliance with incidental take permits. It will be useful to wildlife managers and wind energy operators to estimate, with reasonable certainty, that a certain number of birds or bats have been killed at wind energy facilities, even when no carcasses are found. 


Date Taken:

Length: 00:56:45

Location Taken: Corvallis, OR, US


Good morning, or good afternoon, whichever the  
case may be. Welcome to this webinar on 

evidence of compliance with incidental take 

My name is Manuela Huso, and I am a research 
statistician with the U.S. Geological Survey in 

My colleague and co-presenter 
Dan Dalthorp is also a statistician 
with the USGS here in Corvallis.

Before we get started, I'd like to thank Rick 
Amidon and T.J. Miller from Fish and Wildlife for 
organizing this webinar, and the Fish and  

Wildlife Service's Regions 1 and 3 for their 
strong support of this work, that's actually 
applicable to any Region considering issuing 

an ITP for any species ranging from Indiana bats 
to golden eagles.

And, of course, I would like to thank you for your 
interest in what we have familiarly come to know 
as Evidence of Absence (that's what we've 

named the software that we've produced) but 
what might be more aptly called Evidence of 

As many of you know, requests to the U.S. Fish 
and Wildlife Service for Incidental Take permits 

from wind developers have been rapidly 
increasing in recent years,

to the point that it's starting to feel overwhelming 
for some wildlife managers.

Before issuing the permit, managers must 
assure that any incidental take is already 

minimized and that the taking will not reduce 
likelihood of survival and recovery of the species.

So the first question is, well how many is that?

This is determined typically through collision 
risk models or through evidence from similar 

and through knowledge, sometimes very little, of 
what populations can sustain.

In this example from a facility in the Midwest,

the number that they arrived at was three 
Indiana bats and two northern long-eared bats

that might arrive somewhere out here over the 
course of a year.

Often the permit states that if the permitted 
number is exceeded,

the company will have to compensate through 
some form of additional mitigation or 
minimization, which can sometimes be quite

expensive. So the question we're interested in is 
how will we know when the limit has been 

Or, how can we be reasonably sure that the 
company is in compliance with its ITP,

both in a given year or over the course of the life 
of the permit?

So here's what we're facing:

Out there somewhere there might be no Indiana 
bats, actually. But on the other hand, there 
might be some.

Same for northern long-eared. When the ITP 
was issued, we used the best available science 

to determine that we think there will be three 
Indiana bats and two northern long-eared bats

taken in a typical year.

Does this mean there will be exactly that 
number every year?

Well, quite unlikely.

So in any given year there might be five. There 
might be 10. But there also might be 50 out 

If we didn't have enough information to 
accurately set the ITP level we could be quite 

So how can we know, given what we find, that 
it's closer to one or two than 50?

If we only find only one or two, or maybe even 
none in our searches, well really it all depends 

the probability of detecting a carcass. Or 
equivalently, the probability of missing a 

Why do we miss some carcasses? Well all you 
have to do is look at the sort of landscapes that 
we're dealing with and you intuitively know that

it's quite likely that we will miss some of the 
carcasses on our searches.

But we can quantify our chance of missing a 
carcass by looking at our protocol and the 
reasons for missing them.

In a typical search protocol, we start by 
designating an area around a turbine to be 

This area can vary from facility to facility, from 
study to study.

But in addition to having variable search areas 
designated, there may be parts within it that are 
just too dangerous or brushy to actually search.

Within the searchable area we do our best to 
systematically comb the area searching for 
fallen and birds and bats.

But what we find is certainly not necessarily all 
that was killed.

First, because we often only search a subset of 
the turbans. 

Some animals land outside our designated 
search areas, while others might land inside but 
in unsearchable areas.

Some carcasses are removed by scavengers 
before we can even find them,

and some are present but missed because they 
fall in a hole, in a deep clump of vegetation, or 
shadows hide them. Just chance.

Searchers don't find them. So there's many 
reasons combined why we don't find everything 
that's out there.

But in order to estimate fatality accurately, we 
first need to estimate what the probability of 

a carcass is so that we can understand what 
fraction we've actually observed.

So let's briefly go through all these components.

First we'll start with the proportion of carcasses 
in our searched area,

which depends on the relative density of 
carcasses and where they land.

This composite image of bat carcass locations 
across several turbines shows a higher density 
in the center and towards the edges.

If rather than using this particular configuration 
for the search plot,

we were to reduce it by 25%.

Well we wouldn't necessarily be losing 25% of 
the animals out there.

If we simply shrink the plot by removing 25% of 
the perimeter, we only lose maybe 1%, maybe 
5%...certainly not 25%.

On the other hand, if for some reason

the center 25% of the plot is unsearchable,

we'd lose only about 85% the bats at this site.

The question to answer then is not what fraction 
of the plot is searchable

but what fraction of the carcasses can we 
expect to be in the searched area?

One approach is to model the density as a 
function of distance, taking into account

search effort or, essentially, detection 

This model, that shows that the relative density 
is highest at about 20 m and tapers off to zero 
at around 80 m

can be translated into a three dimensional 

that integrates to one and associates a relative 
density with every point beneath the turbine.

The light color in this graphic, close to the 
center, indicates that we have a higher 
probability that 

an animal will be in the square meter near the 
turbine and farther from the turbine where it's 

So what if we were able to search a plot with 
this kind of configuration,

essentially only the roads and pads and the 
area very close by.

If we don't consider the different densities at 
different distances, we would say that since we 
searched only 25% of the plot,

we would find 25% of the carcasses.

However, if we take that three dimensional 
surface that we had before and superimpose 
that on this plot,

we would say although we searched 25% of the 
plot within that searched area,

we would expect to have about 60% of the 

We'll see later how we can use this idea to our 
advantage when considering coverage in a study 

Our next factor, carcass persistence,

is typically measured by placing trial carcasses 
in the landscape and recording how long it takes

before they are removed by scavengers or 
otherwise no longer identifiable as a carcass by 
a searcher.

When we do this, typically a carcass may be 
removed but feather spots or feathers may 
remain behind.

Those would be identified as a carcass or at 
least as a former carcass by a searcher and so 
those are included as still being identifiable.

Our next factor, searcher efficiency, is also 
typically measured by placing trial carcasses in 

the landscape and recording whether a carcass 
is observed by a searcher.

Often searcher efficiency depends on vegetation 
as well as carcass size and perhaps even 

Just for fun I, show you what I hope is a pretty 
rare event but was

captured by Michael Schirmacher of Bat 
Conservation International to show a lonely bat 

on the top of the nacelle of one of these 
turbines. Of course we're not typically searching 
that area.

To put this in algebraic terms, we start with M,  
the actual numbers killed at a site.

But we know it doesn't necessarily equal X, the 
observed number of carcasses.

Only the fraction that we call a, that arrives 
within the searched area, is even potentially 

Of that, then, only a fraction r of those remain 
unscavenged until the next search.

And finally, a fraction that we call p of those that 
arrive in the searched area and remain 

will actually be observed by a searcher combing 
the area for carcasses.

So with this equation we can see that it's the 
product of these three proportions

that forms the overall probability g that an animal 
struck by the turbine will be detected during a 

Actually, it's not quite that simple. The equation 
for g really looks something like this:

the double sum of the product of an integral. But 
for our purposes and for just thinking about it

thinking about it as the product of these 
proportions gets as a long way.

So we can take this equation and simply 
rearrange it so that we estimate M, the total 

number killed, by dividing the number that we 

by the probability of detection.

We should note that because all the factors in 
the denominator are less than one that the 
estimated fatality

can never be less than what we observe.

To put some real numbers on this, let's say we 
had a site at which 28 of the 40 turbines that 
were out there were searched,

and our search radius, or our search 
configuration, was such that we estimated 51% 
of the 

carcasses that could land out there to be within 
our searchable area.

Scavengers removed about 1/4 of the carcasses 
before we had a chance to search

and the vegetation, etc. and chance prevented  
searchers from finding about 40% of them.

Each of these proportions by itself

 doesn't seem particularly small.

 But when you put them together

it results in an overall probability

of about 0.16.

That's the same as finding about one in every 
six carcasses that arrive, or from another 

missing five out of about every 6 that arrive.

In addition, we really don't know g and we have 
to estimate it so there's uncertainty in that 0.16 

Perhaps we have a 95% confidence interval 
around g that says it's likely between 0.07

and 0.25.

If we find a substantial number of carcasses we 
can use this estimator to arrive at a

95% confidence interval for M that extends 
between 124 and 443 for example.

And while this is a fairly large interval for many 
purposes, this is often

quite useful.

What happens though when we don't find any 

To bring this idea into more familiar context, let's 
considering an analogy to a parlor game.

We're at a party and someone says "Hey, let's 
play Stump the Statistician!"

We need a volunteer, so

I'm a good volunteer and I leave the room. The 
rest agree to roll the die five times.

When I come back, they report "We rolled no 
sixes. How many times did we roll the die?"

So, I'm a good statistician and I know how to 
calculate the best answer.

I divide the number of sixes observed (that's 
zero) by the probability of rolling a six on any 
given roll. That's one in six, or 0.167,

and the answer is zero.

The party breaks into laughter. They love 
stumping the statistician. "The answer was five!"

So what happened?

In this analogy, a roll of a die represents a 
carcass killed,

so five rolls, five carcasses.

The probability of a six represents the probability 
that the carcass will be detected in our search 
process. It's as if, for every carcass 

we roll a die, if it comes up six, which it will on 
average about one in six rolls, we find a 

If it doesn't, on average five out of six rolls, we 
don't find the carcass.

In this case it just happened that none of the five 
carcasses were found.

Our job as statisticians

is to play that game, Stump the Statistician:

guess how many carcasses there are based on 
how many carcasses we found, and our 
estimate of the probability of finding a carcass.

Now let's look at this graphically.

On the X-axis is the number of possible rolls. It  
could be that they didn't roll at all,

or they could have rolled one or twice or maybe 
20 of 25 times.

On the Y-axis is the probability of observing 
what we did.

That is, observing zero 6s given the number of 
rolls on the X-axis. Of course, if you don't roll 

then the probability of observing no sixes is 

This is, in fact, the most likely case, and it is 
the maximum likelihood estimate.

But it's just barely more likely than the next 
case, when we roll once.

Even if you roll nine times

there is still a 20% chance that you will observe 
no sixes.

This means that if your overall probability of 
detecting a carcass is one in six, then even if 

there are nine dead animals out there, you have 
a 20% chance of observing none of them.

It's not until you roll 17 times that your 
probability of observing no sixes

drops to 5%.

So really it wouldn't be too surprising for us to 
have 10 or 15 animals out there and observe 
none of them.

So the main point I'm trying to make here is that 
even if we know precisely the probability of 
detection, which we don't (although we do with 

the roll of a die, but we don't in practice), the 
best we can do is bracket the range of possible 

In this case we can be 95% certain that there 
were fewer than 17 animals out there,

having observed none, and having a one in six 
chance of detection.

But now let's change the game. Instead of rolling 
a die, we flip a coin and count the number of 
heads observed.


the party reports zero heads, and again, I 
estimate no flips. In fact, no matter what the 

probability of the event when zero observations 
are made, my Horvitz-Thompson estimator,

the maximum likelihood estimate, will always 
give the best guess as zero. But more troubling, 
it will not give me any bracket on my estimate.

The variance around this is also zero.

I show you now the same graph as before, but 
modified to reflect the probability

of observing a head as being 0.5.

In this case, given that we observed zero, we 
can assert with 95% credibility

that we flip the coin four or fewer times.

Or, if this were a real-life search process,

having observed zero animals,

we could assert with 95% credibility that there 
were four or fewer animals killed.

But we can't claim, that is, we don't have 

that there were indeed absolutely none, even 
though we didn't find any.

So my second point

is that by increasing the probability of detection, 
we can narrow the bracket around our estimate 
of fatality.

As the probability of detection approaches one, 
the more evidence we have that the actual 
fatality was indeed zero, or very close,

when we observe none.

Our current site monitoring protocol and 
statistical tools that we have for estimating 
actual fatality from observed carcasses are fairly 

robust when the number of observed carcasses 
is relatively high.

But a non-zero estimate of the dead population 
using a Horvitz-Thompson based estimator can 

only be achieved if at least one carcass is found 
and even then

it is likely to be biased, particularly when we 
don't know the detection probability.

This type of estimator is not designed to 
address compliance.

When we expect to find a small number of 
carcasses, or maybe even zero carcasses, we 

need a new protocol and estimators that can 

give us precise estimates and allow zero or few 
observed carcasses to provide evidence

that a company is likely in compliance with its 

The approach we've developed

is based on Bayes theorem,

and it's appropriate when we expect low 
numbers, or even no observed carcasses and 
when we need 

precise estimates that the set limit has not been 

Our focus changes from asking "What is the 
estimated fatality given our observed count and 
our probability of detection?"

to "What is the minimum level of take we can 
reasonably rule out,

given our observed count (which might be zero)

and our probability of detection?"

With that, I would like to now introduce Dan 
Dalthorp, who will talk about the 

approach that we've developed based on Bayes 

talk about the Evidence of Absence software 
that he's written, and give examples of how the 
software can be used

to design protocols and estimate likelihood of 
compliance at sites with ITPs.

I'm going to start out by estimating the total 

of the Indiana bats in a single season to show 
how the Evidence of Absence software takes 

account all of the factors that Manuela 
discussed in her excellent introduction.

We'll start off with the sampling coverage, which 

the fraction of carcasses that land in the 
searched area,

rather than the fraction of the total area that has 
been searched. What goes into the 

sampling coverage is the fraction 
of turbines searched,

the search radius,

and the unsearched areas within the search 

The next very important factor is after the 
carcasses arrive in the searched area, they have 
to persist until

they're actually discovered on the ground.

So we model that (the scavenging process)

using a persistence distribution.

The exponential model is the most common,

and it's the most familiar, but we've also included 
the possibilities for some other more flexible 
models that in practice,

end up fitting the carcass persistence 
distribution a lot better. So here is a plot of the 

fraction of carcasses that remained in a search 

vs. the number of days that the trial has been 
going on. The best fit exponential function is 
shown and it doesn't really fit the line very well.

But a Weibull gives a lot better fit 
and that's fairly typical. Also 
typical is if the log-logistic or a log 
normal will fit better as well.

The next factor that goes into the estimation is 
the searcher efficiency, or the probability of 

finding a carcass given that it's out there at the 
time of the search.

And in this particular example we're going to find 
50% of the carcasses, given that they are there 
at the time of the search. 

You can also see with the searcher efficiency a 
confidence interval, and that's because this is 
based on field trials where

even if we know that we found 10 out of 20 
carcasses, there's still some uncertainty about 
what the actual searcher efficiency is.

It's going to be maybe 50%, it could be 60%, 
but it will be around 50%...the 0.4. to 0.6 range, 
0.5 searcher efficiency is 

typical of what you would see if you have 100 
carcasses in your search trial.

So if we take these three basic parameters:

the search coverage, searcher efficiency, and 
the probability that a carcass persists until the 
first search afterward,

we can combine those into a rough estimate of 
the overall detection probability as Manuela 
discussed earlier.

But, multiplying those three factors is only a 
rough estimate,

and there are two big issues. One is that it 
assumes that a carcass that is missed in one 

cannot be found in a later search.

We know that's not true. A lot of times if a 
carcass is missed in one search, if a search 
comes along

within the next few days there's a reasonable 
chance you can find it in the next search.

But if the

carcasses are missed in one search, they're 
probably less likely to be found on the next 
search because

carcasses tend to deteriorate with age. They get 
covered with dust, they get covered with leaves, 

they blow into a hole, they get dragged around 
and hidden by scavengers partially.

Also, the easy-to-find carcasses are removed 

and so in later searches the more difficult 
carcasses remain, and so naturally the searcher 
efficiency is going to decrease with each

successive search. Evidence of Absence 
includes a parameter for that and it's k 

we call it, and it's the factor by which searcher 
efficiency changes with each search.

And k can be estimated along with p in the 
search trials.

You can enter the sampling dates, what dates 
you have sampled,

enter an arrival function,

the arrival function tells when the carcasses 
arrive in the system (at what point in the 

 In most normal circumstances,

the arrival function is

not important. Where might be important as if all 
the carcasses arrive at the very very end of 

the monitoring season or if they all arrive at the 
very very beginning of the monitoring season.

All other patterns

really will not have much of an impact in most 
cases. So, in that case we just pick the uniform 
which is the simplest, and we can take some 

shortcuts in calculations if it's uniform.

The final box for inputs is the prior distribution.

The prior distribution

is an important part of the estimation because 
we base it on

Bayes' theroem, which requires a prior 

There are two big advantages for using Bayes' 
theorem over classical statistics, 

and the first is that it gives better accuracy when 
X is small. In particular,

if X is zero or one, we can get meaningful 
estimates out the other side and we cannot do 
that with classical statistics.

The other big advantage is that it offers the 
possibility of using

prior information

to improve the current estimates. In most cases 
there won't be good prior information available, 
and in that case the Bayes' estimation will be 

very similar to classical statistics. But when 
there is good prior information available,

the Bayes' analysis gives better estimates.

So if you don't have any prior information 
available, the option is to use a uniform prior,

which gives no external prejudice to the 
estimation and it just lets the data speak for 

The uniform prior is easy to use and it's easy to 

but it does not confer the advantage of 
incorporating reliable prior information. 

And, we don't have any, so in that case it's the 
natural choice.

Another possibility is to use a user-defined prior

where we do have

prior distribution available

that is reliable.

The advantage again is that there's potential for 
improved estimates.

But a big disadvantage is that it may be difficult 
to construct properly and it may be difficult to 

modeling choices. This is an advanced option 
that is available to advanced users.

And the third possibility is to use an informed 
prior. This can be used if there are search 
results from prior years at the same site.

The advantage is that you've got the improved 
estimates and we've already done the work of

of creating an informed prior and programming it 
into Evidence of Absence software for easy use.

A disadvantage is that it's limited in scope. We 
can only use data from one particular site in 
previous years for estimating

this year's take only.

And one final parameter that we enter is the 
level of credibility required

to conclude that a threshold is not exceeded. 
So this is

like a confidence bound in classical statistics. 
We want to be 80% sure that we're not 
exceeding the threshold in this case.

So, we enter all the parameters and we

plug it in and ask EoA to get to work for us

and it begins with calculating the detection 
probability with this

very messy looking formula that

Manuela showed a little bit earlier. It takes 
account of the arrival,

the persistence, the detection probability,

the decrease in deduction probability,

a factor to make sure that we don't

double count

carcasses, and it sums over all the potential

landing times for carcasses and all the potential 
search times.

So once we have that, we combine that into a 
Stump the Statistician game, all pre-

into a binomial just like Manuela was showing 
earlier, and then it takes another step of 

the overall detection probability

with the Stump the Statistician game

with a prior information

and constructs a posterior.

And then when it's all done

it gives us a nice graph that tells us everything 
we need to know.

In the upper right-hand corner, we 
can see that the overall detection 

 is 0.167 or about 1 in 6.

This is a little greater than the 
rough estimate of 0.13 that we got 
earlier by multiplying the coverage 

× persistence × searcher 

The difference is that the EoA 
model properly accounts for the 

that if you miss a carcass in one 
search, you could find it later on.  

That rough estimate we made 
earlier does not account for that 

so it tends to have lower detection 
probability estimates

than the more accurate EoA 

Below that, we see that the 
probability that the number of 
fatalities is less than or equal to

8 is greater than 80%.

In other words, we can be 80% 

that there were no more than 8 

Overall, what the graph shows us, 
on the X-axis

is the total number of fatalities that 
may have occurred

and on the Y-axis,

the probability that the number of 
fatalities in acutality exceeded the 
number on the X-axis.

For example, at m = 0,

the probability that the actual 
number of fatalities exceeded 
zero, given our observed data

or seeing zero carcasses in this 

the probability that the number of 
fatalities exceeded zero or more is 

On the far right hand side of the 
graph, we see that it's almost 
impossible, or a zero% probability

that there were greater than 35 
fatalities, it's very unlikely that 
there were more than 20 fatalities 

about 2%, and, in the edge of the 
red region, we see that

there's less than 20% chance that 
there are greater than or equal to 
9 fatalities

or we can assert with 80% 
credibility that the actual number 
of fatalities is somewhere in the 

red region between 0 and 8.

In other words, the red region 
gives us an interval estimate of the 
number of fatalities.

Given the strength of the search 
protocol and the fact that we 
found zero carcasses,

we can rule out the black bars 
with 80% credibility.

It may be natural to also ask what 
the single best point estimate of 
the number of fatalities is, but

there are a number of difficulties 
with this question when zero or 
few carcasses are observed. 

A naïve, rule-of-thumb point 
estimate that works well when 
there are a lot of carcasses 


is just to divide the observed 
number of carcasses by the 
detection probability

or X/ĝ.

But when X = 0, and no 
carcasses are found,

this estimate makes no distinction 
between the case where detection 
probability is 95%, 

and we could be fairly certain that 
there were no fatalities,

and the case where detection 
probability is 5%, 

where we can't rule out that there 
were 20, or 30, or even 40 

and we just happen to miss all of 

An estimator that makes no 
distinction between those very 
different cases

has some difficulties...and there 
are additional problems as well.

When g is small, X/ĝ is biased, 
and when X is small

X/ĝ is unreliable 

because the uncertainty is very 
high compared to the mean.

So, for example, if we want to 
demonstrate with 80% credibility 
that take was no more than tao=5,

when no carcasses are observed, 
we can use the software

to come up with a search plan that 
will get us the desired level of 

and we can take into account the 
searcher efficiency, the search 
coverage, and the search interval

can what combination of those 
would give us the required 
detection probability

to demonstrate with 80% 

and the solution is to use the 
design tradeoffs module to explore 
the possibilities.

So within the software, we enter 
the threshold

or the number of fatalities that we 
want to demonstrate we have not 

along with the credibility level, and 
in this case it's 0.8

or we want to be 80% sure that we 
have not killed more than 8

assuming that we have found zero 
carcasses overall.

We enter the persistance 
distribution and the k

just as we did before in the prior 
and the arrival function

and then we can look at the trade-
offs between searcher efficiency, 
coverage and search interval

to find out what combination of 
these parameters 

will give us the best optimal way

of attaining the credibility level that 
we require.

So in this particular example, 
we're going to go with a searcher 
efficiency of 0.5

and then we're going to compare 
coverages from 25% up to 100%

and compare search intervals 
from 1 to 14 days to see which 
one give us the best results.

We'll ask Evidence of Absence to 
draw the graph, and what we see 

in color, the probability that the 

the probablity that the number of 
fatalities exceeded the threshold, 

given that we've counted zero 

So let's start with the blue. The 
blue tells us that it is very unlikely

that we have exceeded the 
threshold if we are within search 
coverage of 90-100%

and we're searching on an interval 
of 1 or 2 days.

Under those conditions, we would 
be very unlikely to see

zero carcasses and so that's a 
strong sign that the mortality

does not exceed 5.

On the other hand, if we only 
searched 30% of the carcasses,

and our search interval were 10 
days, we'd be way up here in the 
yellow region

which would say we had very little 
evidence that the fatality did not 
exceed 5.

So what we're trying to do is

find the set of parameters that will 
get us

as far into this blue region as we 

and, what we've said, 

is the real target is to get us to 
80% credibility

which is only 20% probability

that we are in excess of the 

So we're shooting for this line, 
essentially, this 20% line.

And there are a couple of ways 
we can get to that 20% line.

The first is if we have 100% 
coverage, and we're searching

once every 9 days. Another 
possibility is to have 50%

coverage and search everyday.

There are actually a few ways we 
can get those parameters. One is 
if we search all turbines out to

a long search radius, far enough 
out so that we're sure that we'll get 
all the carcasses

and we search 100% of the area 
within each of those

then we can get coverage of 1. If 
we do that, we only have to 
search once every 9 days.

On the other hand, it's very 
difficult to search out

to search all the turbines, so 
maybe we can search half the 

and get a search coverage of 
50%. In that case, we'd need to 
search everyday

and those are 2 ways to get the 
same results.

But the top way is preferable. We 
have to search twice as many 
turbines, but

we're only searching them once 
every 9 days instead of 

everyday. 22% as much effort, 
but we get the same results

of 80% credibility.

Another possibility

is that we search all of the 
turbines, and we have a choice

are we going to search them out 
to a long, long radius?

Far enough so that we have 100% 
coverage, and

in that case, we'd have to search 
once every 9 days.


we can search every turbine, but 
only go out to 30 meters

which might cover half the 
carcasses, so our coverage would 
be 50%

but if we did that, we'd have to go 
out every day.

In this case, the area, or the ratio 
of the areas,

would be 30 squared or 100 
squared, looking at the area within 
30 or 100 meters

but we'd have to do 9 times as 
many searches, because we're 
searching everyday

instead of once every 9 days. But 
in that case, we'd end up with

81% as much effort as the other 

So we can use the software

to help design what set of 
parameters would be the most 
efficient to get the results

that are required.

Another trade-off we might 
consider is searcher efficiency 
vs. the sampling coverage.

If we have higher searcher 
efficiency and greater coverage,

well that puts us more into the blue 
range. If we have low searcher 

and low coverage, that gets us 
into the yellow range. Not good.

To get on to the target, what's the 
best way to do that?

One way this trade-off can work in 
practice is

to compare road and pad 
searches vs. cleared plots.

The road and pad searches, you 
can get great searcher efficiency

for bats, you can be in the 
neighborhood of 80%, but the 
roads and pads only cover

a small part of the area

that usually comprises around 
15% or less of the carcasses.

You can increase the coverage by 
searching on cleared plots,

but the search conditions tend to 
be more difficult on cleared plots

and so the searcher efficiency 
might be only 30%, but

the amount of coverage can go up 
to 80, 90, 100%. Expensive, but it 
can be done.

So if we translate those ideas over 
to our graph, the road and pad 

we can get the high searcher 
efficiency, 80-90%, 

but we can only get a coverage of 
15%, roughly,

which brings us well short of the 
target 20% line.

So to move up to that line, it's 
going to be tough to get more 
searcher efficiency

but if we get higher coverage, we 
can move up to that line, as long 

our searcher efficiency doesn't 
get too low.

What we can do is, add some 
cleared plots, enough to boost the 
coverage to 50%,

and as along as our searcher 
efficiency doesn't drop below, say 

we'll be at the target of 0.2.

A third feature of Evidence of 
Absence that will come in very 
handy is

estimation of a multiple year total.

So for example, we have several 
years of search data

and we want to estimate not the 
total in any particular year, 

but the total that have accumulated 
throughout a number of years

through years 1, 2, 3, and so on.

The software is used to calculate 
the detection probability for each 
of the years

and then, the series of data is 
entered into a multiple years 

That looks something like this.

So in the first year, we had a 
detection probability of 0.3, 

within some confidence interval, 
and didn't find any carcasses. 
Didn't find any the second year

detection probability was slightly 

and in the third year, we did find a 
carcass, and at the same time,

we changed the search protocol 
to only do roads and pads and 
only got 7% 

detection probability in that year 
instead of the 25-30% we had in 
the previous years.

Combining all this data, we can

ask Evidence of Absence to 
estimate the total number of 

and again

it will give us the posterior 
distribution for the total fatalities

over the 3 years. In this case, we 
found one carcass

the detection probability averaged 
20% over the 3 years, 

and we can rule out fatalities 
exceeding 15

but 15 or fewer, it could well be.

As a quick sanity check, let's 
divide our count

which was 1, we found 1 in the 
third year, divide that by the 
detection probability of 0.21

and that is 5, roughly. So if we go 
up to 5, well that puts us in about 

the 50, 60% zone so there is a 
reasonable chance that we have

more or less than 5, that's kind of 
in the center of the distribution.

But if we want to be sure that we 
have fewer than a certian number

we run up to the roughly 80% 
credibility level or confidance level 
in classical statistics.

A final use of the Evidence of 
Absence software that we're going 
to discuss today is

in the context of a long-term 
permit. For example, suppose a 
30-year permit

allows an average take of 2 per 

In other words, we can allow 60 
total over 30 years.

Ideally, what we could do is track 
the true number of fatalities

and when that true number of 
cumulative fatalities exceeds the 
threshold of 60

that we're allowed over 30 years

Then we impliment

some sort of adaptive 
management action because the 
fatalities have exceeded the 

threshold and we're not in 
compliance with the permit 

Unfortunately, we never know for 
sure the true number of fatalities 
and we need to estimate.

The simpliest, most obvious 
approach would be to use 
Evidence of Absence and track 

the cumulative fatality though time.

So we can do that, and end up 
with a graph of the estimated 
number of fatalities

and we can see that the total 
estimated fatalities exceeds

the premitted threshold of 60 at 
year 26, which is 1 year after the 
true fatality exceeded that 26.

It could be a year or 2 late, or it 
could detect it a year or 2 or 3 

there's a lot of uncertainty about 
when it will kick in, but ideally on 
average it'll be pretty close.

But there are some additional 
issues. First, the population my 
well be able to sustain a take of 

T spread out over 30 years, but 
the long-term trigger will not 

that take from happening over the 
period of just a few years. 

And if that take is all 

concentrated in the first few years 
of a project, say

it may be difficult for the 
population to recover.

Another issue is that the acutal 
take rate

which we're going to call lambda, 
may not be at all in line

with the premitted take rate, which 
we'll call tau

and we may get a signal of that 
very early in the project and have

warning that we can do something 
to get the project on track.

So if take rate does look like it's 
higher than the premitted rate, 

we can implement some sort of 
adaptive management

to reduce that rate to avoid the 
final take 

rate exceeding what's allowed 
from the permit in the end

or in other cases, we might adjust 
the permitted take level to match 
what's really going on in the field.

So if we could design a 
secondary trigger that will fire to 
give warning that our

take rate is sustainable under the 
permit, that could be very helpful 

bringing down the take rate to 
acceptable levels and with 
maintaining compliance

with the permit. So the goal is to 
define a short-term trigger to 
signal when

the actual take rate is out of line 
with expectations, tau.

take of 60 in 30 years is much more strict than 
take within 30 years

So to do this, the first thing to note 
is that an annual take

of 2 is much more strict than 
permitting a total of 60 over 30 

For example, 

this is a graph of the exact same 
data that I showed you earlier

with the cumulative take 
approaching 60 over the course 
of the permit

but instead of looking at 
cumulative take, it's the year by 
year take.

This data was all generated from a 

that has a mean of 2 per year, 

but about half the time,

you're getting take that exceeds 2, 
half the time you're getting take 
that is less than 2.

So if the idea is that your take can 
never exceed 2

Well, the first year of the project, 
you've exceeded 2

and you're in violation of the 
permit if

you define the permit in that way.

So the point is that allowing a total 
take of 60

in 30 years is much less strict 

requiring take to be less than or 
equal to 2 each and every year.

So we'll step back and think about 
this for a moment

if the primary concern is the 
effect on viability of a population

or whether actual take is in line 
with expectations,

a short-term trigger to test whether 
the average take rate is 
compatible with the data that we're

seeing, we can define a short-
term trigger to test whether the

probability that the actual rate 
exceeds the threshold

is too high.

So more intuitively, we can look at 

annual take over the course of the 
30 years

and see if this pattern of take

is compatible with an average take 
rate of 2 per year.

So if you think about it for a 

if the average take were 1 per 
year, it would be pretty

strange to see a pattern like this 
because almost every year, 

it exceeds 1, some years it's 
below, but typically it's well above 

So an average take rate of 1, 
highly unlikely with data like that.

On the other hand, an average 
take rate of 4

also highly unlikely

usually we're well below 4

sometimes we're above 4, but it's 
almost implausible to have a take 

of 4 if that's our data. So what we 
can do is draw a 99% confidance 

around the average take rate and 
it's going to be somewhere 
between 1.33 and 3.

Of course, for a short-term 
trigger, we don't want to go the

whole length of the project before 
we run our test, we want to check 
it over the short term.

So those first 3 years, what is a 
plausible range of actual take

average rate over these first 3 

Well, the most likely is a take rate 
of around 3 on average

but if it's 4

is this a plausible pattern to see? 
Yeah, it doesn't look bad.

Or if the rate were 8, it would be 
surprising to see that many counts 
so much smaller than 8.

Or if it were 1, it would be 
surprising to see that many counts 
so much greater than 1.

So what range of plausibility is 
there on that average take rate 
over that period of time

we can calcluate a 99% credibility 

using Evidence of Absence and 
we come up with a range

somewhere between 1.61 and 

is the plausible range for take in 

particular 3-year span.

The most extreme 3-year span in 
this 30-year period is right in this 
case where they're all above 2

and the total is 4 + 5, 9, 11, 
12...12 in a 3-year span

and it turns out that creating a 
99% credible interval for the take 
rate in that period

we come up with 2.06 to 7.68, 
which excludes 2.

So the permitted take level is 
outside the plausible range

of what the take actually is. In that 
case, the short-term trigger fires.

So with our short-term trigger, 
we're going to look at 3-year 
moving averages

and test whether the data from the 
3 years is compatible, or not, with 
a permitted take of 2 per year.

So we check in that 3 year 
period, and we do that throughout 
the whole period of

the permit and at one point, the 
data were

not very compatible with an 
average rate of 2 per year.

Ok, so the short-term trigger has 
indicated that the actual take does 

line up with the expected take or 
the threshold.

And so

there are a couple of scenarios 
where that can happen. One, is 
that the average take rate is 

clearly higher than anticipated. In 
that case, there are a couple of 

One is to implement some sort of 
minimization and monitoring. It 
could be, in the case of bats,

curtailment at lower wind speeds,

increased monitoring to make 
sure that

our rate really is in line with 

Or another option would be to 
reset the take limit based on the 
projected take

but require some sort of mitigation 

Another scenario would be that 
the observed take rate is

significantly lower than anticipated 
at the start of the project.

For example, with bats in the 
Midwest, there's new discussion 
about permitting

with a prophylactic curtailment 
required at 5 meters per second 
windspeed at the start of projects

but if the average take rate is 
clearly lower than anticipated,

one option might be to loosen that 
5 meter per second requirement

and allow either free operation or 
curtailment at

not quite as extreme levels.

However, to demonstrate that 
average take rate is lower than a 

is more difficult than 
demonstrating actual take in a 
given year is lower than threshold.

So this turns out to be a multiple 
year proposition, but

over the course of several years, 
it could turn out to be

an important aspect of fatality and 
wildlife management.

Thank you, Dan. So in summary, I 
want to emphasize

that it's the combination of what 
was actually killed and our 
probability of detection

that results in what we observe.

If actual fatality is very small, 
we're quite likely to find none.

But if actual fatality is large, and 
our detection probability is low, 

then we are also quite likely to find 

So when we're seeking evidence 
of compliance with an incidental 
take permit

it's quite important for us to 
distinguish these two cases.

And the current Horvitz-Thompson 
based estimators that we have 
available to us

and there's several

can't provide the answers that we 

The optimal monitoring protocol 
may be very different

if your objective is to estimate 
fatality of general groups of 
species, like bats or passerines,

than when your objective is to 
provide evidence of compliance 
with an ITP for a rare species.

The target probability of detection 
is determined by the incidental 
take permit itself

by the limit that's set.

And it won't differ by the species.

What will differ is the cost of 
achieving it. 

As generally, large species are 
easier to detect than small ones.

But the Evidence of Absence 
software provides tools for 
optimizing design

under site-specific conditions.

The protocol can be quite flexible 
and can trade off search area,

search interval, and sampling 

The new Evidence of Absence 
modules that Dan talked about 

take advantage of continuous, 
low-level monitoring over the life of 
the project to identify,

on a short-term basis, when 
compliance has not been met.

and on a long-term basis when the 
total take exceeds the overall 
permitted take.

This approach, based on Bayes' 
theorem, can be used to provide 

to pre-construction risk models. It 
can inform post-construction 
monitoring design,

and can be used to inform 
management decisions.

The calculations are not simple,

but we've packaged them into a 
user friendly, peer-reviewed 
package called 

Evidence of Absence, the 
software which is publicly 
available at this website.

It's available for download, it has a 
users' guide with it

and as always, we are very very 
pleased to hear feedback from 
our users.

So with that, I want to again 
acknowledge the financial support 
of the Fish and Wildlife Service

and the USGS, as well as our co-
authors David Dail, Lisa Madsen, 
and Jessica Tapley

for their contributions. And of 
course, thank you for your