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__American Alligator__

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__Production Index (API) Model__

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**Empirical basis and model assumptions:**

The ATLSS American Alligator Production Index (API) Model
was developed as a coarse indicator of
the yearly production potential (probability of producing nests and offspring
successfully) for the American Alligator in South Florida, based upon local
habitat and hydrologic conditions. The API model addresses only the effects of
relative local habitat quality and hydrological dynamics on production. Consequently, this model should not be
interpreted as providing estimates of
population dynamics or viability.

Spatial Constraints. - The spatial resolution for the model
is 500x500 meters. Historical
observations suggest that this roughly corresponds to the home-range of nesting
female alligators, so it is a useful scale of resolution. All data (water depth, vegetation type,
ground elevation, breeding indices) represent values for a 500x500 meter area.

Temporal Constraints. - The temporal resolution for the
model is one day for all water data (height and depth) and is static for the
vegetation habitat types. The model produces a single yearly value for each
spatial cell that takes account of the daily water data affecting the nesting
and offspring production during that year.

Model Components

Breeding. - Water levels encountered during the period
ranging from May 16 of the current nesting year to April 15 of the previous
year are used as an indicator of the probability of breeding occurrence in an
area. The probability that nesting will occur correlates positively with the
amount of time spent in flooded conditions during this period. This model component is defined to be the
proportion of this period for which there was water depth greater than 0.5
feet. Biologists at ARM
Loxahatchee NWR have suggested that a static value of 1.0 for this model
component is appropriate for WCA 1.

Nest Construction. - The mean water depth during the peak of
the mating season from April 16 through May 15 is used as an indicator of the
probability that mating and nest construction will occur in a given area. Two
linear functions are applied to define the value of this model component such
that the highest probability of nest construction occurs at a mean level of 1.3
feet. Mean water depth values higher or lower than this reduce the probability
of nest construction.

Nest Flooding. - The probability of a nest being flooding is
calculated from a combination of the mean water level during nest construction
and the maximum water level during egg incubation. Field observations indicate that the mean water level between
June 15 and June 30 will determine the elevation at which a nest will be
constructed. A linear function is
applied to the difference between the maximum water level during the egg
incubation period (July 1 through
September 1) and the mean water level during nest construction to give the
probability of nest flooding.
Biologists at ARM Loxahatchee NWR have suggested that a static value of
1.0 for this model component is appropriate for WCA 1.

Relative Habitat Quality - Available evidence suggests that
the type of vegetative cover and elevation within an area greatly influence the
probability of nesting. This model uses
a static ranking of the dominant vegetation type within a 500-meter spatial
cell as a measure of habitat quality.

**Selected References**

Fleming, D. M. 1991. Wildlife Ecology Studies, Annual Report,
South Florida Research Center, Everglades National Park, Homestead, Fl,
V-10-1-52.

Kushlan, J. A. and T.
Jacobsen. 1990. Environmental Variability and Reproductive Success of
Everglades Alligators. J Herpetol. 24(2):176-184.

Mazzotti, F. J. and L.
Brandt, 1994. Ecology of the American
Alligator in a Seasonally Fluctuating Environment. Pgs:485-505 in S. M. Davis and J. Ogden (eds.) Everglades: The
Ecosystem and Its Restoration. St.
Lucie Press, Delray Beach, Fl.

__Flow Chart for Construction of American Alligator
Production Index (API) Model
Page 1
Page 2
Yearly Breeding Cycle
__

The flow chart shows the steps in computing an
index value for a cell.

__Calculation of effects of water levels during periods of
year__

Four periods of the year are used:

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(1) period from April 15 of previous year to May 16 of current
year;

The model iterates over this time period and sums up all of
the days in which the water depth is less than 0.5 feet. Then the proportion of non-nesting days
during that period with water depth less than 0.5 feet (proportion_dry_days) is
computed;

proportion_dry_days =
[1.0 - (non_nesting_days - dry_days)/non_nesting_days]

This is used to compute the probability of females breeding
in a cell located at x,y, as

females_nesting(x,y) =
1.53 - 4.88*proportion_dry_days(x,y)

This is set to 1.0 if females_nesting(x,y) > 1.0 and to
0.0 if females_nesting(x,y) < 0.0

This value of females_nesting(x,y) is rescaled for each cell
by using the maximum and minimum values of this quantity on the entire modeled
grid. The rescaled value is

females_nesting
= 1/(MAX(females_nesting) -
MIN(females_nesting))*(females_nesting -

MIN(females_nesting))

(2) peak mating season, April 16 through May 15 of current year;

The current water levels are kept track of and the mean
water depth (mean_mating_depth) during mating is calculated by averaging over
the values of (mating_water_depth) during that period. This is used to determine if a nest will be
built. If the mean water depth is less
than 1.3 feet, then the probability of a nest being built is

prob_nest_built(x,y) =
0.212 + 0.457*mean_mating_depth

If the mean water depth is greater than 1.3 feet, then the
probability of a nest being built is

prob_nest_built(x,y) =
3.15 - 1.67*mean_mating_depth

This value is set to 1.0 if prob_nest_built(x,y) > 1.0
and to 0.0 if prob_nest_built(x,y) < 0.0

The variables females_nesting(x,y) and prob_nest_built(x,y)
are combined into a single variable

nesting(x,y) =
(females_nesting(x,y) + probability_nest_built(x,y))/2.0

(3) peak early nest construction season, June 15 through June
30;

The probability of a nest being flooding is calculated from
a combination of the mean water level during nest construction and the maximum
water level during egg incubation. The
current water levels (nest_water_depth) during this period are kept track of
and the mean value during this time (mean_nest-water) is calculated. This will provide an estimate of the
elevation at which the nest is built.

(4) incubation period, July 1 through September 30

A linear function is applied to the difference between the
maximum water level during the egg incubation period (July 1 through September
1) and the mean water level during nest construction to give the probability of
nest flooding. An indication of
flooding potential is given by the difference between the maximum water depth
during the incubation period (max_gest_water) and the mean water depth during
the nest-building period (mean_nest_water)

flooding(x,y) =
(max_gest_water - mean_nest_water)*2

In addition to factors derived from these hydrologic
conditions, a habitat weighting factor, representing the quality of different
FGAP habitat types, is defined:

Relative Habitat Quality - Available evidence suggests that
the type of vegetative cover and elevation within an area greatly influence the
probability of nesting. This model uses
a static ranking of the dominant vegetation type within a 500-meter spatial
cell as a measure of habitat quality.
Habitat quality is given by the parameter habitat_weight. If this is zero for a cell, then there is no
reproduction.

The values of habitat_weight are:

__ Veg. Class Weight Veg.
Class Weight__

0 0 22 0

1 1.0 23
1.0

2 1.0 24
0.6

3 Exclude 25
0.5

4 0 26
Exclude

5 0 27
0

6 0 29
1.0

7 1.0 30
0.7

8 0.1 31
0.5

9 Exclude 32
0.4

10 0 33
0.5

12 0 34
0.8

13 0 35
0

14 0 36
0

15 1.0 37
0

17 Exclude 38
0.7

18 0 39
0.5

20 1.0 40
0

21 0 41
0

42 0

The potential for alligator nesting success is then
calculated as a combination of the terms representing nesting, habitat quality,
and the potential for flooding.

gator_potential(x,y) =
((nesting(x,y) * NESTING_WEIGHT) + habitat_weight(x,y)

+
(1.0 - flooding(x,y))*FLOODING_WEIGHT)/FACTOR

where: NESTING_WEIGHT = 2.0

FLOODING_WEIGHT
= 3.0

FACTOR
= 6.0