einat sandbank , robert glazer , gabriel delgado , and ...€¦ · implementing an adaptable survey...

Implementing an adaptable survey technique for population assessment

Einat Sandbank1, Robert Glazer1, Gabriel Delgado1, and Erin Leone2

1Florida Fish and Wildlife Conservation CommissionFish and Wildlife Research Institute

Marathon, Florida USA

2Florida Fish and Wildlife Conservation CommissionFish and Wildlife Research Institute

Center for Biostatistics and ModelingGainesville, Florida USA

Queen ConchLobatus (Strombus) gigas• Large, long lived marine gastropod

Queen ConchStrombus (Lobatus) gigas• Large, long lived marine gastropod

Conch Life Cycle

MatingSpawning

Egg Strand

LarvaMetamorphosis(3-4 weeks)

Adult(~4 years)

Juvenile(1 year)


• Found in the tropical western Atlantic



• Was once an abundant fishery in Florida

• Now closed and protected



• Was once an abundant fishery in Florida

• Now closed and protected

• FWC has been monitoring the species for almost 30 years• 1993-2013 aggregation based

methodology• 2014-present stratified adaptable

sampling

Goals• Improve our knowledge about queen conch

abundance and distribution Keys-wide

• Develop a methodology that can be used to survey animals where little information is available

Previous Knowledge

• 40 known queen conch aggregations

Previous Knowledge

• 40 known queen conch aggregations

• Size and shape of aggregations were mapped

• Density and abundance calculated

• Habitat preferences• Substrate • Depth

Sampling Area • Fishnet tool

• Grid of 100 meter by 100 meter squares

• Northern border on Hawk Channel

• Southern border on 60 foot contour line

Sampling Area • Fishnet tool

• Grid of 100 meter by 100 meter squares

• Northern border on Hawk Channel

• Southern border on 60 foot contour line

• Four geographic regions

• Upper Keys• Middle Keys• Lower Keys• Marquesas

Sampling Area

• Three zones• High

• Aggregation area shapefiles (with buffer)

• Medium• 750m from northern edge

of aggregation to 60 foot contour

• Low• Hawk Channel to medium

zone

Site Allocation • ~250 sites

chosen randomly

• 15% in Low Probability Zone

• 70% in Medium Probability Zone

• 15% in High Probability Zone

• at least two cells within each high probability aggregation are surveyed

Site Allocation

N

Weight

W E

S

Weight

Field Surveys

• Surveys start at the center of each cell

• One diver lays out a 30 meter transect tape in all four cardinal directions

Field Surveys

• Surveys start at the center of each cell

• One diver lays out a 30 meter transect tape in all four cardinal directions

• A second diver follows and collects data

In the Lab

• Maps changed to reflect the best new information

• Cells reclassified, if needed, based on field data and observations

Original map showing the locations where conch were found during the

2014 sampling season

Altered map based on the number of conch found and the habitat in

the surveyed cells

Abundance Calculations

N = total area, �𝑦𝑦 = mean density, n = sampled area, and y = sample mean

�̂�𝜏 = 𝑁𝑁�𝑦𝑦 =𝑁𝑁∑𝑖𝑖=1𝑛𝑛 𝑦𝑦𝑖𝑖

𝑛𝑛

• Abundance was calculated for each probability zone in each region separately

Region Probability Ha Total Cumulative Sampled Mean Var total SD SE P sample

UpperHi 82 4,100 4,100 18 3.722222 32.44771

Upper Abundance 173,274 112,009

Keys-Wide Abundance 2,486,075 595,278

0.069231Med 25,282 1,264,100 1,268,200 56 0.125 0.438636 0.215385Low 21,616 1,080,800 2,349,000 15 0 0 0.057692

MiddleHi 55 2,750 2,351,750 11 4.090909 38.49091

Middle Abundance 570,685 200,274

0.042308Med 11,385 569,250 2,921,000 58 0.982759 7.175136 0.223077Low 13,914 695,700 3,616,700 7 0 0 0.026923

LowerHi 140 7,000 3,623,700 16 2.9375 27.12917

Lower Abundance 344,524 162,507

0.061538Med 11,189 559,450 4,183,150 36 0.416667 2.078571 0.138462Low 5,452 272,600 4,455,750 3 0.3333 0.3333 0.011538

MarquesasHi 35 1,750 4,457,500 6 6.833333 40.56667

Marquesas Abundance 1,397,592 524,682

0.023077Med 7,185 359,250 4,816,750 29 2.103448 15.66749 0.111538Low 2,739 136,950 4,953,700 5 4.6 54.8 0.019231

Population Estimates using original map

Region Probability Ha Total Cumulative Sampled Mean Var total SD SE P sample

UpperHi 82 4,100 4,100 18 3.722222 32.44771


Keys-Wide Abundance 2,486,075 595,278

0.069231Med 25,282 1,264,100 1,268,200 56 0.125 0.438636 0.215385Low 21,616 1,080,800 2,349,000 15 0 0 0.057692

MiddleHi 55 2,750 2,351,750 11 4.090909 38.49091

Middle Abundance 570,685 200,274

0.042308Med 11,385 569,250 2,921,000 58 0.982759 7.175136 0.223077Low 13,914 695,700 3,616,700 7 0 0 0.026923

LowerHi 140 7,000 3,623,700 16 2.9375 27.12917

Lower Abundance 344,524 162,507

0.061538Med 11,189 559,450 4,183,150 36 0.416667 2.078571 0.138462Low 5,452 272,600 4,455,750 3 0.3333 0.3333 0.011538

MarquesasHi 35 1,750 4,457,500 6 6.833333 40.56667

Marquesas Abundance 1,397,592 524,682

0.023077Med 7,185 359,250 4,816,750 29 2.103448 15.66749 0.111538Low 2,739 136,950 4,953,700 5 4.6 54.8 0.019231

Population Estimates using original map

Population Estimates using updated map Region Probability Ha Total Cumulative Sampled Mean Var total SD SE P sample

UpperHi 96 4,800 4,800 20 3.7 29.06316


Keys-Wide Abundance 195,822 47,026

0.076923Med 14,501 725,050 729,850 27 0 0 0.103846Low 32,260 1,613,000 2,342,850 42 0 0 0.161538

MiddleHi 97 4,850 2,347,700 26 3.846154 25.89538

Middle Abundance 45,112 19,1040.1

Med 11,377 568,850 2,916,550 43 0.046512 0.045404 0.165385Low 8,806 440,300 3,356,850 7 0 0 0.026923

LowerHi 157 7,850 3,364,700 18 3.333 24.70588

Lower Abundance 72,560 35,3960.069231

Med 11,135 556,750 3,921,450 36 0.083333 0.135714 0.138462Low 1,265 63,250 3,984,700 1 0 0 0.003846

MarquesasHi 97 4,850 3,989,550 16 7.75 27.5333

Marquesas Abundance 60,390 23,670

0.061538Med 8,665 433,250 4,422,800 19 0.052632 0.052632 0.073077Low 2,927 146,350 4,569,150 5 0 0 0.019231

Future Work

• Changes are still being done to the probability zones

• With time, cell reclassifications will decrease

Future Work

• Changes are still being done to the probability zones

• With time, cell reclassifications will decrease

• Predictive modeling• Can we predict conch presence

using environmental parameters?

einat sandbank , robert glazer , gabriel delgado , and ...€¦ · implementing an adaptable survey...

Documents