Einat Sandbank , Robert Glazer , Gabriel Delgado , and ...€¦ · Implementing an adaptable survey...
Transcript of 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)
Queen ConchStrombus (Lobatus) gigas• Large, long lived marine gastropod
• Found in the tropical western Atlantic
Queen ConchStrombus (Lobatus) gigas• Large, long lived marine gastropod
• Found in the tropical western Atlantic
• Was once an abundant fishery in Florida
• Now closed and protected
Queen ConchStrombus (Lobatus) gigas• Large, long lived marine gastropod
• Found in the tropical western Atlantic
• 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
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
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
Upper Abundance 17,760 5,774
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?