DEER POPULATION MANAGEMENT Lecture ... amazonaws com
Lecture Topics DEER POPULATION MANAGEMENT Guest Lecture: WF 4153/6153 Dr. Steve Demarais
Population Concept
• Population Concept and Important population characteristics • Population growth and regulation • Estimates of habitat carrying capacity
Deaths
Births
Population
• Group of animals occupying a common area • Basic management unit to which we apply our “tools” • Boundary depends on animal movement potential, habitat variation, management intensity, and barriers to movement
Adults
Males
Females Young
Dispersal Immigration
Management Success • Depends on ability to manipulate or control population composition (sex ratio, age structure, density) relative to management goals • Without that ability – you are only managing the HARVEST, not the population
Emigration
Important Population Characteristics • Fawn recruitment • Sex ratio • Age structure • Density
1
Fawn Recruitment • Addition of young animals from reproduction
• Factors affecting recruitment
Importance of Fawn Recruitment Can Be Used to Determine Harvest Harvest = Recruitment – Mortality
•Reproductive rates of adults •Survival of newborns
(assuming immigration = emigration)
• Estimated by •Corpora luteal and fetal counts •Fawn at heel counts from incidental observations, spotlight counts, etc.
Sex Ratio (buck:doe) • Polygynous breeding strategy allows a range of biologically-acceptable ratios • Balanced (1 buck: 1-2 does) – More, older bucks and fewer fawns
• Unbalanced (1 buck: 3+ does) – Fewer, younger bucks and more fawns
Age Structure • Older age structure improves antler development and maybe breeding season • Estimated using harvest data – Percent of harvest by age class – Absolute harvest per area – Results may vary (see WMAs and UPC DMAP)
• Estimated using survey data – Percent by age class or antler class
Density • Number of animals per unit of habitat • Variety of methods used to estimate
Techniques to Estimate Population Characteristics • Harvest-Based : Population reconstruction • Animal-Based : Survey and extrapolation • Habitat-Based : Relative Density
2
Is The Effort Justified?
Population Reconstruction
Effective management requires knowledge of existing population characteristics and future responses to management actions!
• Determine the minimum possible number of individuals alive in a given cohort for a given year by summing all the individuals from that cohort retrieved in subsequent years.
• The virtual population estimate approaches reality as the proportion of each cohort ultimately recovered approaches 100%.
Problems Associated With Harvest Data
Population Reconstruction Assumptions:
• Differential vulnerability among sex and age classes
• The proportion of deaths accounted for is relatively constant over time for each cohort
• Selectivity among hunters
• Age determination is accurate
• Incorrect age determination
ORIGINAL HARVEST RECORDS (BUCKS)
ORIGINAL HARVEST RECORDS (BUCKS)
Age
Age
Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
0.5 18 8 25 32 11 8 19 2 0 0 2 13 11 12 8 11
1.5 55 23 39 23 23 29 22 2 13 4 20 39 23 24 27 19
2.5 47 49 60 23 56 92 83 23 90 15 42 48 56 59 48 97
3.5 31 58 39 29 54 70 58 29 56 66 86 30 19 23 15 30
> 4.5 44 96 41 45 65 61 34 8 13 70 100 12 19 18 16 41
Total kill 195 234 204 152 209 260 216 64 172 155 250 142 128 136 114 198
Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
0.5 18 8 25 32 11 8 19 2 0 0 2 13 11 12 8 11
1.5 55 23 39 23 23 29 22 2 13 4 20 39 23 24 27 19
2.5 47 49 60 23 56 92 83 23 90 15 42 48 56 59 48 97
3.5 31 58 39 29 54 70 58 29 56 66 86 30 19 23 15 30
> 4.5 44 96 41 45 65 61 34 8 13 70 100 12 19 18 16 41
Total kill 195 234 204 152 209 260 216 64 172 155 250 142 128 136 114 198
Mean SE %
11.25 2.22 6.36
24.06 3.23 13.61
55.50 6.19 31.39
43.31 5.20 24.50
42.69 7.28 24.14
176.81 13.34 100.00
Mean SE %
11.25 2.22 6.36
24.06 3.23 13.61
55.50 6.19 31.39
43.31 5.20 24.50
42.69 7.28 24.14
176.81 13.34 100.00
3
RECONSTRUCTED BUCK POPULATION Age Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 Mean SE %
0.5 195 185 208 213 165 179 277 128 95 105 136 151 148 201 156 142 167.75 11.40 28.29
1.5 188 177 177 183 181 154 171 258 126 95 105 134 138 137 189 148 160.06 9.83 26.99
2.5 146 133 154 138 160 158 125 149 256 113 91 85 95 115 113 162 137.06 10.13 23.11
3.5 127 99 84 94 115 104 66 42 126 166 98 49 37 39 56 65 85.44 9.33 14.41
> 4.5 44 96 41 45 65 61 34 8 13 70 100 12 19 18 16 41 42.69 7.28 7.20
Minimum Sum of the bucks alive cohort f x 700 690 664 673 686 656 673 585 616 549 530 431 437 510 530 558 593.00 22.41 100.00
685 676 689 626 499 695 1057 477 349 350 420 501 498 644 528 469 572.69 43.55
Population Reconstruction Problems • Sensitive to changes in harvest
Population Reconstruction Works best for intensively managed populations where you can account for most of the annual mortality (harvest and nonharvest). It is a MINIMUM estimate.
Animal-Based Survey and Estimates Not A Total Count!
• Harvest is sensitive to hunter effort and hunter selection biases and harvest regulations
Highly secretive – can’t count all animals
• Tells you what happened 5 years ago
Habitat visibility and variation – can’t survey the entire area Total counts almost impossible
Common Population Sampling Methods •
Spotlight Survey
•
Camera Survey
•
Hunter Observation
Spotlight Surveys
4
Spotlight Surveys
Visibility (width of survey) estimated every 0.1 mile
Stratify your sample and sample the habitat proportionately. Bottomland Hardwood
Pine
Area Surveyed = average width x length ex., 275 ft. x 52800 ft. = 14,520,000 sq. ft. = 333 ac. Replication : 3-5 times and take the average values Similar weather and moon conditions (avoid full moon) Pasture Pine / Hardwood Mix
Population Estimates Density = deer / acre
Camera Technique
ex., 33 deer/333 ac. = 1 deer / 10 acres Research Advance Photo
Sex Ratio = # bucks/# does ex., 6 bucks/12 does = 1 buck/2 does Fawn Crop = (# fawns/# does) x 100 ex., (15 fawns/12 does) x 100 = 125 %
Bring the deer to you!
Only use “known” identifications
Camera Technique
Estimate is based on the relationship between the number of unique bucks photographed and the total number of bucks photographed.
Camera Technique “Population Factor”
Total Bucks Photographed = 178 Unique Bucks Photographed = 37 Proportion of Unique Bucks = 37 ÷ 178 = 0.21 This is called the “Population Factor” This is used to determine the number of unique does and fawns photographed
5
80% or 0.8 of the population is observed 100%
Estimate the number of unique deer using the “population factor”
90% 80% Winter Fall
70%
Total photographs of bucks= 178 Total photographs of does= 236 Total photographs of fawns= 124
× 0.21 = 37 Does = 236 × 0.21 = 49 Fawns = 124 × 0.21 = 26
If 7-day survey in winter with a camera density of 1 per 100 acres...
Extrapolation Factor
Camera Technique
60% 50% 40% 30% 20%
Bucks = 178
10%
= 112 Total Deer
0% 1
2
Estimate the number of deer on the property using the “extrapolation factor” 0.8 of the population surveyed (from graph) Extrapolation Factor = 1 ÷ 0.8 = 1.25
× 1.25 = 46 Does = 49 × 1.25 = 61 Fawns = 26 × 1.25 = 33 Bucks = 37
= Total of 140 Deer
Hunter Observations
3
4
5
6
7 8 DAYS
9
10 11 12 13 14
Camera Technique
Considerations •
Attractiveness of bait relative to natural forage
•
Closed population is best
•
Equipment expense – minimize by moving cameras
•
Excellent educational tool – shoot/don’t shoot examples
•
Conservative Approach –MINIMUM Population
Hunter Observations Potential Problems • Accuracy • Consistency Positives • “free data” • Hunter involvement • People believe their own results!
6
Hunter Observations • Hunters record # of does, bucks, and fawns seen every outing • Early seasons best (e.g., archery season) • Daily use card or notebooks in stands
Habitat-based Index to Deer Density Browse Survey
Hunter Observation Estimates • Sex ratio – good estimate, but may be biased against sex with heavier harvest pressure • Fawn Crop – good estimate, if hunters can distinguish fawns vs does
Preferred and Important Indicator Species
• Measure relative browse pressure on preferred and important forages.
Deer Are Selective Foragers !
• Qualitative Sampling: “random” areas.
> 400 plant species reported in the SE
Deer sample and evaluate a variety of plant species to meet their needs
• Quantitative Sampling: permanent transects.
Preferred and Important Indicator Species
Oak Leaf Hydrangea Highly preferred, so its presence indicates a relatively lower population density
Preferred and Important Indicator Species
New Jersey Tea Highly preferred, so its presence indicates a relatively lower population density
7
Preferred and Important Indicator Species
Greenbrier Preferred and Important winter forage
Preferred and Important Indicator Species
Honey Suckle Moderately preferred And Important
Assessing Browse Pressure
Assessing Browse Pressure
Greenbrier Honeysuckle
100% Browsing Pressure
Past pressure
Indicates severe overpopulation
Some recovery
Assessing Browse Pressure
Assessing Browse Pressure
Verbena Moderate Preference Heavy use indicates overpopulation
Baccharis Low Preference Heavy use indicates severe overpopulation!
8
George Reserve Deer Population (McCullough 1979)
Population Concepts in Deer Ecology and Management
180 160
Population
140 120 100 80 60 40 20 0 0
1
2
3
4
5
6
7
8
9
Year
George Reserve Deer Population (McCullough 1979) Population
40 Time Population Change
Population Change
60
20
0 0
20
40
60
80
100
120
140
160
180
Post-hunt Population
Density Dependent Regulatory Factors Environmental factors impact population “health” more significantly as density increases
Time
Recognized Density Dependent Regulatory Factors Infectious and Parasitic Disease effects may increase as density increases due to : 1. Increased chance of contact and transfer of disease agents 2. Decreased resistance to disease due to nutritional deprivation and/or other stressors
- e.g. Lungworm - Pneumonia Complex (Davidson and Doster 1997)
9
“Intra-specific Competition for Nutrition”
Recognized Density Dependent Regulatory Factors
Nutritional impacts on deer fawn mortality in pens in Michigan (Verme 1962) (not density per se)
Intra-specific competition for nutrition • There is a limited amount of nutrition per unit of habitat. Limited nutrition can negatively affect reproductive success, survival, condition, etc.
Optimum nutrition – 5 % fawn mortality Low nutrition winter – 33 % fawn mortality Low nutrition winter & spring – 90 % fawn mortality
“Intra-specific Competition for nutrition” Reproduction in white-tailed deer in NY arranged by habitat quality (Cheatum and Severinghaus 1950) Region
Percent of females pregnant
Embryos per female
CLs per female
Western (best range) Catskill Periphery
94
1.7
2.0
92
1.5
1.8
Catskill Central Adirondack Periphery
87 86
1.4 1.3
1.8 1.7
Adirondack Center (worst range)
79
1.1
1.1
Compensatory Effects of Density Dependent Regulation Slater Ranch, Texas Hill Country
• 1980 – 1983 : limited harvest • 1984 : First spotlight survey estimated 1680 deer • 1984-1989 : Annually harvested 28% of estimated doe population • 1989 : Spotlight survey estimated 1734 deer
Compensatory Effects of Density Dependent Regulation • Presence of one density dependent regulatory factor is compensated for by reduced effects of other density dependent factors • Improved nutrition due to lowered density should improve herd health with increases in reproductive success and body condition
Compensatory Effects of Density Dependent Regulation South Fork Ranch , Texas Hill Country (35,000 acres)
• Pre-1979 : essentially NO management, severe overpopulation of whitetails, exotic deer, and livestock with long-term habitat degradation and brush encroachment • 1979 : new managers began INTENSIVE population and habitat management including brush control, prescribed burning, and animal reductions
10
Compensatory Effects of Increased Harvest and Improved Habitat
Population Growth Without Harvest Roe Deer (Harvest ended 1990) (Andersen and Linnell 2000)
1979
1980
CLs
n
Mean
n
Mean
1.5 Yrs 2.5+ Yrs
5 5
0.0 0.8
2 10
1.0 1.8
Conception Mean Range
NUMBER OF DEER
South Fork Ranch , Texas Hill Country (35,000 acres) 300 250 200 150 100 50
Dec 31 Nov 15-Feb 7
Nov 15 Oct 20-Dec 10
Density Dependent Regulation With No Harvest Roe Deer (1991-1994)(Andersen and Linnell 2000)
0 1990
1991
1992
1993
1994
Compensatory Effects Are Not Always Obvious
Fawns / Adults Female
3 2.5
Deer populations on poor soils with abundant, low quality vegetation may not show classic compensatory effects in response to “reasonable” harvest rates
2 1.5 1 0.5 0 0
50
100
150
200
250
300
Abundance
No Compensatory Reproduction (Petrick 1994)
Florida Panhandle – adjacent management areas with heavy and no harvest
Tracks/Mile Harvest No Harvest
Deer/Hour
Fetuses/Doe
2.5
0.0002
1.4
11.7
0.0106
1.3
Conclusion • Density Dependent Regulation of deer population parameters exists • Compensatory effects of density reductions would be more difficult to document on habitats with an abundance of low quality forage.
11
Carrying Capacity Concepts
Population Density
K – Carrying Capacity Maximum Sustainable Density Maximum Harvest Density
I – Carrying Capacity
Optimum Carrying Capacity
Carrying Capacity • Significant variation - seasonally, annually, and as habitats change • Examples of variable environments - South Texas : rainfall dependent - Appalachian Mtns.: hard mast dependent - Pennsylvania winter
Time
sapling stands - 23.1 deer/km2 pole timber stands - 1.9 deer/km2
Relative Abundance
Plant & Animal Dynamics Habitat-Based Estimates of Carrying Capacity
Plants
Use of forage nutrients (food) to predict carrying capacity
Animals
Time
Basic Carrying Capacity Model A k= B × Days
Example: Basic Model • Biomass estimated at 2,000 kg/ha • Use 50% of biomass to avoid habitat damage • Elk eat about 5 kg/day ( 2.5% of body mass) • The habitat is used for 180 days
K = carrying capacity A = usable forage B = average daily intake (dry matter) Days = season length
1,000 kg / ha . elk / ha = 111 5.0 kg / day × 180 days
12
Expanded CC Models 1. Nutrition-based models use only forages which fulfill nutritional requirements (energy or protein) for specific life processes. 2. Fat reserves can be included since they provide energy supplement
Considering Forage Nutrient Concentration and Animal Requirements ME kcal/g 3.00 2.75 2.50 2.25 2.00 1.75 1.50
C.C. C.C. Burned Unburned 14 0 28 0 84 1 185 4 206 160 206 330 206 359
Lactation: 2.67
Gestation: 2.02 Maintenance: 1.62
(Hobbs and Swift 1985)
Extensive data requirements and assumptions limit the absolute accuracy of all nutritional-based estimates of carrying capacity
Arsenal/burn effects on white-tailed deer forage in mid-rotation pine plantations enrolled in cost-share programs Melinda Ragsdale and Steve Demarais
Greatest value is in relative comparisons across time, areas, or habitat treatments.
Nutritional Carrying Capacity - UCP 12% crude protein average diet
Indirect Measures of Density Relative to Carrying Capacity (i.e., Condition Indices)
90
Logistical limitations with direct estimates of carrying capacity lead some managers to use indirect measures to index density relative to C.C., such as
80
Deer Days/acre
70 60 50 40 30 20 10 0
Arsenal/burn
Control
– Fat stores – Reproductive performance – Body mass
= P < 0.05
13
Population Concept
• Population Concept and Important population characteristics • Population growth and regulation • Estimates of habitat carrying capacity
Deaths
Births
Population
• Group of animals occupying a common area • Basic management unit to which we apply our “tools” • Boundary depends on animal movement potential, habitat variation, management intensity, and barriers to movement
Adults
Males
Females Young
Dispersal Immigration
Management Success • Depends on ability to manipulate or control population composition (sex ratio, age structure, density) relative to management goals • Without that ability – you are only managing the HARVEST, not the population
Emigration
Important Population Characteristics • Fawn recruitment • Sex ratio • Age structure • Density
1
Fawn Recruitment • Addition of young animals from reproduction
• Factors affecting recruitment
Importance of Fawn Recruitment Can Be Used to Determine Harvest Harvest = Recruitment – Mortality
•Reproductive rates of adults •Survival of newborns
(assuming immigration = emigration)
• Estimated by •Corpora luteal and fetal counts •Fawn at heel counts from incidental observations, spotlight counts, etc.
Sex Ratio (buck:doe) • Polygynous breeding strategy allows a range of biologically-acceptable ratios • Balanced (1 buck: 1-2 does) – More, older bucks and fewer fawns
• Unbalanced (1 buck: 3+ does) – Fewer, younger bucks and more fawns
Age Structure • Older age structure improves antler development and maybe breeding season • Estimated using harvest data – Percent of harvest by age class – Absolute harvest per area – Results may vary (see WMAs and UPC DMAP)
• Estimated using survey data – Percent by age class or antler class
Density • Number of animals per unit of habitat • Variety of methods used to estimate
Techniques to Estimate Population Characteristics • Harvest-Based : Population reconstruction • Animal-Based : Survey and extrapolation • Habitat-Based : Relative Density
2
Is The Effort Justified?
Population Reconstruction
Effective management requires knowledge of existing population characteristics and future responses to management actions!
• Determine the minimum possible number of individuals alive in a given cohort for a given year by summing all the individuals from that cohort retrieved in subsequent years.
• The virtual population estimate approaches reality as the proportion of each cohort ultimately recovered approaches 100%.
Problems Associated With Harvest Data
Population Reconstruction Assumptions:
• Differential vulnerability among sex and age classes
• The proportion of deaths accounted for is relatively constant over time for each cohort
• Selectivity among hunters
• Age determination is accurate
• Incorrect age determination
ORIGINAL HARVEST RECORDS (BUCKS)
ORIGINAL HARVEST RECORDS (BUCKS)
Age
Age
Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
0.5 18 8 25 32 11 8 19 2 0 0 2 13 11 12 8 11
1.5 55 23 39 23 23 29 22 2 13 4 20 39 23 24 27 19
2.5 47 49 60 23 56 92 83 23 90 15 42 48 56 59 48 97
3.5 31 58 39 29 54 70 58 29 56 66 86 30 19 23 15 30
> 4.5 44 96 41 45 65 61 34 8 13 70 100 12 19 18 16 41
Total kill 195 234 204 152 209 260 216 64 172 155 250 142 128 136 114 198
Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
0.5 18 8 25 32 11 8 19 2 0 0 2 13 11 12 8 11
1.5 55 23 39 23 23 29 22 2 13 4 20 39 23 24 27 19
2.5 47 49 60 23 56 92 83 23 90 15 42 48 56 59 48 97
3.5 31 58 39 29 54 70 58 29 56 66 86 30 19 23 15 30
> 4.5 44 96 41 45 65 61 34 8 13 70 100 12 19 18 16 41
Total kill 195 234 204 152 209 260 216 64 172 155 250 142 128 136 114 198
Mean SE %
11.25 2.22 6.36
24.06 3.23 13.61
55.50 6.19 31.39
43.31 5.20 24.50
42.69 7.28 24.14
176.81 13.34 100.00
Mean SE %
11.25 2.22 6.36
24.06 3.23 13.61
55.50 6.19 31.39
43.31 5.20 24.50
42.69 7.28 24.14
176.81 13.34 100.00
3
RECONSTRUCTED BUCK POPULATION Age Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 Mean SE %
0.5 195 185 208 213 165 179 277 128 95 105 136 151 148 201 156 142 167.75 11.40 28.29
1.5 188 177 177 183 181 154 171 258 126 95 105 134 138 137 189 148 160.06 9.83 26.99
2.5 146 133 154 138 160 158 125 149 256 113 91 85 95 115 113 162 137.06 10.13 23.11
3.5 127 99 84 94 115 104 66 42 126 166 98 49 37 39 56 65 85.44 9.33 14.41
> 4.5 44 96 41 45 65 61 34 8 13 70 100 12 19 18 16 41 42.69 7.28 7.20
Minimum Sum of the bucks alive cohort f x 700 690 664 673 686 656 673 585 616 549 530 431 437 510 530 558 593.00 22.41 100.00
685 676 689 626 499 695 1057 477 349 350 420 501 498 644 528 469 572.69 43.55
Population Reconstruction Problems • Sensitive to changes in harvest
Population Reconstruction Works best for intensively managed populations where you can account for most of the annual mortality (harvest and nonharvest). It is a MINIMUM estimate.
Animal-Based Survey and Estimates Not A Total Count!
• Harvest is sensitive to hunter effort and hunter selection biases and harvest regulations
Highly secretive – can’t count all animals
• Tells you what happened 5 years ago
Habitat visibility and variation – can’t survey the entire area Total counts almost impossible
Common Population Sampling Methods •
Spotlight Survey
•
Camera Survey
•
Hunter Observation
Spotlight Surveys
4
Spotlight Surveys
Visibility (width of survey) estimated every 0.1 mile
Stratify your sample and sample the habitat proportionately. Bottomland Hardwood
Pine
Area Surveyed = average width x length ex., 275 ft. x 52800 ft. = 14,520,000 sq. ft. = 333 ac. Replication : 3-5 times and take the average values Similar weather and moon conditions (avoid full moon) Pasture Pine / Hardwood Mix
Population Estimates Density = deer / acre
Camera Technique
ex., 33 deer/333 ac. = 1 deer / 10 acres Research Advance Photo
Sex Ratio = # bucks/# does ex., 6 bucks/12 does = 1 buck/2 does Fawn Crop = (# fawns/# does) x 100 ex., (15 fawns/12 does) x 100 = 125 %
Bring the deer to you!
Only use “known” identifications
Camera Technique
Estimate is based on the relationship between the number of unique bucks photographed and the total number of bucks photographed.
Camera Technique “Population Factor”
Total Bucks Photographed = 178 Unique Bucks Photographed = 37 Proportion of Unique Bucks = 37 ÷ 178 = 0.21 This is called the “Population Factor” This is used to determine the number of unique does and fawns photographed
5
80% or 0.8 of the population is observed 100%
Estimate the number of unique deer using the “population factor”
90% 80% Winter Fall
70%
Total photographs of bucks= 178 Total photographs of does= 236 Total photographs of fawns= 124
× 0.21 = 37 Does = 236 × 0.21 = 49 Fawns = 124 × 0.21 = 26
If 7-day survey in winter with a camera density of 1 per 100 acres...
Extrapolation Factor
Camera Technique
60% 50% 40% 30% 20%
Bucks = 178
10%
= 112 Total Deer
0% 1
2
Estimate the number of deer on the property using the “extrapolation factor” 0.8 of the population surveyed (from graph) Extrapolation Factor = 1 ÷ 0.8 = 1.25
× 1.25 = 46 Does = 49 × 1.25 = 61 Fawns = 26 × 1.25 = 33 Bucks = 37
= Total of 140 Deer
Hunter Observations
3
4
5
6
7 8 DAYS
9
10 11 12 13 14
Camera Technique
Considerations •
Attractiveness of bait relative to natural forage
•
Closed population is best
•
Equipment expense – minimize by moving cameras
•
Excellent educational tool – shoot/don’t shoot examples
•
Conservative Approach –MINIMUM Population
Hunter Observations Potential Problems • Accuracy • Consistency Positives • “free data” • Hunter involvement • People believe their own results!
6
Hunter Observations • Hunters record # of does, bucks, and fawns seen every outing • Early seasons best (e.g., archery season) • Daily use card or notebooks in stands
Habitat-based Index to Deer Density Browse Survey
Hunter Observation Estimates • Sex ratio – good estimate, but may be biased against sex with heavier harvest pressure • Fawn Crop – good estimate, if hunters can distinguish fawns vs does
Preferred and Important Indicator Species
• Measure relative browse pressure on preferred and important forages.
Deer Are Selective Foragers !
• Qualitative Sampling: “random” areas.
> 400 plant species reported in the SE
Deer sample and evaluate a variety of plant species to meet their needs
• Quantitative Sampling: permanent transects.
Preferred and Important Indicator Species
Oak Leaf Hydrangea Highly preferred, so its presence indicates a relatively lower population density
Preferred and Important Indicator Species
New Jersey Tea Highly preferred, so its presence indicates a relatively lower population density
7
Preferred and Important Indicator Species
Greenbrier Preferred and Important winter forage
Preferred and Important Indicator Species
Honey Suckle Moderately preferred And Important
Assessing Browse Pressure
Assessing Browse Pressure
Greenbrier Honeysuckle
100% Browsing Pressure
Past pressure
Indicates severe overpopulation
Some recovery
Assessing Browse Pressure
Assessing Browse Pressure
Verbena Moderate Preference Heavy use indicates overpopulation
Baccharis Low Preference Heavy use indicates severe overpopulation!
8
George Reserve Deer Population (McCullough 1979)
Population Concepts in Deer Ecology and Management
180 160
Population
140 120 100 80 60 40 20 0 0
1
2
3
4
5
6
7
8
9
Year
George Reserve Deer Population (McCullough 1979) Population
40 Time Population Change
Population Change
60
20
0 0
20
40
60
80
100
120
140
160
180
Post-hunt Population
Density Dependent Regulatory Factors Environmental factors impact population “health” more significantly as density increases
Time
Recognized Density Dependent Regulatory Factors Infectious and Parasitic Disease effects may increase as density increases due to : 1. Increased chance of contact and transfer of disease agents 2. Decreased resistance to disease due to nutritional deprivation and/or other stressors
- e.g. Lungworm - Pneumonia Complex (Davidson and Doster 1997)
9
“Intra-specific Competition for Nutrition”
Recognized Density Dependent Regulatory Factors
Nutritional impacts on deer fawn mortality in pens in Michigan (Verme 1962) (not density per se)
Intra-specific competition for nutrition • There is a limited amount of nutrition per unit of habitat. Limited nutrition can negatively affect reproductive success, survival, condition, etc.
Optimum nutrition – 5 % fawn mortality Low nutrition winter – 33 % fawn mortality Low nutrition winter & spring – 90 % fawn mortality
“Intra-specific Competition for nutrition” Reproduction in white-tailed deer in NY arranged by habitat quality (Cheatum and Severinghaus 1950) Region
Percent of females pregnant
Embryos per female
CLs per female
Western (best range) Catskill Periphery
94
1.7
2.0
92
1.5
1.8
Catskill Central Adirondack Periphery
87 86
1.4 1.3
1.8 1.7
Adirondack Center (worst range)
79
1.1
1.1
Compensatory Effects of Density Dependent Regulation Slater Ranch, Texas Hill Country
• 1980 – 1983 : limited harvest • 1984 : First spotlight survey estimated 1680 deer • 1984-1989 : Annually harvested 28% of estimated doe population • 1989 : Spotlight survey estimated 1734 deer
Compensatory Effects of Density Dependent Regulation • Presence of one density dependent regulatory factor is compensated for by reduced effects of other density dependent factors • Improved nutrition due to lowered density should improve herd health with increases in reproductive success and body condition
Compensatory Effects of Density Dependent Regulation South Fork Ranch , Texas Hill Country (35,000 acres)
• Pre-1979 : essentially NO management, severe overpopulation of whitetails, exotic deer, and livestock with long-term habitat degradation and brush encroachment • 1979 : new managers began INTENSIVE population and habitat management including brush control, prescribed burning, and animal reductions
10
Compensatory Effects of Increased Harvest and Improved Habitat
Population Growth Without Harvest Roe Deer (Harvest ended 1990) (Andersen and Linnell 2000)
1979
1980
CLs
n
Mean
n
Mean
1.5 Yrs 2.5+ Yrs
5 5
0.0 0.8
2 10
1.0 1.8
Conception Mean Range
NUMBER OF DEER
South Fork Ranch , Texas Hill Country (35,000 acres) 300 250 200 150 100 50
Dec 31 Nov 15-Feb 7
Nov 15 Oct 20-Dec 10
Density Dependent Regulation With No Harvest Roe Deer (1991-1994)(Andersen and Linnell 2000)
0 1990
1991
1992
1993
1994
Compensatory Effects Are Not Always Obvious
Fawns / Adults Female
3 2.5
Deer populations on poor soils with abundant, low quality vegetation may not show classic compensatory effects in response to “reasonable” harvest rates
2 1.5 1 0.5 0 0
50
100
150
200
250
300
Abundance
No Compensatory Reproduction (Petrick 1994)
Florida Panhandle – adjacent management areas with heavy and no harvest
Tracks/Mile Harvest No Harvest
Deer/Hour
Fetuses/Doe
2.5
0.0002
1.4
11.7
0.0106
1.3
Conclusion • Density Dependent Regulation of deer population parameters exists • Compensatory effects of density reductions would be more difficult to document on habitats with an abundance of low quality forage.
11
Carrying Capacity Concepts
Population Density
K – Carrying Capacity Maximum Sustainable Density Maximum Harvest Density
I – Carrying Capacity
Optimum Carrying Capacity
Carrying Capacity • Significant variation - seasonally, annually, and as habitats change • Examples of variable environments - South Texas : rainfall dependent - Appalachian Mtns.: hard mast dependent - Pennsylvania winter
Time
sapling stands - 23.1 deer/km2 pole timber stands - 1.9 deer/km2
Relative Abundance
Plant & Animal Dynamics Habitat-Based Estimates of Carrying Capacity
Plants
Use of forage nutrients (food) to predict carrying capacity
Animals
Time
Basic Carrying Capacity Model A k= B × Days
Example: Basic Model • Biomass estimated at 2,000 kg/ha • Use 50% of biomass to avoid habitat damage • Elk eat about 5 kg/day ( 2.5% of body mass) • The habitat is used for 180 days
K = carrying capacity A = usable forage B = average daily intake (dry matter) Days = season length
1,000 kg / ha . elk / ha = 111 5.0 kg / day × 180 days
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Expanded CC Models 1. Nutrition-based models use only forages which fulfill nutritional requirements (energy or protein) for specific life processes. 2. Fat reserves can be included since they provide energy supplement
Considering Forage Nutrient Concentration and Animal Requirements ME kcal/g 3.00 2.75 2.50 2.25 2.00 1.75 1.50
C.C. C.C. Burned Unburned 14 0 28 0 84 1 185 4 206 160 206 330 206 359
Lactation: 2.67
Gestation: 2.02 Maintenance: 1.62
(Hobbs and Swift 1985)
Extensive data requirements and assumptions limit the absolute accuracy of all nutritional-based estimates of carrying capacity
Arsenal/burn effects on white-tailed deer forage in mid-rotation pine plantations enrolled in cost-share programs Melinda Ragsdale and Steve Demarais
Greatest value is in relative comparisons across time, areas, or habitat treatments.
Nutritional Carrying Capacity - UCP 12% crude protein average diet
Indirect Measures of Density Relative to Carrying Capacity (i.e., Condition Indices)
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Logistical limitations with direct estimates of carrying capacity lead some managers to use indirect measures to index density relative to C.C., such as
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Deer Days/acre
70 60 50 40 30 20 10 0
Arsenal/burn
Control
– Fat stores – Reproductive performance – Body mass
= P < 0.05
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