Mule Deer Management -

Informational

Presentation Overview

• Primary recommendations from peer review

– Agency addressing all aspects

– Specific feedback and discussion

• Current process – Cody Schroeder

• Survey innovation – Cody McKee

• Field implementation – Joe Bennet

• Hitting the target – Brian Wakeling

Quick observation

"Fish and game administration is also complicated by certain human attributes without parallel in other democratic processes. It has been said that among the various attributes of the human male, three are almost universal: …each considers himself a good driver; …each, for a period at least, considers himself attractive to the opposite sex; …each considers himself expert in fish and game management." Nevada Legislative Counsel Bureau. 1959. Survey of fish and game problems in Nevada. Bulletin 36, Carson City, Nevada. Pages 35–36.

The Process

Harvest Data

Survey and

Inventory

Population Modeling

Develop Arrays &

Recommend Quotas

When are surveys conducted?

Pronghorn – Fall to Winter

Bighorn Sheep - Late Summer Deer - Late Fall and Spring

Elk - Winter

Survey and inventory: Mule Deer

• Surveys are typically conducted late Fall (post-season) and late Spring.

• Fall counts = Classify Bucks, Does, Fawns – Ideally as close to the rut as possible to minimize bias in sightability of bucks

(peak of aggregation between sexes)

– Calculate a ratio of bucks:ratio and fawn:doe

• Spring counts = Classify Adults, Fawns and calculate a fawn to adult ratio (recruitment)

Male Female

Peak of Rut

Peak timing of rut based on activity level and home range size occurs between Nov 10 – Nov 24

Harvest Data

• Mandatory harvest reporting for all big game species

• Typically about 95% return rate

• # harvested animals removed from population estimate for following year

Population Models: Why do we estimate numbers?

• No survey method has perfection detection to counts all animals

• Populations constantly change because of mortality, births, movements.

• To provide a number of males and females for determining harvestable surplus (quotas)

• Sometimes we may be interested in other factors – What is driving fawn recruitment?

– What is the predicted age class of bucks in the population

• Currently NDOW uses a deterministic spreadsheet model – Sometimes referred to as “accounting model” – Basic input parameters

• Start with a baseline population number • Spring fawn ratios (Recruitment) • Harvest data • Project number of deer going into Fall hunting season • Makes certain assumptions about survival of adults,

juveniles, birth rates, etc. • No measure of variance or uncertainty (ie confidence levels)

– Calibration of model estimate may occur if predicted buck ratio varies substantially from what is observed on post-season helicopter survey

Population Models: What are the different types?

Population Models

Sex ratios allow for calibration of model estimate

• Ex. Population Estimate = 1,000 animals

• Observed ratios of 30 bucks:100 does:40 fawns

– Pre-hunt population = 176 bucks 589 does 235 fawns

• 30 bucks removed by harvest:

– Should result in observed buck ratio of 25:100

• If the observed ratio is higher, the population is likely under-estimated and if a lower ratio is observed, the population is likely over-estimated.

Population Models: Are there other models we could use?

• AIC model selection based – Used by Colorado and Utah

– Basic input parameters (Similar to Deterministic) • Start with a baseline population number

– Uses independent abundance estimate from count data

• Fawn survival estimated from radio collars

• Adult survival estimated from radio collars

• Projects number of deer with confidence intervals – Measure of uncertainty

• Makes certain assumptions about fecundity, birth rates, summer survival rates, and others.

Population Models: Are there other models we could use?

• Integrated Population Model (IPM) – Currently being tested for Sage Grouse

populations in Nevada and other states

– Basic input parameters • Count data

• Juvenile survival or recruitment (collars or ratios)

• Adult survival

• Harvest

– IPM’s use Bayesian statistics to estimate parameters and integrate all sources of data and their variances together

Quota Calculation Process

• Determine projected animal harvest for each unit group – Population estimate – Male ratio or % of males

• Apportion the projected animal harvest into the various weapon classes – Based on previous year’s demand

• Expand the projected harvest to quotas – Divide the projected harvest by the previous years hunter success

Generally speaking, there are 3 steps

Demand

Measure of interest among hunter groups within a specific unit group

Muzzleloader Archery Any Legal Weapon

Measure of Interest based on the applicant’s 1st Choice Only

Allocation of Tag Quotas

Available Buck Harvest 100 - 23

77 Remaining Bucks

Bucks allocated to Juniors

Archery Muzzleloader Any Legal Weapon

Demand (%) 5% 10% 85%

Available Bucks 4 8 65

Success Rate (%) 20% 40% 50%

Final Tag Quota 20 20 130

Public Review and Wildlife Commission Process

• All 17 County Wildlife Advisory Boards receive NDOW quota recommendations end of April

• Each county holds public meeting to discuss and develop alternative quota recommendations

• 2nd weekend in May, Board of Wildlife Commissioners hold meeting that includes final quota determination

• County advisory boards present their alternative quotas to Commission and Commission makes final decision on tag quotas for all draw hunts.

Questions?

An evaluation of a sample-based

approach to mule deer surveys

Objective

Optimize timing and duration of aerial surveys to collect the best data for input into population models and, ultimately, to provide informed management recommendations to the trustees.

Questions

• What is the optimal “count” needed for ratios and confidence intervals to stabilize?

• Will this allow us to fly more unit groups closer to the peak-of-the-rut?

• Can data be collected in a method that obtains accurate ratios with precision allowing for valid statistical inference about the population?

What is a Directed

Search Aerial Survey?

Classified 500 deer

Bucks:100 Does = 27

Fawns:100 Does = 45

What is a Sample-

Based Aerial Survey?

Roadmap

• Review ratios and confidence limits obtained from existing data collected during past surveys.

• Determine point of stabilization using historical data.

• Develop survey protocol and review results under various scenarios of sampling.

Stabilization – Area 17 percent sampled

total count fawn ratio

90% CI buck ratio

90% CI

1 4 0.00 - 1.00 -

2 14 0.43 0.067 0.57 0.381

5 81 0.26 0.099 0.36 0.158

10 125 0.35 0.105 0.34 0.141

20 286 0.35 0.072 0.34 0.094

30 455 0.36 0.062 0.32 0.086

40 584 0.31 0.051 0.29 0.063

50 749 0.32 0.043 0.34 0.059

60 910 0.35 0.041 0.31 0.056

70 1070 0.35 0.035 0.31 0.051

80 1180 0.34 0.034 0.32 0.050

90 1334 0.36 0.031 0.34 0.049

100 1488 0.35 0.031 0.32 0.045

Stabilization – Area 17

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Sampled (%)

Random Subsampling of Survey Data (2013)

fawn ratio buck ratio

Sample-Based Survey

• Stratify deer densities across mountain ranges into “bins” of high, medium, and low density using historical survey data (Keegan et al. 2011).

• Randomly select plots to be surveyed from each stratification to obtain representative sample of the entire area

Stratification – Area 17

Subsample Analysis – Area 17

• Area 17 was divided into 55 plots

• Analyzed:

▫ Entire post-season survey dataset from 2012 -2014.

▫ Sub1 – 55% of plots randomly selected

▫ Sub2 – 55% of plots randomly selected

▫ Sub3 – 62% of plots randomly selected

Subsample Analysis – Area 17

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Actual Subsample1 Subsample2 Subsample3

Area 17 Post-Season Fawn Ratio

2012

2013

2014

Subsample Analysis – Area 17

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Actual Subsample1 Subsample2 Subsample3

Area 17 Post-Season Buck Ratio

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2014

Summary

• Directed search surveys can inadvertently introduce bias into survey results.

▫ May yield the wrong number

• Sample-based sampling incorporates randomization into surveys and can be done to obtain valid buck and fawn ratios of mule deer in Nevada.

• What do we lose? Fewer record counts and ancillary information from surveying more area.

Questions?

Exploration of Polygon Surveys

for Mule Deer in Area 17

Area – 17 Fall 2016 Results

• 1,018 Deer Observed

• 30 Bucks :100 Does

• 61 Fawns :100 Does

0.000

0.200

0.400

0.600

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1.000

2005 2012 2013 2014 2016

Area 17 Post-Season Survey Results (+/- 90% CI) fawnr buckr

Comparison to Past Surveys

Established Method

(2014) Test Trial (2016)

• 1,338 deer observed

• 13.8 Total Survey Hours

• 5.9 Ferry Hours

• 1,018 deer observed

• 15.1 Total Survey Hours

• 5.4 Ferry Hours

Comparison to Past Surveys “Old” methodology “New” Methodology

Strengths

• Objectively directs biologist to survey area

• A statistically rigorous method of data collection

• Seeing areas not typically visited using existing survey techniques

• Potential for abundance estimates and incorporation into Integrated Population Models.

• Improved replicability

Exploration Challenges

• A possible net loss of ancillary habitat and species distribution information

• Added distractions for flight crew

• Survey intensity may increase in years with limited snow cover

• Initial exploration did not reduce flight time.

• Public perceptions (i.e., loss of credibility)

Improving Logistics

• Modify flight protocol in specific situations (i.e., faster speeds, higher AGL)

• Simplify plots to reduce pilot and crew distractions

• Purchase better GPS device for in-flight navigation –

• Data collection: only record activity and snow cover in-flight. Remaining variables derived in GIS.

• Fitting all flights into the “optimal” period may require flying on weekends and using contractors to meet demand.

• Every area may not have the same “optimal” period.

Questions?

Investing in technology

Improved survey

Improved modeling

Decisions informed by science

Targets defined

Science defines potential routes to target

Multiple layers of conservatism built in Target, survey, model, recommendations

Policy for Management of Pronghorn Antelope (2003) contains an objective of 20–30 total bucks per 100 doe; Buck ratio be based on modeled pronghorn bucks ≥2

years of age.

Bighorn Sheep Management Plan (2001) does not identify specific ram harvest criteria; Minimum of 8% of the total estimated rams and not to

exceed 50% of rams ≥6 years of age.

No plan exists for mountain goats. Harvest 2-5% of total population.

Bear harvest at light level.

Elk Species Management Plan (1997) identified a bull to cow ratio of 15–40; Main beam length for 25-35% of harvested bulls are to

evaluate age structure of harvest, which is not contained in the plan.

Draft mule deer plans, but nothing final Buck to doe ratios of 30:100 for most of the state.

Comprehensive Mountain Lion Management Plan (1995) identifies that harvest limits by administrative region; Combines harvest limits into a statewide objective until need

for a specific area-specific objective.

Department will continue to provide recommendations to meet objectives as described. Use the best science to meet objectives.

Commission may request further review of Harvest Objectives as presented. Department needs a target to assist in hitting objectives.

Quotas vs. Seasons.