# Presentation: 447617

## Building Models from Breeding Bird Surveys

**Wayne E. Thogmartin**
**USGS Upper Midwest Environmental Sciences Center**

## Where can we expect to find species of high conservation concern?

**Motivation: **
**Focus scarce conservation resources**
**Provide regional context to local conservation action**
**Lay the groundwork for estimating regional population size**

## What is the Breeding Bird Survey?

**1966 inception**
**50 stops on 2ndary**
**road, 0.5 mile apart**
**All birds seen or **
**heard w/in 3 min**
**3700 active routes**
**2900 annually run**
**Spatially hetero-**
**geneous**

## Important Issues to Address When Modeling Bird-habitat Associations

**Count-based**
**Road-side **
**Annual, spring**
**Volunteer**
**Potentially spatially correlated**
**Areally dimensionless**
**Species detectability**
**Index to abundance (relative abundance)**

## Count-based

**Use of linear regression for count-based outcomes results **
**inefficient, **
**inconsistent, and **
**biased estimates**
**Particularly problematic when counts are low**

- Expectation = 10

- Expectation = 1

- CERW < **0.1** (90% zeroes)
- HESP < **0.1** (95%)
- GWWA = **0.4**
- GRSP = **0.8**
- SEWR = **2.8**
- BOBO = **9.7** (15%)

- 0

- 5

- 10

- 15

- 20

- Value of Random Variable

- 0.0

- 0.1

- 0.2

- 0.3

- 0.4

- Probability

## Road-side survey

**Biggest criticism of the BBS**
**How much does this bias the counts?**

## Annual spring survey

**Each route comprised of 50 stops, each 3 minutes long**
**Completed only one time in spring**
**Total route time surveyed: 150 minutes**
**Is a 3 min (stop) or 150 min (route) survey sufficient?**
**Is it better to include multiple years to reduce noise in the expectation?**

## BBS surveys (primarily) breeding males

**Non-floaters and females are less frequently counted**
**Is it enough to simply double the observed counts to obtain an estimate of the female population?**
**What about the non-territorial birds?**

## Time of day and season

**Calling propensity varies over the course of the day and season**

- y = 2E-07x

- 6

-  + 3E-05x

- 5

-  - 0.0074x

- 4

-  + 0.4953x

- 3

-  - 15.145x

- 2

-  + 215.1x - 136.78

**0**

**200**

**400**

**600**

**800**

**1,000**

**1,200**

**1**

**3**

**5**

**7**

**9**

**11**

**13**

**15**

**17**

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**21**

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**25**

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**29**

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**35**

**37**

**39**

**41**

**43**

**45**

**47**

**49**

**STOP NUMBER**

**NUMBER OF BIRDS**

- 6th-order polynomial fit

- Number of Brown-headed Cowbird detected

**Rosenberg and Blancher, unpubl. data**

## Volunteers with varying levels of ability

**Observers differ in how they see and hear birds**
**Novice observers often overwhelmed**

- Probability of detecting Dickcissels

- 6x difference between best and worst

## Spatial correlation: nuisance or insight?

**Bias parameter estimates **
**Improperly narrow confidence intervals**

## Spatial Correlation

**Correlogram of Cerulean Warbler abundance in the Appalachians**
**Rho > 0.25**
**at distances **
**< 50 km**

## Species Detectability

**Detectability varies as a fcn of species, observer, year, and landcover**
**~50% of known territory individuals were detected by auditory means ****(Earnst and Heltzel 2005)**

**Probability of detecting **
**Yellow-billed Cuckoos**

## Areally dimensionless

**Is a 400 m listening radius reasonable for all birds?**
**No. Amer. Landbird Cons. Plan assigned various listening radii**
**Are 80 m, 125 m, 200 m, 400 m, 800 m reasonable?**

- Assuming no overlap

**80 m radius ******** 2.0 ha ****	**** ********     100 ha**
**125 m ******** 5 ha  ****	****	********      250 ha**
**200 m ******** 13 ha ****	**** ****	********      630 ha**
**400 m ******** 50 ha ****	****	********   2,500 ha**
**800 m ******** 201 ha  ****	********  10,000 ha**

**2 orders of magnitude**

## Areally dimensionless

**Positionally uncertain because most ****stops**** are ****not geo-located**** and ****routes**** are ****not**** always ****updated**** when changes occur**
**Uncertainty as to where surveys are taken and how much area to attribute to them**
**Density**** = Count of Species / ****Area of Habitat**

## Index to abundance (relative abundance)

**If these various factors are not accommodated, resulting counts from BBS are only indices of abundance rather than estimates of population size**

## Building Models of Rare Bird Abundance in the Prairie Hardwood Transition with Breeding Bird Survey Data

**Building Models of Rare Bird Abundance in the Prairie Hardwood Transition with Breeding Bird Survey Data**

## Modeling BBS Counts ~ f(Environmental Variables)

**Counts derived between ****1981**** and ****2001**
**Environmental Variables were only those which could be remotely sensed or regionally mapped**
**Spatially correlated counts, Poisson distribution of counts, observer and year effects**

## Spatial Poisson Count Model

*********  *****Environmental effects**
********* *****Observer effects****:***** ******Individual effect, with novice observer counts deleted***
*********  *****Year effects****:***** ******to accommodate observed annual variation and decline in abundance***
**Spatial CAR**** ****(****C****onditional ****A****uto****R****egression)****:***** ******correlation ***
********  ****Extra-poisson**** ****variation****:***** ******zero-inflation***

***Z*****(s****i****)**** ****=**** *****μ*****(s****i****)**** ****+**** ********c****ik****[*****Z*****(s****k****)**** - **********(s****k****)****]**** ****+**** **********(s****i****)**** ****+**** **********(s****i****)**** ****+ **********(s****i****)**** **

**Notes:**
The expectation is treated as Poisson.

## Hierarchical Modeling

**Correlation may occur because of ****design****, ****over time****, and/or ****across**** ****space**
**Hierarchical: clustering of ******** for ****observer****, ****year****, and ****route**** effects because of group-level correlation**
**Bayesian: Data and prior specification used to identify a posterior distribution for parameter estimates (********)**
**Standardized Likelihood x Data ****= Posterior Probability**
**Combine prior belief with the likelihood of the data to obtain posterior inferences**

**Notes:**
To fit this model, the approach we employ is described as a hierarchical model.  In this workshop, we’re most interested in effects over space, but to identify those spatial effects free of the clutter of the sample design and the temporal correlation between survey’s, we need to accommodate nuisance effects that would otherwise obfuscate the spatial effects.

## Markov chain Monte Carlo

**There is ****NO**** frequentist approach that would accomodate ****1****) Poisson nature of BBS, ****2****) nuisance effects due to correlated observer and year effects, AND ****3****) potential spatial correlation**
**Model fitting in ****WinBUGS**

- Posterior Distribution

-    -2.5

-     0.0

-     2.5

-     5.0

-     0.0

-     0.2

-     0.4

-     0.6

## Observer Effect (rank ordered)

**CERW counts in the Appalachians; ****486**** observers**

## Year Effect

**CERW counts in the Appalachians**
**Annual variation AND trend used to adjust counts**
**Bayesian approach allows imputation to future years**

## Route Effect (rank ordered)

**CERW counts in the Appalachians**
**Environmental covariates alone will not likely be sufficient**
**Route effect can be mapped**

## Regional Models of Rare Forest Bird Abundance

- Wood Thrush

- Black-billed
- Cuckoo

## Regional Models of Rare Grassland Bird Abundance

- Grasshopper Sparrow

- Bobolink

## Federal Lands

**The Conservation Estate**

## Tribal Lands

**The Conservation Estate**

## The Conservation Estate

- State Lands

**The Conservation Estate**

## Private Lands Context in the Prairie Hardwood Transition

**Area under ****state****/federal/****tribal**** land management ****~9****%**** **
**CERW ****66****% of population under management**
**SEWR ****7****%**
**State lands provide ****3-4**** times the management opportunities**
**95****% of rare grassland bird conservation to occur on private lands (vs ****73****% for rare forest birds)**

## Stepping Down Regional Population Goals to Local Management Action

**Stepping Down Regional Population Goals to Local Management Action**

**17,274 predicted GWWA**

**281 GWWA**

**290 GWWA**

- Golden-winged Warbler

## Conclusion: BBS data can be used to model avian habitat

**Focus habitat management on areas of predicted high or medium abundance**
**Consider location of public lands**
**Build conservation partnerships**
**Focus monitoring to detect change in vital rates (local) or population trend (regional)**

## Questions?

**For more information**
**http://****www.umesc.usgs.gov****/terrestrial/**
**	****migratory_birds/bird_conservation.html**
**wthogmartin@usgs.gov**