1. Conservation Design for Sustainable Avian Populations
Modeling species occurrence dynamics at landscape levels
NC Cooperative Fish & Wildlife Research Unit, NCSU
Patuxent Wildlife Research Center
AL Cooperative Fish & Wildlife Research Unit, Auburn University
Atlantic Coast Joint Venture
Gap Analysis Program (BaSIC)

2. Abundance…and vital rates…
Why not abundance as a starting point?
BBS-derived abundance estimates cannot account/adjust for factors that affect the detection process
Availability and perception
Multiple species and habitats
Background noise (Simons et al. 2007)
Discrepancy between adjusted and unadjusted estimates might be substantial. Thus there could be profound implications for
Modeling species dynamics
Conservation design
Vital rates…
Available for game and endangered species
Remaining community members--fragmentary at best.
Simons, T.R., M.W. Alldredge, and K.H. Pollock Experimental analysis of the auditory detection process on avian point counts. Auk 124:
Nichols, J. D., L. Thomas, and P. B. Conn. In Press. Inferences about landbird abundance from count data: recent advances and future directions. Environmental and Ecological Statistics. 2

3. Patch Occupancy Models
Patch Occupancy offers an alternative approach.
Survey counts are re-tallied as “presence-absence” (detection-nondetection),
Assumptions and inferences are more tractable,
Preserves ability to estimate selected vital rates and community-level metrics over time.
Patch Occupancy (MacKenzie et al. 2006) is an appropriate framework for this project given the number of species and habitats in question, and the issues with detection probability concerning BBS raw data. It generates parameters of interest (e.g., vital rates) and is appropriate to evaluate range dynamics potentially associated with Global Climate Change (e.g., Royle and Kéry 2007; Ecology 88: ).
MacKenzie, D. I., J. D. Nichols J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press pp.

4. Patch Occupancy
Patch Occupancy (Psi) is defined as the probability that a site is occupied. It is conditioned by fact that species is not always detected with certainty, even when present (p < 1)
Notation: probability site i is occupied
- probability of detecting the species in site i at time j, given species is present
The model framework permits relating  and p to site and/or sampling characteristics via the logistic model (or logit link). Most applicable to this project will be:
Site-specific: model  and/or p
e.g., habitat type, patch size, patch isolation
Newer versions of PRESENCE generate Psi-conditional. The Psi-conditional is the estimate of occupancy, given that the species was never detected at a site. So, Psi is the probability that any site is occupied, some of which you will detect the species. And Psi-conditional is the probability that sites where you had no detections are occupied.

5. Patch Occupancy and Conservation Design
Abundance – revisited?
It is possible to estimate abundance from presence-absence data (Royle and Nichols 2003). Two assumptions need to be met:
the probability of detecting an animal at a site is a function of how many animals are actually at that site,
the spatial distribution of the animals across the survey sites follows a specified prior distribution, such as the Poisson distribution,
BUT approach based on temporal replication, not spatial.
If review of literature or data suggests that discrepancy between adjusted and unadjusted counts is deemed acceptable
W. Thogmartin’s (2004) provides a comprehensive approach to estimate abundance from BBS data
Incorporates multiple factors influencing abundance estimates including possible changes in detection due to changes in observers over time.
It would be very useful to further qualify (rank) landscape contexts (patches) with low extinction rates using abundance estimates generated via Patch Occupancy framework. Abundance estimates would also provide a link with population objectives, a metric used in most conservation planning documents.
There are options but they might require relaxing assumptions (e.g., equal detection across a segment or primary sampling unit) in the case of BBS-based data. But other issues are being examined before formally proposing using BBS data. Such assumptions might not be needed for species like the Bobwhite Quail where temporal and spatial replication is part of routine surveying protocols.
Royle, J.A. and J.D. Nichols Estimating abundance from repeated presence-absence data or point counts. Ecology 84(3):
Thogmartin, W. E., J. R. Sauer, and M. G. Knutson A hierarchical spatial model of avian abundance with application to Cerulean Warblers. Ecological Applications 14:

6. Conservation Design Project - Approach
SAMBI Region
Focal Regions
SAMBI
Eastern United States
Assessments based on BBS and remotely-sensed data.
Analytical Approach -
MacKenzie, D. I., J. D. Nichols J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press pp.
Mackenzie, D.I., J.D. Nichols, J.E. Hines, M.G. Knutson, and A.B. Franklin Estimating site occupancy, colonization and local extinction probabilities when a species is not detected with certainty. Ecology 84:
Patch Occupancy
MacKenzie et al and 2006
Monica Iglecia, MS Student
Post-Doctoral Research Associate 6

7. Data Source and Sampling Units
Motivation
Reduces extent and number of habitat classes within sampled unit
Minimizes heterogeneity in detection probability
Occupancy-habitat relationship “tighter”
Improved interpretation
Potential to increase sample size
If deemed necessary, spatial correlation can be incorporated into models
Primary Sampling Units
BBS route segments/year
Each BBS route split into 4 segments
Secondary Sampling Units
Each segments contains 8 stations (spatial replicates)
“tighter” refers to increasing the chance that occupancy can be more closely linked with a habitat class. Also, measures of context are more interpretable as a smaller sample unit is buffered.
Sample sizes can certainly be doubled (extreme segments). Whether all 4 segments are replicates is still a matter of debate. There are ways to incorporate spatial correlation, including a recent development that deals between-station correlation. It might be applicable if we believe that birds can move between stations within a segment during a survey.
Finally, the detection process among spatial replicates is not as confounded as compared to the standard 50 mile BBS route. Reducing such heterogeneity may allow estimating abundance as per Royle and Nichols (2003)—still being evaluated.

8. Multi-Season Data Framework
Local Extinction
Colonization
Year
1990
2001
2008
Surveys/
sampled rt/
year
The MSG project draws from 3 primary landcover dates, not equally spaced. Landcover from 1992 needs to be “cross-walked” to be compatible with 2001 and The idea is to draw inferences about interval predictions. Examples of inferences that can be drawn from this approach (Pollock’s robust design) will be illustrated using 2001 and 2006. 1 2
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Closure

9. Multi-season Models
Modeling dynamics or changes in occupancy over time (occupancy as a state variable)
Parameters of interest for this work:
t = t+1/ t = rate of change in occupancy
t = P(absence at time t+1 | presence at t) = patch extinction probability
t = P(presence at t+1 | absence at t) =
patch colonization probability
The multi-season framework will be the “workhorse” in this project. To go beyond “pattern” (static), we need to quantify the dynamics of patch occupancy. This framework offers that capability. Here I list three parameters of interest.
Colonization and local extinction probabilities (t to t+1) could be functions of the number of ‘neighboring’ occupied sites in the season t (autologistic function). Nichols et al. (Patuxent researchers) will be developing analytical approach to deal with situations where occupancy of neighboring locations is not known.
Nichols highlights some applications:
Direct modeling of extinction probability as function of proportion of patches that are suitable: direct estimation of “extinction threshold” relationships
Incorporation of succession and habitat alteration into occupancy modeling
Changes in extinction/colonization in response to changes in habitat: much stronger inferences than from static habitat-occupancy patterns
Multi-species extension permits tests of Caswell-Cohen ideas about predator-mediated and disturbance-mediated coexistence of competing species in patch systems.

10. Patch Dynamics – Multiple Seasons
(Not Ext.)
(Occupied)
(Ext.)
(Col.)
Patch dynamics over 3 seasons (years)…using model notation (words parenthetically).
The idea is to envision patches ”winking in and out” over time, hopefully, as a function of covariates distilled from the literature review process, consultation with regional biologists (workshops), and ecological theory.
(Unoccupied)
(Not Col.)

11. Community Dynamics Multi-species, multi-season Occurrence
Local Turnover
Probability that a species selected at random from the community in year j was not present in year i
Local Extinction or Colonization
Probability that a species present in year i is not present in some later year j (i < j)
The number of species not present at time i that colonize and are present at time j
Rate of change in species richness
Ratio of estimated richness in successive time periods
Co-occurrence
Model local rates of extinction and colonization as functions of occupancy of other species
The community-level parameters and inferences are borne from the multi-season framework. Examples of rates of interest are listed in the slide. 11

12. Essentials… Formulate a priori hypotheses
Explicit statements about processes and predictions reflecting knowledge from the literature, expert opinion, and ecological theory
Useful to think in terms of the following question:
What is the ecological (landscape) basis for sensitivity of a species?
Life History
What biological process or requirement is (are) a determinant driver of the species’ continued survival?!
Habitat classes (states) need to be kept to a minimum and defined keeping in mind:
The scale at which it can be modeled and the interplay/relevance to biological processes

13. Patch Dynamics…and Conservation Design
2001
Landcover covariates
2006
Landcover covariates
Landcover
Change Analysis
Initial set of
a priori models
Interval predictions
and models
2000
2001
2002
2003
2004
2005
2006
2007
2008
E/C1
E/C2
E/C3
E/C4
E/C5
E/C6
E/C7
E/C8
We set ourselves to assess patch dynamics, and hence, address questions regarding predicted impacts and expressions of persistence (also see possible applications as per Nichols on previous multi-season slide). Before analyses begin, a change analysis should be conducted to help us define expectations. Two paths are possible with regards to dynamics. The first would focus around available data on landcover from 2001 and 2006 (we can generalized this approach to include 1992). The second is to use the full time series data set from BBS. This approach is preferred because it “bridges” two intervals defined by landcover data. Benefits: 1) We create an opportunity to identify alternative explanations to account for patch extinction or colonization rates, and 2) foundation to understand “state transition probabilities between intervals”, crucial to predict potential impacts on bird occurrence (ultimately populations) due to changes in landcover predicted by habitat and global climate models. Noteworthy-approach incorporates “history” – Markov process.
NOTE: MSG project deals with only 1992, 2001 and 2006 landcover data, but for global climate, LandSat data will enable working with multi-year landcover data over 3 decades (B. Grand new project). Continous landcover data might allow us to develop a more rigorous understanding of transitions probabilities over time intervals of various lengths.
Transition probabilities between patch occupancy
and changes in habitat “states”
Expressions of Persistence

14. Inferential benefits from time series…  
Plausible outcomes and uses of patch dynamics based on all available BBS data between three widely spaced landcovers (illustrated with 2001 and 2006).
Correlational - two figures on left: a) an increase in patch occupancy with increasing suitable habitat classes (holding context constant); b) the opposite situation. Hopefully hypotheses will leave us with something more than a mere correlation--strong support for a biologically sound model.
The scenario depicted on the top-right figure suggest that something other than habitat is influencing patch occupancy. In other words, other processes are operating.
The bottom right figure depicts annual changes in patch occupancy. Insights from its variation are invaluable. For example, variation might be large relative to detectable changes in landcover stemming from a change analysis—not good! If that were not an issue, then the time series helps us discern when the “down-turn” might have started. One could seek out auxiliary information to determine IF the change in landcover, detected in 2006, occurred before. Alternatively, and as a competing explanation, we could assess alternative biological processes (e.g., change in avian community composition). Such analysis also serves to determine whether patterns on one species are a community-wide phenomenon (co-variation).
The interpretational benefits of patch dynamics apply to the work on global climate as per Nichols et al. Inferences (e.g., transitional probabilities) will likely be stronger because landsat change analyses over 30 years will generate more continuous landcover data, and hence, a nearly “ideal” match between estimates of patch occupancy and corresponding landcover.  14

15. Patch Occupancy and Conservation Design
Modeling Species-Habitat Relationships
Brown-headed Nuthatch
Evolved in the southeast
Mature, pine forests.
Local extinctions.
Notable culprits short-age rotations, fire suppression.
Poor disperser
Secondary Cavity Nester – snags
Types of data/information on life history that could be modeled or used to generate a priori hypotheses.

16. Brown-headed Nuthatch
Structural and Functional Covariates
Colonization Process
Community – Presence of primary Cavity Nesters (+)
Inter-Patch Distance – Poor Disperser (-)
Inter-Patch Matrix – Distance*Composition
Extinction Process
Habitat – Mature Pine (-)
Fire Rotation – Increasing time since last burn (-)
Community – Presence of Cavity Competitors (+)
For the nuthatch, a priori models might reflect the interplay between habitat class (type, age) with isolation or distance (due to poor dispersal capabilities), isolation might be further modeled as a function of the type of “matrix” (dominant type - urban or agric. between pine forest patches). Community-level analyses (co-occurrence) can also be invoked as presence/absence of “primary cavity nesters” might be hypothesized to be essential element of nuthatch persistence. Conversely, presence/absence of nest cavity competitors, as suggested in BNA acct., could be a competing model to assess persistence. Some predicted effects in parenthesis.

17. Patch Occupancy and Conservation Design
Predicting the possible impacts of urban growth and global climate are central themes of the project.
Projected impacts 5, 10, out to 100yrs
Global Climate – Range Dynamics
Temperature and landscape changes over past 30 yrs
Urban growth – Community Dynamics
Past sprawl on the SAMBI area and projected in 2 focal areas

18. Range Dynamics of North American Landbirds
Range Boundaries – transition zones - demographic flux
With changing temperatures populations on boundaries are hypothesized to exhibit differing rates of extinction and colonization.
Some predictions by regions are:
Rate of local extinction: S>EW>N,C
Rate of local colonization: N,C>EW>S
Spatial Comparisons: Mean change in rate of local extinction: S>EW,C>N
Assessment will rely on BBS data ( ) and landcover assessments since the 1970s (landsat).
Nichols et al. will lead this work. Benefits extend to the MSG project, both in terms of analytical tools and applications, but also in terms of shared objectives to predict impacts stemming from global climate.
(background from Nichols et al.) For selected landbird species, we make specific predictions about locations near northern portions of historical ranges (N), near southern portions of historical ranges (S), near eastern and western portions of historical ranges (EW), and in central areas of historical ranges (C). Some predictions involve differences among these groups of locations during the last 2 decades. Other predictions involve differences in relative changes in dynamic rate parameters between and among these groups of locations. 18

19. Community Dynamics in Urban Landscapes
Evokes biological integrity (Karr & Karr 1996)
We will frame hypotheses as per life history/functional traits (McGill et al. 2006, Croci et al. 2008).
Example from Croci et al. (2008)
Urban Adapters Urban Avoiders
Forested, shrubs Open landscapes
Resident Migratory
Longer Life Expectancy Shorter
Enclosed nesters Open
Widely Distributed Narrower
> 2 clutches clutches
Croci, S., A. Butet, and P. Clergeau Does urbanization filter birds on the basis of their biological traits?! Condor 110:
McGill, B. J., B. J. Enquist, E. Weiher, and M. Westoby Rebuilding community ecology from functional traits. TREE 21:
McKinney, M. L Urbanization as a major cause of biotic homogenization. Biological Conservation 127:
Karr, J Karr, J. R Ecological integrity and ecological health are not the same. In P.C. Schulze (ed.), Engineering Within Ecological Constraints, pp. 97–109. National Academy Press, Washington, D.C.

20. Groupings by 4 life history traits…10 species
Survival
No. Broods
Nest Structure
Migratory Status
An useful approach is to follow the framework and findings outlined by several authors on the previous slide. We could formulate hypotheses regarding the ability of birds to sustain/adapt to continued encroachment and/or habitat alteration based on their life history traits.
The dendrogram illustrates how 10 species, 5 migratory and 5 resident, were grouped based on 3 other life history features (arbitrary values for adult survival rates, number of broods per season, and nest structure (enclosed or open cup). We could make predictions about which of the emerging groups will be the “adapter” and which will be the “avoider.” Analyses of turnover rates, species richness, etc. will provide the basis to confront our hypothesized predictions and identify which species fit or not within the hypothesized scheme. 20

21. Community Dynamics in Urban Landscapes
Species Turnover – predictions over past 18 years based on life history…who comes in and leaves?
Species Richness – predictions about a “homogenized” avian community—any evidence?
Co-occurrence – predictions about functional groups, brood parasite-hosts
We will be looking for “thresholds” …patterns in the dynamic process of extinction and colonization under hypothesized prediction about
The basic question is not only what’s the “surviving species assemblage”, but why? In other words, what are the life history attributes of the “winners” as the landscape change. In other words, test whether Psi (constrained patterns) have anything to do with hypothesized predictions based on life history traits. What are colonization or extinction rates as a function of road density, edge index, number of forested patches (frequency), average size of forested patches, distance among forested patches, and matrix (proportion of hostility-dominated by urban). As benchmark, we could frame hypotheses as per Croci et al. (2008).

22. Summary of Expected Results
Patch Occupancy and Conservation Design
Summary of Expected Results
Understand interplay between pattern and process in the SAMBI region,
Understand range and community dynamics of avian species in Southern United States,
Enable the development of decision-support tools for conservation design
Incorporate transition probabilities between patch occupancy and habitat “states” given projected changes on landscapes 22