Transcript Sustainable Landscapes

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)
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.
Patch Occupancy Models
Patch Occupancy offers an alternative approach.
• Survey counts are re-tallied as “presenceabsence” (detection-nondetection),
• Assumptions and inferences are more
tractable,
• Preserves ability to estimate selected vital
rates and community-level metrics over time.
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:
i
pij
- 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
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.
Conservation Design Project - Approach
SAMBI Region
Focal Regions
• SAMBI
• Eastern United States
Assessments based on BBS
and remotely-sensed data.
Analytical Approach Patch Occupancy
Monica Iglecia, MS Student
Post-Doctoral Research Associate
MacKenzie et al. 2003 and 2006
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
• Primary Sampling Units
– BBS route segments/year
• Each BBS route split into 4
segments
• Secondary Sampling Units
• Each segments contains 8
stations (spatial replicates)
– Potential to increase
sample size
• If deemed necessary,
spatial correlation can be
incorporated into models
Multi-Season Data Framework
Local Extinction
Colonization
Year
Surveys/
sampled rt/
year
1990
1
2 ... k8
2001
1
2 ... k8
Closure
2008
1
2 ... k8
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
Patch Dynamics – Multiple Seasons
S1
S2
1  1
1
(Occupied)
1  1
(Unoccupied)
1  2
(Not Ext.)
(Ext.)
1
2
1
(Col.)
1 1
(Not Col.)
S3
2
1  2
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
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
Patch Dynamics…and Conservation Design
2001
Landcover covariates
2006
Landcover covariates
Landcover
Change Analysis
Initial set of
a priori models
2000
E/C1
2001
Interval predictions
and models
2002
E/C2
2003
E/C3
Expressions of Persistence
2004
E/C4
2005
E/C5
2006
E/C6
2007
E/C7
2008
E/C8
Transition probabilities between patch occupancy
and changes in habitat “states”
Inferential benefits from time series…
1
1
0.9
0.9

0.8
0.7
0.8
0.6
Psi
0.6
Psi

0.7
0.5
0.5
0.4
0.4
0.3
0.3

0.2
0.2
0.1
0.1
0
0
2000
2001
2002
2003
2004
2005
2006
2007
2000
2008
2001
2002
2003
2004
2005
2006
2007
2008
1
1
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.5

0.4
0.3
Psi
Psi
0.6
0.5
0.2
0.4
0.1
0.3
0
2000
2001
2002
2003
2004
2005
2006
2007
2008
0.2
0.1
0
2000
2001
2002
2003
2004
2005
2006
2007
2008
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
Brown-headed Nuthatch
Structural and Functional Covariates
• Colonization Process
o Community – Presence of primary Cavity Nesters (+)
o Inter-Patch Distance – Poor Disperser (-)
o Inter-Patch Matrix – Distance*Composition
• Extinction Process
o Habitat – Mature Pine (-)
o Fire Rotation – Increasing time since last burn (-)
o Community – Presence of Cavity Competitors (+)
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
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 (1996-2008) and landcover
assessments since the 1970s (landsat).
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
•
•
•
•
•
•
Open landscapes
Migratory
Shorter
Open
Narrower
1-2 clutches
Forested, shrubs
Resident
Longer Life Expectancy
Enclosed nesters
Widely Distributed
> 2 clutches
Groupings by 4 life history traits…10 species
sp1
sp2
sp5
sp3
sp8
sp4
sp6
sp7
sp9
sp10
Survival
No. Broods
Nest Structure
Migratory Status
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
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