Bird ReturnsDynamic Conservation in the Central Valley of California

Steve Kelling

95% of historic habitat

destroyed

California Migratory Waterbird Habitats

Estuary

4%

Saline

12%

Wetland

27%

Agriculture

57%

Stralberg, Cameron, Reynolds et al. 2010 Biodiversity & Conservation

> 9 M acres

total

Source: US NAAS CropScape Cropland Data Layer 2011

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Rice Farming Annual CycleP

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Jan Feb Mar Apr May Jun July Aug Sep Oct Nov DecJan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

Solution: Only Pay for What We Need When We Need It

Can Farming Cycles be Synchronized with Bird Migration Cycles?

Where and When Birds Occur

Birders

• 200,000 eBird participants

• 14 million hours

• 200,000 million observations

The eBird network is growing 35% annually.

The Multi-scale Modeling Challenge

Goal: Analysis at broad-scale with fine resolution

Challenge: spatiotemporal patterning at multiple scales

• Local-scale– Fine-scale spatial and temporal resource patterns

• Large-scale– Regional & seasonal variation in species’ habitat utilization

Wood Thrush

Observational Data Sensors

Sensors, sensor networks, and remote sensing gather observations

Remote Imagery Sources

• North America elevation, collected by the ASTER instrument onboard Terra

• North America landcover, collected by MODIS via the global land cover product (MCD12Q1) using the University of Maryland classification scheme

• Conterminous U.S. Cropland Data Layer (USDA-NASS 2013), generated by satellite imagery from Landsat TM 5 and ETM+7.

Annual land cover data were summarized as proportional cover of each category within 1.5km × 1.5km squares

centered on each checklist location.

SpatioTemporal Exploratory Model (STEM)

Nonparametric SDM’s are good for local-scale modeling by relating environmental predictors (X) to observed occurrences (y)

 

y = f (X)y = f (X)

SpatioTemporal Exploratory Model (STEM)

Nonparametric SDM’s are good for local-scale modeling by relating environmental predictors (X) to observed occurrences (y)

Multi-scale strategy: differentiate between local and global-scale ST structure.

1. Make explicit time (t) and location (s)

 

f (X,s,t)

y = f (X)

f (X, s, t)

SpatioTemporal Exploratory Model (STEM)

Nonparametric SDM’s are good for local-scale modeling by relating environmental predictors (X) to observed occurrences (y)

Multi-scale strategy: differentiate between local and global-scale ST structure.

1. Make explicit time (t) and location (s)

2. “Regionalize” by restricting support

 

y = f (X)

Restricted Support Set (Q)

 

y = f (X)

 

f (X,s,t)I(s,tÎq)

SpatioTemporal Exploratory Model (STEM)

Nonparametric SDM’s are good for local-scale modeling by relating environmental predictors (X) to observed occurrences (y)

Multi-scale strategy: differentiate between local and global-scale ST structure.

1. Make explicit time (t) and location (s)

2. “Regionalize” by restricting support

3. Predictions at time (t) and location (s) are made by averaging across a set of local models containing that time and location

Restricted Support Set (q)

Number of models supporting (s,t)

ith ST explicit base model

 

1

n(s,t)f i(X,s,t)I(s,tÎq i)

i=1

m

å

 

y = f (X)

 

f (X,s,t)I(s,tÎq)

“Slice and dice” ST extent into stixels

• With sufficient overlap

• Adapt to different dynamics

Temporal Design:

• 40 day intervals

• 80 evenly spaced windows throughout year

Spatial Design

• For each time interval

• Random Sample rectangles

(12 deg lon x 9 deg lat)

• Minimum 25 unique locations.

The Spatio-Temporal Ensemble

Data Products

• Weekly spatial data layers that

• Estimated the species abundance within the 3 km x 3 km grid across North America

Shorebird OccurrenceP

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Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

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Rice Farming Annual CycleP

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Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

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Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

Shorebird Abundance

October

Shorebird Abundance

January

Forward Auction vs. Reverse Auctions

Sell

Buy Buy BuyBuy

Forward Auction

Goal: highest price

Buy

Sell Sell SellSell

Reverse Auction

Goal: lowest price

Avian Abundance Estimation Across the Pacific Flyway for Full Life-cycle Conservation Planning

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Pacific Migration Flyway

Targeted Estimates

Bids selected based ontargeted estimates

Reverse Auction and Bid Selection

Farmers

Submit Bids

eBird Data Models

NASA MODIS and ASTER earth imagery, bird observations gathered by citizen-science

volunteers, big data analytics, and a market-based conservation approaches are applied to

fine-scale conservation of bird habitat

• Results: 20,000 more acres of habitat for migrating waterbirds in California

Dunlin Response Spring 2014

BirdReturns: Vision• A defined tool in the conservation portfolio

• Able to achieve dynamic habitat at scale

• Optimizes conservation investments

• Funded by mix of public and private sources

• Scientific foundations for adaptive management

BirdReturns 2.0Program Options

•Spring 2015:

2 weeks Feb 1-14

4 weeks Feb 1-28

6 weeks Feb 1- Mar

15

8 weeks Feb 1 - Mar

28

Fall 2014:

2 weeks Sept 2-15

2 weeks Sept 16-30

2 weeks Oct 4-17

2 weeks Oct 18-31