Algorithmic Methods in Conservation Biology

Steven PhillipsAT&T Labs-Research

Vignettes: Data Models Policies

• Species detection: tree swallow roosts from radar

• Modeling species distributions– Challenge 1: Presence-only data (Maxent)– Challenge 2: Non-stationarity (STEM)

• Planning protected areas to allow dispersal– Network flow, mixed integer programming

• Thanks to Tom Dietterich, Rebecca Hutchinson & Dan Sheldon (Oregon State University) for many slides!

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Dover, DE, 10/2/2010@6:52AM

The Dream

• Automatic detection of roosts at continent-scale on daily basis– Data gathering and repurposing

• Unprecedented view of species distribution– Spatial coverage– Temporal resolution

• Analyze results to learn about– Roost biology– Migration patterns– Climate change

• Data archived since 1991

Source: NOAA

[Winkler, 2006]

Research by D. Sheldon & T. Dietterich (OSU) and D. Winkler (Cornell)

Progress: Machine Learning

• Challenging image recognition task!– Primarily shape features to-date – no temporal sequencing– High precision for roosts with “perfect appearance”– Variability in appearance is challenging low recall

100 positive examples

Top 100 predicted roosts

(shape features + SVM)

Progress: Ecology

• Locating roosts– Identifying roosts in radar

images• Labeling efforts

– Estimate ground location within a few km

• Previously difficult task• 15+ roosts located in 2010-2011

– Oregon, Florida, Louisiana

• Analysis of labeled data– Understand regional patterns– Roost growth dynamics

• Very predictable• Potential species ID from radar!

Florida

Vignettes: Data Models Policies

Species Distribution Models (SDM)

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SDM Challenge #1: Presence-only data

occurrence points

Predicted distributionenvironmental

variables

…

Yellow-throatedVireo

A solution: Maxent• Given:

• Training examples x1, …, xn

• Assumed to be from an unknown distribution π = P(x|y=1)• Environmental variables f1(x), …, fm(x)

• Find:• A good estimate of π (as a function of f1, …, fm) …and

P(y=1|x)

• Method: L1-regularized Maxent• Maximum entropy principle: among distributions consistent with

the data, prefer one of maximum entropy (Jaynes, 1957)• Consistency given by relaxed moment constraints:

• | Eπ[fi] –∑j fi(xj)/m | ≤ βi

• E.g., “mean rainfall must be close to mean rainfall at training examples”S. J. Phillips, R. E. Schapire and M. Dudík 2004; S. J. Phillips, R. P. Anderson and R. E. Schapire 2006

Application: Protected area design

Application: Protected area design

(a)Dracula ant (Mystrium mysticum)(b)Grandidier’s baobab (Adansonia grandidieri)(c) Common leaf-tailed gecko (Uroplatus fimbriatus)(d)Indri, the largest lemur species (Indri indri)

Application: Protected area design

Kremen et al., Science 320(5873), 2008, pp 222-226

Application: Invasive species

Cane toad: knownoccurrences

Cane toad: areasvulnerable to invasion

Elith et al., Methods in Ecology & Evolution 1, 2010, pp 330-342.

Figures by Richard Pearson, AMNH

Application: guiding field surveys

Chameleons (Brookesia & Chamaeleo)

Target survey areasHighest priority

Lower priority

Leaf-tailed geckos (Uroplatus)

Day geckos (Phelsuma)

Application: guiding field surveys

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Application: guiding field surveys

Calumma sp. 1

Calumma sp. 2Results: new species of chameleon

Oplurus sp. Liophidium sp. and others…

Results: new species of iguana, snake

Application: Giant exploding palm

J. Dransfield et al., Botanical Journal of the Linnean Society, 2008, 156, 79-91.

SDM Challenge #2: Non-stationarity

• Problem: predictor-response relationships can change over space and time

• A solution: Spatial-Temporal Exploratory Models (STEM)– Create ensembles with local spatial/temporal support– Base learner = classification trees

• eBird– Citizen Science– Dataset publicly available for analysis – LOTS of data!

• ~3 million observations reported this May

STEM

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D. Fink et al., Ecological Applications, 2010, 20(8):2131-47

STEM SDM: Indigo Bunting

Animation courtesy of Daniel Fink

Vignettes: Data Models Policies

Reserve planning for Protea Dispersal ~300 endemic species in the fynbos of the Western Cape of S. Africa

Suitable conditions will shift under climate changeLimited dispersal ability (ants, rodents…)

Modeled distributions of Protea lacticolor

Source: Hannah et al., BioScience, 2005

Shifting suitable conditions

Interpretation: a patch of suitable conditions moving slowly enough to support the species over time

Dispersal chain:– Sequence of suitable cells (one per time slice)– Physical distance between cells limited by dispersal ability

The goal: find disjoint dispersal chains for each species:– At least 35 (100 km2) chains per species, if possible

Minimize #cells with proposed protection– Union of all chains, non counting already protected

P. Williams et al., Conservation Biology 19(4) pp 1063—1074, 2005

Dispersal as network flow in a layered graph

• Path from source to sink = dispersal chain for one species• With unit capacity arcs, an integral flow of size 35 represents a

set of 35 non-overlapping chains

cell suitable for speciesIn this slice

dispersal possibilities

S. J. Phillips et al., Ecological Applications 18(5), 2008, pp. 1200-1211

Solution: network flow and linear programming• Flow conservation constraints are linear• Integer variables: Preserve for each cell (0 or 1)• Exact solution of MIP:

– Minimum possible number of protected cells to achieve the conservation goal

Light grey: transformedGreen: already protectedBlack: goal essentialOrange: MIP solution

Questions?