algorithmic methods in conservation biology
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Algorithmic Methods in Conservation Biology. Steven Phillips AT&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) - PowerPoint PPT PresentationTRANSCRIPT
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!
2
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)
•
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
•
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?