1. Wildlife Applications
“Visualizing the distribution of rare or threatened species is necessary for effective implementation of conservation initiatives.”
“With the purpose of refining existing maps, we used survey results, radio-telemetry locations and GIS data to construct resource selection functions (RSF) that quantified the habitat affinities and predicted the relative probability of occurrence of mountain caribou at two spatial scales.”
Johnson, Seip and Boyce 2004
Very rapid shift from the old-school 1:20,000 scale maps to modern GIS

2. Movie Screening Probably some GIS involved
Pre-Register Here

3. Visualizing Human Impact
Raster analysis combining built environment, cropland, pasture, population density, night-time lights, railways, roads and navigable waterways
Conclusion: impact slower than economic growth (doing more on less land)

4. Visualizing Endangered Species
But what’s wrong with this picture? The great plains looks empty…

5. Visualizing Extinctions

6. Underlying Tabular Data
Recent mammal extinctions (1500 AD to present)
Common name\scientific name
Extinction date
Range
Antillean giant rice rat
1902
France (Martinique)
Megalomys desmarestii[1]
Anthony's woodrat
1926
Mexico (only on Isla Todos Santos)
Neotoma anthonyi[2]
Banks Island wolf
1920
Canada
Canis lupus bernardi
Barbados raccoon
1964
Barbados
Procyon lotor gloveralleni
Bunker's woodrat
1932
Mexico (Coronados Islands)
Neotoma bunkeri[3]
California golden bear
1922
California
Ursus arctos californicus
Caribbean monk seal
1952
Caribbean Sea
Monachus tropicalis[5]
Cascade mountain wolf
1940
Canada and USA
Canis lupus fuscus
Cuban coney
1500s
Cuba
Geocapromys columbianus[6]
Eastern cougar
Unknown (declared in 2011)
Eastern Canada and USA
Puma concolor couguar
Eastern elk
1887
Cervus canadensis canadiensis
Goff's pocket gopher
1955
Florida
Geomys pinetis goffi
Gull Island vole
1897
Gull Island, New York
Microtus pennsylvanicus nesophilus
Hispaniolan edible rat
Hispaniola
Heteropsomys insulans [7]
Insular cave rat
1600s
Puerto Rico
Heteropsomys insulans[8]
Imposter hutia
Hexolobodon phenax[9]
Jamaican monkey
Jamaica
Xenothrix mcgregori[10]
Jamaican rice rat
1870s
Oryzomys antillarum[11]
Little Swan Island hutia
Swan Islands, Honduras
Geocapromys thoracatus[12]
Marcano's solenodon
Solenodon marcanoi[13]
Merriam's elk
1906
Southwestern USA
Cervus canadensis merriami
Mexican grizzly bear
Mexico and Southwestern USA
Ursus arctos nelsoni
A subset of mammalian extinctions

7. North American Species Richness
What’s up with BC? There’s blue there…. Suggesting positive? Crappy symbology.

8. Extinction vs. Richness

9. Impact vs. Species Richness
This impact assessment does not consider species richness or anthropogenic extinctions

10. Extinction: Stratification by Biome
Bars indicate means, numbers following means indicate maxima. The proportion of North American land that each biome constitutes is shown as a percentage after the biome’s name.

11. Visualizing Ocean Habitats

12. BC Salmon Farms
Interactive map: spatial locations connected to database
The same effect as “info” button in ArcMap Dekstop
Living Oceans advocacy group

13. Invasive Aquatic Species

14. Invasive Fish Over Time

15. Angler’s Atlas

16. Wildlife meets Planning
Buffer!
These guidelines are for building homes and other permanent structures, not timber harvest

17. Common Buffering Application
Harvest block falls within 600m buffer but not 500m buffer
All access roads go through both buffers
Road is established; best to wait until nesting complete

18. Vernal Pools

19. Visualizing Home Range
Defined as the space an animal uses, containing the essential requirements for life (food, water, cover)
(Vs. defended territory)

20. Minimum Convex Polygon vs. Kernel Density Estimate

21. 95 % Kernel Density (Red) vs. 50% Kernel Density (Blue)
Home range (95%) vs core range (50%)

22. Wolves Captured in Washington
Each color is one wolf (others may be tagging along); data is from 2013 only
What’s wrong with this map?
At least two commenters concerned that wolves are in their neighborhoods
How might this backfire?

23. Geog300 Projects

24. Risk determined by road size, surface and density
Might have been better to divide polygons into smaller pieces or use raster as distant overlaps affect the entire area.

25. Visualizing Interactions
Assumptions:
Collared caribou cover the same ground as the whole herd.
The sampled herds are the only herds in the region.

26. Spatial Joins
Attaching the attributes of underlying features to a point layer
In this case, I am joining the pop_places layer to its nearest lake over 40 hectares, so I know the nearest place to go swimming

27. Result of Many Spatial Joins
Each point has:
Number of years moderately or severely defoliated
Number of years defoliated
Year of most recent fire, if any
Cause of fire, if any
Size of fire, if any
Mean annual temperature
Mean annual precipitation
Elevation
Fundamental to analysis

28. Rea, Johnson, Emmons 2014
“Characterizing Moose-Vehicle Collision Hotspots in Northern British Columbia”
Study Area

29. What’s the difference between areas with high moose collisions and no moose collisions?

30. Experimental Design Result: Single, averaged attribute per feature
29 hotspots (four or more collisions per km, )
15 control sites with no collisions recorded
0.5-3km segments of road
500m and 5km buffers for fine + coarse scale attributes
Faked example: Selected two random road segments that happened to be next to each other. Buffer acts only on the selected features—good thing, too, or I would still be watching it buffer.
Elevation, aspect, rivers, at 500m and 5km distance
Result: Single, averaged attribute per feature

31. Reading Confidence Intervals
What is the null value (in this figure, it is 1.0; it is often 0)
Do the upper and lower confidence intervals contain the null value?
If yes, nonsignificant. If no, significant.

32. Look for… Big coefficients = big effect 95% CI that does NOT include 0
(both positive or both negative)
Conclusion: time to move the
signs

33. Expert-Based Habitat Modeling
Ask a trapper where he sees bears spring
Identify key characteristics
Distance to roads
Distance to eskers
Distance to old forest
Distance to swamp
Extrapolate to the landscape

34. Work-Through Lacking experts, any parameters will do
‘Twas brillig, and the slithy toves    
Did gyre and gimble in the wabe;
All mimsy were the borogoves,    
And the mome raths outgrabe.
“Beware the Jabberwock, my son    
The jaws that bite, the claws that catch!
Beware the Jubjub bird, and shun    
The frumious Bandersnatch!”
He took his vorpal sword in hand;    
Long time the manxome foe he sought—
So rested he by the Tumtum tree,    
And stood awhile in thought.
And, as in uffish thought he stood,    
The Jabberwock, with eyes of flame,
Came whiffling through the tulgey wood,    
And burbled as it came!
One, two! One, two! And through and through    
The vorpal blade went snicker-snack!
He left it dead, and with its head    
He went galumphing back.
“And hast thou slain the Jabberwock?    
Come to my arms, my beamish boy!
O frabjous day! Callooh! Callay!”    
He chortled in his joy.

35. Objective
Identify routes that the Jabberwocky is likely to use as it travels from Purden to Aleza Lake and prioritize them according to the % of the landscape that may be devoted to Jabberwocky conservation

36. Toolset: Corridor Design
Assumption: animal movement follows path of least risk (food, water, cover)
Food, water, cover differ by species
By finding routes that provide food, water, cover, we can maintain a travel corridor between patches

37. Jabberwocky will prefer to be
Parameters
During the summertime, when adventure-seeking knights (and graduate students) roam the countryside, the Jabberwocky tends to avoid travelled roads. Rivers and swamps are its preferred haunts, where Bandersnatches and Jubjub birds are present to keep watch for would-be heroes. Finally, the creature is easily scared off by its arch nemesis the feller-buncher, and does not return to a stand until the area has been successfully regenerated.
Jabberwocky will prefer to be
100m or more from roads
Less than 50m from a river
Less than 100m from a swamp
More than 500m from an not-successfully regenerated block

38. Coding the Parameters 100 = preferred habitat 75 = good but not great
Road
0 50 : 12
: 45
: 64
: 100
River
0 50 : 100
: 60
: 40
: 10
Swamp
0 100 : 100
: 66
: 15
NSR
0 100 : 10
: 50
100 = preferred habitat
75 = good but not great
50 = acceptable
25 = avoided
0 = terrible
Distance Range (m): Weight

39. % of Landscape Devoted to Corridor Under Different Constraints

40. Summary Modern wildlife management is GIS-heavy
Statistics are becoming more complex
Output maps are getting better
More interactive applications
Online, Google Earth
Check out the movie screening on the 25th
Next slides: midterm!

41. Midterm Exam: October 11 Multiple choice/short answer
Samples from last year (not this year):
2. In an attribute table, a field containing the total number of trees in each polygon, would be which data type:
a. Text
b. Date
c. Integer
d. Float
e. Double
30. If GIS software gave us a distance of metres, should this answer be described as ‘accurate’ or ‘precise’ (or both) ? Why ?

42. Fast Assignment Work
When you forget where in the labs we covered converting .e00 files
Open lab PDF>Ctrl+F>.e00

43. Google Earth Safari