The challenge of statistically identifying The challenge of statistically identifying species-resource relationships on an species-resource relationships on an uncooperative landscape uncooperative landscape Or… Or… Facts, true facts, and statistics: a lesson in Facts, true facts, and statistics: a lesson in numeracy numeracy Barry D. Smith & Kathy Martin Barry D. Smith & Kathy Martin Canadian Wildlife Service, Pacific Wildlife Canadian Wildlife Service, Pacific Wildlife Research Centre Research Centre Delta, B.C., Canada Delta, B.C., Canada Clive Goodinson Clive Goodinson Free Agent, Vancouver, B.C., Canada Vancouver, B.C., Canada
The challenge of statistically identifying species-resource relationships on an uncooperative landscape Or… Facts, true facts, and statistics: a lesson in numeracy Barry D. Smith & Kathy Martin Canadian Wildlife Service, Pacific Wildlife Research Centre Delta, B.C., Canada Clive Goodinson - PowerPoint PPT Presentation
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The challenge of statistically identifying The challenge of statistically identifying species-resource relationships on an species-resource relationships on an
Facts, true facts, and statistics: a lesson in numeracyFacts, true facts, and statistics: a lesson in numeracy
Barry D. Smith & Kathy MartinBarry D. Smith & Kathy MartinCanadian Wildlife Service, Pacific Wildlife Research CentreCanadian Wildlife Service, Pacific Wildlife Research Centre
Objective: To incorporate habitat suitability predictionsinto a stand-level forest ecosystem model
Can we show statistically that the relative quantity of a resource on the landscape predicts the
presence of a species such as Northern Flicker?
0
1
0 1Predicted
Observed
Logistic regression model output
123 16
9 74
0 1Predicted
Observed Groups and Predicted Probabilities
20 + 1 + I 1 I I 1 IF I 1 1 IR 15 + 1 1 +E I 1 1 1 1 IQ I 1 1 1 111 1 1 IU I 11 11 11 111 1 11 IE 10 + 1 11111 11 11111 11 1 +N I 1 1 10111101 11111111 1 IC I 011110011001110101111 1 1 IY I 01110000100111000111111 1 I 5 + 00 001100000000110000001111111 11 + I 001000100000000000000001111101 1 11 I I 0 00000000000000000000000010001000110 11 I I 0 1 000000000000000000000000001000000000011011 11 1 IPredicted --------------+--------------+--------------+--------------- Prob: 0 .25 .5 .75 1 Group: 000000000000000000000000000000111111111111111111111111111111
Logistic regression model
0 = Absent 1 = Present
Sampling intensity is too low; birds occur within good habitat but sampling does not capture all occurrences.
Habitat is not 100% saturated; there are areas of good habitat which are unoccupied.
Habitat is over 100% saturated; birds occur in areas of poor habitat.
0
1
0 1
Predicted
Observed
Spatial variability is too low or spatial periodicity of key habitat attributes is too high, given sampling intensity.
The playback tape pulls in individuals from outside the point-count radius.
So, can we expect be successful in detecting So, can we expect be successful in detecting species-habitat associations when they exist?species-habitat associations when they exist?
We use simulations where:We use simulations where:
we generated a landscape, thenwe generated a landscape, then
• populated that landscape with a populated that landscape with a (territorial) species, then(territorial) species, then
• sampled the species and landscape sampled the species and landscape repeatedly to assess our ability to repeatedly to assess our ability to
detect a known associationdetect a known association
It might help to conceptualize required It might help to conceptualize required resources by consolidating them into four resources by consolidating them into four fundamental suites:fundamental suites:
……but in either case sufficient resources must be accumulated for but in either case sufficient resources must be accumulated for an individual to establish a territoryan individual to establish a territory
If territory establishment is…If territory establishment is…
Species centredSpecies centred
……then the ‘Position function” sets the parameters for territory then the ‘Position function” sets the parameters for territory establishmentestablishment
Territory establishmentTerritory establishment
Saturation
Half-saturation
Territory densities may be…Territory densities may be…
LowLow
……so realistic simulations must be calibrated to the real worldso realistic simulations must be calibrated to the real world
HighHigh
To be as realistic as possible we had to make To be as realistic as possible we had to make decisions concerning:decisions concerning:
•The characteristics of the landscapeThe characteristics of the landscape
•The species’ distribution on theThe species’ distribution on the landscapelandscape
• The sampling methodThe sampling method
• The statistical model(s)The statistical model(s)
Detection FunctionDetection Function
Point-count radius
Vegetation plot radius
To be as realistic as possible we had to make To be as realistic as possible we had to make decisions concerning:decisions concerning:
•The characteristics of the landscapeThe characteristics of the landscape
•The species’ distribution on theThe species’ distribution on the landscapelandscape
• The sampling methodThe sampling method
• The statistical model(s)The statistical model(s)
The statistical modelThe statistical model
•Deterministic model structureDeterministic model structure
•Use AIC to judge the best of several trial modelsUse AIC to judge the best of several trial models
•The ‘best’ model must be statistically significant The ‘best’ model must be statistically significant from the ‘null’from the ‘null’ model to be accepted model to be accepted
If If =0.05, then Bonferroni’s adjusted =0.05, then Bonferroni’s adjusted is: is:
• A-priori hypotheses concerning species-habitat associations are essential
• Required resources should be amalgamated by suite
• Resource contrast is essential and should be planned:
•Ratio of ‘between-point:within-point’ variability must be increased for both resources and species-of-interest
•Point-count method must be designed with spatial period considerations in mind
At best:
Affirmative conclusions about the importance of ‘critical resources’ based on statistical correlations alone are not justified!
Key Conservation Conclusion
At worst:
Affirmative conclusions about the importance of ‘critical resources’ based on statistical correlations alone, and without documenting the spatial characteristics of the landscape etc., are completely indefensible!