Copyright
P.(Paul) Hendricks.

Surveys for grassland birds of the Malta Field Office-BLM, including a seven-year study in north Valley County (Volume 2008) online

. (page 7 of 10)
Online LibraryP.(Paul) HendricksSurveys for grassland birds of the Malta Field Office-BLM, including a seven-year study in north Valley County (Volume 2008) → online text (page 7 of 10)
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□ .□



























Fraction of background predicted

Omission on training samples

Omission on test samples

Predicted omission



10



20



30



70



80



90



100



40 50 60
Cum Illative til res It old
Figure C18. An evaluation of omission error rates for training (dark blue line) and test (light blue line) data as a function of the
cumulative threshold and overall predicted area. The red line indicates the overall fraction of the map area fitting each value of
the cumulative threshold. The black line is the predicted omission rate for each cumulative threshold.



Appendix C - 25









Sens


itivity


vs. Specificity for


Sprag


ue_s_


Pipit






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D.9

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Training data (AUC = 0.988)

Test data (AUG =0.976)

Random Prediction (AUC = 0.5)



0.0



0.1



0.:



0.9



1.0



0.3 0.4 0.5 0.6 0.7 0.8
G|3ecifi:ity (1 - Fractional Predicted Area)

Figure C19. Receiver Operating Characteristic (ROC) curve evaluating the overall predictive power of the model with the Area
Under the Curve (AUC). The AUC value indicates that when two random locations are chosen the model has that probability
of assigning a higher cumulative threshold value to the location with more suitable habitat. The light blue line indicates how a
neutral or random model would perform (i.e., it only has a 50% probability of assigning a higher cumulative threshold value to
a random location with more suitable habitat than a random location with less suitable habitat). The further toward the top left
of the graph the training (red) and test (blue) data lines are, the better the model is at predicting the presences contained in the
training data. Sensitivity (plotted on the y-axis) is the proportion of positive locations that were correctly classified by the model.
Sensitivity is also known as the true positive rate and can be thought of as the degree of absence of omission errors. Specificity is
the proportion of random locations chosen from the background (these pseudo-absences are used instead of true negative loca-
tions) that were correctly classified by the model as negative. One minus the Specificity (plotted on the x-axis) is known as the
false positive rate and represents the commission error rate.



Appendix C - 26



Log response of Sprague_5_Plpltto aspect90m_mtS3cllp



TO







aspect9(]m_mtB3cli|i
Log response of Sprague_5_Pipit to nlGd90m_mtS3





Hh i


t t


I


ir










Log res


pc


nse


of Sprag


ue


s Pi


3i


to


statsgo90m


mt83


clip






































































































































































































































1



















































































































Log response of Sprague


Pipit to curve planSOir


mfSZ






























































1.0
































































































a A

















































































































Log respo


nse


of Sprague s Pipit to elevSOm


mtS3clip




















































































































































































|\






















\l






















1
















































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Log res


ponse


cuive_
of Sprague


|ilan90m_mtB3
s Pipit to precip


annSOm mtS3




a, 3

^ 0.2

I""

5-11,1

-D3


















































































































































































































-QA





















































Log response of Sprague s Pipit to slopes


m mtSSclip























































































































































































Log response Of Sprague s Pipit to tmax90m mt83


















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3nse of Sprague s Pipit to tminSOm mtS^clip




















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Log resp


onse


of Sprague


s Pipit to geo


mbrr


g90mt83clJF






















































































































































































































































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1















































































































































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_


_


_


_


_


_


_


_


_


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_


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Log respo


nseo


Sprague s


Pipit to so


il tmpSOm


mtS3























































































































































































































































































































































msi(3am_mt83



Figure C20. Response curves for individual environmental variables showing how the logistic prediction changes as each environmental variable is varied while all other envi-
ronmental variables are held constant at their average sample values. The value on the y-axis is predicted probability of suitable conditions as given by the logistic formula P(x)
= exp(cl * fl(x) + c2 *f2(x) + c3 *f3(x)...) / Z. Note that if any of the environmental variables are correlated, the marginal response curves can be misleading (e.g., two highly
correlated variables with opposite response curves could effectively cancel each other out). Value definitions and/or links to metadata containing these definitions can be found in
the Descriptions of Environmental Input Layers section of the appendix above.



tmin9Dm_mte3clip

tmax90m_mt83

statsgo90m_mt83_clip

soil_tmp90m_mt83

slope9Dm_mt83clip

piecip_ann90m_mt83

nlcd90m_mt83

geombmg90mt83clip

elev9Dm_mt83clip

c u ive_p I a n 9 m_mt8 3

aspect90m_mt83clip



Q.Q



Jackknife of Training gain for SpraguesPipit



Without variable
With only variable
With all variables



0.5



1.0



1.5
Training gain



2.0



2.5



3.0



Figure C21. Jackknife chart showing the relative importance of environmental variables as a function of the change in "gain"
(the log of the number of grid cells minus the average of the negative log probabilities of the sample locations) resulting from the
exclusion or sole inclusion of the environmental variable in the model Variables with the highest training gain resulting from
sole inclusion of those variables (dark blue bars) are the best individual variables at describing suitable habitat for the species.
Jariables with the greatest reduction in training gain resulting from their exclusion (light blue bars) contain information on the
species habitat use that is not present in other variables. The red bar indicates the maximum gain achieved with inclusion of all
variables.



Appendix C - 28



Brewer's Sparrow {Spizella breweri)



TO

s
X







Figure C22. The hot-to-cold color map indicates the suitability of each grid cell as a function of the environmental variables at that grid cell. Hotter colors indicate areas that
are predicted to have more suitable habitat for the species. Black dots are positive data used to build the model Gray dots are locations where a survey capable of detecting the
species has been performed. A shaded relief map, BLM Field Office boundaries, and county lines are included for reference.







Omission vs. Predicted Area for Brewer_s_


Sparrow




□ .9
O.B
0.7

■io.6

|0.5

^0.4

D.3

n.2

□ .1

□ .□














































4






















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/




















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1






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y










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r












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^^


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<i


^


















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-— .










































Fraction of background predicted

Omission on training sampies

Omission on test sampies

Predicted omission



10



20



30



70



80



90



100



40 50 60
Cumulative tiiiesiiold
Figure C23. An evaluation of omission error rates for training (dark blue line) and test (light blue line) data as a function of the
cumulative threshold and overall predicted area. The red line indicates the overall fraction of the map area fitting each value of
the cumulative threshold. The black line is the predicted omission rate for each cumulative threshold.



Appendix C - 30









Sensitivity vs. Specificity for E


rewer


_s_Sp


narrow






i.a

D.9

o.s

I0.7

£=



S0.6

E


;go.4
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Training data (AUC = 0.959)

Test data (AUC= 0.940]

Random Prediction (AUG = 0.5)



0.0



0.1



0.3 0.4 0.5 0.6 0.7 0.8
5|3ecifi:ity (1 - Fractional Predicted Area)



0.9



1.0



Figure C24. Receiver Operating Characteristic (ROC) curve evaluating the overall predictive power of the model with the Area
Under the Curve (AUC). The AUC value indicates that when two random locations are chosen the model has that probability
of assigning a higher cumulative threshold value to the location with more suitable habitat. The light blue line indicates how a
neutral or random model would perform (i.e., it only has a 50% probability of assigning a higher cumulative threshold value to
a random location with more suitable habitat than a random location with less suitable habitat). The further toward the top left
of the graph the training (red) and test (blue) data lines are, the better the model is at predicting the presences contained in the
training data. Sensitivity (plotted on the y-axis) is the proportion of positive locations that were correctly classified by the model.
Sensitivity is also known as the true positive rate and can be thought of as the degree of absence of omission errors. Specificity is
the proportion of random locations chosen from the background (these pseudo-absences are used instead of true negative loca-
tions) that were correctly classified by the model as negative. One minus the Specificity (plotted on the x-axis) is known as the
false positive rate and represents the commission error rate.



Appendix C - 31



TO






Log response of Brewer_5_Sparrowto a5pect90m_mt83clip



f












Log response


aspec19Dm_mta3tli|i
of Brewer s Sparrow to


nIcddOm mt83














1


1 1


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1


M.


, ;










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03 B3 B4 35 91 93







Log response of Brewer


5 Sparrow


tc


statsgo90m


mtS3 clip




,









































































































ll














































































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1




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~T ! i ! 1 ! ! ! 1 ! !



: 513 557 E03 647 E91





Log response of Brewer s Sparrow to curve planSO


m mtS3

























































































































































































































































































































































Log response of Brewer_s_Sparrowto elev90m_mt83clip







curve
Log response of Brewer s


plan9[lm_mta3
Sparrow to precip annSDm


mtS3




































r






















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ID 30 30





Log response of Brewer s Sparrow to tmaxSOm mts:




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1









































































































































































































































1


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Log response of Brewer_s_Sparrowto geombmg90mt83clip



5(10 ICIDCI





Log respon


se of Brew


r 5 Sparrow to 5lope90m mt83t:ljp







ll














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Log response of Brewer s Sparrow to tmin90m mt83clip




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1-0.

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Log response


of Brewer s Sparrow to s


oil tmpSOm


mtS3






'










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,













































































































































































































tmln90m_m1S3cli|i



Figure C25. Response curves for individual environmental variables showing how the logistic prediction changes as each environmental variable is varied while all other envi-
ronmental variables are held constant at their average sample values. The value on the y-axis is predicted probability of suitable conditions as given by the logistic formula P(x)
= expfcl * fl(x) + c2 *f2(x) + c3 *f3(x)...) / Z. Note that if any of the environmental variables are correlated, the marginal response curves can be misleading (e.g., two highly
correlated variables with opposite response curves could effectively cancel each other out). Value definitions and/or links to metadata containing these definitions can be found in
the Descriptions of Environmental Input Layers section of the appendix above.



Jackknife of Training gain for BrewersSp arrow



tmin9Dm_mte3clip

tmax90m_mt83

statsgo90m_mt83_clip

soil_tmp90m_mt83

slope9Dm_mt83clip

piecip_ann90m_mt83

nlcd90m_mt83

geombmg90mt83clip

elev9Dm_mt83clip

c u ive_p I a n 9 m_mt8 3

aspect90m_mt83clip




Without variable
With only variable
With all variables



0.0 0.2 0.4 0.6



0.8 1.0 1.2
Training gain



1.4 1.6 1.8 2.0 2.2



Figure C26. Jackknife chart showing the relative importance of environmental variables as a function of the change in "gain"
(the log of the number of grid cells minus the average of the negative log probabilities of the sample locations) resulting from the
exclusion or sole inclusion of the environmental variable in the model Variables with the highest training gain resulting from
sole inclusion of those variables (dark blue bars) are the best individual variables at describing suitable habitat for the species.
Jariables with the greatest reduction in training gain resulting from their exclusion (light blue bars) contain information on the
species habitat use that is not present in other variables. The red bar indicates the maximum gain achieved with inclusion of all
variables.



Appendix C - 33



Lark Bunting {Calamospiza melanocorys)



TO

s
X







Figure C27. The hot-to-cold color map indicates the suitability of each grid cell as a function of the environmental variables at that grid cell. Hotter colors indicate areas that
are predicted to have more suitable habitat for the species. Black dots are positive data used to build the model Gray dots are locations where a survey capable of detecting the
species has been performed. A shaded relief map, BLM Field Office boundaries, and county lines are included for reference.









Omission vs. Predicted Area for Lark_Bunting






1.D
D.9

□.e

□ 7

15
|0.5

(J
CIS

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Fraction of background predicted

Omission on training sampies

Omission on test sampies

Predicted omission



10



?0



30



70



SO



90



100



40 50 60
Cumuiative tiiresiioid
Figure C28. An evaluation of omission error rates for training (dark blue line) and test (light blue line) data as a function of the
cumulative threshold and overall predicted area. The red line indicates the overall fraction of the map area fitting each value of
the cumulative threshold. The black line is the predicted omission rate for each cumulative threshold.



Appendix C - 35



Sensitivity vs. Specificity for Lark_Bunting



i.a



D.9



o.s



q;0.7



0.6



:eo.4



0.3



0.2



0.1



0.0













1 1








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Training data (AUC = 0.956)

Test data (AUC= 0.940]

Random Prediction (AUG = 0.5)



0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Specificity (1 - Fractionai Predicted Area)



0.9



1.0



Figure C29. Receiver Operating Characteristic (ROC) curve evaluating the overall predictive power of the model with the Area
Under the Curve (AUC). The AUC value indicates that when two random locations are chosen the model has that probability
of assigning a higher cumulative threshold value to the location with more suitable habitat. The light blue line indicates how a
neutral or random model would perform (i.e., it only has a 50% probability of assigning a higher cumulative threshold value to
a random location with more suitable habitat than a random location with less suitable habitat). The further toward the top left
of the graph the training (red) and test (blue) data lines are, the better the model is at predicting the presences contained in the
training data. Sensitivity (plotted on the y-axis) is the proportion of positive locations that were correctly classified by the model.
Sensitivity is also known as the true positive rate and can be thought of as the degree of absence of omission errors. Specificity is
the proportion of random locations chosen from the background (these pseudo-absences are used instead of true negative loca-
tions) that were correctly classified by the model as negative. One minus the Specificity (plotted on the x-axis) is known as the
false positive rate and represents the commission error rate.



Appendix C - 36



TO

s








Log response of Lark Bunting to aspectSOrr


mtSSclJp




























II






1


1




11










1


1




r


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por




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itin


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Log response of Lark_Bunting to &tatsgD9Dm_mt83_clip






I 244 289 333 377 ■



510 554 599 543 687







Log res


ponse


of Lark Bunting to curve planSOm


mtS3






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Log response of Lark_Bunting to geombmg90mtS3cllp







cuive_|ilan9Dn
Log response of Lark Buntin


_nitB3
to precip


ann90m mt83




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-□55





















































Log response of Lark_Bunting to tmaxS0m_mtS3











-r-






































































































\














\





Log response of Lark Bunting


5lape90m mtSSclip

















































































































slopegDm_m183clip
Log response of Lark_Bunting to tmln90m_mt83clip





















J


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res


por


se


>f Lark


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iting to


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Log response


of Lark


Sunting to soil


tmpSOm mt83












































r


~




























/




















































































































































































































































/




























/




























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tmln90m_m183clip


1 2 3 4 5 7 9 10

Online LibraryP.(Paul) HendricksSurveys for grassland birds of the Malta Field Office-BLM, including a seven-year study in north Valley County (Volume 2008) → online text (page 7 of 10)