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 6 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 6 of 10)
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Response Of Greater Sage-grouse to nIcdSOm mt83


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Re


sponse


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clip















































































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153 199 244



332 37B 422 466 510 555 599
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Respo


nse of Greater


Saqs


-grouse to curve p


anSO


m mtS3















































































































































































































































Response of Greater_Sage-grouse to elev90m_mt83cllp







Response


of Greater Sage-grouse to precip annSO


m mtS3




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Response of Greater


_8nn90m_mt83

Sage-grouse to tmax90m mt83


























































































































































































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-500 500



Response of Greater_Sage -grouse to slope90m_mtS3clJp



:





Response


of Greater


pe90i-n_mt83clip

Sag e -grouse to tminSOm mtS3c


p












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Response


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re ate r






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Respon

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5 □ 70

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se of Greater


Sage-grouse to soil tmpSDm mt83

mm


ttttttt



I_tmp9am_mt83



tiTisx9am_m183



tmin9[)m_mta3clip



Figure C5. 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.



Jackknife of regularized training gain for GreaterSage-grous

aspect90m_mtS3clip



cuive_plan90m_mt83

elev90m_mtS3clip

geombmg90mt83elip

nlcd90m_mt83

piecip_ann90m_mt83

slope9Qm_mtS3clip

soil_tmp90m_mt83

statsgo90m_mt83_clip

tma5{90m_mt83

tmin90m_mtS3clip



i



Without variable
Witli only variable
With all variables



Q.Q Q.2 0.4 0.6 0.8 1.0

regularized training gain



1.2



1.4



Figure C6. 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 - 13



Mountain Plover {Charadrius montanus)



TO

s
X







Figure C7. 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 A shaded relief map, BLM Field Office boundaries, and county lines
are included for reference.










mission vs.


Predicted Area for Mountain,


Plover




1.D














































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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 C8. 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 - 15









Sens


tivity


vs. Sp


ecificity for MountainPlover






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Tmining data [AUC = 0.994)

Test data (AUG =0.979)

Random Prediction [AUG = 0.5)



0.0



0.1



0.9



1.0



0.2 0.3 0.4 0.5 0.6 0.7 0.8
Specificity (1 - Fractional Predicted Area)
Figure C9. 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 - 16



TO

s

n







Log response of Mountain P


over to


aspectSOm in1S3


lip




















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Log response of Mountain


Plover to curve p


an90m mt83




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□ a









































Log response of MDuntain_Plover to elev90m_mt83clip











aspect9am_
Log response of Mountain


mtB3cllp
Plover


to nIcdSOm


mt83












































1,3


















































































1












































%










































B
g 04

io2

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1


































































































































































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, 1







Log response


of Mountain Plover to


precip


ann90m mt83








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s




























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on











































































































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-SOD n 50C



Log response of Mountaln_Plover to 5lope90m_mtS3cllp









Log response


of Mo


untain


Plo^




ip




























































































































































































2 '








































r'








































E-"




































3








































05
0.0














































































1

































^0,6
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Loare


ponse


of Mountain


Plover


to soil


tmpSO


Ti mtsa




































































































































































I


1
















^






















T










1









Logrespo


nse


of Mounts


n Plover to


stats go90


m


Tit83


clip








5.0

t

[35

ho




















































































































































































































































































































































E














































05
DO














































































































,











j10 554 599 043 6B7







Log respon


se of Mountain Plover to tmax90m mtS3




































□ ,1S

2 0,14
1

,^012
































































-§0 00

1

^0 04
0,03

0,00

















































































































Log response of Mountain Plover to tminSOm mISSclip


5.0
4.5

,°4

h

|2,D














_____


















-~














"*


'


























































s
















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□ □













































tmax90m_m183



Figure CIO. 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 MountainPlover



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



0.5



1.0



2.5



3.0



3.5



1.5 2.0

Training cjain
Figure Cll. 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.
Variables 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 - 18



Long-billed Curlew {Numenius americanus)



TO

s
X







Figure CI 2. 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 Long-billedCurlew



D.g

D.B

□ .7


I 0.5

■^0.4

□ .3

□ .2
□.1

□ .□



Fraction of background predicted

OrTiission on training samples

Omission on test samples

Predicted orTiission



10



30 40 50 60
Cumulative threslioli



70



SO



90



100



Figure C13. 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 - 20



i.a

D.9

o.s

™ ™ -.
q;0.7

£Z
O

E
o
■ 0.5



:eo.4



0.3



0.2



0.1



0.0







Sensitivity vs. Specificit


/ for Long-billed_Curlew




















— -




/




, /-


-^


' "










/




h




f












/








1














/










I












/












\










/












-










/




















/






















/






















y






















y















































Training data (AUC = 0.984)

Test data (AUG =0.934)

Random Prediction (AUG = 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 CI 4. 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 - 21



Log response of Long-t>illed_Curlew to a5pect90m_mtS3clip



TO

s

n







^



aspect90m_mta3clip
Log response of LDng-bJlled_CurlewtD nlcd90m_mtS3









JL _ ^^


t













Logrespo


3 31

seof


33 33 41

nif
Long-billed


42 43 51 T1 B1 B3 03 B4

9Om_ml03
Curlew to statsgoSOm mtS3


B5
Cl


91
P


2


6I]

1"

5:35

K3[]

s










1
























































































































































i





















































































-










































'^ID










































°










































l,U










































U..












1






















□.D




1 1

















Log response of


Long-


|}illed


Curlew to curve


plan90m mt83





















































































































































































































Log response of Long-billed


Curlew to


elev90m mtSSclip












































































s








U
















K.
























-E










1
















o








°




























































































Log resp


cuive_|ilan9[)m_mtB
3nse of Long-billed Curlew to


precip ann90m


mt83






























1


























































'



























































































































































Lo


S response of Long-


billed Curlew to tma)(90m mt83
















1














\
















\
















\
















\
















\
















\
















\
















\
















^


















\




:^ J



Log response of Long-billed_Curlew to 5lDpe90m_mtS3cllp








.ogrespon


si
se of Long


pe9[]m_m183clip

billed Curlew to tmin90m mtS3


lip






1
















































^


- \
































N














\
















fs









Log resp


onse of Long


billed Curlew to georr


b


mg90mtS3cll






4.5

4.:




















































































































E




















































































s ^










































1














































^'"
















































□ 5


















































1




























D □











































Log response




Curlew to soil tmpSOm


miss




































"^
































LI
































1"

3
ins

□ 3

□ .1

□ □











































































































































































































































































































































Figure CIS. 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.



Jackknife of Training gain for Long-billedCurlew



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



"C



0.0



Without variable
With only variable
With all variables



0.5



1.0



2.0



2.5



1.5

Training gain
Figure C16. 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.
Variables 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 - 23



Sprague's Pipit (Anthus spragueii)



TO

s
X







Figure CI 7. 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 5prague_


sPipIt




1.D

D.g

D.8
0.7

Hi

I0.6

>

li














































/j






















A


f




















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//


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0.i5


^0.4
0.3
D.2
□ .1












/


J


r


















/


r/.


cy












1




A


^


^
















\


A


^


f^


















^


/^










1 2 3 4 6 8 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 6 of 10)