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 8 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 8 of 10)
Font size
QR-code for this ebook


Figure C30. 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



I



0.0



Jackknife of Training gain for LarkBunting



"T 1 1 1 1 1 1 1 1 1 r



_] L



Without variable
With only variable
With all variables



0.2



0.4



o.e



0.8 1.0 1.2
Trainintj cjain



1.4



1.6 1.8



2.0



Figure C31. 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 - 38



Grasshopper Sparrow {Ammodramus savannarum)



TO

s
X







Figure C32. 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 Grasslioppei


'_Sp arrow




1.D

□ .S
0.8

□ 7

>
15
|0.5
























































































Z'


/




















/^


r

J


r








I










/


J


/










\








*^ —


r


/










0.4
□.3

□ .2

□ .1




\






/


f^


r^














\


V ,


A


<>


y


















>^




/


















^


^


P^


■■—-

















D.D



























Fraction of background predicted

Omission on training samples

Omission on test samples

Predicted omission



10



20



30



70



en



90



100



40 50 60
CurTuilative tlireshold
Figure C33. 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 - 40



Sensitivity vs. Specificity for Grassliopper_Sparrow



I.D



□ .9



0.3



d;0.7



U.d



E
o
^0.5

:eo.4



0.3



0.2



0.1



0.0



(^^^^" ^^^^^^ ^ .Jf^



Training data (AUC = 0.963)

Test data [AUG = 0.91 9)

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 C34. Receiver Operating Characteristic (ROC) curve evaluating the overall predictive power of the model with the Area
Under the Curve (AUC). TheAUC 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 - 41



Log response of Grasshopper_Sp arrow to aspect90m_mt83cljp



TO

s

n






□ 20




i














1 „,„














1
















ll


1


1






i
















1 1


-'














i
















t


"







aspects Dm,
Log response of Grasshopper


mtB3cli|i
Sparrow to n


cd90m


mtS3
























































































































































































































































































































1










































1








III






1




ll
















T




1





















Log response


of Grasshopper Sparrow to sUtsgodOm mtS3


lip


















































































































































































|35










































































1
















































1


















01 r




























□ 5

□ □






















































1 1



510 554 599 e43 I





Log response of Grassh


spper


Sparrow to


curve


planSOm mtS3




□ ,5D
i|ll4[l

In...


















































































































































































































."-




























D05






















































□ a:











































Log response of Grasshopper Sp


arrow


to precip ann90m mtS3































































































































































































































































































































Log


response


of Grasshopper Sparrow to tmaxSOm mt83




















-□,1














\




































1'
|-a5

E-or,

-D7






























\


















\
































V-





















Log response of Grasshopper Sparrow to elevSOm mtS3cllp






























L














































































































































































































































^



-500 son



Log response of Grasshopper_Sparrowto slope90m_mtS3clip












































































\






































































Log response of Grasshopper Sparrow to tmin90m mt83cljp


'












\






























\














\_

































































Lob


respon


se of Grass


hopper Sparrow to geo


nt


m


390mtS3c


P


















































































































































































































































































































Log response


of Grassh


pper


Sparrow


to


soil


mp90


m mtS3








1














































^






































































































/


/


























/





















































msi<9Qm_mt83



in90m_m1S3cli|i



Figure C35. 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 GrassliopperSparrow



tmin90m_mt83clip

tma){90m_mt83

stats g 9 m_mtS 3_c I i p

soil_tmp90m_mt83

slope90m_mt83clip

piecip_ann90m_mt83

nlcd90m_mt83

geombmg90nnt83clip

elev90m_mt83clip

CLiiYe_plan90m_mt83

aspect9Qm_mta3clip






Without variable
With only variable
With all variables



0.0 0.2 0.4 0.6



0.8 1.0 1.2 1.4
Training cj;^in



1.6 1.S 2.0 2.2



Figure C36. 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 - 43



Baird's Sparrow (Ammodramus bairdii)



TO

s
X







Figure C37. 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 BairdsSp arrow




1.D
0.9

D.e

□ 7

>
li
|0.5

□ .3

□ .2

□ .1














































/I






















/






















/


J


I


















/




/


















/


_i


/"


r
















/


^


:^


















/


A


r














I




/<


^


&^
















V


A


tr^




















A


r^


X—T


















D.D

























Fraction of background predicted

Omission entraining samples

Omission on test samples

Predicted omission



10 20 30 40 50 60 70

Cumulative tliresh old



SO



90



10Q



Figure C38. 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 - 45









Sens


tivity


vs. Sp


ecificityfor Baird


sSparrow






1.0

D.g

0.8

I0.7

£=


tn 0.6

E

^0.5

I0.3
0.2
0.1
0.0
















— -




/


















/




f
















/








/














/






















/






















/






















//






















/^






















/






















y






















/















































Tmining data (AUC= 0.984)

Test data [AUG = 0.979)

Random Prediction tAUC = 0.5)



0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

S|]ecifici1y (1 - Fractional Predicted Area)



0.9



1.0



Figure C39. Receiver Operating Characteristic (ROC) curve evaluating the overall predictive power of the model with the Area
Under the Curve (AUC). TheAUC 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 - 46



TO



■fcv



LO!


respon




rd_s_Sp


arrow to


aspects


1


clip

1






















11




■ 1 1


fj


^


rJ


h




-\


ri


H\


1
















1
















1-

















5spect9[]m_mta3cl[p
Log response of Balrd_s_SparrDwtD nlcd90m_mtS3







1




J







Log response


of Baird


s Sparrow





statsgoSOm


Tit83


clip




















































































































































































































































































































































1





































































































































































































30 E7 109 154 200 245 2B9 334 379 423 46B 513 557 ED2 647 E91

sta1sgo9aiTi_m183_cli|i







Lofl


response of Baird


s Sparrow


tocu


rve planSOm mtS3




1"

s °-°

1 [1.3

i"

S D,1

3-nn
































































































































































































































































































-□2



































































































Log


response


ofBa


cuive_
rd s


lan90ni_mtB3
Sparrow to precl


annSOm mtS3
































s




























1

^ [1.05

B

1-ii.an

°-ll05





























































































































































































Log response of Baird s Sparrow to tmaKSOm mtS3
















1












\
















\




































































































y





















Loqre


ponse


>f Baird


s Sparrow to elevSOm


mtSSclip










































































































/I








































r








































/


















/




















/




















/



















































350[] iDDD





.og response of Bairc


s Sparrow to slopeSDm mtSScll



























































































































































Log respo


slope9Dm_m103clip
nse of Baird s Sparrow to tmin90m mtS3ci


P



-□.5


















\














s












^ 1




' — 1












1-30
1-3,5










































































^








'"
















■5.0
-5 5










~~ ..^
















" - -■^^























Log respon


se


of Baird


.s_Sparrowt


get


mbmg90mt83Glip



























































































































































































































































































































































































































































































































33 72 12[] ■







Lofl


esponse of Bs


ird


5 Sparrow to sc


II tmpSDm


mt83







































































































































































































































































































































































































































1mj*90m_mt83



tmln90m_m183clip



Figure C40. 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 BairdsSp arrow



tmin90m_mt83:lip

tmax90m_mtS3

stats g 9 m_mt8 3_c I i p

soil_tmp90m_mtS3

slope90m_mt83:lip

precip_ann90m_mtS3

nlcd90m_rrit83

geombmg90mt83:lip

elev90m_mt83:lip

cuiYe_plan90m_mtS3

aspect90m_mt83:lip



Q.Q



Without variable
With only variable
With all variables



0.5



1.0



1.S
Training gain



2.0



2.5



3.0



Figure C41. 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 - 48



McCown's Longspur {Calcarius mccownii)



TO

s
X







Figure C42. 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.







Om


ssion vs. Predicted Are


a for McCown_s_


Longs


pur




1.D
D.9

□ .8

□ 7

15
|0.5

(J
CIS

□ .3

□ .2

□ .1














































^






















X


7




















/


J




















A


y


y


















/j


-^

r^


7


















/,


A


y


















/,


fO


^
















-i


^


i'


/
















^^


^




r


















K-






















D.G

























Fraction of background predicted

Omission on training sampies

Omission on test sampies

Predicted omission



10 20 30 40 50 60

Cumuiative tiiresiioii



70



SO



90



100



Figure C43. 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 - 50







Sensitivity vs


. Spec


if i city


for IVIcCown


_s_Lq


ngspur




1.D

D.9

O.S

I0.7

£=


SO.6

E


;go.4

I0.3

0.2

□.1

a.u




























[^


^
















/






1
















/








f














/






















/






















/






















/






















/






















/






















/






















y















































Training data (AUC = 0.993)

Test data (AUG =0.984)

Random Prediction (AUC = 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 C44. 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-


1 2 3 4 5 6 8 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 8 of 10)