DAVID STONER and THOMAS C. EDWARDS Utah State University, US Geological Survey

Transcription

1 DAVID STONER and THOMAS C. EDWARDS Utah State University, US Geological Survey

2 Science Needs Identified by the Mule Deer Focus Group 1. Synthesis / delivery of information pertaining to factors limiting mule deer populations; 2. Development of tools that use remotely sensed data to assess mule deer habitat quality; 3. Improve the efficiency, accuracy, and cost effectiveness of mule deer population monitoring i.e. use of satellite imagery to monitor mule deer habitat and demography

3 States in the SRLCC are the fastest growing!

4 .the driest Mean total annual mm ppt

5 !and getting drier!,"*+-%()*+,*"!./"01+-".,//"01"(012,*"3" /4*0156"!"#$%&'%()!!$#%!.23-"7057,*"01"(012,*"3" /4*0156"*"+$#%%&'%()!!$#% (012,*" /4*015" />++,*" """""8*,90402):;1" "

6 More people * less water what does this mean for large, migratory mammals?!.'+)(#4'$%5/'6&7e"!>*f)10g):;1"!,1,*5h6"()2,*"i,j,.;4+,12"!(,,i/6"k*,6"*,9*,):;16",29c"!l"!"#$%&'%()*$&+,%"-&./"))! 8(1*#&"%+/'9"04'$:E"!()*+,*"2,+4/"M"F0+;I)."442"*,50+,"!0)!"#$%&'!"+)&$!(!-1)

7 Why is understanding the drivers of deer abundance so important? NC! O15>.)2,")F>1I)19,")"9,12*)."9;19,*1"P;*"233) )4:3;3:<5)2=59>:56) ;;<! 30'$'*10%1*+#0&:%! Q>1:156"7057()H/6")5*09>.2>*," ;;;<! 30'('510#(%1*+#0&:%! R0;I0J,*/02H"A)/4,16"4*,I)2;*"9;12*;.D" ;=<! >'01#(%0'$?10&:-%%! ST;*,"I,,*UV"J/C"SW;;"+)1H"I,,*"X&'%!,%-./0,.#1YUV"

8 Research Concept: 8AFD;=CF3>% Predator Density & Distribution Prey Density & Distribution Climate & Weather T)1)5,+,12" A+)104>.):;1D" E3FG;=CF3>% =3B3,A,;CD% Primary Productivity Topography Z;"9;12*;." A4*,I09:;1D"

9 Changes in primary production transfer across trophic levels 10,000 J

10 Research Questions Use NDVI to evaluate mule deer abundance, migra:on, and habitat use with respect to climate and land- use; I. How does climate affect mule deer demography? II. How do climate and land- use influence mule deer migra:on? III. How can satellite observa:ons of vegeta:on phenology inform wildlife management?

11 Study region (1.5 million km") `a" Z\" OW"?b"?c" cd" ZT"

12 Vegetation metrics derived from The Stacks a=ce" f>.0)1"i)2,/"$g#6$$!"" A"$"f)1"%!!!"2;"&$"[,9"%!$'D" %!$'" Tb[NB"Z[\N"A%!!!<%!$'6"I)0.H6"#!!"+D" e,/4;1/,"\)*0)f.,"h"z[\ne"/9).,i"!<$" 9;)*/,"/4):).6"K1,"2,+4;*).6"" " %!!!" [,*0J,I"I)2)""

13 Study areas & sampling polygons %%%%%%%%%%%>)/H"I%J#&#%K$%L%MN%)$1&:O%%%%%%%%%%%%%%%%%%%%%%%%%%%B.>%J#&#%K$%L%P%)$1&:O%

14 Response variables:! i)(1/"j"$!!"i;,/"aow6"cdd"! %!!'<%!$%"! TO"4;4>.):;1",/:+)2,/"AOWD"! %!!k<%!$&"! l8b"p*;+")i>.2"p,+).,/"aowd"! 1"h"$!!")10+)./"! %!!!<%!$#"

15 Question 1: How does climate affect mule deer demography?

16 Climate varies along a latitudinal gradient within the study area: Note three seasonal pamerns: 1. Winter snow proceeds from north to south 2. Heavy winter snowpack ends ~ 37 N 3. Monsoonal moisture progressing from south to north ~ mid- summer

17 Precipitation varies with latitude across the study region: 60% 50% cd"""ow" % WINTER % SUMMER % SPRING TOTAL ANNUAL PRECIP 40% 30% R! = 0.02 R! = % 10% R! = % LATITUDE

18 Timing of green-up (~ forage quality) varies with latitude: º `a" Mean NDVI º 33º Latitude Z\" OW"?b" 0.2 winter snow (July-Sept: 17%) snow-rain mix (July-Sept: 29%) summer rain (July-Sept: 52%) 0.1 MAR APR MAY JUN JUL AUG SEP OCT NOV GROWING SEASON cd" Longitude ZT"

19 Birthing dates track Peak-of-Growing- Season across latitude: ID WY NV Latitude UT CO AZ NM Longitude Southern deer :me birthing to monsoonal moisture.

20 Fawn production varies with summer precipitation: MEAN_FAWNS CV_FAWNS R! = % 45% 40% Z\" N[" `a" Fawn counts % 30% 25% 20% 15% CV (fawn counts) Latitude OW"?b" 20 10% 10 5% R! = % Latitude cd" Longitude ZT" B1;("I;+01)2,I"/H/2,+/",q70F02"7057,*"/>++,*"P)(1"/>*J0J).")1I".;(,*"012,*)11>)."J)*0):;1C"

21 Fawn production varies with mean annual AET: R! = N[" `a" Evapotranspiration R! = 0.59 EVAPOTRANSPIRATION =J)4;2*)1/40*):;1" i)(1"9;>12/" MEAN_J100F Fawns / 100 Does Latitude Z\" OW" cd" ZT"?b" Latitude Longitude i)(1"4*;i>9:;1"j)*0,/"01j,*/,.h"(027"+,)1")11>)."c=wc"

22 Density varies with summer range NDVI during parturition: R 2 = 0.58 N[" N[" `a" `a" (deer / 100 km 2 ) Latitude Z\" Z\" OW" OW" cd" cd"?b"?b" ZT" NDVI (Date-Of-Birth) Longitude e)15,"9;1i0:;1/"i>*015"f0*27"4*,i092"4;4>.):;1"i,1/02hc"

23 Question 2: How do climate and land-use influence mule deer seasonal movements?

24 Monroe Mtn elk: spring migration in response to climatic factors Note two seasonal pamerns: 1. Probing behavior to snowline during late winter; 2. Tracking green- up during late spring

25 Cedar Mtn deer: migration in response to climatic factors Note two seasonal pamerns: 1. Spring migra:on ini:ated when summer range is green (May 4); 2. Fall migra:on ini:ated arer first major snowstorm (October 20)

26 Migration is predictable in space! N[" 1000 MEAN ELEVATION (m) N Latitude 1200 SNOW 3000 MEAN (± 95% CI) ANNUALSNOWPACK (mm) MEAN_ELEV Z\" `a" OW" cd" Longitude LATITUDE N [,,*"+05*)2,"01")*,)/"(7,*,"/1;("I,427"L"%#!"++"Ao"$!VDC"?b" ZT"

27 !and time: >F% B4*015"+05*):;1"A#j$'j%!$%D-" " i).."+05*):;1"auj%'j%!$%d-" YF% v6!!!w" >F% u6#!!w" YF% QRS%61$&"/%#$T%:)**"/%E'*"%F#$5":%KG')(T"/%!&$U%V,U%RWXRO%!C#!"!C'#"!C'!" M2J#I%!3AD%DJ=;%!C&#"!C&!"!C%#"!C%!"!C$#"!C$!"!C!#" %!$%t`e" %!$%tbe"!c!!" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" s$<#s" s$$<$#s" s%$<%#s" P,F" +)*" )>5" /,4" ;92" 1;J"

28 Mule deer are vulnerable to land use change:

29 Mule deer are vulnerable to land use change: 2#$.3%4.*3%5.0$% by>0**7"t21/" B).2"x)n,"?02H" 63.7%5.0$%! `012,*"*)15,".0+02):;1E"! x;//";p"+05*)2;*h"*;>2,/e""! i*)5+,12):;1"27*,/7;.i/e"" 8*;J;<b*,+"

30 Mule deer are vulnerable to land use change: [,,*"i%-"9)>572")2"?)+4"`0..0)+/" $juj%!$%" lbx" zo??" B).2"x)n,"?02H" B>++,*"*)15,"" A%!$%6"%!$&D" T05*):;1"*;>2,"A$D-"" c4*0."3"b92"%!$%6""?`" T05*):;1"*;>2,"A%D-" B4*015"%!$&" O2)7" x)n," 8*;J;" (012,*"*)15,"A%D"

31 Question 3: How can satellite observations of plant phenology inform wildlife management? $C! Q)F02)2-"9;1/,*J):;1"j"*,/2;*):;1" %C! [,+;5*)4709/-"4;4>.):;1"I,1/02H"j"2*,1I/"

32 Habitat: where possible, conserve / restore aspen communities B8c?=" WNT=" JUNE NDVI JUNE NDVI R! = R! = DECIDUOUS VEG SPRING PRECIP (mm)

33 Demographics: combined with field data, NDVI can be used to predict population abundance / trends DEER / 100 km JUNE NDVI SPRING NDVI SPRING NDVI FAWN COUNTS YEAR FAWNS / 100 DOES

34 The goal is to improve p/a maps by including demographic parameters: NDVI is correlated with density, produc:vity, and survival

35 Take Home Points: I. Climate (~ vegeta:on) can affect mule deer demography; detectable through standard survey data; II. Interannual varia:on in plant phenology is measurable with satellite imagery; III. Deer abundance varies predictably with NDVI; can be used to es:mate / calibrate popula:on objec:ves; IV. NDVI provides an affordable, :mely, synop:c tool for indexing deer habitat (state, regional, MU scales); can be used to: I. predict deer movements / iden:fy conflict hotspots / migra:on routes; II. Priori:ze management ac:ons (habitat restora:on, predator control, harvest, etc.).

36 Acknowledgements