1. Most mountainous state in the USA? · 1. Most mountainous state in the USA? 2. Most-mountainous...

GBLCC: Sept 2015

POP QUIZ:

1. Most mountainous state in the USA?

POP QUIZ:

1. Most mountainous state in the USA? NV

GBLCC: Sept 2015

POP QUIZ:


2. Most-mountainous ecoregion in N. America?

GBLCC: Sept 2015

POP QUIZ:



hydrographic Great Basin

GBLCC: Sept 2015

POP QUIZ:



3. # of mtn. ranges in Nevada?

GBLCC: Sept 2015

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186 314

Fiero 1986 McLane 1978

GBLCC: Sept 2015

POP QUIZ:




4. # of COOP stations (of 342) in NV at >7,500 feet?

GBLCC: Sept 2015

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4

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4 0

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5. # of mtn. ranges in GB w/ high-elev weather station?

GBLCC: Sept 2015

POP QUIZ:





5. # of GB mtn. ranges w/ any high-elev weather station?

~13? White Mtns., Snake, Toiyabe, Ruby Mtns., Schell Creek,

other SNOTEL locations (many <5-10 yrs old)

GBLCC: Sept 2015

POP QUIZ:






6. ~% of USA in most-strictly-protected status that

occurs in mountains?

GBLCC: Sept 2015

POP QUIZ:






6. ~% of USA in most-strictly-protected status that

occurs in mountains?

>90 GBLCC: Sept 2015

Beever

et

al. 2

011

Basin weather stations are usually NOT in mountains

Problematic, because:

Rain shadows

Varying lapse rates **

Cold-air pooling

Inversion layers

This affects:

Microrefugia

Climate adaptation

Restoration options

Conservation portfolio

GBLCC: Sept 2015

Minder et al. (2010): Lapse rate in WA Cascades

High: Spring Tmax (7.4 Cº/km)

Low: Summer Tmin (2.7 Cº/km)

Windward: 4-5 Cº/km vs. leeward: 3-8 Cº/km

GBLCC: Sept 2015

So what … ?!?

6.5 Cº/km J. Geophys. Research 115(D14)

Mountain ecosystems: Cauldrons of dispersal,

endemism, & climate adaptation

Beever

et

al. 2

011

Sharp gradients in abiotic, biotic

conditions => high diversity,

corridors for re-distributing

Water for ~2/3 of world’s people

Aesthetic, recreational values

Constitute >90% of current

strict-conservation portfolio

GBLCC: Sept 2015

What other questions might you be

interested in, to inform mgmt &

conservation efforts?

314 sensors now in place, in 21 mountain ranges

30 sensors paired with sensors at typical weather-station

height (2 m)

Data on:

GPS locations of all animals (+abundance)

ID & local %cover of top 5 most dominant plant spp.

T & RH, 6x/day, to 2005

In situ dates of snow cover, frequency of T exceedances

GBLCC: Sept 2015

Species

vulnerability

to climate

change GBLCC: Sept 2015

Sensitivity

# of microclimate sensors, Basin-wide

Testing effects of microclimate

J. Wilkening

30

14

8

35

227

314

2 m

old, current

N =

GBLCC: Sept 2015

S. Weber

Practical considerations

Pikas to understand climate-species interactions

Locally abundant, unlike most mammals

Relatively stable population sizes

Highly detectable (HPs, calls)

Obligate to easily defined habitat, NOT changing over time

losses not confused by habitat change

Central-place forager

Simultaneous present & past survey

GBLCC: Sept 2015

S. Weber

S. Weber

Call

Active haypile, sighting

Sighting

eep-eep

S. Weber

S. Weber

S. Weber

Ochotona princeps evidences

Ochotona princeps old evidence Old haypiles

Feces: dry

Feces:

moist

S. Weber

S. Weber S. Weber

S. Weber

GBLCC: Sept 2015

5%

In

‘mainland’

regions:

Stewart et al. 2015

Extirpation rates have varied spatially:

GBLCC: Sept 2015

3-6%

5%

In

‘mainland’

regions:

Erb et al. 2011


GBLCC: Sept 2015

3-6%

5%

In

‘mainland’

regions:

2% Millar & Westfall 2010


GBLCC: Sept 2015

44%

In more-

isolated

areas

(Great

Basin):

Beever et al., in review



GBLCC: Sept 2015

44%

In more-

isolated

areas

(Great

Basin): >33%

50%

Erb et al. 2011

Millar & Westfall 2010

Collins &

Bauman 2012

Anatomy of a decline: persistence

6 local extinctions (once every

10.7 yrs)

4 add’l local extinctions (once

every 2.2 yrs)

0 add’l extirpations among

original 25 sites, but 5 of 9

‘new’ sites extirpated

(radioC dating)

4 periods of sampling

Historic 1898-1956

Recent_1 1994-1999

Recent_2 2003-2008

Recent_3 2012-2015

{ { {

Beever et al. 2011

S. Weber

GBLCC: Sept 2015

Minimum elevation of detections, Historic to

my first (1990s) sampling: 13.2 m/decade

Minimum elev. of detections, 1st to 2nd

sampling: 145.1 m/decade

Minimum elev. of detections, 2nd to 3rd

sampling: 54.1 m/decade

Parmesan & Yohe (2003): 6.1 m / decade

Chen et al. (2011): 11.0 m / decade

No ∆ in max, mean, or median elev, at most sites

Historic min: 2,366 m

1999 min: 2,461 m

2008 min: 2,588 m

Beever et al. 2011

S. Weber

Anatomy of a decline: upslope migrations

Basin-wide avg. (>40M ha)

GBLCC: Sept 2015

What is exposure?

Acute heat stress not most important (behavior)

Unrecognized stresses appear most important

Recent > modeled long-term >> climate change

Beever et al. 2010

GBLCC: Sept 2015

Essential for

adaptation, mitigation,

management, and

conservation strategies

Mechanisms are very important !

Why and how …

GBLCC: Sept 2015

Extinctions and declines rarely

effected through direct stress

Instead, indirectly, via species

interactions, food supplies,

habitat loss, pathogens

Evidence of climatic influence on pikas

Comparison of microclimates at sites where

pikas remained vs. became extirpated

iButton field data, 2005-2006

# Days > 28˚C

Avg summer

temperature (˚C) # Days < 0˚C # Days < -5˚C

Pika-extant sites (N = 15 sites) 2.8 + 1.0 12.05 + 1.01 204.4 + 13.2 15.0 + 4.6

Pika-extirpated sites (N = 10) 10.9 + 4.0 17.02 + 0.72 159.6 + 9.7 28.7 + 7.8

Beever et al. 2010, Ecol. Appl.

GBLCC: Sept 2015 J. Wilkening

Evidence of climatic influence on pikas

Comparison of microclimates at sites where

pikas remained vs. became extirpated

iButton field data, 2005-2006

# Days > 28˚C

Avg summer

temperature (˚C) # Days < 0˚C # Days < -5˚C

Pika-extant sites (N = 15 sites) 2.8 + 1.0 12.05 + 1.01 204.4 + 13.2 15.0 + 4.6

Pika-extirpated sites (N = 10) 10.9 + 4.0 17.02 + 0.72 159.6 + 9.7 28.7 + 7.8

Beever et al. 2010, Ecol. Appl.

GBLCC: Sept 2015

Three Lks. (Lamoille Cnyn), Ruby Mtns., ne NV

(classic pika habitat)

GBLCC: Sept 2015

S. Weber

Swager Cnyn, Sweetwater Mtns., Sierra NV

(pikas extant at site)

GBLCC: Sept 2015

S. Weber

Location: Smith Lk., E. Humboldt Range, ne NV

GBLCC: Sept 2015

S. Weber

Long Cnyn, Ruby Mtns., ne NV

(pikas remain at site, but lowest taluses unoccupied)

GBLCC: Sept 2015

S. Weber

Peterson Crk., Shoshone Range, central NV

(recent local extinction; multi-scale patchiness of talus habitat)

GBLCC: Sept 2015

S. Weber

Pinchot Crk., White Mtns.

GBLCC: Sept 2015

S. Weber

Greenmonster Cnyn., Monitor Range, central NV Photo: S. Weber

GBLCC: Sept 2015

Peterson Crk., Shoshone Range, central NV

(recent local extirpation)

GBLCC: Sept 2015

S. Weber

Insights from density … NDVI strongly predicted pika density in 2000s surveys

P = 0.0003

GBLCC: Sept 2015

Insights from density …

Water-balance metrics predicted pika density in

2000s surveys better than did temperature

Beever et al. 2013, Ecology

GBLCC: Sept 2015

The rules are

changing…

Multiple working hypotheses (Chamberlin 1965)

Biogeography

Climate

Proximate anthropogenic AICc

GBLCC: Sept 2015

The rules are

changing…

1990s abundance

Grazed?

Pika-equivalent elev.

Precipitation

Grazing intensity

Amount of habitat

Precipitation

Grazing intensity

Pika-equivalent elev.

Amount of habitat

Grazed?

2000s abundance

GBLCC: Sept 2015

Multiple working hypotheses (Chamberlin 1965)

Are assumptions of IBT likely to be true for all wildlife spp.?

Not all island area can be habitat, due to strong effects of climate

GBLCC: Sept 2015

NV is the driest state, and the GB is a

water-limited ecoregion…

Does RH, vapor pressure deficit, or dew point better

predict pika persistence than temp measures?

GBLCC: Sept 2015

Research in progress …

p = 0.0006





Avg., year-round RH

GBLCC: Sept 2015


p = 0.0006





Avg., year-round RH

Avg. winter RH

GBLCC: Sept 2015


p = 0.0006





Avg., year-round RH

Avg. winter RH

Avg. summer RH

GBLCC: Sept 2015


p = 0.0006





Avg., year-round RH

Avg. winter RH

Avg. summer RH

Avg. daily low (& high & spread) RH, in summer

GBLCC: Sept 2015


p = 0.0006





Avg., year-round RH

Avg. winter RH

Avg. summer RH


Frequency of RH < 15%, > 85%, > 70%

GBLCC: Sept 2015


p = 0.0006





Avg., year-round RH

Avg. winter RH

Avg. summer RH


Frequency of RH < 15%, > 85%, > 70%

Variability in RH GBLCC: Sept 2015


p = 0.0006


Very-fine-

resolution aerial-

photo imagery, to

create a predictive

model of pika

occupancy

True color

Thermal infrared

Color-infrared

T. Millette, Mt. Holyoke College

A. Johnston, USGS

All images: T. Millette


Assessing whether plant chemistry is

mediating pika distributional changes

% water

% N

% fiber

Phenolics

Limiting micronutrients … ?

Extant vs. extirpated sites

“preferred” vs. non-pref. plant spp.

GBLCC: Sept 2015 S. Weber

Denise Dearing, Jo Varner

Univ. of UT


Assessing whether limitation of surface

activity constrains pika distribution > Temp

sensu Sinervo

et al., but for an

endothermic

mammal

GBLCC: Sept 2015

Paul Mathewson, Univ. WI

Warren Porter, Univ. WI

Lucas Moyer-Horner, U. UT

Mike Kearney, U. Melbourne

Unpublished results; do not cite

How management and conservation actions may

affect adaptive capacity

Fundamental

(intrinsic)

adaptive

capacity:

Realized

adaptive

capacity

Management (e.g., climate-adaptation actions)

Climate-

change

stressors

Managed relocation, genetic engineering

Extrinsic factors: • e.g., pollution

Beever et al. in press,

Conservation Letters

GBLCC: Sept 2015

Adaptive capacity: when, where, and how do

species have the ability to accommodate CC?

vs.

Great Basin

Greater Yellowstone Ecoregion



GBLCC: Sept 2015

A. Loosen

A. Loosen A. Loosen

E. Beever

Importance of

microrefugia

Shorter generation time

Higher mutation rate

Ecological generalists: diet, space use,

activity budgets, broad physiological tolerances

Greater dispersal capacity

Greater ability to learn behavior

Which species likely to have

higher adaptive capacity?

E. Beever

J. Smith

Nicotra et al.,

in press, Cons.

Biology

GBLCC: Sept 2015

Shorter generation time

Higher mutation rate

Ecological generalists: diet, space use,

activity budgets, broad physiological tolerances

Greater dispersal capacity

Greater ability to learn behavior

Which species likely to have

higher adaptive capacity?

E. Beever

J. Smith

Nicotra et al.,

in press, Cons.

Biology

GBLCC: Sept 2015

Where might spp. have higher AC…?

Does climate act differently on wildlife in the

GB, for widely-distributed spp.?

elevation

precip driest quarter

precip seasonality

mean annual temp

temp of hottest month

elevation

temp of warmest quarter

mean annual temp

temp of wettest quarter

fig. courtesy of Adam Smith GBLCC: Sept 2015


Ecotypic and genotypic climate vulnerability Fu

ture d

irection

s

Summer Temperature

Winter Temperature

Annual Precipitation

Summer Precipitation

Winter Precipitation

low high Importance

Non-stationary distribution modeling

GBLCC: Sept 2015 fig. courtesy of Adam Smith

Hypothesized mechanisms driving dynamics reflect our understanding of life history

Resiliency: assisted re-introductions

0

0.2

0.4

0.6

0.8

1

-4 -2 0 2 4 6 8

pik

a p

ers

iste

nce

linear predictor: 9.696 - 0.584 * r(JJAave) - 0.035 * r(ND<-5)

observed

predicted

Adj. r2 = 0.62

GBLCC: Sept 2015



Drought FX: Results of 2012-2014 sampling

Compared to 2000s, low-elevation boundary of

pika occupancy in 2010s:

Retracted

upslope

Expanded

downslope

Stayed same

(changed <15 m)

Mean change,

yellow cells

Basin-wide avg.,

n = 8 sites

2012 1 4 3 -191 m -75 m

2013 5 0 3 +223 m +140 m

Totals 6 4 6 +33 m

GBLCC: Sept 2015


Results of 2012-2014 sampling

Compared to 2000s, abundance in 2010s was:

2012: 55% of earlier (geometric mean)

2013: 137% of earlier (geometric mean)

GBLCC: Sept 2015


Core strengths of our research program

focus on an otherwise largely un-sampled part of the Basin

addressing all kinds of Qs @ microclimate, and multi-scale

patterns about climate and wildlife responses to it

21 yrs of data collection (plus, informed by a historical data set

that spanned 1898-1956)

Up to10 years of contin-

fine-scale resolution, spatially (meters) and temporally (T & RH

measured 6x/day, year-round)

uous microclimatic data

GBLCC: Sept 2015

Take-homes: The Big Picture

Illustrate heterogeneity, nuance of GCC effects

GBLCC: Sept 2015



Iteratively refining mechanistic understanding

GBLCC: Sept 2015




Often, re-introductions would not be helpful

GBLCC: Sept 2015





Manipulative climate-adaptation expts. needed

GBLCC: Sept 2015






Much more is coming, esp. on microclimate data

GBLCC: Sept 2015






Much more is coming, esp. on microclimate data

Need to complement these data with non-talus spp.

GBLCC: Sept 2015

Thanks !

S. Weber

M. Russello

Great Basin LCC

Co-authors & colleagues G. Collins T. Millette

K. Goehring A. Johnston

M. Flores Adam Smith

J.P. Clark D. Thoma

C. Waters T. Rickman

B. Yardley T. Chesley-Preston

S. Weber M. Manguson

J. Perrine N. Nordensten

J. Varner M. Nelson

M. Jeffress C. Ray

A. Nicotra A. Robertson

S. Dobrowski

GBLCC: Sept 2015

Colleagues and collaborators

Thomas Albright, Univ. of NV-Reno (UNR)

Gail Collins, USFWS Sheldon-Hart NWR

Rachel Mazur, U.S. Forest Service

John Axtell, BLM Carson City office

Thomas Rodhouse, NPS Upper Columbia Basin

Inventory & Monitoring Network

Thomas Millette, Mt. Holyoke College

GeoProcessing Lab

Solomon Dobrowski, Univ. of MT College of

Forestry and Conservation

Mackenzie Jeffress, NDOW

GBLCC: Sept 2015

An old story: climate shapes mammal distribution

Joseph Grinnell, ca. 1922

Field notes,

Charles C.,

Yosemite NP,

1915

E, Raymond Hall, 1960s

C. Hart

Merriam,

1960s Merriam 1892, 1894

Grinnell 1917

Hall 1946

GBLCC: Sept 2015

An old story: climate shapes mammal distribution

GBLCC: Sept 2015



Optimal body shape for conserving heat, when cold

vs.

J. Jacobson J. Jacobson

Beever et al. in review,

Frontiers in Ecology

and the Environment

GBLCC: Sept 2015




vs.




and the Environment

When cold : 70.6% 29.4%

GBLCC: Sept 2015




vs.




and the Environment

When cold : 73.7% 26.3%

When warm: 12.0% 88.0%

Pearson’s χ2 = 44.104 , p < 0.0001

Fisher’s exact test: P < 0.0001 that Prob BodyShape = Sphere-like is greater for Cold than Warm

Fisher’s exact test: P < 0.0001 that Prob BodyShape is different across Temps

GBLCC: Sept 2015

Hypothesized mechanisms Potential mechanism of climate

effect Possible measures

Acute heat stress Existence, frequency, or duration of temps above a certain threshold (e.g., 25 or 28 deg C)

Acute cold stress Existence, frequency, or duration of temps below a certain threshold

Chronic cold stress

1]modeled temp only, and 2] if we can get a reasonably reliable snow-cover layer, modeled temp + low

snow cover

Melt/re-freeze cycles or events Existence or frequency of modeled warm temperatures, during the cold season(s)

Plant-mediated NDVI, MODIS, or otherwise -- iff available everywhere

Growing-season PPT this variable is an output of water-balance modeling

Duration of snowpack [iff we can get this for all points]; modeled maximum SWE; 1-km-resolution snow depth

Water stress AET, water deficit, some other measure -- either annual, or summer only

Chronic heat stress Average summer temperature, growing degree-days

Relative humidity Chris Daly's new Vapor Pressure Deficit layer (available early 2015, EAB thinks)

Cumulative effects Additive effects of time exceeding high and low temperature thresholds within a season or year

Synergistic effects Muplicative or interacting effects of different stressors, within the same season or year.

Variability of weather, through time CV, 95% CI, or another measure of unpredictability of weather, inconsistent climate

shoulder-season-cold-stress

in lieu of snow cover data (if unavailable), data identifying the probable snow-free period (including

annual variation), intersected with temperature data

amount of suitable habitat and where Presence of talus, elevation, slope, and vegetation

Cascading (indirect) effects Changed conditions in matrix (non-talus) habitat, which diminishes dispersal capacity

Abundance of predator(s)

Will not be used as a predictor (impossible to get fine-scale data on distribution and esp. on abundance; just list

as caveat in Discussion

GBLCC: Sept 2015

Potential mechanisms of CC on montane spp.

Food abundance or quality

Habitat fragmentation

Disease, pests, parasites

Competitors, predators

Physical conditions (snow cover, streamflow, RH, precip)

Exceeding (narrow) physiological tolerances


S. Weber

GBLCC: Sept 2015

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