Ecosystems and Biodiversity: overview of
mechanistic studies
David Chandler, Walter Dodds, Mark Eberle, Michelle Evans-White,
Keith Gido, David Hoeinghaus, Tony Joern, Angela Laws, Justin
Murdock, Jesse Nippert, Jim Thorp
Ecosystems and Biodiversity: overview of
mechanistic studies
Natural
History
Collections
Integration
Mechanistic
Studies
Modeling
Ecoforecasting
Terrestrial Studies
What is the physiological plasticity and potential for adaptive evolution in Panicum virgatum in response to altered precipitation and N deposition?
4 native genotypes in the Central Plains
• Objective 1:
j
Measure the ecological g
responses of P. virgatum to projected changes in precipitation and nitrogen deposition
• Objective 2: Assess the presence and expression of genetic variation within and among populations to changes in
and among populations to changes in precipitation and nitrogen deposition
• Objective
Objective 3: Model productivity 3: Model productivity
responses to changes in precipitation and nitrogen deposition to assess potential future ecosystem change resulting from the physiological lti f
th h i l i l
plasticity of natural populations
Nippert et al., pending NSF support
We are manipulating rainfall timing and soil N availability using the Mesocosm
soil N availability using the Mesocosm facility at the Konza Prairie. P. virgatum
genotypes are planted in replicated individual mesocosm ‘cells’ (64 total) and receive 1 of 4 rainfall treatments and 1 of 2 nitrogen deposition treatments (8 total). 2 rainfall intervals
2 rainfall intervals
All mesocosm cells (and genotypes) receive the same amount of annual precipitation as per the native site but the
native site, but the timing of rainfall is altered according to climate change
climate change predictions for the Central Plains every 12 days every 6 days
Seasonal rainfall distribution
Equal
Growing‐
50 / 50
season / Dormant ‐
season
Ambient
75 / 25
Altered
Ambient
+N
+N
+N
+N
ambient N or +N (5g/m2) added to each rainfall treatment
How will species interactions vary with climate change?
Temperature,
Food quality, etc
• Altered temperature and food quality, which are predicted to vary with climate change, have potential to affect invertebrate species interactions. ((‐))
(+)/(‐)
(+)
()
(‐)
• Potential for non
Potential for non‐linear,
linear, indirect indirect
interactions make predictions difficult without an understanding of underlying mechanisms
•Understanding the mechanisms will enable us to make predictions about responses of other systems to climate change.
Laws and Joern, pending NSF support
Field Experiments
Treatments:
• Temperature (ambient, increased, decreased)
• Food Quality (ambient, decreased)
• Grasshopper
G
h
D
Density
i (hi
(high,
h low)
l )
• Predation Risk (predators present, absent)
Measure grasshopper performance (survival, body mass, fecundity)
in each treatment
Grasshopper fecundity at Konza
The relative importance of biotic and abiotic factors in controlling fecundity in grasshopper populations is not well understood
Conducting a long term survey of 4 grasshopper species in eight watersheds at Konza to measure fecundity
• Management practices (fire, bison grazing)
• Grasshopper density
• Food quantity and quality
• Weather/temperature
• Hatching phenology
Orphulella speciosa
Aquatic Studies
Eco-Forecasting in the Kansas River:
EcoS i Communities,
Species,
C
i i andd the
h Ecosystem
E
James H. Thorp and KU River Ecology Lab Grad Students
(Brian O’Neil, Sarah Schmidt, and Bradley William)
Ecoforecasting in the
Kansas River: KU
River Ecology Lab
Eco-Forecasting Research Strategies for the Kaw
Predictions of Climate Change Models
[Changes in Precipitation & Runoff Patterns]
[Results from other EPSCoR & national scientists]
Changes
g in River Hydrogeomorphology
y g
p
gy
[Direct effects from altered hydrology and
indirect effects from altered channel geomorphology]
[River Ecology group at KU]
Microalgal Community Structure
aandd Net Ecosystem
cosyste Metabolism
etabo s
Zoobenthic
Community
Co
u ty St
Structure
uctu e
Species
st but o s
Distributions
[Sarah Schmidt]
[Brian O’Neill]
[Bradley Williams]
Community & Ecosystem Effects [see posters for details]
Microalgal Community Structure
and Net Ecosystem Metabolism
Zoobenthic
Community Structure
[[Sarah Schmidt;; Ph.D. project]
p j ]
[[Brian O’Neill;; Masters;; going
g g on to Ph.D]]
• Effects on NEM from variation
in geomorphic complexity
(longitudinal & lateral) and
hydrologic changes (amount
& variability)
• Differences in algal community
in thalweg and slackwater areas
Masters Research:
Effects of hydrogeomorphic
changes on benthic community
di
diversity,
it species
i composition,
iti
and density (emphasis on
chironomid midges)
Ph.D. Research:
• Contribution of benthic algae &
phytoplankton to photosynthesis
• Changes in microbial loop (future
project)
[In development but topic
related to eco-forecasting
eco forecasting
in Great Plains rivers]
Effects on Species Distribution [see poster for details]
[ Bradley Williams; Ph.D. project ]
Medium
M
di
Spatial
S i l Scale
S l
(river network; valley-to-reach)
Large Spatial
L
S i l Scale
S l
(ecoregional and above)
Models emphasizing
hydrogeomorphic patches
and river complexity (e.g., the
Riverine Ecosystem Synthesis;
Thorp
p et al. 2006,, 2008)) and
the ecological importance of
Functional Process Zones (FPZs)
Models emphasizing
temperature water hardness,
temperature,
hardness
and river network
connectivity (e.g., Garp)
KU River Ecology Lab
KU Biodiversity Institute
Ecoforecasting in the
Kansas River: Land use
effects
ff t on metabolism
t b li
and diversity
Patterns in metabolism and diversity in
Kansas River basin
Patterns in Stream Metabolism in the Kansas
River Basin
•Land
Land cover data
•In-stream habitat
•Dissolved
Di l d nutrients
t i t
•Benthic and sestonic algae
•Nutrient Limitation via diffusing substrata
12
DO(mg/L)
10
8
6
4
2
0
8/2/07
12:00
8/3/07
0:00
8/3/07
12:00
Time
8/4/07
0:00
8/4/07
12:00
Patterns in Macroinvertebrates and Fishes
Across a Productivity Gradient in the Kansas
River Basin
•Metabolism
•Water
W t chemistry
h it
•Benthic organic matter
standing stocks
•In-stream habitat
Ecoforecasting in the
Kansas River: nutrient
l di andd aquatic
loading
ti
consumers
Gido et al., pending NSF support
Theoretical predictions: grazer x
nutrient interactions
Experimental Stream: Konza Prairie
Measuring ecosystem responses
• Structure
– Algal filament length
– Algal
Al l bi
biomass
• Chlorophyll a extracted
from natural pebbles
– Particulate Organic Matter
(POM)
– Macroinvertebrate
abundance
• Function
– Gross Primary Productivity
(
(GPP)
)
– Nutrient retention
Preliminary results: grazer and
nutrient
t i t loding
l di
30
tio
25
:N ra
Periphyyton C
ass (chl a
Algal bioma
mg/m2)
1000
100
5
10
20
i en
1
20
)
mb
h
Fis
2
(x a
25
1
15
m
i ng
10
/
(g
o ad
5
s
nt l
bio
s
ma
0
as
trie
15
)
5
om
Nu
g /m
s(
10
bi
10
2
15
sh
Fi
0
10
20
10
25
t)
Nu
Periphyton quantity controlled
by nutrient loading
t lo
t r ie n
a din
g
mb
(x a
ient
)
Periphyton quality controlled
by grazers and nutrient loading
Summary
• Terrestrial studies
– Effects of pprecipitation
p
and nutrient on physiology
p y
gy of P.
virgatum
– Effects of temperature and food quality on trophic
i t
interactions
ti
in
i grassland
l d food
f d webb
• Aquatic studies
– Climate and hydrogeomorphic
h drogeomorphic effects on Kansas Ri
River
er
ecosystem, communities and species distributions
– Effects of land use on stream metabolism and
biodiversity
– Effects of nutrient loading and grazers on stream
ecosystem
t structure
t t
andd function
f ti