Transcript Condition Monitoring

EI Monitoring – Science Challenges
Condition Monitoring
Monitoring conducted over the whole park in the long
term to detect major trends in park EI - “What is the state
of park EI?”
Management Effectiveness
Monitoring
Monitoring conducted over small areas to assess the
effectiveness of specific park management actions – “What
are we doing to improve park EI?”
Common Issues/Common Solutions
Generally the same elements are missing in almost
all park monitoring programs
Permanent, long term monitoring of ecosystem process measures
at local and landscape scales
Given the same missing program
elements
can
work
together
conceptual
ecosystem we
models
linking
EI components
(biodiversity,
processes,
stressors)
for major park
ecosystems toto
EI
to
develop
common
solutions
Measures and Indicators
park EI monitoring and reporting
‘final suite’ of EI measures
issues
management targets and thresholds for EI Measures
assessment methodologies for EI Indicators
Science Challenges
1. How do we ‘capture EI’?
2. What do we measure?
3. What do our measurements
mean?
4. Communicate!!!
‘Capturing EI’
• EI monitoring framework
• major park ecosystems as EI indicators
• core conceptual ecosystem models
• local and landscape scales of
measurement
Ecosystem
Realms and
Major Park
Ecosystems
COASTAL
UPLANDS
forests/woodlands
arctic/alpine tundra
grasslands
other non-forested
WETLANDS
beaches
riparian,
dunes
wetlands
cliffs
estuaries
inter-tidal
sub-tidal
near-shore
pelagic
MARINE
lagoons
rivers/streams
lakes/ponds
FRESHWATER
*MPEs for Great Lakes Bioregion
EIecosystems
major park
Indicator
Concerned
EI Impaired
High EI
Public
environment
Science
environment
biodiversity/processe
s
models
statistic
s
measures/dat
a
human dimension
stressors
EI INDICATORS by BIOREGION
The North
Pacific
Coastal
Interior
Plains
Great
Lakes
Quebec
Atlantic
Montane
Cordilleran
Forest
Forests and
Forest
Forest
Forest
Terrestrial
woodlands
Ecosystems
Tundra
Non-forest
Grasslands
Non-forest
‘Barrens’
Wetlands
Lakes and
Wetlands
Wetlands
Wetlands
wetlands
Freshwater
Streams and
Ecosystems
Lakes
Lakes
rivers
Glaciers
Islets/shorelin
Streams
Streams
es
Coastal
Inter-tidal
Great Lakes
Freshwater
Native
(Lakes)
Biodiversity
Freshwater
Geology and
(Streams)
landscapes
Coast
Climate and
Shore
Marine
Sub-tidal
Aquatic
atmosphere
Marine
support for EI
Ecological Integrity Monitoring Framework
Biodiversity
Process and Function
Stressors
Species richness
Succession/retrogression
Human land-use patterns
- change in species richness*
- numbers and extent of
exotics*
- disturbance frequencies and size
(fire. insects, flooding)*
- vegetation age class distributions*
- land use maps, roads
densities, population densities.*
Habitat fragmentation
Population Dynamics
Productivity
- mortality/natility rates of
indicator species*
- immigration/emigration of
indicator species*
- population viability of indicator
species*
- landscape or by site
- patch size, inter-patch
distance, forest interior*
Decomposition
Pollutants*
-by site
- sewage, petrochemicals etc.
- long-range transport of toxics
Nutrient retention
-Ca, N by site
Trophic structure
- size class distribution of all
taxa
-predation levels
Climate*
- weather data
- frequency of extreme events
Other*
-park specific issues
Ecologically Comprehensive
EI FRAMEWORK
EI INDICATOR*
Forests
Biodiversity
√
√
√
Wetlands
√
√
√
Lakes
√
√
√
Streams
√
√
√
‘Barrens’
√
√
√
Coastal
√
√
√
Marine
√
√
√
Processes
* EI indicators for Atlantic-Quebec Bioregion
Stressors
Forest EI
Indicator
Models
Measure
s
Data
Concerne
d
Critical
Healthy
Stand Level
Forest EI
tree productivity, songbird
index, salamander
populations change, foliar
nutrient index,
decomposition efficiency
dbh, canopy condition, species
composition, chopstick dry weight
loss, songbird/salamander density,
relative soil arthropod abundance,
foliar nutrient concentrations
Landscape Level
Forest EI
FF BioD Index (SAR, top
predators, ungulates), CFBioD
Index (ecosystem representation),
connectivity, productivity
SAR and other species population
assessments, relative ecosystem
abundance, Fragstats, AVHRR
Core Bioregional Forest Stand Model
carnivores
climate
change
predation
trampling/
disturbance
herbivores
herbivory
decomposition
vegetation
soil humus
nutrient/moisture
uptake
hyper-abundant
ungulates
acid
deposition
mineral soil
Core Bioregional Forest Landscape Model
size, vigour and genetic diversity
of focal carnivore populations
predation
size, vigour and genetic diversity
of focal herbivore populations
habitat effects
hunting
trapping
herbivory
spatial character, composition
and productivity of forest communities
landform
processes
climate
change
distribution and character of
park landforms (floodplains,
moraines, karst, organics, avalanche
tracks, glaciers, glacial outwash)
acid
deposition
Roles of Ecosystem Conceptual Models
• reduce ecosystem complexity: essential components of
biodiversity, processes and stressors (EI) to prioritize
monitoring measures; organize protocols and measures
• COMMUNICATE approach and results:
science peers inside and outside parks
park managers, interpreters etc
all Canadians
• improve EI assessments: conceptually related and colocated measures (long term plot data) provides internal logic
• incorporate other park management activities:
ecological frame for including restoration, infrastructure
changes, visitor changes, operational changes, etc
Forest EI
Indicator
Models
Measure
s
Data
Concerne
d
Critical
Healthy
Stand Level
Forest EI
tree productivity, songbird
index, salamander
populations change, foliar
nutrient index,
decomposition efficiency
dbh, canopy condition, species
composition, chopstick dry weight
loss, songbird/salamander density,
relative soil arthropod abundance,
foliar nutrient concentrations
Landscape Level
Forest EI
FF BioD Index (SAR, top
predators, ungulates), CFBioD
Index (ecosystem representation),
connectivity, productivity
SAR and other species population
assessments, relative ecosystem
abundance, Fragstats, AVHRR
Conceptual Model – Streams
CWD,habitat
structure/
channel
stability
riparian
filtering
condition
fish diversity index
amphibians
benthic
invertebrate
index
riparian vegetation
allochthonous
inputs
light/heat
fish
predation
benthic macroinvertebrates
riparian
disturbance
herbivory
periphyton
periphyton
index
human effects
(fishing, invasive
aliens, pollution)
flows/temperature/
water chemistry/
nutrients
water flows,
water quality
water temperature
climate change
macrophytes
Reporting Park EI
6-8 EI Indicators
SOP synopsis
(indicators)
science
foundation
(measurements
and models)
Forests Wetlands
Lakes
Streams
Marine
Coastal
What to Measure and
How to Measure it?
• given the vast number of things we could
measure, what do we measure?
• PSOCLCIEIMs – the Holy Grail
• measuring the park – study designs
The Holy Grail
To find a parsimonious suite of colocated, ecologically inter-related EI
measures that provide a
comprehensive summary of park
forest EI at an acceptable financial
and human resources cost
Forest EI
Indicator
Models
Measure
s
Data
Concerne
d
Critical
Healthy
Stand Level
Forest EI
tree productivity, songbird
index, salamander
populations change, foliar
nutrient index,
decomposition efficiency
dbh, canopy condition, species
composition, chopstick dry weight
loss, songbird/salamander density,
relative soil arthropod abundance,
foliar nutrient concentrations
Landscape Level
Forest EI
FF BioD Index (SAR, top
predators, ungulates), CFBioD
Index (ecosystem representation),
connectivity, productivity
SAR and other species population
assessments, relative ecosystem
abundance, Fragstats, AVHRR
Selecting Measures
• cost-effective, information-rich, low signal to
noise
• credible – supported by science
community/research
• feasible to measure (technical field staff); ‘same
day suites’
• comes with a ‘story’, e.g., soil arthropods?
• works well as part of a ecologically-integrated
suite that covers conceptual model components
• shared by monitoring partners
(provinces/territories, communities, model
forests, industry)
FOREST STANDS
Ecosystem
Component
Ecosystem Process
Ecosystem
Stressor
soil humus
soil mineral weathering
acid deposition
mineral soil
humus decomposition
climate change
vegetation
nutrient uptake
air pollution
herbivores
plant productivity
trampling
carnivores
plant recruitment
harvesting
plant mortality
invasive aliens
herbivory
predation
Proposed
Measures
1. soil decomposition
index
2. foliar nutrient
concentrations
3. vegetation plot
data
4. forest songbirds
5. forest salamanders
6. soil arthropods
7. arboreal lichens
Core Bioregional Forest Stand Model
carnivores
climate
change
predation
% dry weight loss
of soil
decomposition
standard
herbivores
relative
abundance of
indicator soil
arthropods
decomposition
herbivory
vegetation
forest songbird guild densities
forest salamander densities
trampling/
disturbance
epidemic insect
outbreaks
(epidemics/5years)
hyper-abundant
ungulates
Forest vegetation plot: DBH/height
increment of stand dominants;
native/alien species diversity, tree
canopy condition;
tree recruitment
acid
and mortality, browse, arboreal
deposition
lichens,
foliar nutrient concentrations
(N, P, K, Ca, Mg)
soil humus
nutrient/moisture
uptake
mineral soil
FOREST LANDSCAPES
Ecosystem
Component
Ecosystem
Process/Function
Ecosystem
Stressor
Proposed
Measures
• landforms/soils
• landscape connectivity
• climate change
 fragmentation metrics
• forest
• interior forest function
• acid deposition
• ecosystem productivity
• landscape level
• other pollutants
• habitat suitability and
communities
• large herbivores
productivity
• large carnivores
• coarse filter biodiversity
infrastructure
• fine filter biodiversity
• visitor effects
• stand-replacing
disturbance
• landform processes
(flooding and
sedimentation, coastal
• park
• harvesting
• invasive aliens
• GPE effects
population viabilities of
managed species
• ecosystem
representation
• phenological
observations
• invasive alien index
• landform changes
Core Bioregional Forest Landscape Model
focal ungulate populations
(moose, deer. caribou, hare)
focal predator populations
(bear, wolf, coyote, fox)
size, vigour and genetic diversity
of focal carnivore populations
predation
size, vigour and genetic diversity
of focal herbivore populations
habitat effects
human
effects
herbivory
change analysis (fragmentation,
focal species habitat suitability,
ecosystem representation),
productivity, phenology, alien
species
climate
change
spatial character, composition
and productivity of forest communities
landform
processes
distribution and character of
park landforms (floodplains,
moraines, karst, organics, avalanche
tracks, glaciers, glacial outwash)
glacier changes, flooding regimes,
ice processes, avalanche rates
acid
deposition
Establishing Long Term
Monitoring
General Rules
1. For all EI indicators data on biodiversity, processes and
stressors should be collected at 2 scales – local and
landscape
Representative local ecosystems of the major park ecosystem
(forest stands, eelgrass beds, stream reaches, kelp beds,
wetland types) need to be selected for measurement
based on available resources, park management priorities
and bioregional approaches
Whole park and greater park measures and assessments of
indicators based on EO/RS – GIS data
Changes in Forest Site - Spatial Variability
Changes in Forest Structure – Temporal
Variability
Forest Site
FOREST ECOSYSTEM
Shrub
Young
Mature
REPRESENTATION
Herb
Forest
Forest
SMR/SNR
Old Forest
dry/poor
0
0
5
0
mesic/poor
1
5
0
5
medium-
mesic/mediu
5
5
25
5
textured tills,
m
moist/rich
1
1
15
2
wet/poor
0
0
5
15
dry outcrops;
coarse soils
coarse-textured
tills, mors
mors
mediumtextured tills
with seepage,
moders
Bogs
Selecting ‘Representative
Ecosystems’
• average (mesic) ecosystems
• most abundant ecosystems
• ecosystems with high conservation
importance
• ecosystems most sensitive to known
stressors
base poor ecosystems susceptible to acid rain
droughty ecosystems where prolonged summer
drought is forecast
N
Arthropod traps
5m
W
E
Legend
= Bird sample point
= Salamander board
= Vegetation plot
= Potential vegetation
plot
songbirds
defoliators
salamanders
foliar nutrients
veg plot
soil insects
decay sticks
A CO-LOCATED, ECOLOGICALLY INTER-RELATED
SUITE OF LOCAL FOREST EI MEASURES
Forest EI
Indicator
Models
Measure
s
Data
Concerne
d
Critical
Healthy
Stand Level
Forest EI
tree productivity, songbird
index, salamander
populations change, foliar
nutrient index,
decomposition efficiency
dbh, canopy condition, species
composition, chopstick dry weight
loss, songbird/salamander density,
relative soil arthropod abundance,
foliar nutrient concentrations
Landscape Level
Forest EI
FF BioD Index (SAR, top
predators, ungulates), CFBioD
Index (ecosystem representation),
connectivity, productivity
SAR and other species population
assessments, relative ecosystem
abundance, Fragstats, AVHRR
Targets and Thresholds
• What’s the question?
• What’s the answer?
• Developing targets and thresholds.
The question is………….
“What is the state of park EI?”
Humus Decomposition Sub-model
acid
deposition
heat/moisture
condition of litter inputs
climate
change
soil biota
interaction
s and
processes
Dry Weight Loss of
Wood Decomposition
Standard
rate of humus
decomposition
(percent dry weight loss)
vertebrate
predators
Ecological Effects
nutrient
availability/uptake
foliar nutrient content
plant productivity
plant vigour
pests and pathogens
herbivore/predator effects
Targets, Baselines and
Thresholds
thresholds
targe
t
confidence interval
‘precautionary principle’
High EI
82
62
baseline (mean)
concerned
EI Impaired
42
30
20
Dry Weight Loss of Wood Decomposition Standard
(percent dry weight loss)
mean % weight loss
(+/- 80% C.I.)
Establishing Targets and Thresholds
Soil Decomposition
Site 1
Landform: beach sands
Soil: O.DYB moderately coarse, rapidly
drained
Veg Comm: Red Oak / Trembling Aspen
35.00
Stand Origin: fire
Site 5
Landform: glacio-marine
Soil – O.GL; very fine, poorly
drained
Veg Comm: White Cedar / Balsam
Fir
Stand Origin: natural
30.00
25.00
20.00
15.00
10.00
5.00
0.00
0
1
2
3
4
site no.
Mean percent weight loss of tongue depressors
(in ground) within varying sites.
5
6
Clear Monitoring Questions
H01: local scale (stand level) forest ecological integrity has not
changed significantly over the last 5 years in mature
eastern hemlock ecosystems in Kejimkujik NP
H01.1: soil humus decomposition has not changed more
than 35%
H01.2: forest salamander population densities have not
decreased more than 12%
H01.3: foliar N concentrations have not decreased more
than 0.5% foliar dry weight
etc
Communicating
EI Monitoring Nutrient Cycling
To monitor changes in
nutrient cycling, we monitor
soil decomposition using
buried tongue depressors and
measuring weight loss of the
wood as an index of soil
decomposition function
Tree needles,
leaves, and
branches fall
to the forest
floor
Trees take up
nutrients from the soil
enhancing growth and
delivering nutrients
back to the ecosystem
Bacteria and fungi in the soil
humus decompose the tree
litter, making nutrients available
for plant growth
‘Desired Condition’ for
Forest Landscapes: Rationale
• most parks are not ‘natural’ and have had historical impacts
that require management/restoration
• active landscape management is required to meet park
conservation needs – prescribed burning, ecosystem
restoration, species re-introductions, alien invasives
• management activities require performance reporting targets
to assess progress towards desired goals; landscape targets
will be set against patterns of natural successions and
disturbance
• ‘Desired condition’ targets for terrestrial landscapes need to
be based on ‘desired conservation services’ the landscape can
realistically provide
‘Desired Condition’ for
Forest Landscapes: Conservation Services
• Habitat suitability: for focal species, e.g., charismatic, major park
ungulates and carnivores, indicators, keystones, species at risk
• Ecosystem representation: rare ecosystems, old forests, structural stage
targets
• Landscape productivity: within historical range of productivity as
measured by NDVI or NPP
• Landscape pattern: desired states for connectivity/fragmentation
• Landscape processes: ice features (permafrost, thermokarst, solifluction
etc), flooding regimes, mass wasting rates,
• Operational and safety needs: fire/fuel management, RoWs, roads and
visitor access/use, harvesting
EI Assessment of
Change Analysis Data
Time
1
Time
2
Desired Landscape
Condition
Hypothesis Testing/Monitoring
Questions
H01: landscape scale forest ecological integrity has not
changed significantly over the last 5 years in Kejimkujik
NP
H01.1: fragstat index target
H01.2: forest ecosystem representation target
H01.3: white tailed deer density is between 0.25 and 0.75
animals/ha
H01.4: cow:calf ratio in white tailed deer is greater than 1.2
H01.5: NPP of forest landscapes is between ? and ?
etc
EI Assessments
• What is the state of park EI?
• How to defensibly Integrate and assess
monitoring results to report the state of the park?
• IBI approaches – stress gradients
• ‘Internal logic’ / rule systems based on
conceptual ecosystem models
Bruce Peninsula
National Park
Stress Gradients
Bruce Peninsula
National Park
Measures to Indicators
Simple Roll Up
1
3
0
15
5
30
45
22
forest bird
richness
78.4
effective
patch size
BIODIVERSITY
0
7.3
0.2
14.6
26.3
52.6
salamander
abundance
decomposition
11%
37%
63%
89%
regeneration
(height class)
PROCESSES
0
0.1
14
3
6
0.4
13
0.7
21
0.9
28
35
250
10%
184
- Pendall Point = 25
5%
117
- Rocky Bay = 39
lichen
diversity
0%
crown
vigor
50
fragmentation
(ENN)
STRESSORS
20%
productivity
(NDVI)
Measures to Indicators
Simple Roll Up
bootstrapped percentiles from across monitoring stations
9
21
33
45
Site Comparison
22 - South Cameron Lake
27 - Fathom Five Landbase
graphical & numerical
representation
29 - Emmett Lake
25 - Pendall Point
Forest Indicator = 31 (±2.4)
34 - Cameron Lake Dunes
42 - Shouldice Lake
30 - Horse Lake Trail
39 - Rocky Bay
but close to
LTEMPs
forest songbirds
forest salamanders
carnivores
climate
change
predation
human
effects
epidemic insect outbreaks
herbivores
humus decomposition
soil arthropods
herbivory
ingress/mortality
growth/health of stand dominants
decomposition
vegetation
species diversity/dominance/abundance
foliar nutrients
soil humus
nutrient/moisture
uptake
mineral soil
EMA
N
plot
data
That man is
so cool – he’s
monitoring EI
The Day Monitoring Became Cool