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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