Objectives-Based Vegetation Management The Florida Fish and Wildlife Conservation Commission s Adaptive Management Program GEORGE OTTO, QUANTITATIVE ECOLOGIST george.otto@myfwc.com 352.327.1761
OBVM has made substantial gains improving the FWCs data driven decision making capacity FWC s Protected Area System 1.5 million acres 50 different areas >60 people 2
Objectives-Based Vegetation Management (OBVM) OBVM is an inventory & monitoring program that collects, stores, interprets, and disseminates quantitative information about vegetation structure to decision makers. OBVM aims to answer the question: Are land management actions producing plant communities of desired quality? 3
Florida s Plant Communities fire maintained, three tiered vegetation structure 4
Historic Longleaf Pine Community 5
Gopher Tortoise 6
OBVM Program Goal *****Increase data driven decision making***** Establish long term queryable GIS database (LMIS) Provide status & trend analysis on progress towards meeting management objectives (JMP) Learn from the outcomes of previous management actions and implement knowledge as feedback to guide future efforts (adaptive management)
SUNLIGHT MANAGEMENT ACTIONS Px fire, logging, mechanical $ Over-story Vegetation MANAGEMENT DECISIONS Mid-story Vegetation VEGETATION QUALITY Groundcover Vegetation OBJECTIVES BASED VEGETATION MANAGEMENT Conceptual Ecological Model 8
Prescribed Fire before, after, +30days 9
Physical / Mechanical 10
Timber Harvest 11
Survey Design Create statewide vegetation community GIS polygon basemap; for locating population sampling stations & to pair management actions to a permanent footprint. (LMIS) Sampling occurs every five years to test a hypothesis which determines if the difference in attributes observed is significant. 12
Sample Point Generation 13
OBVM Vegetation Attributes LAYER ATTRIBUTE ALIAS UNITS Over-story Basal Area of Pine BP ft 2 /acre Over-story Mid-story Non-Pine Stem Density > 4in DBH Subcanopy Stem Density 2-4in DBH NPD SUBCAN Mid-story Shrub Stem Density > 1m STEMS Mid-story Mid-story Maximum Shrub Stem DBH Average Maximum Shrub Height DBH SHBHT Mid-story Shrub Cover < 1m SHBCOV Mid-story Mid-story Serenoa Petiole Density > 1m Average Maximum Serenoa Height SERPET SERHT Mid-story Serenoa Cover < 1m SERCOV Groundcover Herbaceous Cover HERBCOV count/7m radius average count/4m 2 quadrat average count/4m 2 quadrat average maximum DBH in inch/4m 2 quadrat average maximum height in ft/4m 2 quadrat average % cover/4m 2 quadrat average count/4m 2 quadrat average maximum height in ft/4m 2 quadrat average % cover/4m 2 quadrat average % cover/1m 2 quadrat 14
2 m 7 m Sample Station 5 m Design 240 120 15
OBVM quadrat sampling 16
OBVM quadrat sampling 17
Statistics hypothesis test model: Likelihood Ratio Chi-square Test for Two Proportions a sample size of 60 is selected to have 90% power to detect 14% comparisons, alpha = 0.20 (2005 UnifyPow) Raw data is transformed into a twooutcome form by comparison to the attributes desired condition as determined by a reference site project. http://www.fnai.org/reference-natural-communities.cfm 18
Attribute Desired Condition LAYER ATTRIBUTE SANDHILL FLATWOODS Over-story Basal Area of Pine 20-60 10-50 Over-story Mid-story Non-Pine Stem Density > 4in DBH <3 0 Subcanopy Stem Density 2-4in DBH <1 <1 Mid-story Shrub Stem Density > 1m 0 <1 Mid-story Mid-story Maximum Shrub Stem DBH <1 <0.5 Average Maximum Shrub Height <3 <2 Mid-story Shrub Cover < 1m 10-20 <25 Mid-story Mid-story Serenoa Petiole Density > 1m 0 0 Average Maximum Serenoa Height <3 <3 Mid-story Serenoa Cover < 1m <5 10-25 Groundcover Herbaceous Cover >25 >25 19
Statistical Considerations Simple random sampling, independent Two-tailed hypothesis test H O : P 1 = P 2 NULL HYPOTHESIS H A : P 1 P 2 ALTERNATIVE HYPOTHESIS Balance the risk of Type I and Type II errors cost of actions not needed (false+) VS failure to detect resource damage (false -) A higher risk of Type I error is accepted to increase the power of the test - correct rejection of null hypothesis when null hypothesis is false - significant results indicate evidence of management effect - non-significant results indicate little evidence of management effect 20
Although comparing two independent proportions seems like a simple problem, gifted statisticians have been debating the fine points for years and now almost 25 different methods have been suggested just to get good p values. Obtaining power probabilities for these tests is an even tougher research problem. A Tour of UnifyPow: A SAS Module/Macro for Sample-Size Analysis Ralph G. O Brien, Cleveland Clinic Foundation 21
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OBVM Sample data Data from: Fort White Wildlife and Environmental Area Little Gator Creek Wildlife Management Area Go to excel 23
JMP analysis Go to JMP reports data visualization & significance test keystrokes: Analyze->Consumer Research->Categorical->Related->Aligned Responses 24
Management Activities Database 25
Immediate What is Next? Sample size analysis for oversampling and better implementation at regional scale Leaf Area Index Adaptive statistics Future Violin/Bean Plots (data visualization, jmp?) Bootstrap, Generalized linear model Land Acquisition Ranking, Payment for Ecosystem Services, Climate Change 26
Conservation Monitoring Program Development Recommendations Clearly state the purpose of monitoring Determine if monitoring is status seeking or trend seeking Secure funding and institutional support at all levels Develop a systems model that: clearly describes the way the system is structured and functions prioritizes and selects components of the system to be monitored communicates system function to all participants links causes of change (management action/inaction) to outcomes Create legacy program document Ensure adequate training, information management, and QA/QC. Obtain peer review of protocols and statistics Determine hypothesis, statistical power required, minimum detection effect Identify a panel of experts to evaluate data and develop conclusions that: modify future policy/management actions or validate status quo suggest improvements to future monitoring efforts develop an outreach program to disseminate results Ensure future management or monitoring implement knowledge from results 27
OBVM makes the FWC one of the few public agencies in the nation able to constantly fine-tune its land management actions based on data 28