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1 Using GAP data to design and inform field research James B. Grand 1, Amy L. Silvano 2, Mark D. MacKenzie 2, and Edward F. Lowenstein 2 1 USGS, Alabama.

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Presentation on theme: "1 Using GAP data to design and inform field research James B. Grand 1, Amy L. Silvano 2, Mark D. MacKenzie 2, and Edward F. Lowenstein 2 1 USGS, Alabama."— Presentation transcript:

1 1 Using GAP data to design and inform field research James B. Grand 1, Amy L. Silvano 2, Mark D. MacKenzie 2, and Edward F. Lowenstein 2 1 USGS, Alabama Cooperative Fish & Wildlife Research Unit, Auburn University, Alabama 2 Alabama Cooperative Fish & Wildlife Research Unit, School of Forestry & Wildlife Sciences, Auburn University, Alabama 3 School of Forestry & Wildlife Sciences, Auburn University, Alabama

2 2 AcknowledgementsAcknowledgements  Funding  Alabama Department on Conservation and Natural Resources, Wildlife & Freshwater Fisheries Division  State Wildlife Grants, USFWS Federal Aid  GIS data

3 3 Presentation outline  Reason for large-scale research & monitoring  Pitfalls & considerations for large-scale surveys  Using GAP data to improve study design  Example: I nventory & C onservation P lanning in Alabama

4 4 Why inventory & monitor?  Science  Understand ecology of systems  Detect changes in species distribution and abundance  Management/Conservation  Make informed decisions  Learn from actions   Adaptive management

5 5 State Comprehensive Conservation Strategies 1. Distribution and abundance of species 2. Locations and condition of key habitats 3. Problems which may adversely affect species 4. Conservation actions proposed 5. Plans for monitoring and adopting conservation actions 6. Review procedures 7. Plans for coordinating development, implementation, review, and revision 8. Public participation in developing and implementing CWCS Plans for monitoring and adopting conservation actions

6 6 Alabama Species of Greatest Conservation Need 303 Species of Greatest Conservation Need

7 7 ConsiderationsConsiderations  What hypotheses are to be addressed?  Specific questions regarding abundance or distribution  What are the management objectives?  Maintenance, enhancement, or control of populations  Geographic and temporal scale  State, regional, or subregional  How long to complete?  How frequently repeated?  Effort ($) available for monitoring  Quality  Abundance  Distribution  Species richness Highest Lowest

8 8 Basics of inventory design Pollock, K. H., J. D. Nichols, T. R. Simons, G. L. Farnsworth, L. L., Bailey, and J. R. Sauer Large scale wildlife monitoring studies: Statistical methods for design and analysis. Environmetrics 13:  Pitfalls of large scale inventory and monitoring  Failures:  Ignoring heterogeneity in encounter rates for animals  Density  Detection  Results indefensible with very poor precision  Properly designed monitoring programs:  Incorporate heterogeneity in encounters  Produce defensible results Robust Sampling Scheme incorporating Detectability

9 9 Ecological theory – generally  Animals select habitats to optimize fitness  Quality differs among habitats  Abundance & distribution reflect quality  Abundance & distribution differ among habitats Sink Source Sink Source Sink

10 10 Biological hypotheses  Differ among taxa in relation to resource requirements and community interactions  Food availability  Structural habitat requirements  Competition  Predator-prey relationships  Other influences  Species specific  Essential to deductive methods

11 11 Sampling theory  Animal density differs in relation to many factors  Greatest variation is among “habitat types”  Vegetative cover  Physical structure  Location  Heterogeneity in density leads to bias  Stratify by habitat or density  Detection of animals is imperfect  Estimate dectability  Can obtain a representative sample  Systematic  Random

12 12 I nventory & C onservation P lanning  Science-based plan for the conservation of GCN species and the habitats they depend on  Protocol and a baseline for monitoring GCN species  Understand the issues affecting conservation  Provide management recommendations  Foster relationships among public and private stakeholders Adaptive Resource Management

13 13 ObjectivesObjectives  Assess information on current and potential distribution and abundance of GCN species on ADCNR managed lands;  Develop recommended methods for inventories  Develop a matrix of preferred management practices;  Conduct high priority research and inventory projects;

14 14 ObjectivesObjectives  Predictive models of distribution and abundance;  Basis for decision support tools  Prepare management & conservation recommendations;  Outreach regarding conservation and management for GCN species

15 15 ICP for Alabama DCNR lands  Used GAP stewardship to select 13 survey areas  50 watersheds  55,600 ha

16 16 ConsiderationsConsiderations  Hypotheses to be addressed – species specific  Determining habitat relationships/requirements  Response to management practices  Management questions  Which GCN populations occur/can be maintained on ADCNR lands?  Spatial & temporal scale  Selected DCNR lands  Tri-annual completion schedule  Effort  Estimating distribution & abundance

17 17 Approach – survey design  Stratification  Account for large differences in heterogeneity  Ensure that samples were well-distributed  Random selection of sites  Ensure that samples were representative  Covariates  Modeling heterogeneity at finer scales  Based on a priori models  Literature & hypothesized relationships

18 18 StratificationStratification  Landform (slope & slope position)  Solar exposure (soil moisture)  Geology (soil texture/chemistry)  Land use/land cover Gap Ecological Systems 2001

19 19 Selection of survey sites

20 20 Analysis methods  Empirically model habitat relationships  Landscape level  Site & survey specific  Empirically model detection rates  Site & survey specific  Based on a priori hypotheses  Species specific  Habitat specific  Survey specific

21 21 Analysis methods  Patch occupancy analysis  Spatial models of probable use [proportion used]  Incorporates estimates of detectability  Over time – change detection:  Colonization & local extinction rates  Relate to management actions via land cover  Repeated counts  Spatial models of animal abundance [density]  Incorporates estimates of detectability  Over time – change in abundance

22 22 Landscape level GIS data NLCDSystems Forested Early successional Roads & buffers Streams & buffers Landform

23 23 Site & survey specific covariates  Vegetative structure  Time – date & TOD  Vegetative composition  Duff layer  Moisture  Temperature  Stream bed strata  Embeddedness  Dissolved oxygen  Gradient  Soil texture  Canopy closure

24 24 Using GAP Predicted distributions  Literature-based & expert opinion  Known to over-predict  Hypothesis: Percent of suitable habitat  probability of encounter  Bayesian prior expectation  Encounter probability  prior probability x likelihood of the data Gopher tortoiseEastern king snakeWorm-eating warbler

25 25 Using predicted distributions Study Area Species Stimpson Monte Sano Lauderdale- ColbertGuntersvilleColdwaterSipsey Oak Mountain Cerulean warbler Kentucky warbler Red- cockaded woodpecker Swainson's warbler Wood thrush American woodcock Least bittern American kestrel Predicted Occupancy each sample site?

26 26 Using GAP data to inform field research  Stewardship  Identification and delineation of study sites  Land use land cover  Stratification  Selection of sampling sites  Landscape characteristics  Incorporation of uncertainty  Animal distributions  Determination of survey methods  Prior expectation of encounters


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