Tutorial: Species Modeling Approach. Scott Fleury ICF International April 13, 2011

Transcription

1 Tutorial: Species Modeling Approach Scott Fleury ICF International April 13, 2011

2 Filling the Data Gaps: The Role of Species Distribution Modeling Why develop species distribution models? To provide an objective and repeatable way of analyzing and evaluating biological information across the Plan Area. As an important supplemental tool. Not a replacement for field data

3 Rationale for modeling DRECP Science Advisors recommend careful use of species distribution modeling Modeling is a necessary part of the conservation planning gprocess because of the following: Need for prediction and extrapolation in areas lacking adequate data because of lack of comprehensive species data across the Plan Area; Constraints to conducting supplemental surveys: limited access to private land; limited time and budget; Need to look beyond the snapshot in time effect of field data; Need for synthesis and analysis of multiple data sources across the entire Plan Area; Need to identify and rank biological values between areas ; and Need to establish baseline conditions to compare the merits of alternate preserve designs and proposed conservation actions.

4 Application to DRECP Gap analysis Effects analysis Conservation strategy Reserve design Corridor analysis Monitoring Conditions on covered activities (e.g., survey requirements)

5 Potential Species Distribution Modeling Approaches Species Distribution Model Types 1. Expert based models 2. Statistically based models Other models mentioned by DRECP Science Advisor Report UseSpeciesDistribution Modeloutput/resultsoutput/results 1. Decision Support Models Effect of managementor landuse decisions on habitat 2. Spatially Explicit Population Models Dynamic changes in population lti size and distribution ib ti over time

6 Integration of GIS data with Species Habitat Associations Watersheds Vegetation Species A Vegetation Density Landform Soil Texture Species B Soil Parent Material Elevation Slope Rock Outcrops Hydrologic Features Ecological Subregions Aspect Species X

7 Expert-based models More flexible and adaptable to include complex spatial relationships, different habitat use areas Integrate descriptive habitat associations Use species locality data to test model assumptions Yield somewhat subjective results Can be expensive to develop Science advisors recommended cautious use of expert opinion models when data insufficient for statistically-based models

8 Example: Expert-based model Upper Santa Ana River Wash Plan HCP San Bernardino Kangaroo Rat Habitat Model

9 GIS Data Used in the SBKR Model 1. USGS geology data 2. Vegetation data 3. Historic and Recent Aerial Photography 4. SBKR trapping data (for model verification and testing)

10 USGS Geologic Map SBKR correlated with geologic age of substrate. Youngest Qw formations had highest correlation (80% locations), young Qya formations also had high correlation (71%), and low correlation on oldest formations.

11 Vegetation Data SBKR correlated with earlier seral stages of Riversidean alluvial fan sage SBKR correlated with earlier seral stages of Riversidean alluvial fan sage scrub (P, I, and I/M). Less frequent in mature RAFSS, other denser shrub types, or areas dominated by nonnative grasses.

12 SBKR Trapping Data USACE Data Other USFWS Data WSPA Data

13 The SBKR Model Factors High Habitat Suitability Best vegetation types + Best soil indicator types Vegetation Type Pioneer alluvial fan sage scrub, Intermediate alluvial fan sage scrub, or Intermediate/Mature alluvial fan sage scrub AND Soil Indicator Qw, Qw1, or Qw2 geology or Plunge Creek alluvium washout areas

14 The SBKR Model Factors Moderate Habitat Suitability Best vegetation types + OK soil indicator types or OK vegetation types + Best soil indicator types Vegetation Type Pioneer alluvial fan sage scrub, Intermediate alluvial fan sage scrub, or Intermediate/Mature alluvial fan sage scrub AND OR Soil Indicator Qya3, Qya4 or Qya5 Mature alluvial fan sage scrub AND Qw, Qw1, or Qw2

15 The SBKR Model Factors Low Habitat Suitability Poor vegetation types + Any soil indicator types or OK vegetation types + Poor soil indicator types Vegetation Type Soil Indicator Mature alluvial fan sage scrub/nng, AND Any soil indicator type Nonnative grassland (NNG), Chamise chaparral, and Chamise chaparral/nng Mature alluvial fan sage scrub AND Qya3, Qya4 or Qya5 OR

16 The SBKR Model Factors No Habitat Suitability Developed/ruderal, recharge basins, and Riversidian upland sage scrub + Any soil indicator types or Areas mapped as highly disturbed (USFWS) or within the active low flow channel Vegetation Type Soil Indicator Riversidian upland sage scrub, AND Any soil indicator type Developed/ruderal, and recharge basins Anyvegetation type AND Areasmapped by USFWS as highly disturbed Anyvegetation type AND Withinthe the activelow flow channel OR OR

17 Initial SBKR Model Results Abrupt transition from high to low Observed habitat potential lower than modeled habitat potential Account for ecotone by modeling moderate habitat potential within 100 ft buffer of high potential. A tf l it bilit f i l t d ll th b Account for lower suitability of isolated smaller patches by modeling low habitat potential if greater than 500 ft from high potential habitat.

18 Final SBKR Model Results 100 ft ecotone transition of moderate habitat potential (59 ac. of low became moderate). Reduction to low potential (9 ac. of high and 217 ac. of moderate became low).

19 Statistically based models Predictive model of species occurrence Correlates presence and/or absence data with habitat parameters Need extensive occurrence records More than 50 over range of habitat Models work best when model area is same as occurrence data area Example: USGS 2009 desert tortoise model

20 USGS Desert tortoise model Over 15,000 presence points 16 environmental variables Applied Maxtent algorithm 1 km 2 grid cells

21 Mohave Ground Squirrel model USGS and UNR developing model similar to USGS and UNR developing model similar to desert tortoise (CEC PIER grant)

22 Statistically based models Recommended by science advisors when sufficient data are available Need a minimum of 50 occurrence locations, but performs better with hundreds of occurrences Canbe time consuming and expensive Data limitations make it infeasible for most covered species Results require additional interpretation

23 Next Steps for DRECP Use existing statistically based models when available Desert tortoise, Mojave ground squirrel, others? Apply GIS based expert opinion models to most species using known species habitat associations to develop draft model parameters Model parameters based on species accounts Solicit input from scientific experts