RTNCF Species & Habitat Step-down Models

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Benjamin McKenzie
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1 RTNCF Species & Habitat Step-down Models USFWS Science Support Project Ashton Drew Jaime Collazo, John Stanton, Alexa McKerrow

(250,000) Increase 100% Brown-headed nuthatch (1.

2 Project Objective Aid with step-down of national population & habitat objectives Partners in Flight 2004 National Goals Bachman s sparrow (250,000) Increase 100% Brown-headed nuthatch (1.5 mil) Increase 50% RTNCF Ecosystem? National Wildlife Refuges? Other protected lands? Errol Taskin

3 Species-Habitat Models Simplified conceptualization of complex systems summarize current knowledge & gaps force explicit statement of assumptions

4 Species-Habitat Models Simplified conceptualization of complex systems summarize current knowledge & gaps force explicit statement of assumptions Tools for asking What if my knowledge is biased or my assumptions are wrong? I make Choice A or Choice B?

5 Species-Habitat Models Simplified conceptualization of complex systems summarize current knowledge & gaps force explicit statement of assumptions Tools for asking What if my knowledge is biased or my assumptions are wrong? I make Choice A or Choice B? To provide estimates of expected variation around goals risk evaluation of management decisions identification of most serious knowledge gaps

6 RTNCF Ecosystem & Refuges Two spatial scales Terrestrial & aquatic species Data-rich & data- poor scenario Start with GAP products Utilize expert opinion Design for adaptive management use

7 Pilot Model Species King Rail USFWS Focal Species Fresh & brackish wetlands Swainson s Warbler PIF Priority Species Bottomland & upland hardwood forest Blueback Herring NOAA Species of Concern Anadromous fish

8 NC Gap Analysis Project Literature and expert derived habitat associations Suitable for national and regional analyses King Rail live in Fresh or Brackish Marsh Habitat (red)

9 NC Gap Analysis Project What about ecosystem and refuge level planning?

10 Refuge-level Habitat Variability

11 No Local Data? Obvious environmental heterogeneity but no local data to identify relevant environmental gradients or define species response to these gradients Bayesian modeling approach based on literature review and expert opinion

12 King Rail Expert Opinion Probability of detecting a King Rail using the Standardized Marsh Bird Monitoring Protocol (Conway 2005) Individual, refuge-specific sessions Part 1: Discussion-based interview, univariate predictors Part 2: Map-based interview, multivariate predictors

13 Bayesian Modeling Approach Cell X 50% 50% Uninformative Prior Prob(detect KIRA) in a cell of GAP Potential Habitat without any other information; the value we want to update

14 Bayesian Modeling Approach Wet Dry Cell X Wet Dry Lit Review = Expectation KIRA response to environmental gradient affects the probability of detection

15 Bayesian Modeling Approach 7 wet, 1 dry Anything else Cell X 7 wet, 1 dry Anything else Expert Opinion = Best Available Local Data KIRA detections at 7/10 wet, 1/10 dry sites

16 Bayesian Modeling Approach 7 wet, 1 dry Cell X New % New % Anything else 7 wet, 1 dry Anything else Combine our prior probability with the likelihood of the observed data, given our expectations, to define the posterior probability of detecting KIRA at multivariate GAP Cell X.

17 RTNCF SSP Questions: Ashton Drew: or Project Website: Two spatial scales Terrestrial & aquatic species Data-rich & data- poor scenario Start with GAP products Utilize expert opinion Design for adaptive management use

19 Without models Brown-Headed Nuthatch Goal: 50% Increase, 1.5M pairs Step-down population and habitat objectives? Area based? 20% habitat, so provide 20% pairs 80% habitat, so provide 80% pairs

20 Without models Brown-Headed Nuthatch Goal: 50% Increase, 1.5M pairs Step-down population and habitat objectives? Area based? Equal effort? 100 pairs, so provide 150 pairs 10 pairs, so provide 15 pairs

21 Discussion-based Interviews Expert describes general local spatial and temporal variability Expert quantifies detection probabilities for specific univariate gradients

22 Map-based Interviews All GAP predicted habitat divided into 400m cells with multivariate environmental attributes Of 10 locations, identify 3 most likely and 3 least likely to detect a KIRA repeat for total of 100 unique points

GIS Data, Technology, and Models. to Integrate Information and Improve Transportation Decision-Making. within the Eco-Logical* Framework for Oregon

GIS Data, Technology, and Models to Integrate Information and Improve Transportation Decision-Making within the Eco-Logical* Framework for Oregon GIS-T 2009, Oklahoma City, OK April 8, 2009, Session 5..2.2