Multi-scale modeling of species distributions, hydrology, & gene flow

Transcription

1 Multi-scale modeling of species distributions, hydrology, & gene flow Douglas R. Leasure PhD. Candidate University of Arkansas Department of Biological Sciences

2 BIG data in GIS

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5 Future Climate

6 Future Climate

7 Future Climate

8 Future Climate

9 Future Climate

10 Future Climate

11 Future Climate

12 The problem of pattern and scale in ecology Simon A. Levin 1992

13 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Point-based data from a location s X, Y coordinates.

14 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path All geographic areas that drain into a userdefined location

15 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Areas within a location s watershed AND within a user-defined distance (x) from streams.

16 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Areas within a location s watershed AND within a user-defined distance (x) from streams.

17 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Areas within a user-defined radius (x) of a user-defined location.

18 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Areas within a location s local zone AND within its watershed.

19 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Areas within a location s local zone AND within its watershed.

20 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Buffered (x) stream path between all pairwise combinations of user-provided locations.

21 Spatial Scales Point Watershed Riparian Local Local-watershed Local-riparian Linear Path Stream Path Buffered (x) stream path between all pairwise combinations of user-provided locations.

22 Geodata Crawler Centralized national geodatabase & Automated multi-scale data crawler

23 Geodata Crawler Centralized national geodatabase & Automated multi-scale data crawler Python scripting with ESRI ArcGIS

24 1. User-provided Locations

25 2. User-provided Boundary

26 3. Variable Selection Human population within 5 km radius Avg. January rainfall in watershed % forest in upstream riparian zone within 100 m of streams

27 Automated Sample Area Delineation & Data Collection Drainage Area: 926 sq km Forest Cover: 215 sq km Population: 482 people January Rainfall: 6 cm Avg. Terrain Slope: 3.2 degrees

28 Research Applications Species Distribution Modeling Sulphur Springs diving beetle Hydrological Modeling Mapping natural flow regimes & flow alteration Path analysis Gene flow among bluehead sucker populations in the Colorado River basin

29 Species Distribution Modeling

30 Sulphur Springs diving beetle Collaborators: Scott Longing & Pablo Bacon Endemic species of concern Headwater specialist 83 sample locations o Presence/absence Habitat associations at multiple spatial scales Heterosternuta sulphuria Predictive model

31 Sulphur Springs diving beetle Landscape Characteristics of Interest: Watershed Area % Forest % Canopy Cover % Urban % Impervious Surfaces % Agriculture Watershed Local-watershed (200m-2km radius) Avg./Max. Terrain Slope Avg./Max. Stream Channel Slope Human population density Road density Riparian (50-800m buffer)

32 Bayesian Information Criteria (BIC) Effects of urbanization at multiple spatial scales Multi-model comparison with logistic regression Site Radius (meters)

33 Forested Riparian Buffers Non-parametric Multiplicative Regression McCune 2011, McCune & Medford 2004 Forest Cover in 100 m Riparian Zone Low Med High

34 Predicted Occurrences Maxent Presence-only modeling Phillips 2006

35 Predicted Occurrences Maxent Presence-only modeling Phillips 2006 R Package: BIOMOD Thuiller et al BIOMOD - a platform for ensemble forecasting of species distributions. Ecography.

36 Predicted Occurrences Maxent Presence-only modeling Phillips 2006 hydrology? population connectivity?

37 Hydrological Modeling

38 Mapping Natural Flow Regimes 7 natural flow regimes in Arkansas region (Leasure et al., in review) 67 reference streams with USGS gauges Collaborators: Dan Magoulick & Scott Longing Predict natural flow at un-gauged and disturbed streams

39 Landscape-Climate Flow Regime PRECIP SOIL

40 Predicted Natural Flow Regimes Random Forests Classification Breiman ,000 stream segments Predict natural flow regime Error Rate = 37%

41 Assessing Hydrologic Alteration Carlisle et al Random Forest to predict expected natural conditions (E) Gauge data to measure observed current conditions (O) O/E to measure flow alteration

42 Path Analysis & Animal Movements

43 Gene flow among bluehead sucker populations Collaborators: Michael & Marlis Douglas Bluehead Sucker Catostomis discobolus Microsat loci: 16 Specimens: 1092 Locations: 39

44 Barriers to Gene Flow??

45 No barrier = Similar allele freq.

46 Barrier = Dissimilar allele freq.

47 Fst Dissimilarity Matrix Fst

48 Map Fst to Stream Segments StreamTree (Kalinowski 2007) Fst Fst Fst Fst Fst Fst

49 Landscape/segment Fst/segment Fst Fst Fst Fst Fst Fst

50 Map Gene Flow Resistance Gene flow resistance Red = High Green = Low

51 Multi-scale Analysis as an Interdisciplinary Bridge

52 Hydrology Habitat Suitability Population Connectivity

53 Hydrology Population Connectivity Habitat Suitability

54 Hydrology Population Connectivity Habitat Suitability

55 Hydrology Future Climate Future Climate Future Climate Population Connectivity Habitat Suitability

56 Hydrology Population Connectivity Habitat Suitability

57 Hydrology Future Climate Population Connectivity Habitat Suitability

58 Hydrology Population Connectivity Habitat Suitability

59 Strategies Going Forward Big data from GIS & remote sensing Automated multi-scale data collection Contemporary modeling tools Multi-model inference Machine learning Ensemble forecasting Hierarchical Bayesian models*

60 Questions?