Leveraging Big Data and Citizen Science to Understand Sub Continental Scale Ecological Patterns

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Ariel Goodwin
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1 Leveraging Big Data and Citizen Science to Understand Sub Continental Scale Ecological Patterns Noah R. Lottig University of Wisconsin Center for Limnology

2 Roadmap 1. Approach to addressing sub-continental scale research questions. Regional and sub-continental water clarity patterns o Regional patterns (citizen data) o Data driven approach 3. Potential for future contributions and citizen driven research

5 So why use Big Data?

6 Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of escience such as databases, workflow management, visualization, and cloud-computing technologies.

The speed at which any given scientific discipline advances will depend on how well its researchers

7 Big data approaches can be used to study freshwaters at broad scales if 3 conditions are met: 1) Interdisciplinary team science approach ) Conceptual foundation 3) Robust quantitative methods

8 Approach: Interdisciplinary team science Ecoinformatics GIS science Statistics & machine learning Freshwater science

9 Ecoinformatics FW science GIS science Statistics & machine learning

10 LAke GeOSpatial temporal database 17 States 5, lakes ( ha) 8, w/ limno data 1, w/ secchi data

11 Limnological data sources (~9 total) Data volume State & tribal agencies Citizen monitoring programs Federal agencies University researchers

12 What are the long term patterns of water clarity Pietro Angelo Secchi

13 Citizen Science! The Secchi Dip In Project 13k lakes/1k secchi observations

14 Long term lake clarity trends Citizen Data! 8 States 39,71 observations 1, annual values 3,51 different lakes Lottig et al. PLoS ONE (1)

15 Research Questions What are the long-term patterns in water clarity across a broad spatial extent How does spatial location, size, and monitoring time period influence long-term patterns Bayesian Hierarchical Linear Modeling Lottig et al. PLoS ONE (1)

location, size, and monitoring time period influence long-term

16 Long term lake clarity trends Average Secchi (.m) Low inter-annual variability (3%) 1% yr -1 increase in Secchi depth Few lakes with significant long-term trends o o 7% increasing % decreasing Lottig et al. PLoS ONE (1)

in Secchi depth Few lakes with significant long-term

17 Long term lake clarity trends Spatial Location 1. Latitudinal gradients in Secchi depth. Latitudinal gradients in long-term trends 3. Latitudinal gradients in inter-annual variability Lottig et al. PLoS ONE (1)

18 Long term lake clarity trends Time Period 1. Average Secchi hasn t changed. Recent lakes have declining trends 3. Significant reductions in inter-annual variability Lottig et al. PLoS ONE (1)

19 Biases in the lakes monitored Lottig et al. PLoS ONE (1)

20 Lottig et al. PLoS ONE (1) # Lakes

21 What are patterns of ecological change? Linear trend Non linear Threshold Anomalous event

22 Research Questions: 1. What are ecological patterns across large spatial scales?. Do clusters of ecosystems exhibit similar patterns? 3. Can we identify factors influencing clusters o o o Spatial Local Regional LAke GeOSpatial temporal database 17 States 5, lakes ( ha) 8, w/ limno data 1, w/ secchi data ver 1.3.

23 Long term Secchi data 18+ years of data 1 o 75% overlap among timeseries Annual median value o 15 June 15 September 17 lakes / 8 states 7. W 67.5 W 65. W 8. W 77.5 W 75. W 7.5 W 9. W 87.5 W 85. W 8.5 W 5. N 7.5 N 5. N.5 N. N 37.5 N 97.5 W 95. W 9.5 W

24 Approach (Q1 & Q) Patterns and clusters Pang Ning Tan

25 Approach (cont.) Kernel K means clustering Response Variable

26 6 All lakes -

27 Cluster Cluster Cluster 6 - Cluster 1 Cluster Cluster 13 Linear trend -

28 3 Cluster 1 Linear trend

29 Cluster 5 - Linear trend

30 Cluster - Threshold

31 3 Cluster Cluster 7-3 Cluster 8 Cluster Non linear

32 6 All lakes 3 Cluster 1 Cluster Cluster Cluster Cluster 5 6 Cluster 6 Cluster Cluster 8 Cluster 9 Cluster 1 Cluster Cluster 1 6 Cluster

33 Cluster Cluster 1 (# points = 7, s =.19) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

34 Cluster Cluster (# points = 16, s = -.35) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

35 Cluster Cluster 3 (# points = 5, s =.1) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

36 Cluster Cluster (# points = 59, s =.119) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

37 Cluster Cluster 5 (# points = 7, s =.33) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

38 Cluster Cluster 6 (# points = 17, s = -.79) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

39 Cluster Cluster 7 (# points = 6, s =.6) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

40 Cluster Cluster 8 (# points = 3, s =.1) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

41 Cluster Cluster 9 (# points = 9, s =.75) Normalized 75 W 7 W 85 W 8 W 5 N 5 N N 95 W 9 W

42 Cluster Cluster 1 (# points = 97, s = -.63) W 7 W 85 W 8 W Normalized 5 N 5 N N 95 W 9 W

43 Cluster Cluster 11 (# points = 88, s =.7) W 7 W 85 W 8 W Normalized 5 N 5 N N 95 W 9 W

44 Cluster Cluster 1 (# points = 53, s =.355) W 7 W 85 W 8 W Normalized 5 N 5 N N 95 W 9 W

45 Cluster Cluster 13 (# points = 13, s = -.131) W 7 W 85 W 8 W Normalized 5 N 5 N N 95 W 9 W

46 Conclusions Simple models may not be adequate for perceiving of long-term patterns in the age of Big Data Data driven perception of long-term change Diverse set of common water clarity dynamics

47 Opportunities Citizens data is a critical source of information Watras et al. GRL (1)

48 lakechange.org discoverycenter.net Poster Session

REGIONAL CENTRE FOR TRAINING IN AEROSPACE SURVEYS (RECTAS) MASTER IN GEOINFORMATION PRODUCTION AND MANAGEMENT

REGIONAL CENTRE FOR TRAINING IN AEROSPACE SURVEYS (RECTAS) MASTER IN GEOINFORMATION PRODUCTION AND MANAGEMENT PROGRAMME DESCRIPTION October 2014 1. The programme The academic programme shall be referred