Mapping diversity in citizen science participation in the northeastern US Erika Barthelmess1 and Jacob Malcomb2 Biology Department and Nature Up North St. Lawrence University Canton, NY 13617 1 barthelmess@stlawu.edu @porcupinedoc @natureupnorth 2 jm4bg@virginia.edu Department of Environmental Sciences University of Virginia

Background Boston NYC Albany http://natureupnorth.org https://www.stlawu.edu/people/erika-barthelmess

Citizen Science and inclusion We stand for equity as a society CS has the potential to: Engage communities who have traditionally remained uninvolved in science (Newman et al. 2012) Make environmental knowledge more robust and democratic (Ottinger 2010) Connect people to science (Pandya 2012). Participants in CS are not representative Highly educated, more affluent participants (Evans et al. 2005)

Are we meeting this goal? Premise “…the environmental imperative is to get people of all ethnic and economic backgrounds on board with what we need to do to save what we can of the natural world; and the scientific imperative is to figure out what it is that can be done.” -- Dickinson and Bonney 2012 Are we meeting this goal?

Question Can we use data on access to education, income, and race to predict where citizen science happens? Approach Combine US Census data with citizen science data from three large projects.

USA NPN Data

Budburst Data

iNaturalist Data

Process PREDICTOR VARIBLES: Hectares Number of ISE centers (Informal Sci Ed) Number of Colleges & Universities Number of Educational Institutions (# Schools + # C&U) Number of Educational Institutions per/100 people Number of Schools (K-12) Population Size Per Capita Income Region (Urbanized, Not Urbanized) Percent of Population White (vs. other races) Population Density (people per 100 ha) Bachellors’ Degrees per Capita % with Income to Poverty ratio 50% or lower % with Income to Poverty 99% or lower (At_Below) % with Income to Poverty > 100% (Above) RESPONSE VARIABLE: Are there Cit Sci data in the Block Group? (Yes/No)

Results 62,500 possible models Best model: CS ~ 1 + Region + Hectares + NumSch + SumISE + PopSize + PerCapIncome + SumCU + PerWhite + Pop100HA + BAperCap + NumED + Edper100 + Below 10 models with AICc < 2.0 Used model averaging to look at them together.

Results 16 Estimate Std. Error Adjusted SE z value Pr(>|z|) (Intercept) -1.90E+00 1.03E-01 18.364 <<<0.001 *** RegionUrban -1.04E+00 4.23E-02 24.583 Hectares 9.44E-05 6.80E-06 13.879 NumSch 8.26E-02 6.56E-02 1.259 0.207872 NumISE 1.33E+00 1.17E-01 11.307 PopSize 3.14E-04 2.63E-05 2.64E-05 11.922 PerCapIncome 1.55E-06 1.14E-06 1.362 0.1733 NumCU 4.33E-01 8.02E-02 5.399 << 0.001 PerWhite 3.49E-03 9.27E-04 3.767 0.000165 Pop100HA -3.53E-05 2.52E-06 13.996 BAperCap 4.86E+00 2.50E-01 19.468 NumED 4.93E-02 3.14E-02 1.57 0.116379 Edper100 1.46E-01 1.18E-01 1.233 0.217467 Below 6.69E-03 2.86E-03 2.343 0.019152 * AboveP -9.25E-04 2.55E-03 0.364 0.716212 AtBelow 1.90E-05 6.88E-04 0.028 0.977994

Results

Results

Results

Concluding thoughts How can we improve access for all? Where there are more people (absolute) and the area is larger, there are more citizen science data (not a surprise). Where people have more education and greater access to education, including to informal science education centers, there are more citizen science data. Race and poverty status also appear to have a role Need further exploration. We can use Cit Sci data to assess ourselves on access. How can we improve access for all?