SYMP 9-4 - Redistributing conservation resources from monitoring to action using community science

Background/Question/Methods
Community science is rapidly emerging as an effective means to collect vast quantities of data in a cost-efficient manner. This may prove invaluable to conservation efforts, as using crowd-sourced data to inform decisions can allow managers to redirect limited funds towards action rather than monitoring. Our objective was to quantify the benefit of using community science data for setting conservation priorities. Using data from the BirdReturns conservation initiative run by The Nature Conservancy in central California, we prioritized privately-owned farms for conservation action based on the modeled probability of detection of seven species of interest using three datasets: i) eBird community science data, ii) professionally collected monitoring data, and iii) an integrated dataset that combines both community and professionally collected data. Using Value of Information analysis, we quantified the total expected value of prioritizations made using each dataset, accounting for the sensitivity and specificity of the different models, across a range of budgets. We predicted that the integrated model would yield better value at larger budgets, but that decisions based solely on eBird data would be preferable at lower budgets given that more money can be spent on action rather than monitoring.

Results/Conclusions
We found that prioritizing detections across all seven species based on the model using eBird data only or the integrated data model resulted in the greatest overall value across all budgets. eBird data only was the preferred option at smaller budgets due to the costs associated with collecting the professional monitoring data, but also resulted in similar estimated values to the prioritizations made using the integrated model at larger budgets. However, model performance and the resulting value of prioritizations did vary among species, possibly reflecting differences in detectability and habitat specificity. Our case study quantifies the trade-offs between monitoring and action, to better illustrate to conservation managers the potential risks associated with unnecessary data collection. Although the integrated model was more accurate in predicting detections, the eBird-only model performed comparably well, and resulted in more effective conservation action at more limited budgets. Using openly and freely available data, we can redistribute resources towards actions that will directly benefit biodiversity, ultimately resulting in better overall outcomes.