Forecasting biodiversity in breeding birds using best practices

PeerJ

article

David J. Harris¹  Shawn D. Taylor²  Ethan P. White¹ 

[1] Department of Wildlife Ecology and Conservation, University of Florida;School of Natural Resources and Environment, University of Florida;Informatics Institute, University of Florida;Biodiversity Institute, University of Florida

关键词: Birds;    Breeding bird survey;    Species richness;    Biodiversity;    Forecasting;    Prediction;    Climate change;    Species distribution model;    Time series;    Space-for-time substitution;   

DOI  :  10.7717/peerj.4278

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods.

【 授权许可】

CC BY