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Science Enabled by Specimen Data

Zhao, Y., G. A. O’Neill, N. C. Coops, and T. Wang. 2024. Predicting the site productivity of forest tree species using climate niche models. Forest Ecology and Management 562: 121936. https://doi.org/10.1016/j.foreco.2024.121936

Species occurrence-based climate niche models (CNMs) serve as valuable tools for predicting the future ranges of species’ suitable habitats, aiding the development of climate change adaptation strategies. However, these models do not address an essential aspect - productivity, which holds economic significance for timber production and ecological importance for carbon sequestration and ecosystem services. In this study, we investigated the potential to extend the CNMs to predict species productivity under various climate conditions. Lodgepole pine (Pinus contorta Dougl. ex Loud.) and Douglas-fir (Pseudotsuga menziesii Franco.) were selected as our model species due to their comprehensive range-wide occurrence data and measurement of site productivity. To achieve this, we compared and optimized the performance of four individual modeling algorithms (Random Forest (RF), Maxent, Generalized Boosted Models (GBM), and Generalized Additive Model (GAM)) in reflecting site productivity by evaluating the effect of spatial filtering, and the ratio of presence to absence (p/a ratio) observations. Additionally, we applied a binning process to capture the overarching trend of climatic effects while minimizing the impact of other factors. We observed consistency in optimal performance across both species when using the unfiltered data and a 1:1.5 p/a ratio, which could potentially be extended to other species. Among the modeling algorithms explored, we selected the ensemble model combining RF and Maxent as the final model to predict the range-wide site productivity for both species. The predicted range-wide site productivity was validated with an independent dataset for each species and yielded promising results (R2 above 0.7), affirming our model’s credibility. Our model introduced an innovative approach for predicting species productivity with high accuracy using only species occurrence data, and significantly advanced the application of CNMs. It provided crucial tools and insights for evaluating climate change's impact on productivity and holds a better potential for informed forest management and conservation decisions.

Zhao, Y., G. A. O’Neill, and T. Wang. 2023. Predicting fundamental climate niches of forest trees based on species occurrence data. Ecological Indicators 148: 110072. https://doi.org/10.1016/j.ecolind.2023.110072

Species climate niche models (CNMs) have been widely used for assessing climate change impact, developing conservation strategies and guiding assisted migration for adaptation to future climates. However, the CNMs built based on species occurrence data only reflect the species’ realized niche, which can overestimate the potential loss of suitable habitat of existing forests and underestimate the potential of assisted migration to mitigate climate change. In this study, we explored building a fundamental climate niche model using widely available species occurrence data with two important forest tree species, lodgepole pine (Pinus contorta Dougl. ex Loud.) and Douglas-fir (Pseudotsuga menziesii Franco.), which were introduced to many countries worldwide. We first compared and optimized three individual modeling techniques and their ensemble by adjusting the ratio of presence to absence (p/a) observations using an innovative approach to predict the realized climate niche of the two species. We then extended the realized climate niches to their fundamental niches by determining a new cut-off threshold based on species occurrence data beyond the native distributions. We found that the ensemble model comprising Random Forest and Maxent had the best performance and identified a common cut-off threshold of 0.3 for predicting the fundamental climate niches of the two species, which is likely applicable to other species. We then predicted the fundamental climate niches of the two species under current and future climate conditions. Our study demonstrated a novel approach for predicting species’ fundamental climate niche with high accuracy using only species occurrence data, including both presence and absence data points. It provided a new tool for assessing climate change impact on the future loss of existing forests and implementing assisted migration for better adapting to future climates.

Orr, M. C., A. C. Hughes, D. Chesters, J. Pickering, C.-D. Zhu, and J. S. Ascher. 2021. Global Patterns and Drivers of Bee Distribution. Current Biology 31: 451-458.e4. https://doi.org/10.1016/j.cub.2020.10.053

Insects are the focus of many recent studies suggesting population declines, but even invaluable pollination service providers such as bees lack a modern distributional synthesis. Here, we combine a uniquely comprehensive checklist of bee species distributions and >5,800,000 public bee occurrence re…

Grünig, M., D. Mazzi, P. Calanca, D. N. Karger, and L. Pellissier. 2020. Crop and forest pest metawebs shift towards increased linkage and suitability overlap under climate change. Communications Biology 3. https://doi.org/10.1038/s42003-020-0962-9

Global changes pose both risks and opportunities to agriculture and forestry, and biological forecasts can inform future management strategies. Here, we investigate potential land-use opportunities arising from climate change for these sectors in Europe, and risks associated with the introduction an…