Science Enabled by Specimen Data
Zhang, H., W. Guo, and W. Wang. 2023. The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models. Ecology and Evolution 13. https://doi.org/10.1002/ece3.10747
How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.
Medina, L., P. Nascimento, and M. Menezes de Sequeira. 2021. Rediscovering of Chara braunii (Characeae, Charophyta) in Madeira (Macaronesian region, Portugal). Botanica Complutensis 45: e79754. https://doi.org/10.5209/bocm.79754
Chara braunii C.C. Gmelin (Characeae, Charophyta) was found in Madeira Island (Portugal) in a water channel in an agricultural area. This constitutes the first record of that species since 1944 in the Macaronesian region (Azores, Madeira and Canary archipelagos).
TREVIÑO-ZEVALLOS, I., I. GARCÍA-CUNCHILLOS, and C. LADO. 2021. New records of Myxomycetes (Amoebozoa) from the tropical Andes. Phytotaxa 522: 231–239. https://doi.org/10.11646/phytotaxa.522.3.6
The Myxomycetes comprise a remarkably diverse group of organisms within Amoebozoa, with over 1000 species currently recognized. These organisms, at the end of their life cycles produce fruiting bodies which are the basis for their systematics. Despite being a biodiversity hotspot, the tropical Andes…
Fragkopoulou, E., E. A. Serrão, P. A. Horta, G. Koerich, and J. Assis. 2021. Bottom Trawling Threatens Future Climate Refugia of Rhodoliths Globally. Frontiers in Marine Science 7. https://doi.org/10.3389/fmars.2020.594537
Climate driven range shifts are driving the redistribution of marine species and threatening the functioning and stability of marine ecosystems. For species that are the structural basis of marine ecosystems, such effects can be magnified into drastic loss of ecosystem functioning and resilience. Rh…