A statistical assessment of population trends for data deficient Mexican amphibians

Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea

Prediction of rice yields at pixel scale rather than county scale can benefit crop management and scientific understanding because it is useful for monitoring how crop yields respond to various agricultural systems and environmental factors. In this study, we propose a methodology for the early prediction of rice yield at pixel scale combining a crop model and a deep learning model for different agricultural systems throughout South and North Korea. Initially, satellite-integrated crop models were applied to obtain a pixel-scale reference rice yield. Then, the pixel-scale reference rice yields were used as target labels in the deep learning model to leverage the advantages of crop models. Models of five different deep learning network architectures were employed to help determine the hybrid structure of long-short term memory (LSTM) and one-dimensional convolutional neural network (1D-CNN) layers by predicting the optimal model about two months ahead of harvest time. The suggested model showed good performance [R² = 0.859, Nash-Sutcliffe model efficiency = 0.858, root mean squared error = 0.605 Mg ha^-1], with specific spatial patterns of rice yields for South and North Korea. Analysis of the relative importance of the input variables showed the water-related index and maximum temperature in North Korea and the vegetation indices and geographic variables in South Korea to be crucial for predicting rice yields. The proposed approach successfully predicted and diagnosed rice yield at the pixel scale for inaccessible locations where reliable ground measurements are not available, especially North Korea.

Fixing the Canadian Species at Risk Act: identifying major issues and recommendations for increasing accountability and efficiency

Since the implementation of the Canadian Species at Risk Act (SARA) in 2003, deficiencies in SARA and its application have become clear. Legislative and policy inconsistencies among responsible federal agencies and the use of a subjective approach for prioritizing species protection lead to taxonomic biases in protection. Variations in legislation among provinces/territories and the reluctance of the federal government to take actions make SARA’s application often inefficient on nonfederally managed lands. Ambiguous key terms (e.g., critical habitat) and disregard for legislated deadlines in many steps impede the efficacy of SARA. Additionally, the failure to fully recognize Indigenous knowledge and to seek Indigenous cooperation in the species protection process leads to weaker government accountability, promotes inequity, and leads to missed opportunities for partnerships. New legislative amendments with well-defined and standardized steps, including an automatic listing process, a systematic prioritization program, and clearer demands (e.g., mandatory threshold to trigger safety net/emergency order) would improve the success of species at risk protection. Moreover, a more inclusive approach that brings Indigenous representatives and independent scientists together is necessary for improving SARA’s effectiveness. These changes have the potential to transform SARA into a more powerful act towards protecting Canada’s at-risk wildlife. (The graphical abstract follows.)

Emerging semantics to link phenotype and environment

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.